diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fa7e17c --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +**/__pycache__ +/checkpoint/ \ No newline at end of file diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/DH_live.iml b/.idea/DH_live.iml new file mode 100644 index 0000000..cc22754 --- /dev/null +++ b/.idea/DH_live.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..f5543d8 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,157 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..632ebc5 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..897d86e --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..e364551 --- /dev/null +++ b/README.md @@ -0,0 +1,71 @@ +# Mobile and Web Real-time Live Streaming Digital Human! +# 实时直播数字人 [bilibili video](https://www.bilibili.com/video/BV1Ppv1eEEgj/?vd_source=53601feee498369e726af7dbc2dae349) +# 数字人方案对比 + +以下表格展示了不同数字人方案的性能、使用方式、脸部分辨率及适用设备等信息，方便用户根据需求选择合适的方案。 + +| 方案名称 | 单帧算力（Mflops） | 使用方式 | 脸部分辨率 | 适用设备 | +|------------------------------|-------------------|------------|------------|------------------------------------| +| Ultralight-Digital-Human（mobile） | 1100 | 单人训练 | 160 | 中高端手机APP | +| DH_live_mini | 39 | 无须训练 | 128 | 所有设备，网页&APP&小程序 | +| DH_live | 55,046 | 无须训练 | 256 | 30系以上显卡 | +| duix.ai | 1,200 | 单人训练 | 168 | 中高端手机APP | + +### News +## Fastest model released! More demos joins me through the contact information at the bottom! + All checkpoint files are moved to [baiduNetDisk](https://pan.baidu.com/s/1jH3WrIAfwI3U5awtnt9KPQ?pwd=ynd7) +## Training +Details on the render model training can be found [here](https://github.com/kleinlee/DH_live/tree/master/train). +Audio Model training Details can be found [here](https://github.com/kleinlee/DH_live/tree/master/train_audio). +### Video Example + + +https://github.com/user-attachments/assets/7e0b5bc2-067b-4048-9f88-961afed12478 + + +## Overview +This project is a real-time live streaming digital human powered by few-shot learning. It is designed to run smoothly on all 30 and 40 series graphics cards, ensuring a seamless and interactive live streaming experience. + +### Key Features +- **Real-time Performance**: The digital human can interact in real-time with 25+ fps for common NVIDIA 30 and 40 series GPUs +- **Few-shot Learning**: The system is capable of learning from a few examples to generate realistic responses. +## Usage + +### Create Environment and Unzip the Model File +First, navigate to the `checkpoint` directory and unzip the model file: +```bash +conda create -n dh_live python=3.12 +conda activate dh_live +pip install torch --index-url https://download.pytorch.org/whl/cu124 +pip install -r requirements.txt +cd checkpoint +``` +unzip checkpoint files from [baiduNetDisk](https://pan.baidu.com/s/1jH3WrIAfwI3U5awtnt9KPQ?pwd=ynd7) +### Prepare Your Video +Next, prepare your video using the data_preparation script. Replace YOUR_VIDEO_PATH with the path to your video: +```bash +python data_preparation.py YOUR_VIDEO_PATH +``` +The result (video_info) will be stored in the ./video_data directory. +### Run with Audio File +Run the demo script with an audio file. Make sure the audio file is in .wav format with a sample rate of 16kHz and 16-bit single channel. Replace video_data/test with the path to your video_info file, video_data/audio0.wav with the path to your audio file, and 1.mp4 with the desired output video path: +```bash +python demo.py video_data/test video_data/audio0.wav 1.mp4 +``` +### Real-Time Run with Microphone +For real-time operation using a microphone, simply run the following command: +```bash +python demo_avatar.py +``` + +## Acknowledgements +We would like to thank the contributors of [Wav2Lip](https://github.com/Rudrabha/Wav2Lip), [DINet](https://github.com/MRzzm/DINet), [LiveSpeechPortrait](https://github.com/YuanxunLu/LiveSpeechPortraits) repositories, for their open research and contributions. + +## License +DH_live is licensed under the MIT License. + +DH_live_mini is licensed under the Apache 2.0. +## 联系 +| 进入QQ群聊，分享看法和最新咨询。 | 加我好友，请备注“进群”，拉你进去微信交流群。 | +|-------------------|----------------------| +| ![QQ群聊](https://github.com/user-attachments/assets/29bfef3f-438a-4b9f-ba09-e1926d1669cb) | ![微信交流群](https://github.com/user-attachments/assets/b1f24ebb-153b-44b1-b522-14f765154110) | diff --git a/data/face_pts_mean.txt b/data/face_pts_mean.txt new file mode 100644 index 0000000..6156796 --- /dev/null +++ b/data/face_pts_mean.txt @@ -0,0 +1,478 @@ +500.0 701.5 110.2 +500.0 588.2 29.8 +500.0 624.2 113.5 +472.2 470.8 75.8 +500.0 551.6 18.9 +500.0 504.4 32.9 +500.0 389.6 124.6 +266.6 375.6 255.2 +500.0 295.9 148.2 +500.0 247.2 142.4 +500.0 78.0 180.5 +500.0 715.3 114.0 +500.0 729.9 124.9 +500.0 739.6 140.7 +500.0 779.9 151.7 +500.0 797.2 147.6 +500.0 818.5 142.1 +500.0 839.8 149.8 +500.0 867.8 184.2 +500.0 607.1 46.5 +470.4 606.8 91.8 +140.2 243.2 416.6 +356.6 405.7 220.1 +324.8 408.5 222.6 +293.6 407.6 232.1 +252.1 388.8 260.1 +383.6 398.0 223.9 +309.4 302.6 209.1 +346.6 307.0 209.2 +274.6 309.0 220.3 +252.1 324.0 234.0 +223.9 412.4 276.5 +377.4 906.5 221.4 +254.6 367.0 266.4 +125.2 398.4 426.0 +186.7 386.1 310.9 +334.1 559.7 186.7 +460.9 695.2 115.1 +465.3 729.6 130.0 +422.6 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5.850000000000000000e+02 3.900000000000000000e+01 +5.720000000000000000e+02 5.280000000000000000e+02 1.100000000000000000e+01 +5.850000000000000000e+02 4.970000000000000000e+02 -6.000000000000000000e+00 +6.130000000000000000e+02 4.740000000000000000e+02 -1.500000000000000000e+01 +6.350000000000000000e+02 4.590000000000000000e+02 -1.900000000000000000e+01 diff --git a/data/pca.pkl b/data/pca.pkl new file mode 100644 index 0000000..ed2db82 Binary files /dev/null and b/data/pca.pkl differ diff --git a/data_preparation.py b/data_preparation.py new file mode 100644 index 0000000..866c3ae --- /dev/null +++ b/data_preparation.py @@ -0,0 +1,230 @@ +import uuid +import tqdm +import numpy as np +import cv2 +import sys +import os +import math +import pickle +import mediapipe as mp +mp_face_mesh = mp.solutions.face_mesh +mp_face_detection = mp.solutions.face_detection + +def detect_face(frame): + # 剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80的 + with mp_face_detection.FaceDetection( + model_selection=1, min_detection_confidence=0.5) as face_detection: + results = face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if not results.detections or len(results.detections) > 1: + return -1, None + rect = results.detections[0].location_data.relative_bounding_box + out_rect = [rect.xmin, rect.xmin + rect.width, rect.ymin, rect.ymin + rect.height] + nose_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.NOSE_TIP) + l_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.LEFT_EYE) + r_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.RIGHT_EYE) + # print(nose_, l_eye_, r_eye_) + if nose_.x > l_eye_.x or nose_.x < r_eye_.x: + return -2, out_rect + + h, w = frame.shape[:2] + # print(frame.shape) + if rect.xmin < 0 or rect.ymin < 0 or rect.xmin + rect.width > w or rect.ymin + rect.height > h: + return -3, out_rect + if rect.width * w < 100 or rect.height * h < 100: + return -4, out_rect + return 1, out_rect + + +def calc_face_interact(face0, face1): + x_min = min(face0[0], face1[0]) + x_max = max(face0[1], face1[1]) + y_min = min(face0[2], face1[2]) + y_max = max(face0[3], face1[3]) + tmp0 = ((face0[1] - face0[0]) * (face0[3] - face0[2])) / ((x_max - x_min) * (y_max - y_min)) + tmp1 = ((face1[1] - face1[0]) * (face1[3] - face1[2])) / ((x_max - x_min) * (y_max - y_min)) + return min(tmp0, tmp1) + + +def detect_face_mesh(frame): + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5) as face_mesh: + results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + pts_3d = np.zeros([478, 3]) + if not results.multi_face_landmarks: + print("****** WARNING! No face detected! ******") + else: + image_height, image_width = frame.shape[:2] + for face_landmarks in results.multi_face_landmarks: + for index_, i in enumerate(face_landmarks.landmark): + x_px = min(math.floor(i.x * image_width), image_width - 1) + y_px = min(math.floor(i.y * image_height), image_height - 1) + z_px = min(math.floor(i.z * image_width), image_width - 1) + pts_3d[index_] = np.array([x_px, y_px, z_px]) + return pts_3d + + +def ExtractFromVideo(video_path, circle = False): + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return 0 + + dir_path = os.path.dirname(video_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + + totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 + totalFrames = int(totalFrames) + pts_3d = np.zeros([totalFrames, 478, 3]) + frame_index = 0 + face_rect_list = [] + mat_list = [] + model_name = os.path.basename(video_path)[:-4] + + # os.makedirs("../preparation/{}/image".format(model_name)) + for frame_index in tqdm.tqdm(range(totalFrames)): + ret, frame = cap.read() # 按帧读取视频 + # #到视频结尾时终止 + if ret is False: + break + # cv2.imwrite("../preparation/{}/image/{:0>6d}.png".format(model_name, frame_index), frame) + tag_, rect = detect_face(frame) + if frame_index == 0 and tag_ != 1: + print("第一帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80") + pts_3d = -1 + break + elif tag_ == -1: # 有时候人脸检测会失败，就用上一帧的结果替代这一帧的结果 + rect = face_rect_list[-1] + elif tag_ != 1: + print("第{}帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80, tag: {}".format(frame_index, tag_)) + # exit() + if len(face_rect_list) > 0: + face_area_inter = calc_face_interact(face_rect_list[-1], rect) + # print(frame_index, face_area_inter) + if face_area_inter < 0.6: + print("人脸区域变化幅度太大，请复查，超出值为{}, frame_num: {}".format(face_area_inter, frame_index)) + pts_3d = -2 + break + + face_rect_list.append(rect) + + x_min = rect[0] * vid_width + y_min = rect[2] * vid_height + x_max = rect[1] * vid_width + y_max = rect[3] * vid_height + seq_w, seq_h = x_max - x_min, y_max - y_min + x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2 + # x_min = int(max(0, x_mid - seq_w * 0.65)) + # y_min = int(max(0, y_mid - seq_h * 0.4)) + # x_max = int(min(vid_width, x_mid + seq_w * 0.65)) + # y_max = int(min(vid_height, y_mid + seq_h * 0.8)) + crop_size = int(max(seq_w * 1.35, seq_h * 1.35)) + x_min = int(max(0, x_mid - crop_size * 0.5)) + y_min = int(max(0, y_mid - crop_size * 0.45)) + x_max = int(min(vid_width, x_min + crop_size)) + y_max = int(min(vid_height, y_min + crop_size)) + + frame_face = frame[y_min:y_max, x_min:x_max] + print(y_min, y_max, x_min, x_max) + # cv2.imshow("s", frame_face) + # cv2.waitKey(20) + frame_kps = detect_face_mesh(frame_face) + pts_3d[frame_index] = frame_kps + np.array([x_min, y_min, 0]) + cap.release() # 释放视频对象 + return pts_3d + + +def CirculateVideo(video_in_path, video_out_path, export_imgs = False): + # 1 视频转换为25FPS, 并折叠循环拼接 + front_video_path = "front.mp4" + back_video_path = "back.mp4" + # ffmpeg_cmd = "ffmpeg -i {} -r 25 -ss 00:00:00 -t 00:02:00 -an -loglevel quiet -y {}".format(video_in_path, front_video_path) + ffmpeg_cmd = "ffmpeg -i {} -r 25 -an -loglevel quiet -y {}".format(video_in_path, front_video_path) + os.system(ffmpeg_cmd) + + # front_video_path = video_in_path + + cap = cv2.VideoCapture(front_video_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) + cap.release() + + + ffmpeg_cmd = "ffmpeg -i {} -vf reverse -y {}".format(front_video_path, back_video_path) + os.system(ffmpeg_cmd) + ffmpeg_cmd = "ffmpeg -f concat -i {} -c:v copy -y {}".format("video_concat.txt", video_out_path) + os.system(ffmpeg_cmd) + # exit() + print("正向视频帧数：", frames) + pts_3d = ExtractFromVideo(front_video_path) + if type(pts_3d) is np.ndarray and len(pts_3d) == frames: + print("关键点已提取") + pts_3d = np.concatenate([pts_3d, pts_3d[::-1]], axis=0) + Path_output_pkl = "{}/keypoint_rotate.pkl".format(os.path.dirname(video_out_path)) + with open(Path_output_pkl, "wb") as f: + pickle.dump(pts_3d, f) + + if export_imgs: + # 计算整个视频中人脸的范围 + x_min, y_min, x_max, y_max = np.min(pts_3d[:, :, 0]), np.min( + pts_3d[:, :, 1]), np.max( + pts_3d[:, :, 0]), np.max(pts_3d[:, :, 1]) + new_w = int((x_max - x_min) * 0.55) * 2 + new_h = int((y_max - y_min) * 0.6) * 2 + center_x = int((x_max + x_min) / 2.) + center_y = int(y_min + (y_max - y_min) * 0.6) + size = max(new_h, new_w) + x_min, y_min, x_max, y_max = int(center_x - size // 2), int(center_y - size // 2), int( + center_x + size // 2), int(center_y + size // 2) + + # 确定裁剪区域上边top和左边left坐标 + top = y_min + left = x_min + # 裁剪区域与原图的重合区域 + top_coincidence = int(max(top, 0)) + bottom_coincidence = int(min(y_max, vid_height)) + left_coincidence = int(max(left, 0)) + right_coincidence = int(min(x_max, vid_width)) + print("人脸活动范围：{}:{}, {}:{}".format(top_coincidence, bottom_coincidence, left_coincidence, right_coincidence)) + np.savetxt("{}/face_rect.txt".format(os.path.dirname(video_out_path)), + np.array([top_coincidence, bottom_coincidence, left_coincidence, right_coincidence])) + os.makedirs("{}/image".format(os.path.dirname(video_out_path))) + ffmpeg_cmd = "ffmpeg -i {} -vf crop={}:{}:{}:{},scale=512:512:flags=neighbor -loglevel quiet -y {}/image/%06d.png".format( + front_video_path, + right_coincidence - left_coincidence, + bottom_coincidence - top_coincidence, + left_coincidence, + top_coincidence, + os.path.dirname(video_out_path) + ) + os.system(ffmpeg_cmd) + + cap = cv2.VideoCapture(video_out_path) + frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) + cap.release() + print("循环视频帧数：", frames) + + +def main(): + # 检查命令行参数的数量 + if len(sys.argv) != 2: + print("Usage: python data_preparation.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + video_name = sys.argv[1] + print(f"Video name is set to: {video_name}") + + new_data_path = "video_data/{}".format(uuid.uuid1()) + os.makedirs(new_data_path, exist_ok=True) + video_out_path = "{}/circle.mp4".format(new_data_path) + CirculateVideo(video_name, video_out_path, export_imgs=False) + +if __name__ == "__main__": + main() diff --git a/data_preparation_web.py b/data_preparation_web.py new file mode 100644 index 0000000..3a99855 --- /dev/null +++ b/data_preparation_web.py @@ -0,0 +1,291 @@ +import uuid +import tqdm +import numpy as np +import cv2 +import sys +import os +import math +from talkingface.data.few_shot_dataset import get_image +import torch +import mediapipe as mp +from talkingface.utils import crop_mouth, main_keypoints_index, smooth_array +import json +from mini_live.obj.wrap_utils import index_wrap, index_edge_wrap + +mp_face_mesh = mp.solutions.face_mesh +mp_face_detection = mp.solutions.face_detection + +def detect_face(frame): + # 剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80的 + with mp_face_detection.FaceDetection( + model_selection=1, min_detection_confidence=0.5) as face_detection: + results = face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if not results.detections or len(results.detections) > 1: + return -1, None + rect = results.detections[0].location_data.relative_bounding_box + out_rect = [rect.xmin, rect.xmin + rect.width, rect.ymin, rect.ymin + rect.height] + nose_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.NOSE_TIP) + l_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.LEFT_EYE) + r_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.RIGHT_EYE) + # print(nose_, l_eye_, r_eye_) + if nose_.x > l_eye_.x or nose_.x < r_eye_.x: + return -2, out_rect + + h, w = frame.shape[:2] + # print(frame.shape) + if rect.xmin < 0 or rect.ymin < 0 or rect.xmin + rect.width > w or rect.ymin + rect.height > h: + return -3, out_rect + if rect.width * w < 100 or rect.height * h < 100: + return -4, out_rect + return 1, out_rect + + +def calc_face_interact(face0, face1): + x_min = min(face0[0], face1[0]) + x_max = max(face0[1], face1[1]) + y_min = min(face0[2], face1[2]) + y_max = max(face0[3], face1[3]) + tmp0 = ((face0[1] - face0[0]) * (face0[3] - face0[2])) / ((x_max - x_min) * (y_max - y_min)) + tmp1 = ((face1[1] - face1[0]) * (face1[3] - face1[2])) / ((x_max - x_min) * (y_max - y_min)) + return min(tmp0, tmp1) + + +def detect_face_mesh(frame): + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5) as face_mesh: + results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + pts_3d = np.zeros([478, 3]) + if not results.multi_face_landmarks: + print("****** WARNING! No face detected! ******") + else: + image_height, image_width = frame.shape[:2] + for face_landmarks in results.multi_face_landmarks: + for index_, i in enumerate(face_landmarks.landmark): + x_px = min(math.floor(i.x * image_width), image_width - 1) + y_px = min(math.floor(i.y * image_height), image_height - 1) + z_px = min(math.floor(i.z * image_width), image_width - 1) + pts_3d[index_] = np.array([x_px, y_px, z_px]) + return pts_3d + + +def ExtractFromVideo(video_path): + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return 0 + + dir_path = os.path.dirname(video_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + + totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 + totalFrames = int(totalFrames) + pts_3d = np.zeros([totalFrames, 478, 3]) + frame_index = 0 + face_rect_list = [] + ms_list = [] + model_name = os.path.basename(video_path)[:-4] + + # os.makedirs("../preparation/{}/image".format(model_name)) + for frame_index in tqdm.tqdm(range(totalFrames)): + ret, frame = cap.read() # 按帧读取视频 + ms = cap.get(cv2.CAP_PROP_POS_MSEC) + ms_list.append(ms) + # #到视频结尾时终止 + if ret is False: + break + # cv2.imwrite("../preparation/{}/image/{:0>6d}.png".format(model_name, frame_index), frame) + tag_, rect = detect_face(frame) + if frame_index == 0 and tag_ != 1: + print("第一帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80") + pts_3d = -1 + break + elif tag_ == -1: # 有时候人脸检测会失败，就用上一帧的结果替代这一帧的结果 + rect = face_rect_list[-1] + elif tag_ != 1: + print("第{}帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80, tag: {}".format(frame_index, tag_)) + # exit() + if len(face_rect_list) > 0: + face_area_inter = calc_face_interact(face_rect_list[-1], rect) + # print(frame_index, face_area_inter) + if face_area_inter < 0.6: + print("人脸区域变化幅度太大，请复查，超出值为{}, frame_num: {}".format(face_area_inter, frame_index)) + pts_3d = -2 + break + + face_rect_list.append(rect) + + x_min = rect[0] * vid_width + y_min = rect[2] * vid_height + x_max = rect[1] * vid_width + y_max = rect[3] * vid_height + seq_w, seq_h = x_max - x_min, y_max - y_min + x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2 + # x_min = int(max(0, x_mid - seq_w * 0.65)) + # y_min = int(max(0, y_mid - seq_h * 0.4)) + # x_max = int(min(vid_width, x_mid + seq_w * 0.65)) + # y_max = int(min(vid_height, y_mid + seq_h * 0.8)) + crop_size = int(max(seq_w * 1.35, seq_h * 1.35)) + x_min = int(max(0, x_mid - crop_size * 0.5)) + y_min = int(max(0, y_mid - crop_size * 0.45)) + x_max = int(min(vid_width, x_min + crop_size)) + y_max = int(min(vid_height, y_min + crop_size)) + + frame_face = frame[y_min:y_max, x_min:x_max] + print(y_min, y_max, x_min, x_max) + # cv2.imshow("s", frame_face) + # cv2.waitKey(20) + frame_kps = detect_face_mesh(frame_face) + pts_3d[frame_index] = frame_kps + np.array([x_min, y_min, 0]) + cap.release() # 释放视频对象 + return pts_3d,ms_list + + +def step0_keypoints(video_in_path, out_path): + video_out_path = os.path.join(out_path, "01.mp4") + # 1 视频转换为25FPS + ffmpeg_cmd = "ffmpeg -i {} -r 25 -an -y {}".format(video_in_path, video_out_path) + os.system(ffmpeg_cmd) + + # front_video_path = video_in_path + + cap = cv2.VideoCapture(video_out_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) + cap.release() + + print("正向视频帧数：", frames) + pts_3d,_ = ExtractFromVideo(video_out_path) + if type(pts_3d) is np.ndarray and len(pts_3d) == frames: + print("关键点已提取") + + pts_3d = pts_3d.reshape(len(pts_3d), -1) + smooth_array_ = smooth_array(pts_3d, weight=[0.03, 0.06, 0.11, 0.6, 0.11, 0.06, 0.03]) + pts_3d = smooth_array_.reshape(len(pts_3d), 478, 3) + return pts_3d,vid_width,vid_height + +def step1_crop_mouth(pts_3d, vid_width, vid_height): + list_source_crop_rect = [crop_mouth(source_pts[main_keypoints_index], vid_width, vid_height) for source_pts in + pts_3d] + list_source_crop_rect = np.array(list_source_crop_rect).reshape(len(pts_3d), -1) + face_size = (list_source_crop_rect[:,2] - list_source_crop_rect[:,0]).mean()/2.0 + (list_source_crop_rect[:,3] - list_source_crop_rect[:,1]).mean()/2.0 + face_size = int(face_size)//2 * 2 + face_mid = (list_source_crop_rect[:,2:] + list_source_crop_rect[:,0:2])/2. + # step 1: Smooth Cropping Rectangle Transition + # Since HTML video playback can have inconsistent frame rates and may not align precisely from frame to frame, adjust the cropping rectangle to transition smoothly, compensating for potential misalignment. + face_mid = smooth_array(face_mid, weight=[0.20, 0.20, 0.20, 0.20, 0.20]) + face_mid = face_mid.astype(int) + if face_mid[:, 0].max() + face_size / 2 > vid_width or face_mid[:, 1].max() + face_size / 2 > vid_height: + raise ValueError("人脸范围超出了视频，请保证视频合格后再重试") + + list_source_crop_rect = np.concatenate([face_mid - face_size // 2, face_mid + face_size // 2], axis = 1) + + standard_size = 128 + list_standard_v = [] + for frame_index in range(len(list_source_crop_rect)): + source_pts = pts_3d[frame_index] + source_crop_rect = list_source_crop_rect[frame_index] + print(source_crop_rect) + standard_v = get_image(source_pts, source_crop_rect, input_type="mediapipe", resize=standard_size) + + list_standard_v.append(standard_v) + + return list_source_crop_rect, list_standard_v + +def step2_generate_obj(list_source_crop_rect, list_standard_v, out_path): + from mini_live.obj.obj_utils import generateRenderInfo, generateWrapModel + render_verts, render_face = generateRenderInfo() + face_pts_mean = render_verts[:478, :3].copy() + + wrapModel_verts, wrapModel_face = generateWrapModel() + # 求平均人脸 + from talkingface.run_utils import calc_face_mat + mat_list, _, face_pts_mean_personal_primer = calc_face_mat(np.array(list_standard_v), face_pts_mean) + from mini_live.obj.wrap_utils import newWrapModel + + face_wrap_entity = newWrapModel(wrapModel_verts, face_pts_mean_personal_primer) + + with open(os.path.join(out_path,"face3D.obj"), "w") as f: + for i in face_wrap_entity: + f.write("v {:.3f} {:.3f} {:.3f} {:.02f} {:.0f}\n".format(i[0], i[1], i[2], i[3], i[4])) + for i in range(len(wrapModel_face) // 3): + f.write("f {0} {1} {2}\n".format(wrapModel_face[3 * i] + 1, wrapModel_face[3 * i + 1] + 1, + wrapModel_face[3 * i + 2] + 1)) + json_data = [] + for frame_index in range(len(list_source_crop_rect)): + source_crop_rect = list_source_crop_rect[frame_index] + standard_v = list_standard_v[frame_index] + + standard_v = standard_v[index_wrap, :2].flatten().tolist() + mat = mat_list[frame_index].T.flatten().tolist() + # 将 standard_v 中所有元素四舍五入到两位小数 + standard_v_rounded = [round(i, 5) for i in mat] + [round(i, 1) for i in standard_v] + print(len(standard_v_rounded), 16 + 209 * 2) + json_data.append({"rect": source_crop_rect.tolist(), "points": standard_v_rounded}) + # print(json_data) + # break + with open(os.path.join(out_path, "json_data.json"), "w") as f: + json.dump(json_data, f) + +def step3_generate_ref_tensor(list_source_crop_rect, list_standard_v, out_path): + from talkingface.render_model_mini import RenderModel_Mini + renderModel_mini = RenderModel_Mini() + renderModel_mini.loadModel("checkpoint/DINet_mini/epoch_40.pth") + + from talkingface.data.few_shot_dataset import select_ref_index + from talkingface.utils import draw_mouth_maps + + driven_keypoints = np.array(list_standard_v)[:,main_keypoints_index] + ref_img_index_list = select_ref_index(driven_keypoints, n_ref=3, ratio=0.33) # 从当前视频选n_ref个图片 + ref_img_list = [] + + video_path = os.path.join(out_path, "01.mp4") + cap_input = cv2.VideoCapture(video_path) + for index in ref_img_index_list: + cap_input.set(cv2.CAP_PROP_POS_FRAMES, index) # 设置要获取的帧号 + ret, frame = cap_input.read() + + ref_face_edge = draw_mouth_maps(driven_keypoints[index], size=(128, 128)) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + ref_img = get_image(frame, list_source_crop_rect[index], input_type="image", resize=128) + + ref_face_edge = cv2.resize(ref_face_edge, (128, 128)) + ref_img = cv2.resize(ref_img, (128, 128)) + w_pad = int((128 - 72) / 2) + h_pad = int((128 - 56) / 2) + + ref_img = np.concatenate([ref_img[h_pad:-h_pad, w_pad:-w_pad], ref_face_edge[h_pad:-h_pad, w_pad:-w_pad, :1]], + axis=2) + # cv2.imshow("ss", ref_img[:,:,::-1]) + # cv2.waitKey(-1) + ref_img_list.append(ref_img) + ref_img = np.concatenate(ref_img_list, axis=2) + + ref_tensor = torch.from_numpy(ref_img / 255.).float().permute(2, 0, 1).unsqueeze(0).cuda() + + renderModel_mini.net.ref_input(ref_tensor) + + ref_in_feature = renderModel_mini.net.infer_model.ref_in_feature + print(1111, ref_in_feature.size()) + ref_in_feature = ref_in_feature.detach().squeeze(0).cpu().float().numpy().flatten() + print(1111, ref_in_feature.shape) + + np.savetxt(os.path.join(out_path, 'ref_data.txt'), ref_in_feature, fmt='%.8f') + +def main(): + video_in_path = r"E:\data\video\video/5.mp4" + out_path = "web_demo/static/assets" + pts_3d, vid_width,vid_height = step0_keypoints(video_in_path, out_path) + list_source_crop_rect, list_standard_v = step1_crop_mouth(pts_3d, vid_width, vid_height) + step2_generate_obj(list_source_crop_rect, list_standard_v, out_path) + step3_generate_ref_tensor(list_source_crop_rect, list_standard_v, out_path) + +if __name__ == "__main__": + main() diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..defe411 --- /dev/null +++ b/demo.py @@ -0,0 +1,69 @@ +import time +import os +import numpy as np +import uuid +import cv2 +import tqdm +import shutil +import sys +from talkingface.audio_model import AudioModel +from talkingface.render_model import RenderModel + +def main(): + # 检查命令行参数的数量 + if len(sys.argv) < 5: + print("Usage: python demo.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + video_path = sys.argv[1] + print(f"Video path is set to: {video_path}") + audio_path = sys.argv[2] + print(f"Audio path is set to: {audio_path}") + output_video_name = sys.argv[3] + print(f"output video name is set to: {output_video_name}") + try: + model_name = sys.argv[4] + print(f"model_name: {model_name}") + except Exception: + model_name = "render.pth" + + + audioModel = AudioModel() + audioModel.loadModel("checkpoint/audio.pkl") + + renderModel = RenderModel() + renderModel.loadModel(f"checkpoint/{model_name}") + pkl_path = "{}/keypoint_rotate.pkl".format(video_path) + video_path = "{}/circle.mp4".format(video_path) + renderModel.reset_charactor(video_path, pkl_path) + + # wavpath = "video_data/audio0.wav" + wavpath = audio_path + mouth_frame = audioModel.interface_wav(wavpath) + cap_input = cv2.VideoCapture(video_path) + vid_width = cap_input.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap_input.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + cap_input.release() + task_id = str(uuid.uuid1()) + os.makedirs("output/{}".format(task_id), exist_ok=True) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + save_path = "output/{}/silence.mp4".format(task_id) + videoWriter = cv2.VideoWriter(save_path, fourcc, 25, (int(vid_width) * 1, int(vid_height))) + for frame in tqdm.tqdm(mouth_frame): + frame = renderModel.interface(frame) + # cv2.imshow("s", frame) + # cv2.waitKey(40) + + videoWriter.write(frame) + + videoWriter.release() + val_video = "../output/{}.mp4".format(task_id) + os.system( + "ffmpeg -y -i {} -i {} -c:v libx264 -pix_fmt yuv420p -loglevel quiet {}".format(save_path, wavpath, output_video_name)) + shutil.rmtree("output/{}".format(task_id)) + + + +if __name__ == "__main__": + main() diff --git a/demo_avatar.py b/demo_avatar.py new file mode 100644 index 0000000..fa3c706 --- /dev/null +++ b/demo_avatar.py @@ -0,0 +1,38 @@ +import time +import os +import numpy as np +from scipy.io import wavfile +import cv2 +import glob +from talkingface.audio_model import AudioModel +from talkingface.render_model import RenderModel + +audioModel = AudioModel() +audioModel.loadModel("checkpoint/audio.pkl") + +renderModel = RenderModel() +renderModel.loadModel("checkpoint/render.pth") +test_video = "test" +pkl_path = "video_data/{}/keypoint_rotate.pkl".format(test_video) +video_path = "video_data/{}/circle.mp4".format(test_video) +renderModel.reset_charactor(video_path, pkl_path) + +wavpath = "video_data/audio0.wav" +rate, wav = wavfile.read(wavpath, mmap=False) +index_ = 0 +frame_index__ = 0 +import sounddevice as sd +sample_rate = 16000 +samples_per_read = int(0.04 * sample_rate) +with sd.InputStream( + channels=1, dtype="float32", samplerate=sample_rate +) as s: + while True: + samples, _ = s.read(samples_per_read) # a blocking read + pcm_data = samples.reshape(-1) + print(pcm_data.shape) + mouth_frame = audioModel.interface_frame(pcm_data) + frame = renderModel.interface(mouth_frame) + cv2.imshow("s", frame) + cv2.waitKey(10) + index_ += 1 diff --git a/demo_mini.py b/demo_mini.py new file mode 100644 index 0000000..ee19840 --- /dev/null +++ b/demo_mini.py @@ -0,0 +1,152 @@ +import os +os.environ["kmp_duplicate_lib_ok"] = "true" +import os +from mini_live.obj.wrap_utils import index_wrap, index_edge_wrap +current_dir = os.path.dirname(os.path.abspath(__file__)) +from mini_live.render import create_render_model +import pickle +import cv2 +import time +import numpy as np +import glob +import random +import os +import sys +import torch +from talkingface.model_utils import LoadAudioModel, Audio2bs +from talkingface.data.few_shot_dataset import get_image + +def run(video_path, pkl_path, wav_path, output_video_path): + Audio2FeatureModel = LoadAudioModel(r'checkpoint/lstm/lstm_model_epoch_325.pkl') + + from talkingface.render_model_mini import RenderModel_Mini + renderModel_mini = RenderModel_Mini() + renderModel_mini.loadModel("checkpoint/DINet_mini/epoch_40.pth") + + standard_size = 256 + crop_rotio = [0.5, 0.5, 0.5, 0.5] + out_w = int(standard_size * (crop_rotio[0] + crop_rotio[1])) + out_h = int(standard_size * (crop_rotio[2] + crop_rotio[3])) + out_size = (out_w, out_h) + renderModel_gl = create_render_model((out_w, out_h), floor=20) + + from mini_live.obj.obj_utils import generateWrapModel + from talkingface.utils import crop_mouth, main_keypoints_index + wrapModel, wrapModel_face = generateWrapModel() + + with open(pkl_path, "rb") as f: + images_info = pickle.load(f) + + images_info = np.concatenate([images_info, images_info[::-1]], axis=0) + + cap = cv2.VideoCapture(video_path) + vid_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + list_source_crop_rect = [] + list_video_img = [] + list_standard_img = [] + list_standard_v = [] + list_standard_vt = [] + for frame_index in range(min(vid_frame_count, len(images_info))): + ret, frame = cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) + source_pts = images_info[frame_index] + source_crop_rect = crop_mouth(source_pts[main_keypoints_index], vid_width, vid_height) + + standard_img = get_image(frame, source_crop_rect, input_type="image", resize=standard_size) + standard_v = get_image(source_pts, source_crop_rect, input_type="mediapipe", resize=standard_size) + standard_vt = standard_v[:, :2] / standard_size + + list_video_img.append(frame) + list_source_crop_rect.append(source_crop_rect) + list_standard_img.append(standard_img) + list_standard_v.append(standard_v) + list_standard_vt.append(standard_vt) + cap.release() + + renderModel_mini.reset_charactor(list_standard_img, np.array(list_standard_v)[:, main_keypoints_index]) + from talkingface.run_utils import calc_face_mat + mat_list, _, face_pts_mean_personal_primer = calc_face_mat(np.array(list_standard_v), renderModel_gl.face_pts_mean) + from mini_live.obj.wrap_utils import newWrapModel + face_wrap_entity = newWrapModel(wrapModel, face_pts_mean_personal_primer) + + renderModel_gl.GenVBO(face_wrap_entity) + + bs_array = Audio2bs(wav_path, Audio2FeatureModel)[5:] * 0.5 + import uuid + task_id = str(uuid.uuid1()) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + save_path = "{}.mp4".format(task_id) + videoWriter = cv2.VideoWriter(save_path, fourcc, 25, (int(vid_width), int(vid_height))) + + for frame_index in range(len(mat_list)): + if frame_index >= len(bs_array): + continue + bs = np.zeros([12], dtype=np.float32) + bs[:6] = bs_array[frame_index, :6] + # bs[2] = frame_index* 5 + + verts_frame_buffer = np.array(list_standard_vt)[frame_index, index_wrap, :2].copy() * 2 - 1 + + rgba = renderModel_gl.render2cv(verts_frame_buffer, out_size=out_size, mat_world=mat_list[frame_index].T, + bs_array=bs) + # rgb = cv2.cvtColor(rgba, cv2.COLOR_RGBA2RGB) + # rgba = cv2.resize(rgba, (128, 128)) + rgba = rgba[::2, ::2, :] + + gl_tensor = torch.from_numpy(rgba / 255.).float().permute(2, 0, 1).unsqueeze(0) + source_tensor = cv2.resize(list_standard_img[frame_index], (128, 128)) + source_tensor = torch.from_numpy(source_tensor / 255.).float().permute(2, 0, 1).unsqueeze(0) + + warped_img = renderModel_mini.interface(source_tensor.cuda(), gl_tensor.cuda()) + + image_numpy = warped_img.detach().squeeze(0).cpu().float().numpy() + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = image_numpy.clip(0, 255) + image_numpy = image_numpy.astype(np.uint8) + + x_min, y_min, x_max, y_max = list_source_crop_rect[frame_index] + + img_face = cv2.resize(image_numpy, (x_max - x_min, y_max - y_min)) + img_bg = list_video_img[frame_index][:, :, :3] + img_bg[y_min:y_max, x_min:x_max, :3] = img_face[:, :, :3] + # cv2.imshow('scene', img_bg[:,:,::-1]) + # cv2.waitKey(10) + # print(time.time()) + + videoWriter.write(img_bg[:, :, ::-1]) + videoWriter.release() + + os.system( + "ffmpeg -i {} -i {} -c:v libx264 -pix_fmt yuv420p {}".format(save_path, wav_path, output_video_path)) + os.remove(save_path) + + cv2.destroyAllWindows() + +def main(): + # 检查命令行参数的数量 + if len(sys.argv) < 4: + print("Usage: python demo_mini.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + video_path = sys.argv[1] + print(f"Video path is set to: {video_path}") + wav_path = sys.argv[2] + print(f"Audio path is set to: {wav_path}") + output_video_name = sys.argv[3] + print(f"output video name is set to: {output_video_name}") + + pkl_path = "{}/keypoint_rotate.pkl".format(video_path) + video_path = "{}/circle.mp4".format(video_path) + + run(video_path, pkl_path, wav_path, output_video_name) + +# 示例使用 +if __name__ == "__main__": + main() + + + diff --git a/mini_live/bs_texture.png b/mini_live/bs_texture.png new file mode 100644 index 0000000..c19b628 Binary files /dev/null and b/mini_live/bs_texture.png differ diff --git a/mini_live/bs_texture_halfFace.png b/mini_live/bs_texture_halfFace.png new file mode 100644 index 0000000..f28a247 Binary files /dev/null and b/mini_live/bs_texture_halfFace.png differ diff --git a/mini_live/face_fusion_mask.png b/mini_live/face_fusion_mask.png new file mode 100644 index 0000000..8d56c6e Binary files /dev/null and b/mini_live/face_fusion_mask.png differ diff --git a/mini_live/generate_fusion_mask.py b/mini_live/generate_fusion_mask.py new file mode 100644 index 0000000..df0234f --- /dev/null +++ b/mini_live/generate_fusion_mask.py @@ -0,0 +1,16 @@ +import numpy as np +import cv2 +import os + +face_fusion_mask = np.zeros([128, 128], dtype = np.uint8) +for i in range(8): + face_fusion_mask[i:-i,i:-i] = min(255, i*40) + +cv2.imwrite("face_fusion_mask.png", face_fusion_mask) + + +from mini_live.obj.wrap_utils import index_wrap +image2 = cv2.imread("bs_texture.png") +image3 = np.zeros([12, 256, 3], dtype=np.uint8) +image3[:, :len(index_wrap)] = image2[:, index_wrap] +cv2.imwrite("bs_texture_halfFace.png", image3) \ No newline at end of file diff --git a/mini_live/mouth_fusion_mask.png b/mini_live/mouth_fusion_mask.png new file mode 100644 index 0000000..65bd4d5 Binary files /dev/null and b/mini_live/mouth_fusion_mask.png differ diff --git a/mini_live/obj/image_utils.py b/mini_live/obj/image_utils.py new file mode 100644 index 0000000..788fc56 --- /dev/null +++ b/mini_live/obj/image_utils.py @@ -0,0 +1,135 @@ +import numpy as np +import cv2 +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) + +def get_standard_image_(img, kps, crop_coords, resize = (256, 256)): + h = img.shape[0] + w = img.shape[1] + c = img.shape[2] + (x_min, y_min, x_max, y_max) = [int(ii) for ii in crop_coords] + new_w = x_max - x_min + new_h = y_max - y_min + img_new = np.zeros([new_h, new_w, c], dtype=np.uint8) + + # 确定裁剪区域上边top和左边left坐标 + top = int(y_min) + left = int(x_min) + # 裁剪区域与原图的重合区域 + top_coincidence = int(max(top, 0)) + bottom_coincidence = int(min(y_max, h)) + left_coincidence = int(max(left, 0)) + right_coincidence = int(min(x_max, w)) + img_new[top_coincidence - top:bottom_coincidence - top, left_coincidence - left:right_coincidence - left, :] = img[ + top_coincidence:bottom_coincidence, + left_coincidence:right_coincidence, + :] + + img_new = cv2.resize(img_new, resize) + kps = kps - np.array([left, top, 0]) + + factor = resize[0]/new_w + kps = kps * factor + return img_new, kps + +def get_standard_image(img_rgba, source_pts, source_crop_rect, out_size): + ''' + 将输入的RGBA图像和关键点点集转换为标准图像和标准顶点集。 + + 参数: + img_rgba (numpy.ndarray): 输入的RGBA图像，形状为 (H, W, 4)。 + source_pts (numpy.ndarray): 源点集，形状为 (N, 3)，其中N是点的数量，每个点有三个坐标 (x, y, z)。 + source_crop_rect (tuple): 源图像的裁剪矩形，格式为 (x, y, width, height)。 + out_size (int): 输出图像的大小，格式为 (width, height)。 + + 返回: + standard_img (numpy.ndarray): 标准化的图像，形状为 (out_size, out_size, 4)。 + standard_v (numpy.ndarray): 标准化的顶点集，形状为 (N, 3)。 + standard_vt (numpy.ndarray): 标准化的顶点集的纹理坐标，形状为 (N, 2)。 + ''' + source_pts[:, 2] = source_pts[:, 2] - np.max(source_pts[:, 2]) + standard_img, standard_v = get_standard_image_(img_rgba, source_pts, source_crop_rect, resize=out_size) + + standard_vt = standard_v.copy() + standard_vt = standard_vt[:,:2]/ out_size + return standard_img, standard_v, standard_vt + +def crop_face_from_several_images(pts_array_origin, img_w, img_h): + x_min, y_min, x_max, y_max = np.min(pts_array_origin[:, :, 0]), np.min( + pts_array_origin[:, :, 1]), np.max( + pts_array_origin[:, :, 0]), np.max(pts_array_origin[:, :, 1]) + new_w = (x_max - x_min) * 2 + new_h = (y_max - y_min) * 2 + center_x = (x_max + x_min) / 2. + center_y = y_min + (y_max - y_min) * 0.25 + x_min, y_min, x_max, y_max = int(center_x - new_w / 2), int(center_y - new_h / 2), int( + center_x + new_w / 2), int(center_y + new_h / 2) + x_min = max(0, x_min) + y_min = max(0, y_min) + x_max = min(x_max, img_w) + y_max = min(y_max, img_h) + new_size = min((x_max + x_min) / 2., (y_max + y_min) / 2.) + center_x = (x_max + x_min) / 2. + center_y = (y_max + y_min) / 2. + x_min, y_min, x_max, y_max = int(center_x - new_size), int(center_y - new_size), int( + center_x + new_size), int(center_y + new_size) + return np.array([x_min, y_min, x_max, y_max]) + +def crop_face_from_image(kps, crop_rotio = [0.6,0.6,0.65,1.35]): + ''' + 只为了裁剪图片 + :param kps: + :param crop_rotio: + :param standard_size: + :return: + ''' + x2d = kps[:, 0] + y2d = kps[:, 1] + w_span = x2d.max() - x2d.min() + h_span = y2d.max() - y2d.min() + crop_size = int(2*max(h_span, w_span)) + center_x = (x2d.max() + x2d.min()) / 2. + center_y = (y2d.max() + y2d.min()) / 2. + # 确定裁剪区域上边top和左边left坐标，中心点是(x2d.max() + x2d.min()/2, y2d.max() + y2d.min()/2) + y_min = int(center_y - crop_size*crop_rotio[2]) + y_max = int(center_y + crop_size*crop_rotio[3]) + x_min = int(center_x - crop_size*crop_rotio[0]) + x_max = int(center_x + crop_size*crop_rotio[1]) + return np.array([x_min, y_min, x_max, y_max]) + +def check_keypoint(img, pts_): + point_size = 1 + point_color = (0, 0, 255) # BGR + thickness = 4 # 0 、4、8 + for coor in pts_: + # coor = (coor +1 )/2. + cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, point_color, thickness) + cv2.imshow("a", img) + cv2.waitKey(-1) + +if __name__ == "__main__": + from talkingface.mediapipe_utils import detect_face_mesh + import glob + + image_list = glob.glob(r"F:\C\AI\CV\TalkingFace\OpenGLRender_0830\face_rgba/*.png") + image_list.sort() + for index, img_path in enumerate(image_list): + img_primer_rgba = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) + source_pts = detect_face_mesh([img_primer_rgba[:, :, :3]])[0] + img_primer_rgba = cv2.cvtColor(img_primer_rgba, cv2.COLOR_BGRA2RGBA) + + source_crop_rect = crop_face_from_image(source_pts, crop_rotio=[0.75, 0.75, 0.65, 1.35]) + standard_img, standard_v, standard_vt = get_standard_image(img_primer_rgba, source_pts, source_crop_rect, + out_size=(750, 1000)) + print(np.max(standard_vt[:, 0])) + print(np.max(standard_vt[:, 1])) + point_size = 1 + point_color = (0, 0, 255) # BGR + thickness = 4 # 0 、4、8 + pts_ = standard_v + img = standard_img + for coor in pts_: + # coor = (coor +1 )/2. + cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, point_color, thickness) + cv2.imshow("a", img) + cv2.waitKey(-1) \ No newline at end of file diff --git a/mini_live/obj/obj_mediapipe/face3D.obj b/mini_live/obj/obj_mediapipe/face3D.obj new file mode 100644 index 0000000..f62b07a --- /dev/null +++ b/mini_live/obj/obj_mediapipe/face3D.obj @@ -0,0 +1,1817 @@ +v 500.271 739.284 -238.683 +v 500.271 677.808 -282.308 +v 500.271 697.341 -236.893 +v 485.187 614.107 -257.349 +v 500.271 657.949 -288.222 +v 500.271 632.339 -280.626 +v 500.271 570.049 -230.870 +v 373.630 562.453 -160.007 +v 500.271 519.208 -218.065 +v 500.271 492.784 -221.212 +v 500.271 400.977 -200.539 +v 500.271 746.772 -236.622 +v 500.271 754.693 -230.707 +v 500.271 759.957 -222.134 +v 500.271 781.823 -216.166 +v 500.271 791.210 -218.390 +v 500.271 802.767 -221.375 +v 500.271 814.324 -217.197 +v 500.271 829.517 -198.532 +v 500.271 688.063 -273.247 +v 484.211 687.900 -248.667 +v 305.046 490.613 -72.433 +v 422.463 578.785 -179.052 +v 405.209 580.304 -177.696 +v 388.280 579.816 -172.541 +v 365.762 569.615 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263 251 +f 251 263 264 +f 263 252 264 +f 264 252 259 +f 252 257 259 +f 259 257 260 +f 257 262 260 +f 260 262 249 +f 262 265 249 +f 249 265 254 + +f 240 247 254 +f 240 254 255 diff --git a/mini_live/obj/obj_mediapipe/generate_wrap_obj.py b/mini_live/obj/obj_mediapipe/generate_wrap_obj.py new file mode 100644 index 0000000..50b4904 --- /dev/null +++ b/mini_live/obj/obj_mediapipe/generate_wrap_obj.py @@ -0,0 +1,118 @@ +index_wrap = [0, 2, 11, 12, 13, 14, 15, 16, 17, 18, 32, 36, 37, 38, 39, 40, 41, 42, 43, 50, 57, 58, 61, + 62, 72, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, + 96, 97, 98, 100, 101, 106, 116, 117, 118, 119, 123, 129, 132, 135, 136, 137, 138, 140, + 142, 146, 147, 148, 149, 150, 152, 164, 165, 167, 169, 170, 171, 172, 175, 176, 177, + 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 191, 192, 194, 199, 200, 201, 202, + 203, 204, 205, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 227, 234, 262, 266, + 267, 268, 269, 270, 271, 272, 273, 280, 287, 288, 291, 292, 302, 303, 304, 306, 307, + 308, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, + 326, 327, 329, 330, 335, 345, 346, 347, 348, 352, 358, 361, 364, 365, 366, 367, 369, + 371, 375, 376, 377, 378, 379, 391, 393, 394, 395, 396, 397, 400, 401, 402, 403, 404, + 405, 406, 407, 408, 409, 410, 411, 415, 416, 418, 421, 422, 423, 424, 425, 426, 427, + 428, 430, 431, 432, 433, 434, 435, 436, 447, 454] + +# INDEX_MP_LIPS = [ +# 291, 409, 270, 269, 267, 0, 37, 39, 40, 185, 61, +# 146, 91, 181, 84, 17, 314, 405, 321, 375, +# 306, 408, 304, 303, 302, 11, 72, 73, 74, 184, 76, +# 77, 90, 180, 85, 16, 315, 404, 320, 307, +# 292, 407, 272, 271, 268, 12, 38, 41, 42, 183, 62, +# 96, 89, 179, 86, 15, 316, 403, 319, 325, +# 308, 415, 310, 311, 312, 13, 82, 81, 80, 191, 78, +# 95, 88, 178, 87, 14, 317, 402, 318, 324, +# ] +INDEX_MP_LIPS_LOWER = [ +146, 91, 181, 84, 17, 314, 405, 321, 375, +77, 90, 180, 85, 16, 315, 404, 320, 307, +96, 89, 179, 86, 15, 316, 403, 319, 325, +95, 88, 178, 87, 14, 317, 402, 318, 324, +] +INDEX_MP_LIPS_UPPER = [ +291, 409, 270, 269, 267, 0, 37, 39, 40, 185, 61, +306, 408, 304, 303, 302, 11, 72, 73, 74, 184, 76, +292, 407, 272, 271, 268, 12, 38, 41, 42, 183, 62, +308, 415, 310, 311, 312, 13, 82, 81, 80, 191, 78, +] +index_lips_upper_wrap = [] +for i in INDEX_MP_LIPS_UPPER: + for j in range(len(index_wrap)): + if index_wrap[j] == i: + index_lips_upper_wrap.append(j) +index_lips_lower_wrap = [] +for i in INDEX_MP_LIPS_LOWER: + for j in range(len(index_wrap)): + if index_wrap[j] == i: + index_lips_lower_wrap.append(j) +# print(index_lips_wrap) +# exit(-1) + +index_edge_wrap = [111,43,57,21,76,59,68,67,78,66,69,168,177,169,170,161,176,123,159,145,208] +index_edge_wrap_upper = [111, 110, 51, 52, 53, 54, 48, 63, 56, 47, 46, 1, 148, 149, 158, 165, 150, 156, 155, 154, 153, 207, 208] +import numpy as np +def readObjFile(filepath): + v_ = [] + face = [] + with open(filepath) as f: + # with open(r"face3D.obj") as f: + content = f.readlines() + for i in content: + if i[:2] == "v ": + v0,v1,v2 = i[2:-1].split(" ") + v_.append(float(v0)) + v_.append(float(v1)) + v_.append(float(v2)) + if i[:2] == "f ": + tmp = i[2:-1].split(" ") + for ii in tmp: + a = ii.split("/")[0] + a = int(a) - 1 + face.append(a) + return v_, face + +verts_wrap,faces_wrap = readObjFile(r"wrap.obj") +verts_wrap = np.array(verts_wrap).reshape(-1,3) +vert_mid = verts_wrap[index_edge_wrap[:4] + index_edge_wrap[-4:]].mean(axis = 0) + +face_verts_num = len(verts_wrap) +index_new_edge = [] +new_vert_list = [] +for i in range(len(index_edge_wrap)): + index = index_edge_wrap[i] + new_vert = verts_wrap[index] + (verts_wrap[index] - vert_mid) * 0.3 + new_vert_list.append(new_vert) + index_new_edge.append(len(index_wrap) + i) +for i in range(len(index_edge_wrap) - 1): + faces_wrap.extend([index_edge_wrap[i], face_verts_num + i, index_edge_wrap[(i+1)%len(index_edge_wrap)]]) + faces_wrap.extend([index_edge_wrap[(i + 1) % len(index_edge_wrap)], face_verts_num + i, face_verts_num + (i+1)%len(index_edge_wrap)]) + +verts_wrap = np.concatenate([verts_wrap, np.array(new_vert_list).reshape(-1,3)], axis = 0) + +v_teeth, face_teeth = readObjFile("modified_teeth_upper.obj") +v_teeth2, face_teeth2 = readObjFile("modified_teeth_lower.obj") + + +faces_wrap = faces_wrap + [i + len(verts_wrap) for i in face_teeth] + [i + len(verts_wrap) + len(v_teeth) // 3 for i in + face_teeth2] + +verts_wrap = np.concatenate([verts_wrap, np.array(v_teeth).reshape(-1,3)], axis = 0) +verts_wrap = np.concatenate([verts_wrap, np.array(v_teeth2).reshape(-1,3)], axis = 0) + +# 边缘-1 正常0 上嘴唇2 下嘴唇2.01 上牙3 下牙4 +verts_wrap2 = np.zeros([len(verts_wrap), 5]) +verts_wrap2[:,:3] = verts_wrap +verts_wrap2[index_lips_upper_wrap, 3] = 2.0 +verts_wrap2[index_lips_lower_wrap, 3] = 2.01 +verts_wrap2[-36:-18, 3] = 3 +verts_wrap2[-18:, 3] = 4 +verts_wrap2[index_edge_wrap_upper, 3] = -1 +verts_wrap2[index_new_edge, 3] = -1 +verts_wrap2[:, 4] = range(len(verts_wrap2)) + +with open("face_wrap_entity.obj", "w") as f: + for i in verts_wrap2: + f.write("v {:.3f} {:.3f} {:.3f} {:.02f} {:.0f}\n".format(i[0], i[1], i[2], i[3], i[4])) + for i in range(len(faces_wrap)//3): + f.write("f {0} {1} {2}\n".format(faces_wrap[3*i]+1, faces_wrap[3*i+1]+1,faces_wrap[3*i+2]+1)) + +# f 240 247 254 +# f 240 254 255 \ No newline at end of file diff --git a/mini_live/obj/obj_mediapipe/modified_obj.py b/mini_live/obj/obj_mediapipe/modified_obj.py new file mode 100644 index 0000000..8abeb24 --- /dev/null +++ b/mini_live/obj/obj_mediapipe/modified_obj.py @@ -0,0 +1,79 @@ +import numpy as np + +def ModifyObjFile(path): + with_vn = False + verts = [] + vt = [] + vn = [] + face = [] + map_face = {} + with open(path) as f: + content = f.readlines() + for i in content: + if i[:2] == "v ": + verts.append(i) + if i[:3] == "vt ": + vt.append(i) + if i[:3] == "vn ": + with_vn = True + vn.append(i) + if i[:2] == "f ": + tmp = i[2:-1].split(" ") + face.extend(tmp) + print(len(verts),len(vt),len(vn),len(face),len(set(face))) + set_face = set(face) + print(len(set_face)) + for index,i in enumerate(set_face): + map_face[i] = index + + + + with open("modified_" + path, "w") as f: + for i in set_face: + index = int(i.split("/")[0]) - 1 + f.write(verts[index]) + for i in set_face: + index = int(i.split("/")[1]) - 1 + f.write(vt[index]) + if with_vn: + for i in set_face: + index = int(i.split("/")[2]) - 1 + f.write(vn[index]) + for i in range(len(face) // 3): + f.write("f {0}/{0}/{0} {1}/{1}/{1} {2}/{2}/{2}\n".format(map_face[face[3 * i]] + 1, + map_face[face[3 * i + 1]] + 1, + map_face[face[3 * i + 2]] + 1)) + else: + for i in range(len(face) // 3): + f.write("f {0}/{0} {1}/{1} {2}/{2}\n".format(map_face[face[3 * i]] + 1, + map_face[face[3 * i + 1]] + 1, + map_face[face[3 * i + 2]] + 1)) + + +ModifyObjFile("teeth_lower.obj") +ModifyObjFile("teeth_upper.obj") + +# ModifyObjFile("teeth_new.obj") +# +# import cv2 +# import numpy as np +# img0 = cv2.imread(r"texture/Std_Lower_Teeth_diffuse.jpg") +# img1 = cv2.imread(r"texture/Std_Tongue_diffuse.jpg") +# img2 = cv2.imread(r"texture/Std_Upper_Teeth_diffuse.jpg") +# img = np.concatenate([img0, img1, img2], axis = 1) +# cv2.imwrite("teeth.png", cv2.resize(img, (256*3,256))) +# +# img0 = cv2.imread(r"texture/Std_Lower_Teeth_normal.png") +# img1 = cv2.imread(r"texture/Std_Tongue_normal.jpg") +# img2 = cv2.imread(r"texture/Std_Upper_Teeth_normal.png") +# img = np.concatenate([img0, img1, img2], axis = 1) +# cv2.imwrite("teeth_normal.png", cv2.resize(img, (256*3,256))) +# print(img[:100,:100]) +# img0 = cv2.imread(r"teeth.png").astype(float) +# img1 = cv2.imread(r"teeth_ao.png", cv2.IMREAD_GRAYSCALE).astype(float)/255. +# +# img0[:,:,0] = img0[:,:,0]*img1 +# img0[:,:,1] = img0[:,:,1]*img1 +# img0[:,:,2] = img0[:,:,2]*img1 +# +# cv2.imwrite("teeth_.png", img0.astype(np.uint8)) \ No newline at end of file diff --git a/mini_live/obj/obj_mediapipe/modified_teeth_lower.obj b/mini_live/obj/obj_mediapipe/modified_teeth_lower.obj new file mode 100644 index 0000000..827762f --- /dev/null +++ b/mini_live/obj/obj_mediapipe/modified_teeth_lower.obj @@ -0,0 +1,52 @@ +v 448.235634 792.164490 -175.129913 +v 536.373879 770.329956 -192.046249 +v 519.147438 793.500122 -200.416458 +v 481.245979 771.633850 -200.767944 +v 518.879250 771.609314 -200.837250 +v 437.316597 790.634216 -154.143127 +v 564.105385 790.995911 -154.156693 +v 553.047402 792.094604 -175.252625 +v 464.153633 770.373901 -191.748932 +v 564.111916 766.822815 -154.555496 +v 481.376473 793.641968 -200.736969 +v 464.157173 792.896423 -191.986725 +v 536.171364 792.828003 -192.110428 +v 448.281075 768.492249 -175.249176 +v 500.247933 772.330933 -205.285172 +v 500.202187 793.314880 -205.248856 +v 437.176796 766.886230 -154.378220 +v 553.153481 768.487427 -175.145020 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 1.000000 1.000000 +vt 1.000000 0.500000 +vt 1.000000 0.500000 +vt 0.000000 1.000000 +vt 0.000000 1.000000 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 0.000000 0.500000 +vt 1.000000 1.000000 +vt 0.000000 1.000000 +vt 0.000000 1.000000 +vt 1.000000 0.500000 +vt 0.000000 0.500000 +vt 0.000000 1.000000 +vt 0.000000 0.500000 +vt 1.000000 0.500000 +f 10/10 18/18 7/7 +f 7/7 18/18 8/8 +f 18/18 2/2 8/8 +f 8/8 2/2 13/13 +f 2/2 5/5 13/13 +f 13/13 5/5 3/3 +f 5/5 15/15 3/3 +f 3/3 15/15 16/16 +f 15/15 4/4 16/16 +f 16/16 4/4 11/11 +f 4/4 9/9 11/11 +f 11/11 9/9 12/12 +f 9/9 14/14 12/12 +f 12/12 14/14 1/1 +f 14/14 17/17 1/1 +f 1/1 17/17 6/6 diff --git a/mini_live/obj/obj_mediapipe/modified_teeth_upper.obj b/mini_live/obj/obj_mediapipe/modified_teeth_upper.obj new file mode 100644 index 0000000..37491a9 --- /dev/null +++ b/mini_live/obj/obj_mediapipe/modified_teeth_upper.obj @@ -0,0 +1,52 @@ +v 443.795746 765.168640 -176.353269 +v 540.990906 741.596375 -196.660321 +v 522.946716 768.276550 -210.566007 +v 477.799988 741.571777 -210.943952 +v 522.885315 741.454102 -210.641004 +v 434.232269 764.718140 -155.438794 +v 565.570435 764.567688 -155.356763 +v 557.125732 765.103882 -176.335141 +v 458.399994 741.520996 -195.760328 +v 565.995178 741.692261 -155.788221 +v 478.100006 768.462036 -210.878370 +v 458.383057 766.574890 -195.788663 +v 541.328491 766.458435 -196.421415 +v 443.661713 740.884888 -176.518079 +v 500.255157 741.520996 -215.260328 +v 500.232025 769.099976 -215.250638 +v 434.873383 741.719055 -155.635678 +v 557.205872 740.844971 -176.471311 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 1.000000 0.500000 +vt 1.000000 0.000000 +vt 1.000000 0.000000 +vt 0.000000 0.500000 +vt 0.000000 0.500000 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 0.000000 0.000000 +vt 1.000000 0.500000 +vt 0.000000 0.500000 +vt 0.000000 0.500000 +vt 1.000000 0.000000 +vt 0.000000 0.000000 +vt 0.000000 0.500000 +vt 0.000000 0.000000 +vt 1.000000 0.000000 +f 10/10 18/18 7/7 +f 7/7 18/18 8/8 +f 18/18 2/2 8/8 +f 8/8 2/2 13/13 +f 2/2 5/5 13/13 +f 13/13 5/5 3/3 +f 5/5 15/15 3/3 +f 3/3 15/15 16/16 +f 15/15 4/4 16/16 +f 16/16 4/4 11/11 +f 4/4 9/9 11/11 +f 11/11 9/9 12/12 +f 9/9 14/14 12/12 +f 12/12 14/14 1/1 +f 14/14 17/17 1/1 +f 1/1 17/17 6/6 diff --git a/mini_live/obj/obj_mediapipe/teeth_lower.obj b/mini_live/obj/obj_mediapipe/teeth_lower.obj new file mode 100644 index 0000000..b75181a --- /dev/null +++ b/mini_live/obj/obj_mediapipe/teeth_lower.obj @@ -0,0 +1,56 @@ +# This file uses centimeters as units for non-parametric coordinates. + +mtllib teeth_lower.mtl +v 437.176796 766.886230 -154.378220 +v 448.235634 792.164490 -175.129913 +v 518.879250 771.609314 -200.837250 +v 536.171364 792.828003 -192.110428 +v 464.153633 770.373901 -191.748932 +v 500.247933 772.330933 -205.285172 +v 564.111916 766.822815 -154.555496 +v 564.105385 790.995911 -154.156693 +v 500.202187 793.314880 -205.248856 +v 481.376473 793.641968 -200.736969 +v 464.157173 792.896423 -191.986725 +v 448.281075 768.492249 -175.249176 +v 519.147438 793.500122 -200.416458 +v 553.153481 768.487427 -175.145020 +v 437.316597 790.634216 -154.143127 +v 553.047402 792.094604 -175.252625 +v 481.245979 771.633850 -200.767944 +v 536.373879 770.329956 -192.046249 +vt 0.000000 0.500000 +vt 1.000000 0.500000 +vt 0.000000 1.000000 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 0.000000 1.000000 +vt 1.000000 0.500000 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 0.000000 1.000000 +vt 1.000000 0.500000 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 0.000000 1.000000 +vt 1.000000 0.500000 +vt 1.000000 1.000000 +vt 0.000000 0.500000 +vt 0.000000 1.000000 +usemtl initialShadingGroup +f 7/1 14/2 8/3 +f 8/3 14/2 16/4 +f 14/2 18/5 16/4 +f 16/4 18/5 4/6 +f 18/5 3/7 4/6 +f 4/6 3/7 13/8 +f 3/7 6/9 13/8 +f 13/8 6/9 9/10 +f 6/9 17/11 9/10 +f 9/10 17/11 10/12 +f 17/11 5/13 10/12 +f 10/12 5/13 11/14 +f 5/13 12/15 11/14 +f 11/14 12/15 2/16 +f 12/15 1/17 2/16 +f 2/16 1/17 15/18 diff --git a/mini_live/obj/obj_mediapipe/teeth_upper.obj b/mini_live/obj/obj_mediapipe/teeth_upper.obj new file mode 100644 index 0000000..0ac4666 --- /dev/null +++ b/mini_live/obj/obj_mediapipe/teeth_upper.obj @@ -0,0 +1,56 @@ +# This file uses centimeters as units for non-parametric coordinates. + +mtllib teeth_upper.mtl +v 434.873383 741.719055 -155.635678 +v 443.795746 765.168640 -176.353269 +v 522.885315 741.454102 -210.641004 +v 541.328491 766.458435 -196.421415 +v 458.399994 741.520996 -195.760328 +v 500.255157 741.520996 -215.260328 +v 565.995178 741.692261 -155.788221 +v 565.570435 764.567688 -155.356763 +v 500.232025 769.099976 -215.250638 +v 478.100006 768.462036 -210.878370 +v 458.383057 766.574890 -195.788663 +v 443.661713 740.884888 -176.518079 +v 522.946716 768.276550 -210.566007 +v 557.205872 740.844971 -176.471311 +v 434.232269 764.718140 -155.438794 +v 557.125732 765.103882 -176.335141 +v 477.799988 741.571777 -210.943952 +v 540.990906 741.596375 -196.660321 +vt 0.000000 0.000000 +vt 1.000000 0.000000 +vt 0.000000 0.500000 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 0.000000 0.500000 +vt 1.000000 0.000000 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 0.000000 0.500000 +vt 1.000000 0.000000 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 0.000000 0.500000 +vt 1.000000 0.000000 +vt 1.000000 0.500000 +vt 0.000000 0.000000 +vt 0.000000 0.500000 +usemtl initialShadingGroup +f 7/1 14/2 8/3 +f 8/3 14/2 16/4 +f 14/2 18/5 16/4 +f 16/4 18/5 4/6 +f 18/5 3/7 4/6 +f 4/6 3/7 13/8 +f 3/7 6/9 13/8 +f 13/8 6/9 9/10 +f 6/9 17/11 9/10 +f 9/10 17/11 10/12 +f 17/11 5/13 10/12 +f 10/12 5/13 11/14 +f 5/13 12/15 11/14 +f 11/14 12/15 2/16 +f 12/15 1/17 2/16 +f 2/16 1/17 15/18 diff --git a/mini_live/obj/obj_mediapipe/wrap.obj b/mini_live/obj/obj_mediapipe/wrap.obj new file mode 100644 index 0000000..83d5348 --- /dev/null +++ b/mini_live/obj/obj_mediapipe/wrap.obj @@ -0,0 +1,776 @@ +# This file uses centimeters as units for non-parametric coordinates. + +v 500.270996 739.283997 -238.682999 +v 500.270996 697.341003 -236.893005 +v 500.270996 746.771973 -236.621994 +v 500.270996 754.692993 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open(filepath) as f: + # with open(r"face3D.obj") as f: + content = f.readlines() + for i in content: + if i[:2] == "v ": + v0,v1,v2 = i[2:-1].split(" ") + v_.append(float(v0)) + v_.append(float(v1)) + v_.append(float(v2)) + if i[:2] == "f ": + tmp = i[2:-1].split(" ") + for ii in tmp: + a = ii.split("/")[0] + a = int(a) - 1 + face.append(a) + return v_, face + +verts_face,_ = readObjFile(r"face3D.obj") +verts_wrap,_ = readObjFile(r"wrap.obj") + +verts_flame = np.array(verts_face).reshape(-1, 3) +verts_mouth = np.array(verts_wrap).reshape(-1, 3) +index_mouthInFlame = [] +for index in range(len(verts_mouth)): + vert = verts_mouth[index] + dist_list = [] + for i in verts_flame: + dist_list.append(np.linalg.norm(i - vert)) + align_index = np.argmin(dist_list) + index_mouthInFlame.append(align_index) +print(index_mouthInFlame) +# exit() + +# from obj.utils import INDEX_FLAME_LIPS +# index_mouthInFlame = np.array(index_mouthInFlame, dtype = int)[INDEX_FLAME_LIPS] +# np.savetxt("index_mouthInFlame.txt", index_mouthInFlame) \ No newline at end of file diff --git a/mini_live/obj/obj_utils.py b/mini_live/obj/obj_utils.py new file mode 100644 index 0000000..b406271 --- /dev/null +++ b/mini_live/obj/obj_utils.py @@ -0,0 +1,171 @@ +import numpy as np +import cv2 +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) + +INDEX_FACE_EDGE = [ + 234, 127, 162, 21, + 54, 103, 67, 109, 10, 338, 297, 332, 284, 251, + 389, 356, + 454, 323, 361, 288, 397, 365, + 379, 378, 400, 377, 152, 148, 176, 149, 150, + 136, 172, 58, 132, + 93, +] +def readObjFile(filepath): + with_vn = False + with_vt = False + v_ = [] + vt = [] + vn = [] + face = [] + with open(filepath) as f: + # with open(r"face3D.obj") as f: + content = f.readlines() + for i in content: + if i[:2] == "v ": + v0,v1,v2 = i[2:-1].split(" ") + v_.append(float(v0)) + v_.append(float(v1)) + v_.append(float(v2)) + if i[:3] == "vt ": + with_vt = True + vt0,vt1 = i[3:-1].split(" ") + vt.append(float(vt0)) + vt.append(float(vt1)) + if i[:3] == "vn ": + with_vn = True + vn0,vn1,vn2 = i[3:-1].split(" ") + vn.append(float(vn0)) + vn.append(float(vn1)) + vn.append(float(vn2)) + if i[:2] == "f ": + tmp = i[2:-1].split(" ") + for ii in tmp: + a = ii.split("/")[0] + a = int(a) - 1 + face.append(a) + if not with_vn: + vn = [0 for i in v_] + if not with_vt: + vt = [0 for i in range(len(v_)//3*2)] + return v_, vt, vn, face + +def generateRenderInfo_mediapipe(): + v_face, vt_face, vn_face, face_face = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/face3D.obj")) + v_teeth, vt_teeth, vn_teeth, face_teeth = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/modified_teeth_upper.obj")) + v_teeth2, vt_teeth2, vn_teeth2, face_teeth2 = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/modified_teeth_lower.obj")) + + v_, vt, vn, face = ( + v_face + v_teeth + v_teeth2, vt_face + vt_teeth + vt_teeth2, vn_face + vn_teeth + vn_teeth2, + face_face + [i + len(v_face)//3 for i in face_teeth] + [i + len(v_face)//3 + len(v_teeth)//3 for i in face_teeth2]) + v_ = np.array(v_).reshape(-1, 3) + + # v_[:, 1] = -v_[:, 1] + + # 0-2: verts 3-4: vt 5:category 6: index 7-10 bone_weight 11-12 another vt + vertices = np.zeros([len(v_), 13]) + # vertices = np.zeros([len(pts_array_), 6]) + + vertices[:, :3] = v_ + vertices[:, 3:5] = np.array(vt).reshape(-1, 2) + vertices[:, 11:13] = np.array(vt).reshape(-1, 2) + vertices[:, 12] = 1 - vertices[:, 12] + # vertices[:, 5] = 0 + # 脸部为0，眼睛1，上牙2，下牙3 + vertices[468:478, 5] = 1. + vertices[478:478 + 18, 5] = 2. + vertices[478 + 18:478 + 36, 5] = 3. + vertices[:, 6] = list(range(len(v_))) + return vertices, face + +def generateRenderInfo(floor = 5): + v_face, vt_face, vn_face, face_face = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/face3D.obj")) + v_teeth, vt_teeth, vn_teeth, face_teeth = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/modified_teeth_upper.obj")) + v_teeth2, vt_teeth2, vn_teeth2, face_teeth2 = readObjFile(os.path.join(current_dir,"../obj/obj_mediapipe/modified_teeth_lower.obj")) + print(len(v_face), len(vt_face), len(vn_face), len(face_face)) + print(len(v_teeth)//3, len(vt_teeth), len(vn_teeth), len(face_teeth)) + print(len(v_face)//3 + len(v_teeth)//3 + len(v_teeth2)//3) + + v_, vt, vn, face = ( + v_face + v_teeth + v_teeth2, vt_face + vt_teeth + vt_teeth2, vn_face + vn_teeth + vn_teeth2, + face_face + [i + len(v_face)//3 for i in face_teeth] + [i + len(v_face)//3 + len(v_teeth)//3 for i in face_teeth2]) + v_ = np.array(v_).reshape(-1, 3) + + # v_[:, 1] = -v_[:, 1] + + vertices = np.zeros([len(v_), 13]) + # vertices = np.zeros([len(pts_array_), 6]) + + vertices[:, :3] = v_ + vertices[:, 3:5] = np.array(vt).reshape(-1, 2) + + # 脸部为0，眼睛1，上牙2，下牙3， 补充的为9 + vertices[468:478, 5] = 1. + + vertices[len(v_face)//3:len(v_face)//3 + len(v_teeth)//3, 5] = 2. + vertices[len(v_face)//3 + len(v_teeth)//3:len(v_face)//3 + len(v_teeth)//3 + len(v_teeth2)//3, 5] = 3. + vertices[:, 6] = list(range(len(v_))) + return vertices, face + + +def generateWrapModel(): + v_ = [] + face = [] + filepath = os.path.join(current_dir,"../obj/obj_mediapipe/face_wrap_entity.obj") + with open(filepath) as f: + # with open(r"face3D.obj") as f: + content = f.readlines() + for i in content: + if i[:2] == "v ": + v0, v1, v2, v3, v4 = i[2:-1].split(" ") + v_.append(float(v0)) + v_.append(float(v1)) + v_.append(float(v2)) + v_.append(float(v3)) + v_.append(float(v4)) + if i[:2] == "f ": + tmp = i[2:-1].split(" ") + for ii in tmp: + a = ii.split("/")[0] + a = int(a) - 1 + face.append(a) + return np.array(v_).reshape(-1, 5), face + +def NewFaceVerts(render_verts, source_crop_pts, face_pts_mean): + from talkingface.run_utils import calc_face_mat + mat_list, _, face_pts_mean_personal_primer = calc_face_mat(source_crop_pts[np.newaxis, :478, :], + face_pts_mean) + + + + # print(face_pts_mean_personal_primer.shape) + mat_list__ = mat_list[0].T + # mat_list__ = np.linalg.inv(mat_list[0]) + render_verts[:478,:3] = face_pts_mean_personal_primer + + # 牙齿部分校正 + from talkingface.utils import INDEX_LIPS,main_keypoints_index,INDEX_LIPS_UPPER,INDEX_LIPS_LOWER + # # 上嘴唇中点 + # mid_upper_mouth = np.mean(face_pts_mean_personal_primer[main_keypoints_index][INDEX_LIPS],axis = 0) + # mid_upper_teeth = np.mean(render_verts[478:478 + 36,:3], axis=0) + # tmp = mid_upper_teeth - mid_upper_mouth + # print(tmp) + # render_verts[478:478 + 36, :2] = render_verts[478:478 + 36, :2] - tmp[:2] + + # 上嘴唇中点 + mid_upper_mouth = np.mean(face_pts_mean_personal_primer[main_keypoints_index][INDEX_LIPS_UPPER],axis = 0) + mid_upper_teeth = np.mean(render_verts[478:478 + 18,:3], axis=0) + tmp = mid_upper_teeth - mid_upper_mouth + print(tmp) + render_verts[478:478 + 18, :2] = render_verts[478:478 + 18, :2] - tmp[:2] + + # 下嘴唇中点 + mid_lower_mouth = np.mean(face_pts_mean_personal_primer[main_keypoints_index][INDEX_LIPS_LOWER],axis = 0) + mid_lower_teeth = np.mean(render_verts[478:478 + 18,:3], axis=0) + tmp = mid_lower_teeth - mid_lower_mouth + print(tmp) + render_verts[478 + 18:478 + 36, :2] = render_verts[478 + 18:478 + 36, :2] - tmp[:2] + + + return render_verts, mat_list__ diff --git a/mini_live/obj/utils.py b/mini_live/obj/utils.py new file mode 100644 index 0000000..7880865 --- /dev/null +++ b/mini_live/obj/utils.py @@ -0,0 +1,186 @@ +import uuid + +import numpy as np +import cv2 +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) + +def translation_matrix(point): + """生成平移矩阵""" + return np.array([ + [1, 0, 0, point[0]], + [0, 1, 0, point[1]], + [0, 0, 1, point[2]], + [0, 0, 0, 1] + ]) +def rotate_around_point(point, theta, phi, psi): + """围绕点P旋转""" + # 将点P平移到原点 + T1 = translation_matrix(-point) + + # 定义欧拉角 + theta = np.radians(theta) # 俯仰角 + phi = np.radians(phi) # 偏航角 + psi = np.radians(psi) # 翻滚角 + + # 创建旋转矩阵 + tmp = [theta, phi, psi] + matX = np.array([[1.0, 0, 0, 0], + [0.0, np.cos(tmp[0]), -np.sin(tmp[0]), 0], + [0.0, np.sin(tmp[0]), np.cos(tmp[0]), 0], + [0, 0, 0, 1] + ]) + matY = np.array([[np.cos(tmp[1]), 0, np.sin(tmp[1]), 0], + [0, 1, 0, 0], + [-np.sin(tmp[1]),0, np.cos(tmp[1]), 0], + [0, 0, 0, 1] + ]) + matZ = np.array([[np.cos(tmp[2]), -np.sin(tmp[2]), 0, 0], + [np.sin(tmp[2]), np.cos(tmp[2]), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + + R = matZ @ matY @ matX + + # 将点P移回其原始位置 + T2 = translation_matrix(point) + + # 总的变换矩阵 + total_transform = T2 @ R @ T1 + + return total_transform + +def rodrigues_rotation_formula(axis, theta): + """Calculate the rotation matrix using Rodrigues' rotation formula.""" + axis = np.asarray(axis) / np.linalg.norm(axis) # Normalize the axis + cos_theta = np.cos(theta) + sin_theta = np.sin(theta) + K = np.array([[0, -axis[2], axis[1]], + [axis[2], 0, -axis[0]], + [-axis[1], axis[0], 0]]) + R = np.eye(3) + sin_theta * K + (1 - cos_theta) * np.dot(K, K) + return R +def RotateAngle2Matrix(center, axis, theta): + """Rotate around a center point.""" + # Step 1: Translate the center to the origin + translation_to_origin = np.eye(4) + translation_to_origin[:3, 3] = -center + + # Step 2: Apply the rotation + R = rodrigues_rotation_formula(axis, theta) + R_ = np.eye(4) + R_[:3,:3] = R + R = R_ + + # Step 3: Translate back to the original position + translation_back = np.eye(4) + translation_back[:3, 3] = center + + # Combine the transformations + rotation_matrix = translation_back @ R @ translation_to_origin + + return rotation_matrix + +INDEX_FLAME_LIPS = [ +1,26,23,21,8,155,83,96,98,101, +73,112,123,124,143,146,71,52,51,40, +2,25,24,22,7,156,82,97,99,100, +74,113,122,125,138,148,66,53,50,41, +30,31,32,38,39,157,111,110,106,105, +104,120,121,126,137,147,65,54,49,48, +4,28,33,20,19,153,94,95,107,103, +76,118,119,127,136,149,64,55,47,46, + +3,27,35,17,18,154,93,92,109,102, +75,114,115,128,133,151,61,56,43,42, +6,29, 13, 12, 11, 158, 86, 87, 88, 79, +80,117, 116, 135, 134, 150, 62, 63, 44, 45, +5,36,14,9,10,159,85,84,89,78, +77,141,130,131,139,145,67,59,58,69, +0,37,34,15,16,152,91,90,108,81,72, +142,129,132,140,144,68,60,57,70, +] + + +def crop_mouth(mouth_pts, mat_list__): + """ + x_ratio: 裁剪出一个正方形，边长根据keypoints的宽度 * x_ratio决定 + """ + num_ = len(mouth_pts) + keypoints = np.ones([4, num_]) + keypoints[:3, :] = mouth_pts.T + keypoints = mat_list__.dot(keypoints).T + keypoints = keypoints[:, :3] + + x_min, y_min, x_max, y_max = np.min(keypoints[:, 0]), np.min(keypoints[:, 1]), np.max(keypoints[:, 0]), np.max(keypoints[:, 1]) + border_width_half = max(x_max - x_min, y_max - y_min) * 0.66 + y_min = y_min + border_width_half * 0.3 + center_x = (x_min + x_max) /2. + center_y = (y_min + y_max) /2. + x_min, y_min, x_max, y_max = int(center_x - border_width_half), int(center_y - border_width_half*0.75), int( + center_x + border_width_half), int(center_y + border_width_half*0.75) + print([x_min, y_min, x_max, y_max]) + + # pts = np.array([ + # [x_min, y_min], + # [x_max, y_min], + # [x_max, y_max], + # [x_min, y_max] + # ]) + return [x_min, y_min, x_max, y_max] + +def drawMouth(keypoints, source_texture, out_size = (700, 1400)): + INDEX_LIPS_INNER = [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, 415, 310, 311, 312, 13, 82, 81, 80, 191] + INDEX_LIPS_OUTER = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 409, 270, 269, 267, 0, 37, 39, 40, 185, ] + INDEX_LIPS_LOWWER = INDEX_LIPS_INNER[:11] + INDEX_LIPS_OUTER[:11][::-1] + INDEX_LIPS_UPPER = INDEX_LIPS_INNER[10:] + [INDEX_LIPS_INNER[0], INDEX_LIPS_OUTER[0]] + INDEX_LIPS_OUTER[10:][::-1] + INDEX_LIPS = INDEX_LIPS_INNER + INDEX_LIPS_OUTER + # keypoints = keypoints[INDEX_LIPS] + keypoints[:, 0] = keypoints[:, 0] * out_size[0] + keypoints[:, 1] = keypoints[:, 1] * out_size[1] + # pts = keypoints[20:40] + # pts = pts.reshape((-1, 1, 2)).astype(np.int32) + # cv2.fillPoly(source_texture, [pts], color=(255, 0, 0,)) + # pts = keypoints[:20] + # pts = pts.reshape((-1, 1, 2)).astype(np.int32) + # cv2.fillPoly(source_texture, [pts], color=(0, 0, 0,)) + + pts = keypoints[INDEX_LIPS_OUTER] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(source_texture, [pts], color=(0, 0, 0)) + pts = keypoints[INDEX_LIPS_UPPER] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(source_texture, [pts], color=(255, 0, 0)) + pts = keypoints[INDEX_LIPS_LOWWER] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(source_texture, [pts], color=(127, 0, 0)) + + prompt_texture = np.zeros_like(source_texture) + pts = keypoints[INDEX_LIPS_UPPER] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(prompt_texture, [pts], color=(255, 0, 0)) + pts = keypoints[INDEX_LIPS_LOWWER] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(prompt_texture, [pts], color=(127, 0, 0)) + return source_texture, prompt_texture + + + +# +# def draw_face_feature_maps(keypoints, mode = ["mouth", "nose", "eye", "oval"], size=(256, 256), im_edges = None): +# w, h = size +# # edge map for face region from keypoints +# if im_edges is None: +# im_edges = np.zeros((h, w, 3), np.uint8) # edge map for all edges +# if "mouth" in mode: +# pts = keypoints[INDEX_LIPS_OUTER] +# pts = pts.reshape((-1, 1, 2)).astype(np.int32) +# cv2.fillPoly(im_edges, [pts], color=(0, 0, 0)) +# pts = keypoints[INDEX_LIPS_UPPER] +# pts = pts.reshape((-1, 1, 2)).astype(np.int32) +# cv2.fillPoly(im_edges, [pts], color=(255, 0, 0)) +# pts = keypoints[INDEX_LIPS_LOWWER] +# pts = pts.reshape((-1, 1, 2)).astype(np.int32) +# cv2.fillPoly(im_edges, [pts], color=(127, 0, 0)) +# return im_edges \ No newline at end of file diff --git a/mini_live/obj/weights478.txt b/mini_live/obj/weights478.txt new file mode 100644 index 0000000..3ee5f29 --- /dev/null +++ b/mini_live/obj/weights478.txt @@ -0,0 +1,478 @@ +1.603576002379501262e-05 9.999839642399761530e-01 0.000000000000000000e+00 +4.689841752874187837e-04 9.995310158247125987e-01 0.000000000000000000e+00 +1.177883755449854675e-04 9.998822116244551195e-01 0.000000000000000000e+00 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142, 146, 147, 148, 149, 150, 152, 164, 165, 167, 169, 170, 171, 172, 175, 176, 177, + 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 191, 192, 194, 199, 200, 201, 202, + 203, 204, 205, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 227, 234, 262, 266, + 267, 268, 269, 270, 271, 272, 273, 280, 287, 288, 291, 292, 302, 303, 304, 306, 307, + 308, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, + 326, 327, 329, 330, 335, 345, 346, 347, 348, 352, 358, 361, 364, 365, 366, 367, 369, + 371, 375, 376, 377, 378, 379, 391, 393, 394, 395, 396, 397, 400, 401, 402, 403, 404, + 405, 406, 407, 408, 409, 410, 411, 415, 416, 418, 421, 422, 423, 424, 425, 426, 427, + 428, 430, 431, 432, 433, 434, 435, 436, 447, 454] + +index_edge_wrap = [111,43,57,21,76,59,68,67,78,66,69,168,177,169,170,161,176,123,159,145,208] +index_edge_wrap_upper = [111, 110, 51, 52, 53, 54, 48, 63, 56, 47, 46, 1, 148, 149, 158, 165, 150, 156, 155, 154, 153, 207, 208] + +print(len(index_wrap)) + +# index_wrap = index_wrap + [291, 409, 270, 269, 267, 0, 37, 39, 40, 185, 61, +# 146, 91, 181, 84, 17, 314, 405, 321, 375,] +import numpy as np +# 求平均人脸 +def newWrapModel(wrapModel, face_pts_mean_personal_primer): + + face_wrap_entity = wrapModel.copy() + # 正规点 + face_wrap_entity[:len(index_wrap),:3] = face_pts_mean_personal_primer[index_wrap, :3] + # 边缘点 + vert_mid = face_wrap_entity[:,:3][index_edge_wrap[:4] + index_edge_wrap[-4:]].mean(axis=0) + for index_, jj in enumerate(index_edge_wrap): + face_wrap_entity[len(index_wrap) + index_,:3] = face_wrap_entity[jj, :3] + (face_wrap_entity[jj, :3] - vert_mid) * (0.3 - abs(10-index_)/40) + # 牙齿点 + from talkingface.utils import INDEX_LIPS, main_keypoints_index, INDEX_LIPS_UPPER, INDEX_LIPS_LOWER + # 上嘴唇中点 + mid_upper_mouth = np.mean(face_pts_mean_personal_primer[main_keypoints_index][INDEX_LIPS_UPPER], axis=0) + mid_upper_teeth = np.mean(face_wrap_entity[-36:-18, :3], axis=0) + tmp = mid_upper_teeth - mid_upper_mouth + face_wrap_entity[-36:-18, :2] = face_wrap_entity[-36:-18, :2] - tmp[:2] + # 下嘴唇中点 + mid_lower_mouth = np.mean(face_pts_mean_personal_primer[main_keypoints_index][INDEX_LIPS_LOWER], axis=0) + mid_lower_teeth = np.mean(face_wrap_entity[-18:, :3], axis=0) + tmp = mid_lower_teeth - mid_lower_mouth + # print(tmp) + face_wrap_entity[-18:, :2] = face_wrap_entity[-18:, :2] - tmp[:2] + + return face_wrap_entity \ No newline at end of file diff --git a/mini_live/opengl_render_interface.py b/mini_live/opengl_render_interface.py new file mode 100644 index 0000000..e81f844 --- /dev/null +++ b/mini_live/opengl_render_interface.py @@ -0,0 +1,251 @@ +import os +os.environ["kmp_duplicate_lib_ok"] = "true" +import glfw +from OpenGL.GL import * +from OpenGL.GL.shaders import compileProgram, compileShader +import numpy as np +import glm +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) +import cv2 +class RenderModel: + def __init__(self, window_size): + self.window_size = window_size + if not glfw.init(): + raise Exception("glfw can not be initialized!") + glfw.window_hint(glfw.VISIBLE, glfw.FALSE) + print(window_size[0], window_size[1]) + self.window = glfw.create_window(window_size[0], window_size[1], "Face Render window", None, None) + if not self.window: + glfw.terminate() + raise Exception("glfw window can not be created!") + glfw.set_window_pos(self.window, 100, 100) + glfw.make_context_current(self.window) + # shader 设置 + current_dir = os.path.dirname(os.path.abspath(__file__)) + self.program = compileProgram(compileShader(open(os.path.join(current_dir, "shader/prompt.vsh")).readlines(), GL_VERTEX_SHADER), + compileShader(open(os.path.join(current_dir, "shader/prompt.fsh")).readlines(), GL_FRAGMENT_SHADER)) + self.VBO = glGenBuffers(1) + self.render_verts = None + self.render_face = None + self.face_pts_mean = None + + def setContent(self, vertices_, face): + glfw.make_context_current(self.window) + self.render_verts = vertices_ + self.render_face = face + glUseProgram(self.program) + # set up vertex array object (VAO) + self.vao = glGenVertexArrays(1) + glBindVertexArray(self.vao) + + self.GenVBO(vertices_) + self.GenEBO(face) + + # unbind VAO + glBindVertexArray(0) + glBindBuffer(GL_ARRAY_BUFFER, 0) + + def GenEBO(self, face): + self.indices = np.array(face, dtype=np.uint32) + self.EBO = glGenBuffers(1) + glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, self.EBO) + glBufferData(GL_ELEMENT_ARRAY_BUFFER, self.indices.nbytes, self.indices, GL_STATIC_DRAW) + + def GenTexture(self, img, texture_index = GL_TEXTURE0): + glfw.make_context_current(self.window) + glActiveTexture(texture_index) + texture = glGenTextures(1) + glBindTexture(GL_TEXTURE_2D, texture) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR) + image_height, image_width = img.shape[:2] + if len(img.shape) == 2: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RED, image_width, image_height, 0, GL_RED, GL_UNSIGNED_BYTE, + img.tobytes()) + elif img.shape[2] == 3: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, image_width, image_height, 0, GL_RGB, GL_UNSIGNED_BYTE, img.tobytes()) + elif img.shape[2] == 4: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, image_width, image_height, 0, GL_RGBA, GL_UNSIGNED_BYTE, img.tobytes()) + else: + print("Image Format not supported") + exit(-1) + + # def ChangeTexture(self, img, texture_index): + # glBindTexture(GL_TEXTURE_2D, texture1) + # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT) + # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT) + # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR) + # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR) + # # image = cv2.imread(os.path.join(current_dir, "face_mask2.png"), cv2.IMREAD_UNCHANGED) + # image = ref_image + # img_data = image.tobytes() + # image_height, image_width = image.shape[:2] + # glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, image_width, image_height, 0, GL_RGBA, GL_UNSIGNED_BYTE, img_data) + + def GenVBO(self, vertices_): + glfw.make_context_current(self.window) + vertices = np.array(vertices_, dtype=np.float32) + glBindBuffer(GL_ARRAY_BUFFER, self.VBO) + glBufferData(GL_ARRAY_BUFFER, vertices.nbytes, vertices, GL_DYNAMIC_DRAW) + glEnableVertexAttribArray(0) + glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, vertices.itemsize * 13, ctypes.c_void_p(0)) + # 顶点纹理属性 + glEnableVertexAttribArray(1) + glVertexAttribPointer(1, 4, GL_FLOAT, GL_FALSE, vertices.itemsize * 13, ctypes.c_void_p(12)) + + glEnableVertexAttribArray(2) + glVertexAttribPointer(2, 4, GL_FLOAT, GL_FALSE, vertices.itemsize * 13, ctypes.c_void_p(28)) + + glEnableVertexAttribArray(3) + glVertexAttribPointer(3, 2, GL_FLOAT, GL_FALSE, vertices.itemsize * 13, ctypes.c_void_p(44)) + + def render2cv(self, out_size = (1000, 1000), mat_world=None, bs_array=None): + glfw.make_context_current(self.window) + # 设置正交投影矩阵 + # left = 0 + # right = standard_size + # bottom = 0 + # top = standard_size + # near = standard_size # 近裁剪面距离 + # far = -standard_size # 远裁剪面距离 + left = 0 + right = out_size[0] + bottom = 0 + top = out_size[1] + near = 1000 # 近裁剪面距离 + far = -1000 # 远裁剪面距离 + + ortho_matrix = glm.ortho(left, right, bottom, top, near, far) + # print("ortho_matrix: ", ortho_matrix) + + glUseProgram(self.program) + glEnable(GL_DEPTH_TEST) + glEnable(GL_BLEND) + glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) + glEnable(GL_CULL_FACE) + # glEnable(GL_CULL_FACE) + glCullFace(GL_BACK) # 剔除背面 + glFrontFace(GL_CW) # 通常顶点顺序是顺时针 + glClearColor(0, 0, 0, 0) + glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) + + # # 设置视口 + # glViewport(100, 0, self.window_size[0], self.window_size[1]) + + + + glUniform1i(glGetUniformLocation(self.program, "texture_face"), 0) + glUniform1i(glGetUniformLocation(self.program, "texture_bs"), 1) + glUniformMatrix4fv(glGetUniformLocation(self.program, "gWorld0"), 1, GL_FALSE, mat_world) + glUniform1fv(glGetUniformLocation(self.program, "bsVec"), 12, bs_array.astype(np.float32)) + + glUniformMatrix4fv(glGetUniformLocation(self.program, "gProjection"), 1, GL_FALSE, glm.value_ptr(ortho_matrix)) + # bind VAO + glBindVertexArray(self.vao) + # draw + glDrawElements(GL_TRIANGLES, self.indices.size, GL_UNSIGNED_INT, None) + # unbind VAO + glBindVertexArray(0) + + glfw.swap_buffers(self.window) + glReadBuffer(GL_FRONT) + # 从缓冲区中的读出的数据是字节数组 + data = glReadPixels(0, 0, self.window_size[0], self.window_size[1], GL_RGBA, GL_UNSIGNED_BYTE, outputType=None) + rgb = data.reshape(self.window_size[1], self.window_size[0], -1).astype(np.uint8) + return rgb + +def create_render_model(out_size = (384, 384), floor = 5): + renderModel = RenderModel(out_size) + + image2 = cv2.imread(os.path.join(current_dir, "bs_texture.png")) + renderModel.GenTexture(image2, GL_TEXTURE1) + + from obj.obj_utils import generateRenderInfo + render_verts, render_face = generateRenderInfo(floor = floor) + renderModel.setContent(render_verts, render_face) + renderModel.face_pts_mean = render_verts[:478, :3].copy() + return renderModel + +def create_render_model2(out_size = (384, 384), floor = 5): + renderModel = RenderModel(out_size) + + image2 = cv2.imread(os.path.join(current_dir, "bs_texture.png")) + renderModel.GenTexture(image2, GL_TEXTURE1) + + from obj.obj_utils import generateRenderInfo + render_verts, render_face = generateRenderInfo(floor = floor) + renderModel.setContent(render_verts, render_face) + renderModel.face_pts_mean = render_verts[:478, :3].copy() + return renderModel + +model_size_ = 384 + +# 示例使用 +if __name__ == "__main__": + import pickle + import cv2 + import time + import numpy as np + import glob + import random + from OpenGL.GL import * + import os + + start_time = time.time() + standard_size = 384 + crop_rotio = [0.5, 0.5, 0.5, 0.5] + out_w = int(standard_size*(crop_rotio[0] + crop_rotio[1])) + out_h = int(standard_size*(crop_rotio[2] + crop_rotio[3])) + out_size = (out_w, out_h) + renderModel = create_render_model((int(out_w*0.8), int(out_h*0.8)), floor = 20) + + from talkingface.mediapipe_utils import detect_face_mesh + from obj.image_utils import crop_face_from_image, get_standard_image + from obj.obj_utils import faceModeling, NewFaceVerts + + image_list = glob.glob(r"F:\C\AI\CV\TalkingFace\OpenGLRender_0830/face_rgba/*.png") + image_list.sort() + from talkingface.utils import crop_mouth,main_keypoints_index + for index, img_path in enumerate(image_list): + img_primer_rgba = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) + source_pts = detect_face_mesh([img_primer_rgba[:, :, :3]])[0] + img_primer_rgba = cv2.cvtColor(img_primer_rgba, cv2.COLOR_BGRA2RGBA) + source_crop_rect = crop_mouth(source_pts[main_keypoints_index],img_primer_rgba.shape[1], img_primer_rgba.shape[0]) + standard_img, standard_v, standard_vt = get_standard_image(img_primer_rgba, source_pts, source_crop_rect, + out_size=( + int(standard_size * (crop_rotio[0] + crop_rotio[1])), + int(standard_size * ( + crop_rotio[2] + crop_rotio[3])))) + + source_crop_pts = standard_v + + # from obj.image_utils import check_keypoint + # check_keypoint(standard_img, source_crop_pts) + + render_verts = renderModel.render_verts.copy() + render_verts[:len(source_crop_pts), :3] = source_crop_pts + w = standard_size * (crop_rotio[0] + crop_rotio[1]) + h = standard_size * (crop_rotio[2] + crop_rotio[3]) + render_verts[:len(source_crop_pts), 3] = source_crop_pts[:, 0] / w + render_verts[:len(source_crop_pts), 4] = source_crop_pts[:, 1] / h + render_verts,Rmat = NewFaceVerts(render_verts, source_crop_pts, renderModel.face_pts_mean) + + image2 = standard_img.copy() + renderModel.GenTexture(image2, GL_TEXTURE0) + renderModel.GenVBO(render_verts) + + rotate_max = 20 + for i in range(-rotate_max * 5, rotate_max * 5, 5): + # bs = np.array([i*1, 0, 0, 0,0,0], dtype=np.float32)*1.2 + bs = np.zeros([12], dtype=np.float32) + bs[0] = i + # rotationMatrix = mat_list__.T + bs[1] = 1.2 * bs[1] + 5 + i = (bs[0] + bs[1]) / 3. + 3 + rgba = renderModel.render2cv(out_size=out_size, mat_world=Rmat, bs_array=bs) + final_img = cv2.cvtColor(rgba, cv2.COLOR_RGBA2BGRA) + cv2.imshow('scene', final_img[:, :, :3].astype(np.uint8)) + cv2.waitKey(30) diff --git a/mini_live/render.py b/mini_live/render.py new file mode 100644 index 0000000..8e385a4 --- /dev/null +++ b/mini_live/render.py @@ -0,0 +1,300 @@ +import os +os.environ["kmp_duplicate_lib_ok"] = "true" +import glfw +from OpenGL.GL import * +from OpenGL.GL.shaders import compileProgram, compileShader +import numpy as np +import glm +import os +from mini_live.obj.wrap_utils import index_wrap, index_edge_wrap +from mini_live.obj.obj_utils import generateRenderInfo, generateWrapModel +from talkingface.utils import crop_mouth, main_keypoints_index +current_dir = os.path.dirname(os.path.abspath(__file__)) +import cv2 +import torch.nn.functional as F +class RenderModel_gl: + def __init__(self, window_size): + self.window_size = window_size + if not glfw.init(): + raise Exception("glfw can not be initialized!") + glfw.window_hint(glfw.VISIBLE, glfw.FALSE) + print(window_size[0], window_size[1]) + self.window = glfw.create_window(window_size[0], window_size[1], "Face Render window", None, None) + if not self.window: + glfw.terminate() + raise Exception("glfw window can not be created!") + glfw.set_window_pos(self.window, 100, 100) + glfw.make_context_current(self.window) + # shader 设置 + current_dir = os.path.dirname(os.path.abspath(__file__)) + self.program = compileProgram(compileShader(open(os.path.join(current_dir, "shader/prompt3.vsh")).readlines(), GL_VERTEX_SHADER), + compileShader(open(os.path.join(current_dir, "shader/prompt3.fsh")).readlines(), GL_FRAGMENT_SHADER)) + self.VBO = glGenBuffers(1) + self.render_verts = None + self.render_face = None + self.face_pts_mean = None + + def setContent(self, vertices_, face): + glfw.make_context_current(self.window) + self.render_verts = vertices_ + self.render_face = face + glUseProgram(self.program) + # set up vertex array object (VAO) + self.vao = glGenVertexArrays(1) + glBindVertexArray(self.vao) + + self.GenVBO(vertices_) + self.GenEBO(face) + + # unbind VAO + glBindVertexArray(0) + glBindBuffer(GL_ARRAY_BUFFER, 0) + + def GenEBO(self, face): + self.indices = np.array(face, dtype=np.uint32) + self.EBO = glGenBuffers(1) + glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, self.EBO) + glBufferData(GL_ELEMENT_ARRAY_BUFFER, self.indices.nbytes, self.indices, GL_STATIC_DRAW) + + def GenTexture(self, img, texture_index = GL_TEXTURE0): + glfw.make_context_current(self.window) + glActiveTexture(texture_index) + texture = glGenTextures(1) + glBindTexture(GL_TEXTURE_2D, texture) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR) + glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR) + image_height, image_width = img.shape[:2] + if len(img.shape) == 2: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RED, image_width, image_height, 0, GL_RED, GL_UNSIGNED_BYTE, + img.tobytes()) + elif img.shape[2] == 3: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, image_width, image_height, 0, GL_RGB, GL_UNSIGNED_BYTE, img.tobytes()) + elif img.shape[2] == 4: + glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, image_width, image_height, 0, GL_RGBA, GL_UNSIGNED_BYTE, img.tobytes()) + else: + print("Image Format not supported") + exit(-1) + + def GenVBO(self, vertices_): + glfw.make_context_current(self.window) + vertices = np.array(vertices_, dtype=np.float32) + glBindBuffer(GL_ARRAY_BUFFER, self.VBO) + glBufferData(GL_ARRAY_BUFFER, vertices.nbytes, vertices, GL_DYNAMIC_DRAW) + glEnableVertexAttribArray(0) + glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, vertices.itemsize * 5, ctypes.c_void_p(0)) + # 顶点纹理属性 + glEnableVertexAttribArray(1) + glVertexAttribPointer(1, 2, GL_FLOAT, GL_FALSE, vertices.itemsize * 5, ctypes.c_void_p(12)) + + def render2cv(self, vertBuffer, out_size = (1000, 1000), mat_world=None, bs_array=None): + glfw.make_context_current(self.window) + # 设置正交投影矩阵 + # left = 0 + # right = standard_size + # bottom = 0 + # top = standard_size + # near = standard_size # 近裁剪面距离 + # far = -standard_size # 远裁剪面距离 + left = 0 + right = out_size[0] + bottom = 0 + top = out_size[1] + near = 1000 # 近裁剪面距离 + far = -1000 # 远裁剪面距离 + + ortho_matrix = glm.ortho(left, right, bottom, top, near, far) + glUniformMatrix4fv(glGetUniformLocation(self.program, "gProjection"), 1, GL_FALSE, glm.value_ptr(ortho_matrix)) + + # print("ortho_matrix: ", ortho_matrix) + + glUseProgram(self.program) + glEnable(GL_DEPTH_TEST) + glEnable(GL_BLEND) + glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) + glEnable(GL_CULL_FACE) + # glEnable(GL_CULL_FACE) + glCullFace(GL_BACK) # 剔除背面 + glFrontFace(GL_CW) # 通常顶点顺序是顺时针 + glClearColor(0.5, 0.5, 0.5, 0) + glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) + + # # 设置视口 + # glViewport(100, 0, self.window_size[0], self.window_size[1]) + + + glUniform1i(glGetUniformLocation(self.program, "texture_bs"), 0) + glUniformMatrix4fv(glGetUniformLocation(self.program, "gWorld0"), 1, GL_FALSE, mat_world) + glUniform1fv(glGetUniformLocation(self.program, "bsVec"), 12, bs_array.astype(np.float32)) + + glUniform2fv(glGetUniformLocation(self.program, "vertBuffer"), 209, vertBuffer.astype(np.float32)) + + glUniformMatrix4fv(glGetUniformLocation(self.program, "gProjection"), 1, GL_FALSE, glm.value_ptr(ortho_matrix)) + # bind VAO + glBindVertexArray(self.vao) + # draw + glDrawElements(GL_TRIANGLES, self.indices.size, GL_UNSIGNED_INT, None) + # unbind VAO + glBindVertexArray(0) + + glfw.swap_buffers(self.window) + glReadBuffer(GL_FRONT) + # 从缓冲区中的读出的数据是字节数组 + data = glReadPixels(0, 0, self.window_size[0], self.window_size[1], GL_RGBA, GL_UNSIGNED_BYTE, outputType=None) + rgb = data.reshape(self.window_size[1], self.window_size[0], -1).astype(np.uint8) + return rgb + +def create_render_model(out_size = (384, 384), floor = 5): + renderModel_gl = RenderModel_gl(out_size) + + image2 = cv2.imread(os.path.join(current_dir, "bs_texture_halfFace.png")) + image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGBA) + renderModel_gl.GenTexture(image2, GL_TEXTURE0) + + render_verts, render_face = generateRenderInfo() + wrapModel_verts,wrapModel_face = generateWrapModel() + + renderModel_gl.setContent(wrapModel_verts, wrapModel_face) + renderModel_gl.render_verts = render_verts + renderModel_gl.render_face = render_face + renderModel_gl.face_pts_mean = render_verts[:478, :3].copy() + return renderModel_gl + +# 示例使用 +if __name__ == "__main__": + import pickle + import cv2 + import time + import numpy as np + import glob + import random + from OpenGL.GL import * + import os + + import torch + from talkingface.model_utils import LoadAudioModel,Audio2bs + from talkingface.data.few_shot_dataset import get_image + + Audio2FeatureModel = LoadAudioModel(r'../checkpoint\lstm/lstm_model_epoch_325.pkl') + + from talkingface.render_model_mini import RenderModel_Mini + renderModel_mini = RenderModel_Mini() + renderModel_mini.loadModel("../checkpoint/DINet_mini/epoch_40.pth") + + start_time = time.time() + standard_size = 256 + crop_rotio = [0.5, 0.5, 0.5, 0.5] + out_w = int(standard_size*(crop_rotio[0] + crop_rotio[1])) + out_h = int(standard_size*(crop_rotio[2] + crop_rotio[3])) + out_size = (out_w, out_h) + renderModel_gl = create_render_model((out_w, out_h), floor = 20) + + wrapModel,wrapModel_face = generateWrapModel() + + path = r"F:\C\AI\CV\DH008_few_shot/preparation_new" + video_list = os.listdir(r"{}".format(path)) + print(video_list) + for test_video in video_list[:15]: + Path_output_pkl = "{}/{}/keypoint_rotate.pkl".format(path, test_video) + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f) + + images_info = np.concatenate([images_info, images_info[::-1]], axis=0) + + video_path = "{}/{}/circle.mp4".format(path, test_video) + cap = cv2.VideoCapture(video_path) + vid_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + list_source_crop_rect = [] + list_video_img = [] + list_standard_img = [] + list_standard_v = [] + list_standard_vt = [] + for frame_index in range(min(vid_frame_count, len(images_info))): + ret, frame = cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) + source_pts = images_info[frame_index] + source_crop_rect = crop_mouth(source_pts[main_keypoints_index], vid_width, vid_height) + + standard_img = get_image(frame, source_crop_rect, input_type="image", resize = standard_size) + standard_v = get_image(source_pts, source_crop_rect, input_type="mediapipe", resize = standard_size) + standard_vt = standard_v[:, :2] / standard_size + + list_video_img.append(frame) + list_source_crop_rect.append(source_crop_rect) + list_standard_img.append(standard_img) + list_standard_v.append(standard_v) + list_standard_vt.append(standard_vt) + cap.release() + + renderModel_mini.reset_charactor(list_standard_img, np.array(list_standard_v)[:,main_keypoints_index]) + from talkingface.run_utils import calc_face_mat + mat_list, _, face_pts_mean_personal_primer = calc_face_mat(np.array(list_standard_v), renderModel_gl.face_pts_mean) + from mini_live.obj.wrap_utils import newWrapModel + face_wrap_entity = newWrapModel(wrapModel, face_pts_mean_personal_primer) + + renderModel_gl.GenVBO(face_wrap_entity) + + wav2_dir = glob.glob(r"F:\C\AI\CV\DH008_few_shot\wav/*.wav") + sss = random.randint(0, len(wav2_dir)-1) + wavpath = wav2_dir[sss] + bs_array = Audio2bs(wavpath, Audio2FeatureModel)[5:] * 0.5 + + import uuid + task_id = str(uuid.uuid1()) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + save_path = "{}.mp4".format(task_id) + + videoWriter = cv2.VideoWriter(save_path, fourcc, 25, (int(vid_width), int(vid_height))) + + for frame_index in range(len(mat_list)): + if frame_index >= len(bs_array): + continue + bs = np.zeros([12], dtype=np.float32) + bs[:6] = bs_array[frame_index, :6] + # bs[2] = frame_index* 5 + + verts_frame_buffer = np.array(list_standard_vt)[frame_index, index_wrap, :2].copy() * 2 - 1 + + rgba = renderModel_gl.render2cv(verts_frame_buffer, out_size=out_size, mat_world=mat_list[frame_index].T, bs_array=bs) + # rgb = cv2.cvtColor(rgba, cv2.COLOR_RGBA2RGB) + + # rgba = cv2.resize(rgba, (128, 128)) + rgba = rgba[::2, ::2, :] + + gl_tensor = torch.from_numpy(rgba/255.).float().permute(2, 0, 1).unsqueeze(0) + source_tensor = cv2.resize(list_standard_img[frame_index], (128, 128)) + source_tensor = torch.from_numpy(source_tensor/255.).float().permute(2, 0, 1).unsqueeze(0) + + warped_img = renderModel_mini.interface(source_tensor.cuda(), gl_tensor.cuda()) + + image_numpy = warped_img.detach().squeeze(0).cpu().float().numpy() + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = image_numpy.clip(0, 255) + image_numpy = image_numpy.astype(np.uint8) + # print(image_numpy.shape) + # cv2.imshow('scene', image_numpy) + # cv2.waitKey(40) + x_min, y_min, x_max, y_max = list_source_crop_rect[frame_index] + + img_face = cv2.resize(image_numpy, (x_max - x_min, y_max - y_min)) + img_bg = list_video_img[frame_index][:,:,:3] + img_bg[y_min:y_max, x_min:x_max, :3] = img_face[:,:,:3] + # cv2.imshow('scene', img_bg[:,:,::-1]) + # cv2.waitKey(40) + # print(time.time()) + + videoWriter.write(img_bg[:,:,::-1]) + videoWriter.release() + os.makedirs("output", exist_ok=True) + val_video = "output/{}.mp4".format(task_id + "_2") + + wav_path = wavpath + os.system( + "ffmpeg -i {} -i {} -c:v libx264 -pix_fmt yuv420p {}".format(save_path, wav_path, val_video)) + os.remove(save_path) + + cv2.destroyAllWindows() \ No newline at end of file diff --git a/mini_live/shader/prompt3.fsh b/mini_live/shader/prompt3.fsh new file mode 100644 index 0000000..0c16402 --- /dev/null +++ b/mini_live/shader/prompt3.fsh @@ -0,0 +1,34 @@ +# version 330 +precision mediump float; +in mediump vec2 v_texture; +in mediump vec2 v_bias; +out highp vec4 out_color; + +void main() +{ + if (v_texture.x == 2.0f) + { + out_color = vec4(1.0, 0.0, 0.0, 1.0); + } + else if (v_texture.x > 2.0f && v_texture.x < 2.1f) + { + out_color = vec4(0.5f, 0.0, 0.0, 1.0); + } + else if (v_texture.x == 3.0f) + { + out_color = vec4(0.0, 1.0, 0.0, 1.0); + } + else if (v_texture.x == 4.0f) + { + out_color = vec4(0.0, 0.0, 1.0, 1.0); + } + else if (v_texture.x > 3.0f && v_texture.x < 4.0f) + { + out_color = vec4(0.0, 0.0, 0.0, 1.0); + } + else + { + vec2 wrap = (v_bias.xy + 1.0)/2.0; + out_color = vec4(wrap.xy, 0.5, 1.0); + } +} \ No newline at end of file diff --git a/mini_live/shader/prompt3.vsh b/mini_live/shader/prompt3.vsh new file mode 100644 index 0000000..174f371 --- /dev/null +++ b/mini_live/shader/prompt3.vsh @@ -0,0 +1,67 @@ +# version 330 + +layout(location = 0) in vec3 a_position; +layout(location = 1) in vec2 a_texture; +uniform float bsVec[12]; +uniform mat4 gProjection; +uniform mat4 gWorld0; +uniform sampler2D texture_bs; + +uniform vec2 vertBuffer[209]; + +out vec2 v_texture; +out vec2 v_bias; +vec4 calculateMorphPosition(vec3 position, vec2 textureCoord) { + vec4 tmp_Position2 = vec4(position, 1.0); + if (textureCoord.x < 3.0) { + vec3 morphSum = vec3(0.0); + for (int i = 0; i < 6; i++) { + ivec2 coord = ivec2(int(textureCoord.y), i); + vec3 morph = texelFetch(texture_bs, coord, 0).xyz * 2.0 - 1.0; + morphSum += bsVec[i] * morph; + } + ivec2 coord6 = ivec2(int(textureCoord.y), 6); + morphSum += bsVec[6] * texelFetch(texture_bs, coord6, 0).xyz; + tmp_Position2.xyz += morphSum; + } + else if (textureCoord.x == 4.0) + { + // lower teeth + vec3 morphSum = vec3(0.0, (bsVec[0] + bsVec[1])/ 2.7 + 6, 0.0); + tmp_Position2.xyz += morphSum; + } + return tmp_Position2; +} + +void main() { + mat4 gWorld = gWorld0; + + vec4 tmp_Position2 = calculateMorphPosition(a_position, a_texture); + vec4 tmp_Position = gWorld * tmp_Position2; + // vec4 tmp_Position = gWorld * vec4(a_position, 1.0); + // vec3 tmp_Position = a_position; +// vec4 pos_ = gProjection * vec4(tmp_Position.x, tmp_Position.y, tmp_Position.z, 1.0); + // # upper lips 1 lower lips2 teeth3 edge4 + + v_bias = vec2(0.0, 0.0); + if (a_texture.x == -1.0f) + { + v_bias = vec2(0.0, 0.0); + } + else if (a_texture.y < 209.0f) + { + vec4 vert_new = gProjection * vec4(tmp_Position.x, tmp_Position.y, tmp_Position.z, 1.0); + v_bias = vert_new.xy - vertBuffer[int(a_texture.y)].xy; + } + + if (a_texture.x >= 3.0f) + { + gl_Position = gProjection * vec4(tmp_Position.x, tmp_Position.y, 500.0, 1.0); + } + else + { + gl_Position = gProjection * vec4(tmp_Position.x, tmp_Position.y, tmp_Position.z, 1.0); + } + + v_texture = a_texture; +} \ No newline at end of file diff --git a/mini_live/train.py b/mini_live/train.py new file mode 100644 index 0000000..6a466c3 --- /dev/null +++ b/mini_live/train.py @@ -0,0 +1,197 @@ +import os +os.environ["kmp_duplicate_lib_ok"] = "true" +from talkingface.models.common.Discriminator import Discriminator +from talkingface.models.common.VGG19 import Vgg19 +from talkingface.models.DINet_mini import DINet_mini as DINet +from talkingface.util.utils import GANLoss,get_scheduler, update_learning_rate +from talkingface.config.config import DINetTrainingOptions +from torch.utils.tensorboard import SummaryWriter +from talkingface.util.log_board import log +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +import random +import numpy as np +import os +import pandas as pd +import torch.nn.functional as F +import cv2 +from talkingface.data.DHLive_mini_dataset import Few_Shot_Dataset,data_preparation + +def Tensor2img(tensor_, channel_index): + frame = tensor_[channel_index:channel_index + 3, :, :].detach().squeeze(0).cpu().float().numpy() + frame = np.transpose(frame, (1, 2, 0)) * 255.0 + frame = frame.clip(0, 255) + return frame.astype(np.uint8) + +if __name__ == "__main__": + ''' + training code of person image generation + ''' + # load config + opt = DINetTrainingOptions().parse_args() + + model_name = "DINet_mini" + n_ref = 3 + opt.source_channel = 3 + opt.target_channel = 3 + opt.ref_channel = n_ref * 4 + opt.D_num_blocks = 3 + opt.batch_size = 4 + opt.result_path = "checkpoint/{}".format(model_name) + opt.resume = True + opt.resume_path = "checkpoint/{}/epoch_18.pth".format(model_name) + train_log_path = os.path.join("checkpoint/{}/log".format(model_name), "") + opt.seed = 1008 + + # set seed + random.seed(opt.seed) + np.random.seed(opt.seed) + torch.cuda.manual_seed(opt.seed) + + df = pd.read_csv(r"F:\C\AI\CV\DH008_few_shot\DH0119_mouth64_48/imageVar2.csv") + video_list = df[df["imageVar"] > 360000]["name"].tolist() + video_list = [i for i in video_list if os.path.exists(i)] + video_list = [os.path.dirname(os.path.dirname(i)) for i in video_list] + path_ = r"F:\C\AI\CV\DH008_few_shot/preparation_bilibili" + video_list += [os.path.join(path_, i) for i in os.listdir(path_)] + + print("video_selected final: ", len(video_list)) + video_list.sort() + train_dict_info = data_preparation(video_list[:]) + train_set = Few_Shot_Dataset(train_dict_info, n_ref=n_ref, is_train=True) + training_data_loader = DataLoader(dataset=train_set, num_workers=0, batch_size=opt.batch_size, shuffle=True) + + # init network + net_g = DINet(opt.source_channel,opt.ref_channel, cuda= True).cuda() + net_d = Discriminator(opt.target_channel, opt.D_block_expansion, opt.D_num_blocks, opt.D_max_features).cuda() + net_vgg = Vgg19().cuda() + + # set optimizer + optimizer_g = optim.Adam(net_g.parameters(), lr=opt.lr_g) + optimizer_d = optim.Adam(net_d.parameters(), lr=opt.lr_d) + + if opt.resume: + print('loading checkpoint {}'.format(opt.resume_path)) + checkpoint = torch.load(opt.resume_path) + # opt.start_epoch = checkpoint['epoch'] + # opt.start_epoch = 200 + net_g_static = checkpoint['state_dict']['net_g'] + net_g.load_state_dict(net_g_static, strict=False) + net_d.load_state_dict(checkpoint['state_dict']['net_d']) + optimizer_g.load_state_dict(checkpoint['optimizer']['net_g']) + optimizer_d.load_state_dict(checkpoint['optimizer']['net_d']) + + # set criterion + criterionGAN = GANLoss().cuda() + criterionL1 = nn.L1Loss().cuda() + # set scheduler + net_g_scheduler = get_scheduler(optimizer_g, opt.non_decay, opt.decay) + net_d_scheduler = get_scheduler(optimizer_d, opt.non_decay, opt.decay) + + os.makedirs(train_log_path, exist_ok=True) + train_logger = SummaryWriter(train_log_path) + tag_index = 0 + # start train + for epoch in range(opt.start_epoch, opt.non_decay + opt.decay + 1): + net_g.train() + avg_loss_g_perception = 0 + avg_Loss_DI = 0 + avg_Loss_GI = 0 + for iteration, data in enumerate(training_data_loader): + # read data + source_tensor, ref_tensor, target_tensor, image_name = data + source_tensor = source_tensor.float().cuda() + ref_tensor = ref_tensor.float().cuda() + target_tensor = target_tensor.float().cuda() + + # network forward + fake_out = net_g(ref_tensor, source_tensor) + # down sample output image and real image + fake_out_half = F.avg_pool2d(fake_out, 3, 2, 1, count_include_pad=False) + target_tensor_half = F.interpolate(target_tensor, scale_factor=0.5, mode='bilinear') + # (1) Update D network + optimizer_d.zero_grad() + # compute fake loss + _,pred_fake_d = net_d(fake_out) + loss_d_fake = criterionGAN(pred_fake_d, False) + # compute real loss + _,pred_real_d = net_d(target_tensor) + loss_d_real = criterionGAN(pred_real_d, True) + # Combine D loss + loss_dI = (loss_d_fake + loss_d_real) * 0.5 + loss_dI.backward(retain_graph=True) + optimizer_d.step() + # (2) Update G network + _, pred_fake_dI = net_d(fake_out) + optimizer_g.zero_grad() + # compute perception loss + perception_real = net_vgg(target_tensor) + perception_fake = net_vgg(fake_out) + perception_real_half = net_vgg(target_tensor_half) + perception_fake_half = net_vgg(fake_out_half) + loss_g_perception = 0 + for i in range(len(perception_real)): + loss_g_perception += criterionL1(perception_fake[i], perception_real[i]) + loss_g_perception += criterionL1(perception_fake_half[i], perception_real_half[i]) + loss_g_perception = (loss_g_perception / (len(perception_real) * 2)) * opt.lamb_perception + + # gan dI loss + loss_g_dI = criterionGAN(pred_fake_dI, True) + # combine perception loss and gan loss + loss_g = loss_g_perception + loss_g_dI + loss_g.backward() + optimizer_g.step() + message = "===> Epoch[{}]({}/{}): Loss_DI: {:.4f} Loss_GI: {:.4f} Loss_perception: {:.4f} lr_g = {:.7f} lr_d = {:.7f}".format( + epoch, iteration, len(training_data_loader), float(loss_dI), float(loss_g_dI), + float(loss_g_perception), optimizer_g.param_groups[0]['lr'], optimizer_d.param_groups[0]['lr']) + print(message) + # with open("train_log.txt", "a") as f: + # f.write(message + "\n") + + if iteration%200 == 0: + inference_out = fake_out * 255 + inference_out = inference_out[0].cpu().permute(1, 2, 0).float().detach().numpy().astype(np.uint8) + inference_in = (target_tensor[0, :3]* 255).cpu().permute(1, 2, 0).float().detach().numpy().astype(np.uint8) + inference_in_prompt = (source_tensor[0, :3] * 255).cpu().permute(1, 2, 0).float().detach().numpy().astype( + np.uint8) + frame2 = Tensor2img(ref_tensor[0], 1) + frame3 = Tensor2img(ref_tensor[0], 5) + + inference_out = np.concatenate([inference_in, inference_in_prompt, inference_out, frame2, frame3], axis=1) + # inference_out = cv2.cvtColor(inference_out, cv2.COLOR_RGB2BGR) + + log(train_logger, fig=inference_out, tag="Training/epoch_{}_{}".format(epoch, iteration)) + + real_iteration = epoch * len(training_data_loader) + iteration + message1 = "Step {}/{}, ".format(real_iteration, (epoch + 1) * len(training_data_loader)) + message2 = "" + losses = [loss_dI.item(), loss_g_perception.item(), loss_g_dI.item()] + train_logger.add_scalar("Loss/loss_dI", losses[0], real_iteration) + train_logger.add_scalar("Loss/loss_g_perception", losses[1], real_iteration) + train_logger.add_scalar("Loss/loss_g_dI", losses[2], real_iteration) + + avg_loss_g_perception += loss_g_perception.item() + avg_Loss_DI += loss_dI.item() + avg_Loss_GI += loss_g_dI.item() + train_logger.add_scalar("Loss/{}".format("epoch_g_perception"), avg_loss_g_perception / len(training_data_loader), epoch) + train_logger.add_scalar("Loss/{}".format("epoch_DI"), + avg_Loss_DI / len(training_data_loader), epoch) + train_logger.add_scalar("Loss/{}".format("epoch_GI"), + avg_Loss_GI / len(training_data_loader), epoch) + update_learning_rate(net_g_scheduler, optimizer_g) + update_learning_rate(net_d_scheduler, optimizer_d) + + # checkpoint + if epoch % opt.checkpoint == 0: + if not os.path.exists(opt.result_path): + os.mkdir(opt.result_path) + model_out_path = os.path.join(opt.result_path, 'epoch_{}.pth'.format(epoch)) + states = { + 'epoch': epoch + 1, + 'state_dict': {'net_g': net_g.state_dict(), 'net_d': net_d.state_dict()}, + 'optimizer': {'net_g': optimizer_g.state_dict(), 'net_d': optimizer_d.state_dict()} + } + torch.save(states, model_out_path) + print("Checkpoint saved to {}".format(epoch)) \ No newline at end of file diff --git a/mini_live/train_input_validation.py b/mini_live/train_input_validation.py new file mode 100644 index 0000000..46b1956 --- /dev/null +++ b/mini_live/train_input_validation.py @@ -0,0 +1,65 @@ +import os +os.environ["kmp_duplicate_lib_ok"] = "true" +import pickle +import cv2 +import numpy as np +import random +import pandas as pd +import glob +import copy +import torch +from torch.utils.data import DataLoader +from talkingface.data.DHLive_mini_dataset import Few_Shot_Dataset,data_preparation +from talkingface.utils import * +# video_list = glob.glob(r"E:\data\video\video\*.mp4") +# video_list = [os.path.basename(i).split(".")[0] for i in video_list] + +df = pd.read_csv(r"F:\C\AI\CV\DH008_few_shot\DH0119_mouth64_48/imageVar2.csv") +video_list = df[df["imageVar"] > 265000]["name"].tolist() +video_list = [os.path.dirname(os.path.dirname(i)) for i in video_list] +print(len(video_list)) +point_size = 1 +point_color = (0, 0, 255) # BGR +thickness = 4 # 0 、4、8 +video_list = video_list[105:125] + +dict_info = data_preparation(video_list) + +device = torch.device("cuda:0") +test_set = Few_Shot_Dataset(dict_info, is_train=True, n_ref = 3) +testing_data_loader = DataLoader(dataset=test_set, num_workers=0, batch_size=1, shuffle=False) + +def Tensor2img(tensor_, channel_index): + frame = tensor_[channel_index:channel_index + 3, :, :].detach().squeeze(0).cpu().float().numpy() + frame = np.transpose(frame, (1, 2, 0)) * 255.0 + frame = frame.clip(0, 255) + return frame.astype(np.uint8) +size_ = 256 +for iteration, batch in enumerate(testing_data_loader): + # source_tensor, source_prompt_tensor, ref_tensor, ref_prompt_tensor, target_tensor = [iii.to(device) for iii in batch] + source_tensor, ref_tensor, target_tensor = [iii.to(device) for iii in batch[:3]] + print(source_tensor.size(), ref_tensor.size(), target_tensor.size(), batch[3][0]) + + frame0 = Tensor2img(source_tensor[0], 0) + frame1 = Tensor2img(ref_tensor[0], 0) + frame2 = Tensor2img(ref_tensor[0], 1) + frame3 = Tensor2img(ref_tensor[0], 4) + frame4 = Tensor2img(ref_tensor[0], 5) + frame5 = Tensor2img(target_tensor[0], 0) + + # cv2.imwrite("in0.png", frame0) + # cv2.imwrite("in1.png", frame1) + # cv2.imwrite("in2.png", frame2) + # cv2.imwrite("in3.png", frame3) + # cv2.imwrite("in4.png", frame4) + # exit() + + + frame = np.concatenate([frame0, frame1, frame2, frame3, frame4, frame5], axis=1) + + cv2.imshow("ss", frame[:, :, ::-1]) + # if iteration > 840: + # cv2.waitKey(-1) + cv2.waitKey(-1) + # break +cv2.destroyAllWindows() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..0c0fdb1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +kaldi_native_fbank +mediapipe +sounddevice +tqdm +scikit-learn \ No newline at end of file diff --git a/talkingface/__init__.py b/talkingface/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/talkingface/audio_model.py b/talkingface/audio_model.py new file mode 100644 index 0000000..f390518 --- /dev/null +++ b/talkingface/audio_model.py @@ -0,0 +1,129 @@ +import sys +import numpy as np +import kaldi_native_fbank as knf +from scipy.io import wavfile +import torch +import pickle +device = "cuda" if torch.cuda.is_available() else "cpu" +import pickle +import os +def pca_process(x): + a = x.reshape(15, 30, 3) + # a = pca.mean_.reshape(15,30,3) + tmp = a[:, :15] + a[:, 15:][:, ::-1] + a[:, :15] = tmp / 2 + a[:, 15:] = a[:, :15][:, ::-1] + return a.flatten() +class AudioModel: + def __init__(self): + self.__net = None + self.__fbank = None + self.__fbank_processed_index = 0 + self.frame_index = 0 + + current_dir = os.path.dirname(os.path.abspath(__file__)) + Path_output_pkl = os.path.join(current_dir, "../data/pca.pkl") + with open(Path_output_pkl, "rb") as f: + pca = pickle.load(f) + self.pca_mean_ = pca_process(pca.mean_) + self.pca_components_ = np.zeros_like(pca.components_) + self.pca_components_[0] = pca_process(pca.components_[0]) + self.pca_components_[1] = pca_process(pca.components_[1]) + self.pca_components_[2] = pca_process(pca.components_[2]) + self.pca_components_[3] = pca_process(pca.components_[3]) + self.pca_components_[4] = pca_process(pca.components_[4]) + self.pca_components_[5] = pca_process(pca.components_[5]) + + self.reset() + + def loadModel(self, ckpt_path): + # if method == "lstm": + # ckpt_path = 'checkpoint/lstm/lstm_model_epoch_560.pth' + # Audio2FeatureModel = torch.load(model_path).to(device) + # Audio2FeatureModel.eval() + from talkingface.models.audio2bs_lstm import Audio2Feature + self.__net = Audio2Feature() # 调用模型Model + self.__net.load_state_dict(torch.load(ckpt_path)) + self.__net = self.__net.to(device) + self.__net.eval() + + def reset(self): + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + self.__fbank = knf.OnlineFbank(opts) + + self.h0 = torch.zeros(2, 1, 192).to(device) + self.c0 = torch.zeros(2, 1, 192).to(device) + + self.__fbank_processed_index = 0 + + audio_samples = np.zeros([320]) + self.__fbank.accept_waveform(16000, audio_samples.tolist()) + + def interface_frame(self, audio_samples): + # pcm为uint16位数据。 只处理一帧的数据， 16000/25 = 640 + self.__fbank.accept_waveform(16000, audio_samples.tolist()) + orig_mel = np.zeros([2, 80]) + + orig_mel[0] = self.__fbank.get_frame(self.__fbank_processed_index) + orig_mel[1] = self.__fbank.get_frame(self.__fbank_processed_index + 1) + + input = torch.from_numpy(orig_mel).unsqueeze(0).float().to(device) + bs_array, self.h0, self.c0 = self.__net(input, self.h0, self.c0) + bs_array = bs_array[0].detach().cpu().float().numpy() + bs_real = bs_array[0] + # print(self.__fbank_processed_index, self.__fbank.num_frames_ready, bs_real) + + frame = np.dot(bs_real[:6], self.pca_components_[:6]) + self.pca_mean_ + # print(frame_index, frame.shape) + frame = frame.reshape(15, 30, 3).clip(0, 255).astype(np.uint8) + self.__fbank_processed_index += 2 + return frame + + def interface_wav(self, wavpath): + rate, wav = wavfile.read(wavpath, mmap=False) + augmented_samples = wav + augmented_samples2 = augmented_samples.astype(np.float32, order='C') / 32768.0 + # print(augmented_samples2.shape, augmented_samples2.shape[0] / 16000) + + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + fbank = knf.OnlineFbank(opts) + fbank.accept_waveform(16000, augmented_samples2.tolist()) + seq_len = fbank.num_frames_ready // 2 + A2Lsamples = np.zeros([2 * seq_len, 80]) + for i in range(2 * seq_len): + f2 = fbank.get_frame(i) + A2Lsamples[i] = f2 + + orig_mel = A2Lsamples + # print(orig_mel.shape) + input = torch.from_numpy(orig_mel).unsqueeze(0).float().to(device) + # print(input.shape) + h0 = torch.zeros(2, 1, 192).to(device) + c0 = torch.zeros(2, 1, 192).to(device) + bs_array, hn, cn = self.__net(input, h0, c0) + bs_array = bs_array[0].detach().cpu().float().numpy() + bs_array = bs_array[4:] + + frame_num = len(bs_array) + output = np.zeros([frame_num, 15, 30, 3], dtype = np.uint8) + for frame_index in range(frame_num): + bs_real = bs_array[frame_index] + # bs_real[1:4] = - bs_real[1:4] + frame = np.dot(bs_real[:6], self.pca_components_[:6]) + self.pca_mean_ + # print(frame_index, frame.shape) + frame = frame.reshape(15, 30, 3).clip(0, 255).astype(np.uint8) + output[frame_index] = frame + + return output \ No newline at end of file diff --git a/talkingface/config/config.py b/talkingface/config/config.py new file mode 100644 index 0000000..401aa40 --- /dev/null +++ b/talkingface/config/config.py @@ -0,0 +1,110 @@ +import argparse + +class DataProcessingOptions(): + def __init__(self): + self.parser = argparse.ArgumentParser() + + def parse_args(self): + self.parser.add_argument('--extract_video_frame', action='store_true', help='extract video frame') + self.parser.add_argument('--extract_audio', action='store_true', help='extract audio files from videos') + self.parser.add_argument('--extract_deep_speech', action='store_true', help='extract deep speech features') + self.parser.add_argument('--crop_face', action='store_true', help='crop face') + self.parser.add_argument('--generate_training_json', action='store_true', help='generate training json file') + + self.parser.add_argument('--source_video_dir', type=str, default="./asserts/training_data/split_video_25fps", + help='path of source video in 25 fps') + self.parser.add_argument('--openface_landmark_dir', type=str, default="./asserts/training_data/split_video_25fps_landmark_openface", + help='path of openface landmark dir') + self.parser.add_argument('--video_frame_dir', type=str, default="./asserts/training_data/split_video_25fps_frame", + help='path of video frames') + self.parser.add_argument('--audio_dir', type=str, default="./asserts/training_data/split_video_25fps_audio", + help='path of audios') + self.parser.add_argument('--deep_speech_dir', type=str, default="./asserts/training_data/split_video_25fps_deepspeech", + help='path of deep speech') + self.parser.add_argument('--crop_face_dir', type=str, default="./asserts/training_data/split_video_25fps_crop_face", + help='path of crop face dir') + self.parser.add_argument('--json_path', type=str, default="./asserts/training_data/training_json.json", + help='path of training json') + self.parser.add_argument('--clip_length', type=int, default=9, help='clip length') + self.parser.add_argument('--deep_speech_model', type=str, default="./asserts/output_graph.pb", + help='path of pretrained deepspeech model') + return self.parser.parse_args() + +class DINetTrainingOptions(): + def __init__(self): + self.parser = argparse.ArgumentParser() + + def parse_args(self): + self.parser.add_argument('--seed', type=int, default=456, help='random seed to use.') + self.parser.add_argument('--source_channel', type=int, default=3, help='input source image channels') + self.parser.add_argument('--ref_channel', type=int, default=15, help='input reference image channels') + self.parser.add_argument('--audio_channel', type=int, default=29, help='input audio channels') + self.parser.add_argument('--augment_num', type=int, default=32, help='augment training data') + self.parser.add_argument('--mouth_region_size', type=int, default=64, help='augment training data') + self.parser.add_argument('--train_data', type=str, default=r"./asserts/training_data/training_json.json", + help='path of training json') + self.parser.add_argument('--batch_size', type=int, default=24, help='training batch size') + self.parser.add_argument('--lamb_perception', type=int, default=10, help='weight of perception loss') + self.parser.add_argument('--lamb_syncnet_perception', type=int, default=0.1, help='weight of perception loss') + self.parser.add_argument('--lr_g', type=float, default=0.00008, help='initial learning rate for adam') + self.parser.add_argument('--lr_d', type=float, default=0.00008, help='initial learning rate for adam') + self.parser.add_argument('--start_epoch', default=1, type=int, help='start epoch in training stage') + self.parser.add_argument('--non_decay', default=10, type=int, help='num of epoches with fixed learning rate') + self.parser.add_argument('--decay', default=30, type=int, help='num of linearly decay epochs') + self.parser.add_argument('--checkpoint', type=int, default=2, help='num of checkpoints in training stage') + self.parser.add_argument('--result_path', type=str, default=r"./asserts/training_model_weight/frame_training_64", + help='result path to save model') + self.parser.add_argument('--coarse2fine', action='store_true', help='If true, load pretrained model path.') + self.parser.add_argument('--coarse_model_path', + default='', + type=str, + help='Save data (.pth) of previous training') + self.parser.add_argument('--pretrained_syncnet_path', + default='', + type=str, + help='Save data (.pth) of pretrained syncnet') + self.parser.add_argument('--pretrained_frame_DINet_path', + default='', + type=str, + help='Save data (.pth) of frame trained DINet') + # ========================= Discriminator ========================== + self.parser.add_argument('--D_num_blocks', type=int, default=4, help='num of down blocks in discriminator') + self.parser.add_argument('--D_block_expansion', type=int, default=64, help='block expansion in discriminator') + self.parser.add_argument('--D_max_features', type=int, default=256, help='max channels in discriminator') + return self.parser.parse_args() + + +class DINetInferenceOptions(): + def __init__(self): + self.parser = argparse.ArgumentParser() + + def parse_args(self): + self.parser.add_argument('--source_channel', type=int, default=3, help='channels of source image') + self.parser.add_argument('--ref_channel', type=int, default=15, help='channels of reference image') + self.parser.add_argument('--audio_channel', type=int, default=29, help='channels of audio feature') + self.parser.add_argument('--mouth_region_size', type=int, default=256, help='help to resize window') + self.parser.add_argument('--source_video_path', + default='./asserts/examples/test4.mp4', + type=str, + help='path of source video') + self.parser.add_argument('--source_openface_landmark_path', + default='./asserts/examples/test4.csv', + type=str, + help='path of detected openface landmark') + self.parser.add_argument('--driving_audio_path', + default='./asserts/examples/driving_audio_1.wav', + type=str, + help='path of driving audio') + self.parser.add_argument('--pretrained_clip_DINet_path', + default='./asserts/clip_training_DINet_256mouth.pth', + type=str, + help='pretrained model of DINet(clip trained)') + self.parser.add_argument('--deepspeech_model_path', + default='./asserts/output_graph.pb', + type=str, + help='path of deepspeech model') + self.parser.add_argument('--res_video_dir', + default='./asserts/inference_result', + type=str, + help='path of generated videos') + return self.parser.parse_args() \ No newline at end of file diff --git a/talkingface/data/DHLive_mini_dataset.py b/talkingface/data/DHLive_mini_dataset.py new file mode 100644 index 0000000..6d895cf --- /dev/null +++ b/talkingface/data/DHLive_mini_dataset.py @@ -0,0 +1,202 @@ +import numpy as np +import cv2 +import tqdm +import copy +from talkingface.utils import * +import glob +import pickle +import torch +import torch.utils.data as data + +model_size = (256, 256) + +def get_image(A_path, crop_coords, input_type, resize= (256, 256)): + (x_min, y_min, x_max, y_max) = crop_coords + size = (x_max - x_min, y_max - y_min) + + if input_type == 'mediapipe': + pose_pts = (A_path - np.array([x_min, y_min])) * resize / size + return pose_pts[:, :2] + else: + img_output = A_path[y_min:y_max, x_min:x_max, :] + img_output = cv2.resize(img_output, resize) + return img_output +def generate_input(img, keypoints, is_train = False, mode=["mouth_bias"]): + # 根据关键点决定正方形裁剪区域 + crop_coords = crop_mouth(keypoints, img.shape[1], img.shape[0], is_train=is_train) + target_keypoints = get_image(keypoints[:,:2], crop_coords, input_type='mediapipe', resize = model_size) + target_img = get_image(img, crop_coords, input_type='img', resize = model_size) + + source_img = copy.deepcopy(target_img) + source_keypoints = target_keypoints + + source_face_egde = draw_mouth_maps(source_keypoints, im_edges = source_img) + return source_img,target_img,crop_coords + +def generate_ref(img, keypoints, is_train=False, alpha = None, beta = None): + crop_coords = crop_mouth(keypoints, img.shape[1], img.shape[0], is_train=is_train) + ref_keypoints = get_image(keypoints, crop_coords, input_type='mediapipe', resize = model_size) + ref_img = get_image(img, crop_coords, input_type='img', resize = model_size) + + ref_face_edge = draw_mouth_maps(ref_keypoints, size = model_size) + ref_img = np.concatenate([ref_img, ref_face_edge[:,:,:1]], axis=2) + return ref_img + +def select_ref_index(driven_keypoints, n_ref = 5, ratio = 1/3., ratio2 = 1): + # 根据嘴巴开合程度，选取开合最大的那一半 + lips_distance = np.linalg.norm( + driven_keypoints[:, INDEX_LIPS_INNER[5]] - driven_keypoints[:, INDEX_LIPS_INNER[-5]], axis=1) + selected_index_list = np.argsort(lips_distance).tolist()[int(len(lips_distance) * ratio): int(len(lips_distance) * ratio2)] + ref_img_index_list = random.sample(selected_index_list, n_ref) # 从当前视频选n_ref个图片 + return ref_img_index_list + +def get_ref_images_fromVideo(cap, ref_img_index_list, ref_keypoints): + ref_img_list = [] + for index in ref_img_index_list: + cap.set(cv2.CAP_PROP_POS_FRAMES, index) # 设置要获取的帧号 + ret, frame = cap.read() + frame = frame[:,:,::-1] + if ret is False: + print("请检查当前视频， 错误帧数：", index) + ref_img = generate_ref(frame, ref_keypoints[index]) + ref_img_list.append(ref_img) + ref_img = np.concatenate(ref_img_list, axis=2) + return ref_img + + + + +class Few_Shot_Dataset(data.Dataset): + def __init__(self, dict_info, n_ref = 2, is_train = False): + super(Few_Shot_Dataset, self).__init__() + self.driven_images = dict_info["driven_images"] + self.driven_keypoints = dict_info["driven_keypoints"] + self.driving_keypoints = dict_info["driving_keypoints"] + + self.driven_teeth_images = dict_info["driven_teeth_image"] + self.driven_teeth_rect = dict_info["driven_teeth_rect"] + self.is_train = is_train + + assert len(self.driven_images) == len(self.driven_keypoints) + assert len(self.driven_images) == len(self.driving_keypoints) + + self.out_size = (256, 256) + + self.sample_num = np.sum([len(i) for i in self.driven_images]) + + # list: 每个视频序列的视频块个数 + self.clip_count_list = [] # number of frames in each sequence + for path in self.driven_images: + self.clip_count_list.append(len(path)) + self.n_ref = n_ref + + def get_ref_images(self, video_index, ref_img_index_list): + # 参考图片 + ref_img_list = [] + for ref_img_index in ref_img_index_list: + ref_img = cv2.imread(self.driven_images[video_index][ref_img_index])[:, :, ::-1] + # ref_img = cv2.convertScaleAbs(ref_img, alpha=self.alpha, beta=self.beta) + ref_keypoints = self.driven_keypoints[video_index][ref_img_index] + ref_img = generate_ref(ref_img, ref_keypoints, self.is_train) + + ref_img_list.append(ref_img) + self.ref_img = np.concatenate(ref_img_list, axis=2) + + def __getitem__(self, index): + if self.is_train: + video_index = random.randint(0, len(self.driven_images) - 1) + current_clip = random.randint(0, self.clip_count_list[video_index] - 1) + + ref_img_index_list = select_ref_index(self.driven_keypoints[video_index], n_ref = self.n_ref, ratio = 0.33) # 从当前视频选n_ref个图片 + + self.get_ref_images(video_index, ref_img_index_list) + else: + video_index = 0 + current_clip = index + + if index == 0: + ref_img_index_list = select_ref_index(self.driven_keypoints[video_index], n_ref=self.n_ref) # 从当前视频选n_ref个图片 + self.get_ref_images(video_index, ref_img_index_list) + + # target图片 + target_img = cv2.imread(self.driven_images[video_index][current_clip])[:, :, ::-1] + # target_img = cv2.convertScaleAbs(target_img, alpha=self.alpha, beta=self.beta) + ref_face_edge = np.zeros_like(target_img) + target_keypoints = self.driving_keypoints[video_index][current_clip] + source_img, target_img,crop_coords = generate_input(target_img, target_keypoints, self.is_train, mode="mouth") + + + [x_min, y_min, x_max, y_max] = self.driven_teeth_rect[video_index][current_clip] + teeth_img = cv2.imread(self.driven_teeth_images[video_index][current_clip]) + # print(ref_face_edge.shape, crop_coords, [x_min, y_min, x_max, y_max]) + ref_face_edge[int(y_min):int(y_max), int(x_min):int(x_max), 1][ + np.where(teeth_img[:, teeth_img.shape[1] // 2:, 0] == 0)] = 255 + ref_face_edge[int(y_min):int(y_max), int(x_min):int(x_max), 2][ + np.where(teeth_img[:, teeth_img.shape[1] // 2:, 0] == 255)] = 255 + + + # cv2.imshow("s", ref_face_edge) + # cv2.waitKey(-1) + # print(ref_face_edge.shape, crop_coords) + teeth_img = get_image(ref_face_edge, crop_coords, input_type='img', resize = model_size) + + + source_img[:,:,1][np.where(source_img[:,:,0] == 0)] = teeth_img[:,:,1][np.where(source_img[:,:,0] == 0)] + source_img[:, :, 2][np.where(source_img[:, :, 0] == 0)] = teeth_img[:, :, 2][np.where(source_img[:, :, 0] == 0)] + + target_img = cv2.resize(target_img, (128, 128)) + source_img = cv2.resize(source_img, (128, 128)) + self.ref_img = cv2.resize(self.ref_img, (128, 128)) + + + w_pad = int((128 - 72) / 2) + h_pad = int((128 - 56) / 2) + + target_img = target_img[h_pad:-h_pad, w_pad:-w_pad]/255. + source_img = source_img[h_pad:-h_pad, w_pad:-w_pad]/255. + ref_img = self.ref_img[h_pad:-h_pad, w_pad:-w_pad]/255. + + # tensor + source_tensor = torch.from_numpy(source_img).float().permute(2, 0, 1) + ref_tensor = torch.from_numpy(ref_img).float().permute(2, 0, 1) + target_tensor = torch.from_numpy(target_img).float().permute(2, 0, 1) + return source_tensor, ref_tensor, target_tensor, self.driven_images[video_index][current_clip] + + def __len__(self): + if self.is_train: + return len(self.driven_images) + else: + return len(self.driven_images[0]) + # return self.sample_num +def data_preparation(train_video_list): + img_all = [] + keypoints_all = [] + teeth_img_all = [] + teeth_rect_all = [] + for i in tqdm.tqdm(train_video_list): + # for i in ["xiaochangzhang/00004"]: + model_name = i + img_filelist = glob.glob("{}/image/*.png".format(model_name)) + img_filelist.sort() + if len(img_filelist) == 0: + continue + img_all.append(img_filelist) + img_teeth_filelist = glob.glob("{}/teeth_seg/*.png".format(model_name)) + img_teeth_filelist.sort() + teeth_img_all.append(img_teeth_filelist) + + teeth_rect_array = np.loadtxt("{}/teeth_seg/all.txt".format(model_name)) + teeth_rect_all.append(teeth_rect_array) + Path_output_pkl = "{}/keypoint_rotate.pkl".format(model_name) + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f) + keypoints_all.append(images_info[:, main_keypoints_index, :2]) + + print("train size: ", len(img_all)) + dict_info = {} + dict_info["driven_images"] = img_all + dict_info["driven_keypoints"] = keypoints_all + dict_info["driving_keypoints"] = keypoints_all + dict_info["driven_teeth_rect"] = teeth_rect_all + dict_info["driven_teeth_image"] = teeth_img_all + return dict_info diff --git a/talkingface/data/__init__.py b/talkingface/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/talkingface/data/dataset_wav.py b/talkingface/data/dataset_wav.py new file mode 100644 index 0000000..b32f1df --- /dev/null +++ b/talkingface/data/dataset_wav.py @@ -0,0 +1,104 @@ +import torch +import torch.utils.data as data +import numpy as np +from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift, Shift, PolarityInversion +# from audio import melspectrogram,mel_bar +import kaldi_native_fbank as knf +import random + + + +class AudioVisualDataset(data.Dataset): + """ audio-visual dataset. currently, return 2D info and 3D tracking info. + + ''' + 多个片段的APC语音特征和嘴部顶点的PCA信息 + :param audio_features: list + :param mouth_features: list + ''' + + """ + + def __init__(self, audio_features, mouth_features, is_train = True, seq_len = 9): + super(AudioVisualDataset, self).__init__() + + self.fps = 25 + # 每0.2s一个序列 + # self.seq_len = int(self.fps /5) + self.seq_len = seq_len + self.frame_jump_stride = 2 + self.audio_features = audio_features + self.bs_features = mouth_features + self.is_train = is_train + + self.augment = Compose([ + AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5), + # TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5), + PolarityInversion(p=0.5), + PitchShift(min_semitones=-4, max_semitones=4, p=0.5), + # Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5), + ]) + + + # 每个序列的裁剪片段个数 + self.clip_num = [] + for i in range(len(audio_features)): + audio_frame_num = int(len(self.audio_features[i])/(16000/25)) - 2 + self.clip_num.append(min(len(self.bs_features[i]), audio_frame_num) - self.seq_len + 1) + + def __getitem__(self, index): + if self.is_train: + video_index = random.randint(0, len(self.bs_features) - 1) + # print(video_index, self.clip_num[video_index]) + clips_index = random.sample(range(self.clip_num[video_index]), 1) # 从当前视频选1个片段 + current_frame = clips_index[0] + else: + video_index = 0 + # video_index = 0 + # for i in range(len(self.clip_num)): + # if index < np.sum(self.clip_num[:i+1]): + # video_index = i + # break + # current_frame = index - np.sum(self.clip_num[:video_index], dtype=int) + # print(video_index, current_frame, current_frame + self.seq_len, self.clip_num, self.bs_features[video_index].shape) + + # start point is current frame + A2Lsamples = self.audio_features[video_index][current_frame*640: (current_frame + self.seq_len + 2)*640] + # A2Lsamples = copy.deepcopy(A2Lsamples_) + # print("A2Lsamples: ", A2Lsamples.shape, A2Lsamples.dtype, A2Lsamples.__class__) + augmented_samples = self.augment(np.array(A2Lsamples, dtype=np.float32), sample_rate=16000) + # print(augmented_samples.shape, augmented_samples.dtype) + # int16转换为float格式 + augmented_samples2 = augmented_samples.astype(np.float32, order='C') / 32768.0 + # orig_mel = mel_bar(augmented_samples2) + # orig_mel = melspectrogram(augmented_samples2).T + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + fbank = knf.OnlineFbank(opts) + fbank.accept_waveform(16000, augmented_samples2.tolist()) + A2Lsamples = np.zeros([2*self.seq_len, 80]) + for i in range(2*self.seq_len): + f2 = fbank.get_frame(i) + A2Lsamples[i] = f2 + fbank.input_finished() + + target_bs = self.bs_features[video_index][current_frame: current_frame + self.seq_len, :].reshape( + self.seq_len, -1) + + # target_bs = self.bs_features[video_index][current_frame + self.seq_len//2, :] + + A2Lsamples = torch.from_numpy(A2Lsamples).float() + target_bs = torch.from_numpy(target_bs).float() + # print("*****", A2Lsamples.size(), target_bs.size(), len(self.clip_num)) + + return [A2Lsamples, target_bs] + + def __len__(self): + return len(self.clip_num) + # return np.sum(self.clip_num, dtype = int) + # return 10000 \ No newline at end of file diff --git a/talkingface/data/face_mask.py b/talkingface/data/face_mask.py new file mode 100644 index 0000000..8777ed7 --- /dev/null +++ b/talkingface/data/face_mask.py @@ -0,0 +1,66 @@ +import os +os.environ["KMP_DUPLICATE_LIB_OK"] = "true" +import pickle +import cv2 +import numpy as np +import os +import glob +from talkingface.util.smooth import smooth_array +from talkingface.run_utils import calc_face_mat +import tqdm +from talkingface.utils import * + +path_ = r"../../../preparation_mix" +video_list = [os.path.join(path_, i) for i in os.listdir(path_)] +path_ = r"../../../preparation_hdtf" +video_list += [os.path.join(path_, i) for i in os.listdir(path_)] +path_ = r"../../../preparation_vfhq" +video_list += [os.path.join(path_, i) for i in os.listdir(path_)] +path_ = r"../../../preparation_bilibili" +video_list += [os.path.join(path_, i) for i in os.listdir(path_)] +print(video_list) +video_list = video_list[:] +img_all = [] +keypoints_all = [] +point_size = 1 +point_color = (0, 0, 255) # BGR +thickness = 4 # 0 、4、8 +for path_ in tqdm.tqdm(video_list): + img_filelist = glob.glob("{}/image/*.png".format(path_)) + img_filelist.sort() + if len(img_filelist) == 0: + continue + img_all.append(img_filelist) + + Path_output_pkl = "{}/keypoint_rotate.pkl".format(path_) + + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f)[:, main_keypoints_index, :] + pts_driven = images_info.reshape(len(images_info), -1) + pts_driven = smooth_array(pts_driven).reshape(len(pts_driven), -1, 3) + + face_pts_mean = np.loadtxt(r"data\face_pts_mean_mainKps.txt") + mat_list,pts_normalized_list,face_pts_mean_personal = calc_face_mat(pts_driven, face_pts_mean) + pts_normalized_list = np.array(pts_normalized_list) + # print(face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1]) + # print(np.max(pts_normalized_list[:,INDEX_FACE_OVAL[:10], 1], axis = 1)) + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1] = np.max(pts_normalized_list[:,INDEX_FACE_OVAL[:10], 1], axis = 0) + np.arange(5,25,2) + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 0] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 0], axis=0) - (9 - np.arange(0,10)) + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 1], axis=0) - np.arange(5,25,2) + 28 + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 0] = np.min(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 0], axis=0) + np.arange(0,10) + + face_pts_mean_personal[INDEX_FACE_OVAL[10], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[10], 1], axis=0) + 25 + + # for keypoints_normalized in pts_normalized_list: + # img = np.zeros([1000,1000,3], dtype=np.uint8) + # for coor in face_pts_mean_personal: + # # coor = (coor +1 )/2. + # cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, (255, 0, 0), thickness) + # for coor in keypoints_normalized: + # # coor = (coor +1 )/2. + # cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, point_color, thickness) + # cv2.imshow("a", img) + # cv2.waitKey(30) + + with open("{}/face_mat_mask20240722.pkl".format(path_), "wb") as f: + pickle.dump([mat_list, face_pts_mean_personal], f) diff --git a/talkingface/data/few_shot_dataset.py b/talkingface/data/few_shot_dataset.py new file mode 100644 index 0000000..e5aed86 --- /dev/null +++ b/talkingface/data/few_shot_dataset.py @@ -0,0 +1,288 @@ +import numpy as np +import cv2 +import tqdm +import copy +from talkingface.utils import * +import glob +import pickle +import torch +import torch.utils.data as data +def get_image(A_path, crop_coords, input_type, resize= 256): + (x_min, y_min, x_max, y_max) = crop_coords + size = (x_max - x_min, y_max - y_min) + + if input_type == 'mediapipe': + if A_path.shape[1] == 2: + pose_pts = (A_path - np.array([x_min, y_min])) * resize / size + return pose_pts[:, :2] + else: + A_path[:, 2] = A_path[:, 2] - np.max(A_path[:, 2]) + pose_pts = (A_path - np.array([x_min, y_min, 0])) * resize / size[0] + return pose_pts[:, :3] + + else: + img_output = A_path[y_min:y_max, x_min:x_max, :] + img_output = cv2.resize(img_output, (resize, resize)) + return img_output +def generate_input(img, keypoints, mask_keypoints, is_train = False, mode=["mouth_bias"], mouth_width = None, mouth_height = None): + # 根据关键点决定正方形裁剪区域 + crop_coords = crop_face(keypoints, size=img.shape[:2], is_train=is_train) + target_keypoints = get_image(keypoints[:,:2], crop_coords, input_type='mediapipe') + target_img = get_image(img, crop_coords, input_type='img') + + target_mask_keypoints = get_image(mask_keypoints[:,:2], crop_coords, input_type='mediapipe') + + # source_img信息：扣出嘴部区域 + source_img = copy.deepcopy(target_img) + source_keypoints = target_keypoints + + pts = source_keypoints.copy() + + face_edge_start_index = 2 + + pts[INDEX_FACE_OVAL[face_edge_start_index:-face_edge_start_index], 1] = target_mask_keypoints[face_edge_start_index:-face_edge_start_index, 1] + + # pts = pts[INDEX_FACE_OVAL[face_edge_start_index:-face_edge_start_index] + INDEX_NOSE_EDGE[::-1], :2] + pts = pts[FACE_MASK_INDEX + INDEX_NOSE_EDGE[::-1], :2] + + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(source_img, [pts], color=(0, 0, 0)) + source_face_egde = draw_face_feature_maps(source_keypoints, mode=mode, im_edges=target_img, + mouth_width = mouth_width * (256/(crop_coords[2] - crop_coords[0])), mouth_height = mouth_height * (256/(crop_coords[2] - crop_coords[0]))) + source_img = np.concatenate([source_img, source_face_egde], axis=2) + return source_img,target_img,crop_coords + +def generate_ref(img, keypoints, is_train=False, alpha = None, beta = None): + crop_coords = crop_face(keypoints, size=img.shape[:2], is_train=is_train) + ref_keypoints = get_image(keypoints, crop_coords, input_type='mediapipe') + ref_img = get_image(img, crop_coords, input_type='img') + + if beta is not None: + if alpha: + ref_img[:, :, :3] = cv2.add(ref_img[:, :, :3], beta) + else: + ref_img[:, :, :3] = cv2.subtract(ref_img[:, :, :3], beta) + ref_face_edge = draw_face_feature_maps(ref_keypoints, mode=["mouth", "nose", "eye", "oval_all","muscle"]) + ref_img = np.concatenate([ref_img, ref_face_edge], axis=2) + return ref_img + +def select_ref_index(driven_keypoints, n_ref = 5, ratio = 1/3.): + # 根据嘴巴开合程度，选取开合最大的那一半 + lips_distance = np.linalg.norm( + driven_keypoints[:, INDEX_LIPS_INNER[5]] - driven_keypoints[:, INDEX_LIPS_INNER[-5]], axis=1) + selected_index_list = np.argsort(lips_distance).tolist()[int(len(lips_distance) * ratio):] + ref_img_index_list = random.sample(selected_index_list, n_ref) # 从当前视频选n_ref个图片 + return ref_img_index_list + +def get_ref_images_fromVideo(cap, ref_img_index_list, ref_keypoints): + ref_img_list = [] + for index in ref_img_index_list: + cap.set(cv2.CAP_PROP_POS_FRAMES, index) # 设置要获取的帧号 + ret, frame = cap.read() + if ret is False: + print("请检查当前视频， 错误帧数：", index) + ref_img = generate_ref(frame, ref_keypoints[index]) + ref_img_list.append(ref_img) + ref_img = np.concatenate(ref_img_list, axis=2) + return ref_img + + + + +class Few_Shot_Dataset(data.Dataset): + def __init__(self, dict_info, n_ref = 2, is_train = False): + super(Few_Shot_Dataset, self).__init__() + self.driven_images = dict_info["driven_images"] + self.driven_keypoints = dict_info["driven_keypoints"] + self.driving_keypoints = dict_info["driving_keypoints"] + self.driven_mask_keypoints = dict_info["driven_mask_keypoints"] + self.is_train = is_train + + assert len(self.driven_images) == len(self.driven_keypoints) + assert len(self.driven_images) == len(self.driving_keypoints) + + self.out_size = (256, 256) + + self.sample_num = np.sum([len(i) for i in self.driven_images]) + + # list: 每个视频序列的视频块个数 + self.clip_count_list = [] # number of frames in each sequence + for path in self.driven_images: + self.clip_count_list.append(len(path)) + self.n_ref = n_ref + + def get_ref_images(self, video_index, ref_img_index_list): + # 参考图片 + ref_img_list = [] + for ref_img_index in ref_img_index_list: + ref_img = cv2.imread(self.driven_images[video_index][ref_img_index]) + # ref_img = cv2.convertScaleAbs(ref_img, alpha=self.alpha, beta=self.beta) + + + ref_keypoints = self.driven_keypoints[video_index][ref_img_index] + ref_img = generate_ref(ref_img, ref_keypoints, self.is_train, self.alpha, self.beta) + + ref_img_list.append(ref_img) + self.ref_img = np.concatenate(ref_img_list, axis=2) + + def __getitem__(self, index): + + # 调整亮度和对比度 + # self.alpha = random.uniform(0.8,1.25) # 缩放因子 + # self.beta = random.uniform(-50,50) # 移位因子 + # adjusted = cv2.convertScaleAbs(img, alpha=alpha, beta=beta) + + self.alpha = (random.random() > 0.5) # 正负因子 + self.beta = np.ones([256,256,3]) * np.random.rand(3) * 20 # 色彩调整0-20个色差 + self.beta = self.beta.astype(np.uint8) + + + if self.is_train: + video_index = random.randint(0, len(self.driven_images) - 1) + current_clip = random.randint(0, self.clip_count_list[video_index] - 1) + ref_img_index_list = select_ref_index(self.driven_keypoints[video_index], n_ref = self.n_ref) # 从当前视频选n_ref个图片 + self.get_ref_images(video_index, ref_img_index_list) + else: + video_index = 0 + current_clip = index + + if index == 0: + ref_img_index_list = select_ref_index(self.driven_keypoints[video_index], n_ref=self.n_ref5) # 从当前视频选n_ref个图片 + self.get_ref_images(video_index, ref_img_index_list) + + # target图片 + target_img = cv2.imread(self.driven_images[video_index][current_clip]) + # target_img = cv2.convertScaleAbs(target_img, alpha=self.alpha, beta=self.beta) + + target_keypoints = self.driving_keypoints[video_index][current_clip] + target_mask_keypoints = self.driven_mask_keypoints[video_index][current_clip] + + mouth_rect = self.driving_keypoints[video_index][:, INDEX_LIPS].max(axis=1) - self.driving_keypoints[video_index][:, INDEX_LIPS].min(axis=1) + mouth_width = mouth_rect[:, 0].max() + mouth_height = mouth_rect[:, 1].max() + + # source_img, target_img,crop_coords = generate_input(target_img, target_keypoints, target_mask_keypoints, self.is_train) + source_img, target_img,crop_coords = generate_input(target_img, target_keypoints, target_mask_keypoints, self.is_train, mode=["mouth_bias", "nose", "eye"], + mouth_width = mouth_width, mouth_height = mouth_height) + + target_img = target_img/255. + source_img = source_img/255. + ref_img = self.ref_img / 255. + + # tensor + source_tensor = torch.from_numpy(source_img).float().permute(2, 0, 1) + ref_tensor = torch.from_numpy(ref_img).float().permute(2, 0, 1) + target_tensor = torch.from_numpy(target_img).float().permute(2, 0, 1) + return source_tensor, ref_tensor, target_tensor + + def __len__(self): + if self.is_train: + return len(self.driven_images) + else: + return len(self.driven_images[0]) + # return self.sample_num +def data_preparation(train_video_list): + img_all = [] + keypoints_all = [] + mask_all = [] + point_size = 1 + point_color = (0, 0, 255) # BGR + thickness = 4 # 0 、4、8 + for i in tqdm.tqdm(train_video_list): + # for i in ["xiaochangzhang/00004"]: + model_name = i + img_filelist = glob.glob("{}/image/*.png".format(model_name)) + img_filelist.sort() + if len(img_filelist) == 0: + continue + img_all.append(img_filelist) + + Path_output_pkl = "{}/keypoint_rotate.pkl".format(model_name) + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f) + keypoints_all.append(images_info[:, main_keypoints_index, :2]) + + Path_output_pkl = "{}/face_mat_mask.pkl".format(model_name) + with open(Path_output_pkl, "rb") as f: + mat_list, face_pts_mean_personal = pickle.load(f) + + face_pts_mean_personal = face_pts_mean_personal[INDEX_FACE_OVAL] + face_mask_pts = np.zeros([len(mat_list), len(face_pts_mean_personal), 2]) + for index_ in range(len(mat_list)): + # img = np.zeros([1000,1000,3], dtype=np.uint8) + # img = cv2.imread(img_filelist[index_]) + + rotationMatrix = mat_list[index_] + + keypoints = np.ones([4, len(face_pts_mean_personal)]) + keypoints[:3, :] = face_pts_mean_personal.T + driving_mask = rotationMatrix.dot(keypoints).T + face_mask_pts[index_] = driving_mask[:, :2] + + + + # for coor in driving_mask: + # # coor = (coor +1 )/2. + # cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, point_color, thickness) + # cv2.imshow("a", img) + # cv2.waitKey(30) + mask_all.append(face_mask_pts) + + print("train size: ", len(img_all)) + dict_info = {} + dict_info["driven_images"] = img_all + dict_info["driven_keypoints"] = keypoints_all + dict_info["driving_keypoints"] = keypoints_all + dict_info["driven_mask_keypoints"] = mask_all + return dict_info + + +def generate_input_pixels(img, keypoints, rotationMatrix, pixels_mouth, mask_keypoints, coords_array): + # 根据关键点决定正方形裁剪区域 + crop_coords = crop_face(keypoints, size=img.shape[:2], is_train=False) + target_keypoints = get_image(keypoints[:, :2], crop_coords, input_type='mediapipe') + + # 画出嘴部像素图 + pixels_mouth_coords = rotationMatrix.dot(coords_array).T + pixels_mouth_coords = pixels_mouth_coords[:, :2].astype(int) + pixels_mouth_coords = (pixels_mouth_coords[:, 1], pixels_mouth_coords[:, 0]) + + source_face_egde = np.zeros_like(img, dtype=np.uint8) + # out_frame = img.copy() + frame = pixels_mouth.reshape(15, 30, 3).clip(0, 255).astype(np.uint8) + + frame = cv2.resize(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (150, 100)) + sharpen_image = frame.astype(np.float32) + mean_ = int(np.mean(sharpen_image)) + max_, min_ = mean_ + 60, mean_ - 60 + sharpen_image = (sharpen_image - min_) / (max_ - min_) * 255. + sharpen_image = sharpen_image.clip(0, 255).astype(np.uint8) + + sharpen_image = np.concatenate( + [sharpen_image[:, :, np.newaxis], sharpen_image[:, :, np.newaxis], sharpen_image[:, :, np.newaxis]], axis=2) + # sharpen_image = cv2.resize(sharpen_image, (150, 100)) + source_face_egde[pixels_mouth_coords] = sharpen_image.reshape(-1, 3) + # cv2.imshow("sharpen_image", source_face_egde) + # cv2.waitKey(40) + + source_face_egde = get_image(source_face_egde, crop_coords, input_type='image') + source_face_egde = draw_face_feature_maps(target_keypoints, mode = ["nose", "eye"], im_edges=source_face_egde) + # cv2.imshow("sharpen_image", source_face_egde) + # cv2.waitKey(40) + + + target_img = get_image(img, crop_coords, input_type='img') + target_mask_keypoints = get_image(mask_keypoints[:, :2], crop_coords, input_type='mediapipe') + # source_img信息：扣出嘴部区域 + source_img = copy.deepcopy(target_img) + source_keypoints = target_keypoints + pts = source_keypoints.copy() + face_edge_start_index = 3 + pts[INDEX_FACE_OVAL[face_edge_start_index:-face_edge_start_index], 1] = target_mask_keypoints[ + face_edge_start_index:-face_edge_start_index, + 1] + pts = pts[INDEX_FACE_OVAL[face_edge_start_index:-face_edge_start_index] + INDEX_NOSE_EDGE[::-1], :2] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(source_img, [pts], color=(0, 0, 0)) + source_img = np.concatenate([source_img, source_face_egde], axis=2) + return source_img, target_img, crop_coords \ No newline at end of file diff --git a/talkingface/face_pts_mean.txt b/talkingface/face_pts_mean.txt new file mode 100644 index 0000000..c51c311 --- /dev/null +++ b/talkingface/face_pts_mean.txt @@ -0,0 +1,478 @@ +7.010000000000000000e+02 5.450000000000000000e+02 -5.500000000000000000e+01 +7.030000000000000000e+02 4.930000000000000000e+02 -1.010000000000000000e+02 +7.030000000000000000e+02 5.120000000000000000e+02 -5.600000000000000000e+01 +6.900000000000000000e+02 4.300000000000000000e+02 -7.600000000000000000e+01 +7.030000000000000000e+02 4.740000000000000000e+02 -1.070000000000000000e+02 +7.040000000000000000e+02 4.480000000000000000e+02 -9.900000000000000000e+01 +7.060000000000000000e+02 3.870000000000000000e+02 -4.900000000000000000e+01 +5.800000000000000000e+02 3.720000000000000000e+02 2.300000000000000000e+01 +7.070000000000000000e+02 3.380000000000000000e+02 -3.600000000000000000e+01 +7.080000000000000000e+02 3.120000000000000000e+02 -3.900000000000000000e+01 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a/talkingface/mediapipe_utils.py b/talkingface/mediapipe_utils.py new file mode 100644 index 0000000..77d05e9 --- /dev/null +++ b/talkingface/mediapipe_utils.py @@ -0,0 +1,38 @@ +import numpy as np +import cv2 +import mediapipe as mp +mp_face_mesh = mp.solutions.face_mesh +mp_face_detection = mp.solutions.face_detection + +def detect_face_mesh(frames): + pts_3d = np.zeros([len(frames), 478, 3]) + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5) as face_mesh: + for frame_index, frame in enumerate(frames): + results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if results.multi_face_landmarks: + image_height, image_width = frame.shape[:2] + for face_landmarks in results.multi_face_landmarks: + for index_, i in enumerate(face_landmarks.landmark): + x_px = i.x * image_width + y_px = i.y * image_height + z_px = i.z * image_width + pts_3d[frame_index, index_] = np.array([x_px, y_px, z_px]) + else: + break + return pts_3d +def detect_face(frames): + rect_2d = np.zeros([len(frames), 4]) + # 剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80的 + with mp_face_detection.FaceDetection( + model_selection=1, min_detection_confidence=0.5) as face_detection: + for frame_index, frame in enumerate(frames): + results = face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if not results.detections or len(results.detections) > 1: + break + rect = results.detections[0].location_data.relative_bounding_box + rect_2d[frame_index] = np.array([rect.xmin, rect.xmin + rect.width, rect.ymin, rect.ymin + rect.height]) + return rect_2d \ No newline at end of file diff --git a/talkingface/model_utils.py b/talkingface/model_utils.py new file mode 100644 index 0000000..42adb0d --- /dev/null +++ b/talkingface/model_utils.py @@ -0,0 +1,141 @@ +import sys +import numpy as np +import kaldi_native_fbank as knf +from scipy.io import wavfile +import torch +import librosa +import pickle +device = "cuda" if torch.cuda.is_available() else "cpu" +pca = None +def LoadAudioModel(ckpt_path): + # if method == "lstm": + # ckpt_path = 'checkpoint/lstm/lstm_model_epoch_560.pth' + # Audio2FeatureModel = torch.load(model_path).to(device) + # Audio2FeatureModel.eval() + from talkingface.models.audio2bs_lstm import Audio2Feature + Audio2FeatureModel = Audio2Feature() # 调用模型Model + Audio2FeatureModel.load_state_dict(torch.load(ckpt_path)) + Audio2FeatureModel = Audio2FeatureModel.to(device) + Audio2FeatureModel.eval() + return Audio2FeatureModel + +def LoadRenderModel(ckpt_path, model_name = "one_ref"): + if model_name == "one_ref": + from talkingface.models.DINet import LeeNet as DINet + n_ref = 1 + source_channel = 3 + ref_channel = n_ref * 6 + else: + from talkingface.models.DINet import DINet_five_Ref as DINet + n_ref = 5 + source_channel = 6 + ref_channel = n_ref * 6 + net_g = DINet(source_channel, ref_channel).cuda() + checkpoint = torch.load(ckpt_path) + net_g_static = checkpoint['state_dict']['net_g'] + net_g.load_state_dict(net_g_static) + net_g.eval() + return net_g + +def find_silence_frame(wav): + audio_energy = librosa.feature.rms(y=wav, frame_length=800, hop_length=320, + center=False) # 对每一frame计算root-mean-square + audio_energy = np.transpose(audio_energy) # [t,1] + energy = audio_energy[:2 * (len(audio_energy) // 2)] + energy = energy.reshape([-1, 2]).max(axis=1) # downsample with max_pool + is_sil_mask = (energy < 1e-3).astype(int) + sil_index_list = [] + for i in range(len(is_sil_mask)): + if i < 3 or i > len(is_sil_mask) - 4: + sil_index_list.append(is_sil_mask[i]) + else: + if is_sil_mask[i] > 0: + sil_index_list.append(is_sil_mask[i]) + else: + # 七帧里面有四帧都静音，且左右侧都有静音帧，则此帧判别为静音帧 + if np.sum(is_sil_mask[i-3:i+4]) > 3 and np.sum(is_sil_mask[i-3:i+1]) > 0 and np.sum(is_sil_mask[i+1:i+4]) > 0: + sil_index_list.append(1) + else: + sil_index_list.append(0) + sil_index = np.where(sil_index_list)[0] + return sil_index + + +def Audio2mouth(wavpath, Audio2FeatureModel, method = "lstm"): + rate, wav = wavfile.read(wavpath, mmap=False) + augmented_samples = wav + augmented_samples2 = augmented_samples.astype(np.float32, order='C') / 32768.0 + print(augmented_samples2.shape, augmented_samples2.shape[0] / 16000) + + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + fbank = knf.OnlineFbank(opts) + # sss = augmented_samples2.tolist() + # for ii in range(0, len(sss), 10000): + # fbank.accept_waveform(16000, sss[ii:ii+10000]) + fbank.accept_waveform(16000, augmented_samples2.tolist()) + seq_len = fbank.num_frames_ready // 2 + A2Lsamples = np.zeros([2 * seq_len, 80]) + for i in range(2 * seq_len): + f2 = fbank.get_frame(i) + A2Lsamples[i] = f2 + + orig_mel = A2Lsamples + # print(orig_mel.shape) + input = torch.from_numpy(orig_mel).unsqueeze(0).float().to(device) + # print(input.shape) + h0 = torch.zeros(2, 1, 192).to(device) + c0 = torch.zeros(2, 1, 192).to(device) + bs_array, hn, cn = Audio2FeatureModel(input, h0, c0) + # print(bs_array.shape) + bs_array = bs_array[0].detach().cpu().float().numpy() + # print(bs_array.shape) + bs_array = bs_array[4:] + bs_array[:, :2] = bs_array[:, :2] / 8 + bs_array[:, 2] = - bs_array[:, 2] / 8 + + return bs_array +from scipy.signal import resample +def Audio2bs(wavpath, Audio2FeatureModel): + rate, wav = wavfile.read(wavpath, mmap=False) + wav = resample(wav, len(wav) //2) + augmented_samples = wav + augmented_samples2 = augmented_samples.astype(np.float32, order='C') / 32768.0 + # print(augmented_samples2.shape, augmented_samples2.shape[0] / 16000) + + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.samp_freq = 8000 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + fbank = knf.OnlineFbank(opts) + # sss = augmented_samples2.tolist() + # for ii in range(0, len(sss), 10000): + # fbank.accept_waveform(16000, sss[ii:ii+10000]) + fbank.accept_waveform(8000, augmented_samples2.tolist()) + seq_len = fbank.num_frames_ready // 2 + A2Lsamples = np.zeros([2 * seq_len, 80]) + for i in range(2 * seq_len): + f2 = fbank.get_frame(i) + A2Lsamples[i] = f2 + + orig_mel = A2Lsamples + # print(orig_mel.shape) + input = torch.from_numpy(orig_mel).unsqueeze(0).float().to(device) + # print(input.shape) + h0 = torch.zeros(2, 1, 192).to(device) + c0 = torch.zeros(2, 1, 192).to(device) + bs_array, hn, cn = Audio2FeatureModel(input, h0, c0) + # print(bs_array.shape) + bs_array = bs_array[0].detach().cpu().float().numpy() + # print(bs_array.shape) + # bs_array = bs_array[4:] + return bs_array \ No newline at end of file diff --git a/talkingface/models/DINet.py b/talkingface/models/DINet.py new file mode 100644 index 0000000..8450774 --- /dev/null +++ b/talkingface/models/DINet.py @@ -0,0 +1,315 @@ +import torch +from torch import nn +import torch.nn.functional as F +import math +import cv2 +import numpy as np +from torch.nn import BatchNorm2d +from torch.nn import BatchNorm1d + +def make_coordinate_grid_3d(spatial_size, type): + ''' + generate 3D coordinate grid + ''' + d, h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + z = torch.arange(d).type(type) + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + z = (2 * (z / (d - 1)) - 1) + yy = y.view(1,-1, 1).repeat(d,1, w) + xx = x.view(1,1, -1).repeat(d,h, 1) + zz = z.view(-1,1,1).repeat(1,h,w) + meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3)], 3) + return meshed,zz + +class ResBlock1d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features,out_features, kernel_size, padding): + super(ResBlock1d, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.conv1 = nn.Conv1d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv1d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding) + if out_features != in_features: + self.channel_conv = nn.Conv1d(in_features,out_features,1) + self.norm1 = BatchNorm1d(in_features) + self.norm2 = BatchNorm1d(in_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.norm1(x) + out = self.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = self.relu(out) + out = self.conv2(out) + if self.in_features != self.out_features: + out += self.channel_conv(x) + else: + out += x + return out + +class ResBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features,out_features, kernel_size, padding): + super(ResBlock2d, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding) + if out_features != in_features: + self.channel_conv = nn.Conv2d(in_features,out_features,1) + self.norm1 = BatchNorm2d(in_features) + self.norm2 = BatchNorm2d(in_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.norm1(x) + out = self.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = self.relu(out) + out = self.conv2(out) + if self.in_features != self.out_features: + out += self.channel_conv(x) + else: + out += x + return out + +class UpBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1): + super(UpBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = F.interpolate(x, scale_factor=2) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + +class DownBlock1d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size, padding): + super(DownBlock1d, self).__init__() + self.conv = nn.Conv1d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding,stride=2) + self.norm = BatchNorm1d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +class DownBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1, stride=2): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, stride=stride) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +class SameBlock1d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size, padding): + super(SameBlock1d, self).__init__() + self.conv = nn.Conv1d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding) + self.norm = BatchNorm1d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +class SameBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +class AdaAT(nn.Module): + ''' + AdaAT operator + ''' + def __init__(self, para_ch,feature_ch, cuda = True): + super(AdaAT, self).__init__() + self.para_ch = para_ch + self.feature_ch = feature_ch + self.commn_linear = nn.Sequential( + nn.Linear(para_ch, para_ch), + nn.ReLU() + ) + self.scale = nn.Sequential( + nn.Linear(para_ch, feature_ch), + nn.Sigmoid() + ) + self.rotation = nn.Sequential( + nn.Linear(para_ch, feature_ch), + nn.Tanh() + ) + self.translation = nn.Sequential( + nn.Linear(para_ch, 2 * feature_ch), + nn.Tanh() + ) + self.tanh = nn.Tanh() + self.sigmoid = nn.Sigmoid() + + def forward(self, feature_map,para_code): + batch,d, h, w = feature_map.size(0), feature_map.size(1), feature_map.size(2), feature_map.size(3) + para_code = self.commn_linear(para_code) + scale = self.scale(para_code).unsqueeze(-1) * 2 + angle = self.rotation(para_code).unsqueeze(-1) * 3.14159# + rotation_matrix = torch.cat([torch.cos(angle), -torch.sin(angle), torch.sin(angle), torch.cos(angle)], -1) + rotation_matrix = rotation_matrix.view(batch, self.feature_ch, 2, 2) + translation = self.translation(para_code).view(batch, self.feature_ch, 2) + grid_xy, grid_z = make_coordinate_grid_3d((d, h, w), feature_map.type()) + grid_xy = grid_xy.unsqueeze(0).repeat(batch, 1, 1, 1, 1) + grid_z = grid_z.unsqueeze(0).repeat(batch, 1, 1, 1) + scale = scale.unsqueeze(2).unsqueeze(3).repeat(1, 1, h, w, 1) + rotation_matrix = rotation_matrix.unsqueeze(2).unsqueeze(3).repeat(1, 1, h, w, 1, 1) + translation = translation.unsqueeze(2).unsqueeze(3).repeat(1, 1, h, w, 1) + trans_grid = torch.matmul(rotation_matrix, grid_xy.unsqueeze(-1)).squeeze(-1) * scale + translation + full_grid = torch.cat([trans_grid, grid_z.unsqueeze(-1)], -1) + trans_feature = F.grid_sample(feature_map.unsqueeze(1), full_grid).squeeze(1) + return trans_feature + +class DINet_five_Ref(nn.Module): + def __init__(self, source_channel,ref_channel, cuda = True): + super(DINet_five_Ref, self).__init__() + self.source_in_conv = nn.Sequential( + SameBlock2d(source_channel,32,kernel_size=7, padding=3), + DownBlock2d(32, 64, kernel_size=3, padding=1), + DownBlock2d(64, 128,kernel_size=3, padding=1) + ) + self.ref_in_conv = nn.Sequential( + SameBlock2d(ref_channel, 64, kernel_size=7, padding=3), + DownBlock2d(64, 128, kernel_size=3, padding=1), + DownBlock2d(128, 256, kernel_size=3, padding=1), + ) + self.trans_conv = nn.Sequential( + # 20 →10 + SameBlock2d(384, 128, kernel_size=3, padding=1), + # SameBlock2d(128, 128, kernel_size=11, padding=5), + SameBlock2d(128, 128, kernel_size=7, padding=3), + DownBlock2d(128, 128, kernel_size=3, padding=1), + # 10 →5 + SameBlock2d(128, 128, kernel_size=7, padding=3), + # SameBlock2d(128, 128, kernel_size=7, padding=3), + DownBlock2d(128, 128, kernel_size=3, padding=1), + # 5 →3 + SameBlock2d(128, 128, kernel_size=3, padding=1), + DownBlock2d(128, 128, kernel_size=3, padding=1), + # 3 →2 + SameBlock2d(128, 128, kernel_size=3, padding=1), + DownBlock2d(128, 128, kernel_size=3, padding=1), + + ) + + appearance_conv_list = [] + for i in range(2): + appearance_conv_list.append( + nn.Sequential( + ResBlock2d(256, 256, 3, 1), + ResBlock2d(256, 256, 3, 1), + ) + ) + self.appearance_conv_list = nn.ModuleList(appearance_conv_list) + self.adaAT = AdaAT(128, 256, cuda) + self.out_conv = nn.Sequential( + SameBlock2d(384, 128, kernel_size=3, padding=1), + UpBlock2d(128,128,kernel_size=3, padding=1), + ResBlock2d(128, 128, 3, 1), + UpBlock2d(128, 128, kernel_size=3, padding=1), + nn.Conv2d(128, 3, kernel_size=3, padding=1), + nn.Sigmoid() + ) + self.global_avg2d = nn.AdaptiveAvgPool2d(1) + self.global_avg1d = nn.AdaptiveAvgPool1d(1) + def ref_input(self, ref_img): + ## reference image encoder + self.ref_in_feature = self.ref_in_conv(ref_img) + # print(self.ref_in_feature.size(), self.ref_in_feature) + ## use AdaAT do spatial deformation on reference feature maps + self.ref_trans_feature0 = self.appearance_conv_list[0](self.ref_in_feature) + def interface(self, source_img, source_prompt): + self.source_img = torch.cat([source_img, source_prompt], dim=1) + ## source image encoder + source_in_feature = self.source_in_conv(self.source_img) + # print(source_in_feature.size(), source_in_feature) + + ## alignment encoder + img_para = self.trans_conv(torch.cat([source_in_feature,self.ref_in_feature],1)) + img_para = self.global_avg2d(img_para).squeeze(3).squeeze(2) + # print(img_para.size(), img_para) + ## concat alignment feature and audio feature + trans_para = img_para + + ref_trans_feature = self.adaAT(self.ref_trans_feature0, trans_para) + ref_trans_feature = self.appearance_conv_list[1](ref_trans_feature) + # print(ref_trans_feature.size(), ref_trans_feature) + ## feature decoder + merge_feature = torch.cat([source_in_feature,ref_trans_feature],1) + # print(merge_feature.size(), merge_feature) + out = self.out_conv(merge_feature) + return out + def forward(self, source_img, source_prompt, ref_img): + self.ref_input(ref_img) + out = self.interface(source_img, source_prompt) + return out + + +# from torch import nn +# import time +# import torch +# +# device = "cuda" if torch.cuda.is_available() else "cpu" +# model_Generator = DINet_five_Ref(6, 30).to(device) +# torch.save(model_Generator.state_dict(), "DINet_five_Ref.pth") +# +# source_img = torch.zeros([1,3,256,256]).cuda() +# source_prompt = torch.zeros([1,3, 256,256]).cuda() +# ref_img = torch.zeros([1,30,256,256]).cuda() +# +# model_Generator.ref_input(ref_img) +# start_time = time.time() +# for i in range(2000): +# print(i, time.time() - start_time) +# out = model_Generator.interface(source_img, source_prompt) \ No newline at end of file diff --git a/talkingface/models/DINet_mini.py b/talkingface/models/DINet_mini.py new file mode 100644 index 0000000..814211d --- /dev/null +++ b/talkingface/models/DINet_mini.py @@ -0,0 +1,414 @@ +import torch +from torch import nn +from torch.nn import BatchNorm2d +import torch.nn.functional as F +import cv2 +import numpy as np +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) + +class DownBlock(nn.Module): + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1, use_relu=True): + super(DownBlock, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups, stride=2) + self.norm = nn.BatchNorm2d(out_features, affine=True) + self.use_relu = use_relu + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + if self.use_relu: + out = F.relu(out) + return out + + +class UpBlock(nn.Module): + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1, use_relu=True, + sample_mode='nearest'): + super(UpBlock, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = nn.BatchNorm2d(out_features, affine=True) + self.use_relu = use_relu + self.sample_mode = sample_mode + + def forward(self, x): + out = F.interpolate(x, scale_factor=2, mode=self.sample_mode) + out = self.conv(out) + out = self.norm(out) + if self.use_relu: + out = F.relu(out) + return out + + +class ResBlock(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.norm = nn.BatchNorm2d(in_features, affine=True) + + def forward(self, x): + out = self.conv1(x) + out = self.norm(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + +class ResBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features,out_features, kernel_size, padding): + super(ResBlock2d, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding) + if out_features != in_features: + self.channel_conv = nn.Conv2d(in_features,out_features,1) + self.norm1 = BatchNorm2d(in_features) + self.norm2 = BatchNorm2d(in_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.norm1(x) + out = self.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = self.relu(out) + out = self.conv2(out) + if self.in_features != self.out_features: + out += self.channel_conv(x) + else: + out += x + return out + +class UpBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1): + super(UpBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = F.interpolate(x, scale_factor=2) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + +class DownBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1, stride=2): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, stride=stride) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +class SameBlock2d(nn.Module): + ''' + basic block + ''' + def __init__(self, in_features, out_features, kernel_size=3, padding=1): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding) + self.norm = BatchNorm2d(out_features) + self.relu = nn.ReLU() + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.relu(out) + return out + +def make_coordinate_grid_3d(spatial_size, type): + ''' + generate 3D coordinate grid + ''' + d, h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + z = torch.arange(d).type(type) + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + z = (2 * (z / (d - 1)) - 1) + yy = y.view(1,-1, 1).repeat(d,1, w) + xx = x.view(1,1, -1).repeat(d,h, 1) + zz = z.view(-1,1,1).repeat(1,h,w) + meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3)], 3) + return meshed,zz + +class AdaAT(nn.Module): + ''' + AdaAT operator + ''' + def __init__(self, para_ch,feature_ch, cuda = True): + super(AdaAT, self).__init__() + self.para_ch = para_ch + self.feature_ch = feature_ch + self.commn_linear = nn.Sequential( + nn.Linear(para_ch, para_ch), + nn.ReLU() + ) + self.scale = nn.Sequential( + nn.Linear(para_ch, feature_ch), + nn.Sigmoid() + ) + self.rotation = nn.Sequential( + nn.Linear(para_ch, feature_ch), + nn.Tanh() + ) + self.translation = nn.Sequential( + nn.Linear(para_ch, 2 * feature_ch), + nn.Tanh() + ) + self.tanh = nn.Tanh() + self.sigmoid = nn.Sigmoid() + self.cuda = cuda + self.f_dim = (20, 14, 18) + if cuda: + self.grid_xy, self.grid_z = make_coordinate_grid_3d(self.f_dim, torch.cuda.FloatTensor) + else: + self.grid_xy, self.grid_z = make_coordinate_grid_3d(self.f_dim, torch.FloatTensor) + batch = 1 + self.grid_xy = self.grid_xy.unsqueeze(0).repeat(batch, 1, 1, 1, 1) + self.grid_z = self.grid_z.unsqueeze(0).repeat(batch, 1, 1, 1) + + + def forward(self, feature_map,para_code): + # batch,d, h, w = feature_map.size(0), feature_map.size(1), feature_map.size(2), feature_map.size(3) + # batch= feature_map.size(0) + if self.cuda: + batch, d, h, w = feature_map.size(0), feature_map.size(1), feature_map.size(2), feature_map.size(3) + # print(batch, d, h, w) + grid_xy = self.grid_xy.unsqueeze(0).repeat(batch, 1, 1, 1, 1).view(batch, d, h*w, 2) + grid_z = self.grid_z.unsqueeze(0).repeat(batch, 1, 1, 1) + else: + batch = 1 + d, h, w = self.f_dim + grid_xy = self.grid_xy.view(batch, d, h*w, 2) + grid_z = self.grid_z + # print((d, h, w), feature_map.type()) + para_code = self.commn_linear(para_code) + scale = self.scale(para_code).unsqueeze(-1) * 2 + # print(scale.size(), scale) + angle = self.rotation(para_code).unsqueeze(-1) * 3.14159# + rotation_matrix = torch.cat([torch.cos(angle), -torch.sin(angle), torch.sin(angle), torch.cos(angle)], -1) + # rotation_matrix = rotation_matrix.view(batch, self.feature_ch, 2, 2) + translation = self.translation(para_code).view(batch, self.feature_ch, 2) + # grid_xy, grid_z = make_coordinate_grid_3d((d, h, w), feature_map.type()) + # grid_xy = self.grid_xy.unsqueeze(0).repeat(batch, 1, 1, 1, 1) + # grid_z = self.grid_z.unsqueeze(0).repeat(batch, 1, 1, 1) + scale = scale.unsqueeze(2).repeat(1, 1, h*w, 1) + # print(scale.size(), scale) + # rotation_matrix = rotation_matrix.unsqueeze(2).repeat(1, 1, h*w, 1, 1) + rotation_matrix = rotation_matrix.unsqueeze(2).repeat(1, 1, h * w, 1) # torch.Size([bs, 256, 4096, 4]) + translation = translation.unsqueeze(2).repeat(1, 1, h*w, 1) + + # trans_grid = torch.matmul(rotation_matrix, grid_xy.unsqueeze(-1)).squeeze(-1) * scale + translation + trans_grid = torch.matmul(rotation_matrix.view(batch, d, h * w, 2, 2), grid_xy.unsqueeze(-1)) + + trans_grid = trans_grid.squeeze(-1) * scale + translation + # print(trans_grid.view(batch, d, h, w, 2).size(), grid_z.unsqueeze(-1).size()) + # trans_grid = torch.matmul(rotation_matrix.view(batch, d, h * w, 2, 2), grid_xy.unsqueeze(-1)).squeeze( + # -1) * scale + translation + # print(trans_grid.view(batch, d, h, w, 2).size(), grid_z.unsqueeze(-1).size()) + full_grid = torch.cat([trans_grid.view(batch, d, h, w, 2), grid_z.unsqueeze(-1)], -1) + # print(full_grid.size(), full_grid) + + trans_feature = F.grid_sample(feature_map.unsqueeze(1), full_grid).squeeze(1) + # print("trans_feature", trans_feature.size()) + return trans_feature + +class DINet_mini(nn.Module): + def __init__(self, source_channel,ref_channel, cuda = True): + super(DINet_mini, self).__init__() + f_dim = 20 + self.source_in_conv = nn.Sequential( + SameBlock2d(source_channel, 12, kernel_size=3, padding=1), + DownBlock2d(12, 12, kernel_size=3, padding=1), + DownBlock2d(12, f_dim, kernel_size=3, padding=1), + ) + self.ref_in_conv = nn.Sequential( + SameBlock2d(ref_channel, 16, kernel_size=3, padding=1), + DownBlock2d(16, 16, kernel_size=3, padding=1), + SameBlock2d(16, 16, kernel_size=3, padding=1), + DownBlock2d(16, f_dim, kernel_size=3, padding=1), + # DownBlock2d(ref_channel, 1, kernel_size=3, padding=1), + # DownBlock2d(1, f_dim, kernel_size=3, padding=1), + ) + self.trans_conv = nn.Sequential( + # 16 →8 + DownBlock2d(f_dim*2, f_dim, kernel_size=3, padding=1), + # 8 →4 + DownBlock2d(f_dim, f_dim, kernel_size=3, padding=1), + ) + + self.appearance_conv = nn.Sequential( + ResBlock2d(f_dim, f_dim, 3, 1), + ) + self.adaAT = AdaAT(f_dim, f_dim, cuda) + self.out_conv = nn.Sequential( + SameBlock2d(f_dim*2, f_dim, kernel_size=3, padding=1), + UpBlock2d(f_dim, f_dim, kernel_size=3, padding=1), + SameBlock2d(f_dim, 16, 3, 1), + UpBlock2d(16, 16, kernel_size=3, padding=1), + nn.Conv2d(16, 3, kernel_size=3, padding=1), + nn.Sigmoid() + ) + self.global_avg2d = nn.AdaptiveAvgPool2d(1) + + def ref_input(self, ref_img): + ## reference image encoder + self.ref_img = ref_img + self.ref_in_feature = self.ref_in_conv(self.ref_img) + # import pickle + # with open("xxx.pkl", "wb") as f: + # pickle.dump(self.ref_in_feature, f) + # print("ref_in_feature", self.ref_in_feature.size()) + + def interface(self, source_img): + self.source_img = source_img + ## source image encoder + source_in_feature = self.source_in_conv(self.source_img) + img_para = self.trans_conv(torch.cat([source_in_feature, self.ref_in_feature], 1)) + img_para = self.global_avg2d(img_para).squeeze(3).squeeze(2) + + ref_trans_feature = self.adaAT(self.ref_in_feature, img_para) + ref_trans_feature = self.appearance_conv(ref_trans_feature) + merge_feature = torch.cat([source_in_feature, ref_trans_feature], 1) + out = self.out_conv(merge_feature) + return out + + def forward(self, ref_img, driving_img): + self.driving_img = driving_img + self.ref_input(ref_img) + out = self.interface(self.driving_img) + return out + +class DINet_mini_pipeline(nn.Module): + def __init__(self, source_channel,ref_channel, cuda = True): + super(DINet_mini_pipeline, self).__init__() + self.infer_model = DINet_mini(source_channel,ref_channel, cuda = cuda) + + self.grid_tensor = F.affine_grid(torch.eye(2, 3).unsqueeze(0).float(), (1, 1, 128, 128), align_corners=False).to("cuda" if cuda else "cpu") + + face_fusion_tensor = cv2.imread(os.path.join(current_dir, "../../mini_live/face_fusion_mask.png")) + face_fusion_tensor = torch.from_numpy(face_fusion_tensor[:,:,:1] / 255.).float().permute(2, 0, 1).unsqueeze(0) + mouth_fusion_tensor = cv2.imread(os.path.join(current_dir, "../../mini_live/mouth_fusion_mask.png")) + mouth_fusion_tensor = torch.from_numpy(mouth_fusion_tensor[:,:,:1] / 255.).float().permute(2, 0, 1).unsqueeze(0) + + self.face_fusion_tensor = face_fusion_tensor.to("cuda" if cuda else "cpu") + self.mouth_fusion_tensor = mouth_fusion_tensor.to("cuda" if cuda else "cpu") + + def ref_input(self, ref_tensor): + self.infer_model.ref_input(ref_tensor) + + def interface(self, source_tensor, gl_tensor): + face_mask = F.relu(torch.abs(gl_tensor[:, 2:3] * 2 - 1) - 0.9)* 10 + # face_mask = (gl_tensor[:, 2] == 1) | ((gl_tensor[:, 2] == 0)) + # face_mask = face_mask.float() + + deformation = gl_tensor[:, :2] * 2 - 1 + # 假设deformation的前两通道分别代表X和Y方向的位移 + # deformation的形状为[1, 2, H, W]，而grid的形状为[1, H, W, 2] + deformation = deformation * self.face_fusion_tensor + deformation = deformation.permute(0, 2, 3, 1) + + warped_grid = self.grid_tensor - deformation + + # img0应该是一个形状为[1, C, H, W]的tensor + warped_img0 = F.grid_sample(source_tensor, warped_grid, mode='bilinear', padding_mode='zeros', + align_corners=False) + + # print(warped_img0.size(), face_mask.size()) + warped_tensor = warped_img0 * (1 - face_mask) + gl_tensor * face_mask + warped_tensor = F.interpolate(warped_tensor, (128, 128)) + w_pad = int((128 - 72) / 2) + h_pad = int((128 - 56) / 2) + + fake_mouth_tensor = warped_tensor[:, :3, h_pad:-h_pad, w_pad:-w_pad] + fake_out = self.infer_model.interface(fake_mouth_tensor) + # fake_out = fake_mouth_tensor + fake_out = fake_out * self.mouth_fusion_tensor + fake_mouth_tensor*(1-self.mouth_fusion_tensor) + warped_tensor[:, :3, h_pad:-h_pad, w_pad:-w_pad] = fake_out + return warped_tensor + def forward(self, source_tensor, gl_tensor, ref_tensor): + ''' + + Args: + source_tensor: [batch, 3, 128, 128] + gl_tensor: [batch, 3, 128, 128] + ref_tensor: [batch, 12, 128, 128] + + Returns: + warped_tensor: [batch, 3, 128, 128] + ''' + self.ref_input(ref_tensor) + warped_tensor = self.interface(source_tensor, gl_tensor) + return warped_tensor + + +if __name__ == "__main__": + device = "cpu" + import torch.nn.functional as F + # size = (56, 72) # h, w + # model = DINet_mini(3, 4*3, cuda = device=="cuda") + # model.eval() + # model = model.to(device) + # driving_img = torch.zeros([1, 3, size[0], size[1]]).to(device) + # ref_img = torch.zeros([1, 4*3, size[0], size[1]]).to(device) + # from thop import profile + # from thop import clever_format + # + # flops, params = profile(model.to(device), inputs=(ref_img, driving_img)) + # flops, params = clever_format([flops, params], "%.3f") + # print(flops, params) + + size = (128, 128) # h, w + model = DINet_mini_pipeline(3, 4*3, cuda = device=="cuda") + model.eval() + model = model.to(device) + source_tensor = torch.ones([1, 3, size[0], size[1]]).to(device) + gl_tensor = torch.ones([1, 3, size[0], size[1]]).to(device) + ref_tensor = torch.ones([1, 4*3, 56, 72]).to(device) + face_fusion_tensor = torch.ones([1, 1, size[0], size[1]]).to(device) + mouth_fusion_tensor = torch.ones([1, 1, 56, 72]).to(device) + + from thop import profile + from thop import clever_format + + flops, params = profile(model.to(device), inputs=(source_tensor, gl_tensor, ref_tensor)) + flops, params = clever_format([flops, params], "%.3f") + print(flops, params) + + import cv2 + + out = model(source_tensor, gl_tensor, ref_tensor) + image_numpy = out.detach().squeeze(0).cpu().float().numpy() + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = image_numpy.clip(0, 255) + image_numpy = image_numpy.astype(np.uint8) + cv2.imwrite("sss1.png", image_numpy) + diff --git a/talkingface/models/__init__.py b/talkingface/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/talkingface/models/audio2bs_lstm.py b/talkingface/models/audio2bs_lstm.py new file mode 100644 index 0000000..1e3c91d --- /dev/null +++ b/talkingface/models/audio2bs_lstm.py @@ -0,0 +1,47 @@ +import torch.nn as nn +import torch +class Audio2Feature(nn.Module): + def __init__(self): + super(Audio2Feature, self).__init__() + num_pred = 1 + self.output_size = 6 + self.ndim = 80 + APC_hidden_size = 80 + # define networks + self.downsample = nn.Sequential( + nn.Linear(in_features=APC_hidden_size * 2, out_features=APC_hidden_size), + nn.BatchNorm1d(APC_hidden_size), + nn.LeakyReLU(0.2), + nn.Linear(APC_hidden_size, APC_hidden_size), + ) + self.LSTM = nn.LSTM(input_size=APC_hidden_size, + hidden_size=192, + num_layers=2, + dropout=0, + bidirectional=False, + batch_first=True) + self.fc = nn.Sequential( + nn.Linear(in_features=192, out_features=256), + nn.BatchNorm1d(256), + nn.LeakyReLU(0.2), + nn.Linear(256, 256), + nn.BatchNorm1d(256), + nn.LeakyReLU(0.2), + nn.Linear(256, self.output_size)) + + def forward(self, audio_features, h0, c0): + ''' + Args: + audio_features: [b, T, ndim] + ''' + self.item_len = audio_features.size()[1] + # new in 0324 + audio_features = audio_features.reshape(-1, self.ndim * 2) + down_audio_feats = self.downsample(audio_features) + # print(down_audio_feats) + down_audio_feats = down_audio_feats.reshape(-1, int(self.item_len / 2), self.ndim) + output, (hn, cn) = self.LSTM(down_audio_feats, (h0, c0)) + + # output, (hn, cn) = self.LSTM(audio_features) + pred = self.fc(output.reshape(-1, 192)).reshape(-1, int(self.item_len / 2), self.output_size) + return pred, hn, cn diff --git a/talkingface/models/common/Discriminator.py b/talkingface/models/common/Discriminator.py new file mode 100644 index 0000000..d397020 --- /dev/null +++ b/talkingface/models/common/Discriminator.py @@ -0,0 +1,39 @@ +from torch import nn +import torch.nn.functional as F + +class DownBlock2d(nn.Module): + def __init__(self, in_features, out_features, kernel_size=4, pool=False): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size) + self.pool = pool + def forward(self, x): + out = x + out = self.conv(out) + out = F.leaky_relu(out, 0.2) + if self.pool: + out = F.avg_pool2d(out, (2, 2)) + return out + + +class Discriminator(nn.Module): + """ + Discriminator for GAN loss + """ + def __init__(self, num_channels, block_expansion=64, num_blocks=4, max_features=512): + super(Discriminator, self).__init__() + down_blocks = [] + for i in range(num_blocks): + down_blocks.append( + DownBlock2d(num_channels if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + kernel_size=4, pool=(i != num_blocks - 1))) + self.down_blocks = nn.ModuleList(down_blocks) + self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1) + def forward(self, x): + feature_maps = [] + out = x + for down_block in self.down_blocks: + feature_maps.append(down_block(out)) + out = feature_maps[-1] + out = self.conv(out) + return feature_maps, out diff --git a/talkingface/models/common/VGG19.py b/talkingface/models/common/VGG19.py new file mode 100644 index 0000000..98c1a09 --- /dev/null +++ b/talkingface/models/common/VGG19.py @@ -0,0 +1,47 @@ +import torch +from torchvision import models +import numpy as np + + +class Vgg19(torch.nn.Module): + """ + Vgg19 network for perceptual loss + """ + def __init__(self, requires_grad=False): + super(Vgg19, self).__init__() + vgg_model = models.vgg19(pretrained=True) + vgg_pretrained_features = vgg_model.features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + + self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))), + requires_grad=False) + self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))), + requires_grad=False) + + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + X = (X - self.mean) / self.std + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out diff --git a/talkingface/models/speed_test.py b/talkingface/models/speed_test.py new file mode 100644 index 0000000..57624fe --- /dev/null +++ b/talkingface/models/speed_test.py @@ -0,0 +1,21 @@ +import torch +from talkingface.models.audio2bs_lstm import Audio2Feature +import time +import random +import numpy as np +import cv2 +device = "cpu" + +model = Audio2Feature() +model.eval() +x = torch.ones((1, 2, 80)) +h0 = torch.zeros(2, 1, 192) +c0 = torch.zeros(2, 1, 192) +y, hn, cn = model(x, h0, c0) +start_time = time.time() + +from thop import profile +from thop import clever_format +flops, params = profile(model.to(device), inputs=(x, h0, c0)) +flops, params = clever_format([flops, params], "%.3f") +print(flops, params) diff --git a/talkingface/models/sss1.png b/talkingface/models/sss1.png new file mode 100644 index 0000000..e39bb91 Binary files /dev/null and b/talkingface/models/sss1.png differ diff --git a/talkingface/preprocess.py b/talkingface/preprocess.py new file mode 100644 index 0000000..9d212ab --- /dev/null +++ b/talkingface/preprocess.py @@ -0,0 +1,205 @@ +import shutil +import numpy as np +import cv2 +import os +import sys +import time +import argparse +from talkingface.run_utils import video_pts_process, concat_output_2binfile +from talkingface.mediapipe_utils import detect_face_mesh,detect_face +from talkingface.utils import main_keypoints_index,INDEX_LIPS +# 1、是否是mp4，宽高是否大于200，时长是否大于2s,可否成功转换为符合格式的mp4 +# 2、面部关键点检测及是否可以构成循环视频 +# 4、旋转矩阵、面部mask估计 +# 5、验证文件完整性 + +dir_ = "data/asset/Actor" +def print_log(task_id, progress, status, Error, mode = 0): + ''' + status: -1代表未开始， 0代表处理中， 1代表已完成， 2代表出错中断 + progress： 0-1000， 进度千分比 + ''' + print("task_id: {}. progress: {:0>4d}. status: {}. mode: {}. Error: {}".format(task_id, progress, status, mode, Error)) + sys.stdout.flush() + +def check_step0(task_id, video_path): + try: + cap = cv2.VideoCapture(video_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) + fps = cap.get(cv2.CAP_PROP_FPS) + cap.release() + if vid_width < 200 or vid_height < 200: + print_log(task_id, 0, 2, "video width/height < 200") + return 0 + if frames < 2*fps: + print_log(task_id, 0, 2, "video duration < 2s") + return 0 + os.makedirs(os.path.join(dir_, task_id), exist_ok=True) + front_video_path = os.path.join("data", "front.mp4") + scale = max(vid_width / 720., vid_height / 1280.) + if scale > 1: + new_width = int(vid_width / scale + 0.1)//2 * 2 + new_height = int(vid_height / scale + 0.1)//2 * 2 + ffmpeg_cmd = "ffmpeg -i {} -r 25 -ss 00:00:00 -t 00:02:00 -vf scale={}:{} -an -loglevel quiet -y {}".format( + video_path,new_width,new_height,front_video_path) + else: + ffmpeg_cmd = "ffmpeg -i {} -r 25 -ss 00:00:00 -t 00:02:00 -an -loglevel quiet -y {}".format( + video_path, front_video_path) + os.system(ffmpeg_cmd) + if not os.path.isfile(front_video_path): + return 0 + return 1 + except: + print_log(task_id, 0, 2, "video cant be opened") + return 0 + +def check_step1(task_id): + front_video_path = os.path.join("data", "front.mp4") + back_video_path = os.path.join("data", "back.mp4") + video_out_path = os.path.join(dir_, task_id, "video.mp4") + face_info_path = os.path.join(dir_, task_id, "video_info.bin") + preview_path = os.path.join(dir_, task_id, "preview.jpg") + if ExtractFromVideo(task_id, front_video_path) != 1: + shutil.rmtree(os.path.join(dir_, task_id)) + return 0 + ffmpeg_cmd = "ffmpeg -i {} -vf reverse -loglevel quiet -y {}".format(front_video_path, back_video_path) + os.system(ffmpeg_cmd) + ffmpeg_cmd = "ffmpeg -f concat -i {} -loglevel quiet -y {}".format("data/video_concat.txt", video_out_path) + os.system(ffmpeg_cmd) + ffmpeg_cmd = "ffmpeg -i {} -vf crop=w='min(iw\,ih)':h='min(iw\,ih)',scale=256:256,setsar=1 -vframes 1 {}".format(front_video_path, preview_path) + # ffmpeg_cmd = "ffmpeg -i {} -vf scale=256:-1 -loglevel quiet -y {}".format(front_video_path, preview_path) + os.system(ffmpeg_cmd) + if os.path.isfile(front_video_path): + os.remove(front_video_path) + if os.path.isfile(back_video_path): + os.remove(back_video_path) + if os.path.isfile(video_out_path) and os.path.isfile(face_info_path): + return 1 + else: + return 0 + +# def check_step2(task_id, ): +# mat_list, pts_normalized_list, face_mask_pts = video_pts_process(pts_array_origin) + + +def ExtractFromVideo(task_id, front_video_path): + cap = cv2.VideoCapture(front_video_path) + if not cap.isOpened(): + print_log(task_id, 0, 2, "front_video cant be opened by opencv") + return -1 + + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + + totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 + totalFrames = int(totalFrames) + pts_3d = np.zeros([totalFrames, 478, 3]) + frame_index = 0 + face_rect_list = [] + start_time = time.time() + while cap.isOpened(): + ret, frame = cap.read() # 按帧读取视频 + # #到视频结尾时终止 + if ret is False: + break + rect_2d = detect_face([frame]) + rect = rect_2d[0] + tag_ = 1 if np.sum(rect) > 0 else 0 + if frame_index == 0 and tag_ != 1: + print_log(task_id, 0, 2, "no face detected in first frame") + cap.release() # 释放视频对象 + return 0 + elif tag_ == 0: # 有时候人脸检测会失败，就用上一帧的结果替代这一帧的结果 + rect = face_rect_list[-1] + + face_rect_list.append(rect) + + x_min = rect[0] * vid_width + y_min = rect[2] * vid_height + x_max = rect[1] * vid_width + y_max = rect[3] * vid_height + seq_w, seq_h = x_max - x_min, y_max - y_min + x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2 + x_min = int(max(0, x_mid - seq_w * 0.65)) + y_min = int(max(0, y_mid - seq_h * 0.4)) + x_max = int(min(vid_width, x_mid + seq_w * 0.65)) + y_max = int(min(vid_height, y_mid + seq_h * 0.8)) + + frame_face = frame[y_min:y_max, x_min:x_max] + frame_kps = detect_face_mesh([frame_face])[0] + if np.sum(frame_kps) == 0: + print_log(task_id, 0, 2, "Frame num {} keypoint error".format(frame_index)) + cap.release() # 释放视频对象 + return 0 + pts_3d[frame_index] = frame_kps + np.array([x_min, y_min, 0]) + frame_index += 1 + + if time.time() - start_time > 0.5: + progress = int(1000 * frame_index / totalFrames * 0.99) + print_log(task_id, progress, 0, "handling...") + start_time = time.time() + cap.release() # 释放视频对象 + if type(pts_3d) is np.ndarray and len(pts_3d) == totalFrames: + pts_3d_main = pts_3d[:, main_keypoints_index] + mat_list, pts_normalized_list, face_pts_mean_personal, face_mask_pts_normalized = video_pts_process(pts_3d_main) + + output = concat_output_2binfile(mat_list, pts_3d, face_pts_mean_personal, face_mask_pts_normalized) + # print(output.shape) + pts_normalized_list = np.array(pts_normalized_list)[:, INDEX_LIPS] + # 找出此模特正面人脸的嘴巴区域范围 + x_max, x_min = np.max(pts_normalized_list[:, :, 0]), np.min(pts_normalized_list[:, :, 0]) + y_max, y_min = np.max(pts_normalized_list[:, :, 1]), np.min(pts_normalized_list[:, :, 1]) + y_min = y_min + (y_max - y_min) / 10. + + first_line = np.zeros([406]) + first_line[:4] = np.array([x_min,x_max,y_min,y_max]) + # print(first_line) + # + # pts_2d_main = pts_3d[:, main_keypoints_index, :2].reshape(len(pts_3d), -1) + # smooth_array_ = np.array(mat_list).reshape(-1, 16)*100 + # + # output = np.concatenate([smooth_array_, pts_2d_main], axis=1).astype(np.float32) + output = np.concatenate([first_line.reshape(1,-1), output], axis=0).astype(np.float32) + # print(smooth_array_.shape, pts_2d_main.shape, first_line.shape, output.shape) + face_info_path = os.path.join(dir_, task_id, "video_info.bin") + # np.savetxt(face_info_path, output, fmt='%.1f') + # print(222) + output.tofile(face_info_path) + return 1 + else: + print_log(task_id, 0, 2, "keypoint cant be saved") + return 0 + +def check_step0_audio(task_id, video_path): + dir_ = "data/asset/Audio" + wav_path = os.path.join(dir_, task_id + ".wav") + ffmpeg_cmd = "ffmpeg -i {} -ac 1 -ar 16000 -loglevel quiet -y {}".format( + video_path, wav_path) + os.system(ffmpeg_cmd) + if not os.path.isfile(wav_path): + print_log(task_id, 0, 2, "audio convert failed", 2) + return 0 + return 1 + +def new_task(task_id, task_mode, video_path): + # print(task_id, task_mode, video_path) + if task_mode == "0": # "actor" + print_log(task_id, 0, 0, "handling...") + if check_step0(task_id, video_path): + print_log(task_id, 0, 0, "handling...") + if check_step1(task_id): + print_log(task_id, 1000, 1, "process finished, click to confirm") + if task_mode == "2": # "audio" + print_log(task_id, 0, 0, "handling...", 2) + if check_step0_audio(task_id, video_path): + print_log(task_id, 1000, 1, "process finished, click to confirm", 2) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Inference code to preprocess videos') + parser.add_argument('--task_id', type=str, help='task_id') + parser.add_argument('--task_mode', type=str, help='task_mode') + parser.add_argument('--video_path', type=str, help='Filepath of video that contains faces to use') + args = parser.parse_args() + new_task(args.task_id, args.task_mode, args.video_path) \ No newline at end of file diff --git a/talkingface/render_model.py b/talkingface/render_model.py new file mode 100644 index 0000000..4137beb --- /dev/null +++ b/talkingface/render_model.py @@ -0,0 +1,116 @@ +import os.path +import torch +import os +import numpy as np +import time +from talkingface.run_utils import smooth_array, video_pts_process +from talkingface.run_utils import mouth_replace, prepare_video_data +from talkingface.utils import generate_face_mask, INDEX_LIPS_OUTER +from talkingface.data.few_shot_dataset import select_ref_index,get_ref_images_fromVideo,generate_input, generate_input_pixels +device = "cuda" if torch.cuda.is_available() else "cpu" +import pickle +import cv2 + + +face_mask = generate_face_mask() + + +class RenderModel: + def __init__(self): + self.__net = None + + self.__pts_driven = None + self.__mat_list = None + self.__pts_normalized_list = None + self.__face_mask_pts = None + self.__ref_img = None + self.__cap_input = None + self.frame_index = 0 + self.__mouth_coords_array = None + + def loadModel(self, ckpt_path): + from talkingface.models.DINet import DINet_five_Ref as DINet + n_ref = 5 + source_channel = 6 + ref_channel = n_ref * 6 + self.__net = DINet(source_channel, ref_channel).cuda() + checkpoint = torch.load(ckpt_path) + self.__net.load_state_dict(checkpoint) + self.__net.eval() + + def reset_charactor(self, video_path, Path_pkl, ref_img_index_list = None): + if self.__cap_input is not None: + self.__cap_input.release() + + self.__pts_driven, self.__mat_list,self.__pts_normalized_list, self.__face_mask_pts, self.__ref_img, self.__cap_input = \ + prepare_video_data(video_path, Path_pkl, ref_img_index_list) + + ref_tensor = torch.from_numpy(self.__ref_img / 255.).float().permute(2, 0, 1).unsqueeze(0).cuda() + self.__net.ref_input(ref_tensor) + + x_min, x_max = np.min(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 0]), np.max(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 0]) + y_min, y_max = np.min(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 1]), np.max(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 1]) + z_min, z_max = np.min(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 2]), np.max(self.__pts_normalized_list[:, INDEX_LIPS_OUTER, 2]) + + x_mid,y_mid,z_mid = (x_min + x_max)/2, (y_min + y_max)/2, (z_min + z_max)/2 + x_len, y_len, z_len = (x_max - x_min)/2, (y_max - y_min)/2, (z_max - z_min)/2 + x_min, x_max = x_mid - x_len*0.9, x_mid + x_len*0.9 + y_min, y_max = y_mid - y_len*0.9, y_mid + y_len*0.9 + z_min, z_max = z_mid - z_len*0.9, z_mid + z_len*0.9 + + # print(face_personal.shape, x_min, x_max, y_min, y_max, z_min, z_max) + coords_array = np.zeros([100, 150, 4]) + for i in range(100): + for j in range(150): + coords_array[i, j, 0] = j/149 + coords_array[i, j, 1] = i/100 + # coords_array[i, j, 2] = int((-75 + abs(j - 75))*(2./3)) + coords_array[i, j, 2] = ((j - 75)/ 75) ** 2 + coords_array[i, j, 3] = 1 + + coords_array = coords_array*np.array([x_max - x_min, y_max - y_min, z_max - z_min, 1]) + np.array([x_min, y_min, z_min, 0]) + self.__mouth_coords_array = coords_array.reshape(-1, 4).transpose(1, 0) + + + + def interface(self, mouth_frame): + vid_frame_count = self.__cap_input.get(cv2.CAP_PROP_FRAME_COUNT) + if self.frame_index % vid_frame_count == 0: + self.__cap_input.set(cv2.CAP_PROP_POS_FRAMES, 0) # 设置要获取的帧号 + ret, frame = self.__cap_input.read() # 按帧读取视频 + + epoch = self.frame_index // len(self.__mat_list) + if epoch % 2 == 0: + new_index = self.frame_index % len(self.__mat_list) + else: + new_index = -1 - self.frame_index % len(self.__mat_list) + + # print(self.__face_mask_pts.shape, "ssssssss") + source_img, target_img, crop_coords = generate_input_pixels(frame, self.__pts_driven[new_index], self.__mat_list[new_index], + mouth_frame, self.__face_mask_pts[new_index], + self.__mouth_coords_array) + + # tensor + source_tensor = torch.from_numpy(source_img / 255.).float().permute(2, 0, 1).unsqueeze(0).cuda() + target_tensor = torch.from_numpy(target_img / 255.).float().permute(2, 0, 1).unsqueeze(0).cuda() + + source_tensor, source_prompt_tensor = source_tensor[:, :3], source_tensor[:, 3:] + fake_out = self.__net.interface(source_tensor, source_prompt_tensor) + + image_numpy = fake_out.detach().squeeze(0).cpu().float().numpy() + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = image_numpy.clip(0, 255) + image_numpy = image_numpy.astype(np.uint8) + + image_numpy = target_img * face_mask + image_numpy * (1 - face_mask) + + img_bg = frame + x_min, y_min, x_max, y_max = crop_coords + + img_face = cv2.resize(image_numpy, (x_max - x_min, y_max - y_min)) + img_bg[y_min:y_max, x_min:x_max] = img_face + self.frame_index += 1 + return img_bg + + def save(self, path): + torch.save(self.__net.state_dict(), path) \ No newline at end of file diff --git a/talkingface/render_model_mini.py b/talkingface/render_model_mini.py new file mode 100644 index 0000000..72ed235 --- /dev/null +++ b/talkingface/render_model_mini.py @@ -0,0 +1,67 @@ +import os.path +import torch +import os +import numpy as np +import time +from talkingface.utils import generate_face_mask, INDEX_LIPS_UPPER, INDEX_LIPS_LOWER, crop_mouth, main_keypoints_index +import torch.nn.functional as F +from talkingface.data.few_shot_dataset import select_ref_index +device = "cuda" if torch.cuda.is_available() else "cpu" +import pickle +import cv2 + +from talkingface.utils import draw_mouth_maps + +class RenderModel_Mini: + def __init__(self): + self.__net = None + + def loadModel(self, ckpt_path): + from talkingface.models.DINet_mini import DINet_mini_pipeline as DINet + n_ref = 3 + source_channel = 3 + ref_channel = n_ref * 4 + self.net = DINet(source_channel, ref_channel).cuda() + checkpoint = torch.load(ckpt_path) + net_g_static = checkpoint['state_dict']['net_g'] + self.net.infer_model.load_state_dict(net_g_static) + self.net.eval() + + + def reset_charactor(self, img_list, driven_keypoints, standard_size = 256): + ref_img_index_list = select_ref_index(driven_keypoints, n_ref=3, ratio=0.33) # 从当前视频选n_ref个图片 + ref_img_list = [] + for i in ref_img_index_list: + ref_face_edge = draw_mouth_maps(driven_keypoints[i], size=(standard_size, standard_size)) + ref_img = img_list[i] + ref_face_edge = cv2.resize(ref_face_edge, (128, 128)) + ref_img = cv2.resize(ref_img, (128, 128)) + w_pad = int((128 - 72) / 2) + h_pad = int((128 - 56) / 2) + + ref_img = np.concatenate([ref_img[h_pad:-h_pad, w_pad:-w_pad,:3], ref_face_edge[h_pad:-h_pad, w_pad:-w_pad, :1]], axis=2) + # cv2.imshow("ss", ref_img[:,:,::-1]) + # cv2.waitKey(-1) + ref_img_list.append(ref_img) + self.ref_img = np.concatenate(ref_img_list, axis=2) + + ref_tensor = torch.from_numpy(self.ref_img / 255.).float().permute(2, 0, 1).unsqueeze(0).cuda() + + self.net.ref_input(ref_tensor) + + + def interface(self, source_tensor, gl_tensor): + ''' + + Args: + source_tensor: [batch, 3, 128, 128] + gl_tensor: [batch, 3, 128, 128] + + Returns: + warped_img: [batch, 3, 128, 128] + ''' + warped_img = self.net.interface(source_tensor, gl_tensor) + return warped_img + + def save(self, path): + torch.save(self.net.state_dict(), path) \ No newline at end of file diff --git a/talkingface/run_utils.py b/talkingface/run_utils.py new file mode 100644 index 0000000..beb1089 --- /dev/null +++ b/talkingface/run_utils.py @@ -0,0 +1,276 @@ +from talkingface.utils import * +import os +import pickle +import copy +def Tensor2img(tensor_, channel_index): + frame = tensor_[channel_index:channel_index + 3, :, :].detach().squeeze(0).cpu().float().numpy() + frame = np.transpose(frame, (1, 2, 0)) * 255.0 + frame = frame.clip(0, 255) + return frame.astype(np.uint8) + + +def correct_rotation_matrix(R): + # Perform SVD on the 3x3 part of the matrix + U, S, VT = np.linalg.svd(R, full_matrices=True) + + # Ensure the determinant is 1 to avoid reflection + det = np.linalg.det(U @ VT) + if det < 0: + VT[-1, :] *= -1 # or U[:, -1] *= -1 + scale_matrix = np.diag(S) + # Combine scaling and rotation + scaled_rotation_matrix = U @ scale_matrix @ VT + # print("sssssssssssss", det) + return scaled_rotation_matrix +def mat_A(pts): + A = np.zeros([len(pts) * 3, 12]) + for i in range(len(pts)): + A[3 * i + 0, 0:3] = pts[i] + A[3 * i + 0, 3] = 1 + A[3 * i + 1, 4:7] = pts[i] + A[3 * i + 1, 7] = 1 + A[3 * i + 2, 8:11] = pts[i] + A[3 * i + 2, 11] = 1 + return A +from sklearn import decomposition +def calc_face_mat(pts_array_origin, face_pts_mean): + ''' + + :param pts_array_origin: mediapipe检测出的人脸关键点 + :return: + ''' + + A = mat_A(face_pts_mean) + A_inverse = np.linalg.pinv(A) + pts_normalized_list = [] + mat_list = [] + for i in pts_array_origin: + B = i.flatten() + x = A_inverse.dot(B) + rotationMatrix = np.zeros([4, 4]) + rotationMatrix[:3, :] = x.reshape(3, 4) + rotationMatrix[3, 3] = 1 + + R = rotationMatrix[:3, :3] + # Correct the rotation part + corrected_R = correct_rotation_matrix(R) + + centroid_src = np.mean(face_pts_mean, axis=0) + centroid_tgt = np.mean(i, axis=0) + + # Step 4: Compute translation vector + T = centroid_tgt - corrected_R @ centroid_src + rotationMatrix[:3, :3] = corrected_R + rotationMatrix[:3, 3] = T + mat_list.append(rotationMatrix) + + for index_, i in enumerate(pts_array_origin): + rotationMatrix = mat_list[index_] + keypoints = np.ones([4, len(i)]) + keypoints[:3, :] = i.T + keypoints_normalized = np.linalg.inv(rotationMatrix).dot(keypoints).T + pts_normalized_list.append(keypoints_normalized[:, :3]) + + x = np.array(pts_normalized_list).reshape(len(pts_normalized_list), -1) + # print(x.shape) + n_components = min(25, len(pts_array_origin)//20) + pca = decomposition.PCA(n_components=n_components) + pca.fit(x) + y = pca.transform(x) + x_new = pca.inverse_transform(y) + x_new = x_new.reshape(len(x_new), -1, 3) + + pts_normalized_list = [] + mat_list = [] + for index, x in enumerate(x_new): + A = mat_A(x) + A_inverse = np.linalg.pinv(A) + + B = pts_array_origin[index].flatten() + x = A_inverse.dot(B) + rotationMatrix = np.zeros([4, 4]) + rotationMatrix[:3, :] = x.reshape(3, 4) + rotationMatrix[3, 3] = 1 + R = rotationMatrix[:3, :3] + # Correct the rotation part + corrected_R = correct_rotation_matrix(R) + + centroid_src = np.mean(x_new[index], axis=0) + centroid_tgt = np.mean(pts_array_origin[index], axis=0) + + # Step 4: Compute translation vector + T = centroid_tgt - corrected_R @ centroid_src + rotationMatrix[:3, :3] = corrected_R + rotationMatrix[:3, 3] = T + + mat_list.append(rotationMatrix) + + mat_list = np.array(mat_list) + # mat_list必须要平滑，注意是针对每个视频分别平滑 + sub_mat_list = mat_list + smooth_array_ = sub_mat_list.reshape(-1, 16) + import pandas as pd + + smooth_array_ = smooth_array(smooth_array_, weight = [0.04,0.08,0.13,0.5,0.13,0.08,0.04]) + # pd.DataFrame(smooth_array_[200:400]).to_csv("sad.csv") + # exit(-1) + # print(smooth_array_, smooth_array_.shape) + smooth_array_ = smooth_array_.reshape(-1, 4, 4) + mat_list = smooth_array_ + mat_list = [hh for hh in mat_list] + + for index_,i in enumerate(pts_array_origin): + rotationMatrix = mat_list[index_] + keypoints = np.ones([4, len(i)]) + keypoints[:3, :] = i.T + keypoints_normalized = np.linalg.inv(rotationMatrix).dot(keypoints).T + pts_normalized_list.append(keypoints_normalized[:,:3]) + + face_pts_mean_personal = pca.mean_.reshape(-1, 3) + return mat_list,pts_normalized_list,face_pts_mean_personal +face_pts_mean = None +def video_pts_process(pts_array_origin): + global face_pts_mean + if face_pts_mean is None: + current_dir = os.path.dirname(os.path.abspath(__file__)) + face_pts_mean = np.loadtxt(os.path.join(current_dir, "../data/face_pts_mean_mainKps.txt")) + # 先根据pts_array_origin计算出旋转矩阵、去除旋转后的人脸关键点、面部mask、 + mat_list, pts_normalized_list, face_pts_mean_personal = calc_face_mat(pts_array_origin, face_pts_mean) + pts_normalized_list = np.array(pts_normalized_list) + face_mask_pts_normalized = face_pts_mean_personal[INDEX_FACE_OVAL].copy() + face_mask_pts_normalized[:10, 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 1], + axis=0) + np.arange(5, 25, 2) + face_mask_pts_normalized[:10, 0] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 0], + axis=0) - (9 - np.arange(0, 10)) + face_mask_pts_normalized[-10:, 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 1], + axis=0) - np.arange(5, 25, 2) + 28 + face_mask_pts_normalized[-10:, 0] = np.min(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 0], + axis=0) + np.arange(0, 10) + face_mask_pts_normalized[10, 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[10], 1], axis=0) + 25 + + face_mask_pts = np.zeros([len(mat_list), len(face_mask_pts_normalized), 2]) + for index_ in range(len(mat_list)): + rotationMatrix = mat_list[index_] + + keypoints = np.ones([4, len(face_mask_pts_normalized)]) + keypoints[:3, :] = face_mask_pts_normalized.T + driving_mask = rotationMatrix.dot(keypoints).T + face_mask_pts[index_] = driving_mask[:,:2] + + return mat_list, pts_normalized_list, face_pts_mean_personal, face_mask_pts + +def mouth_replace(pts_array_origin, frames_num): + ''' + + :param pts_array_origin: mediapipe检测出的人脸关键点 + :return: + ''' + if os.path.isfile("face_pts_mean_mainKps.txt"): + face_pts_mean = np.loadtxt("face_pts_mean_mainKps.txt") + else: + face_pts_mean = np.loadtxt("data/face_pts_mean_mainKps.txt") + mat_list,pts_normalized_list,face_pts_mean_personal = calc_face_mat(pts_array_origin, face_pts_mean) + face_personal = face_pts_mean_personal.copy() + pts_normalized_list = np.array(pts_normalized_list) + # face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1] = np.max(pts_normalized_list[:,INDEX_FACE_OVAL[:10], 1], axis = 0) + 20 + # face_pts_mean_personal[INDEX_FACE_OVAL[:10], 0] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 0], axis=0) + 10 + # face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 1], axis=0) + 20 + # face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 0] = np.min(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 0], axis=0) - 10 + # face_pts_mean_personal[INDEX_FACE_OVAL[10], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[10], 1], axis=0) + 20 + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1] = np.max(pts_normalized_list[:,INDEX_FACE_OVAL[:10], 1], axis = 0) + np.arange(5,25,2) + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 0] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 0], axis=0) - (9 - np.arange(0,10)) + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 1], axis=0) - np.arange(5,25,2) + 28 + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 0] = np.min(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 0], axis=0) + np.arange(0,10) + + face_pts_mean_personal[INDEX_FACE_OVAL[10], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[10], 1], axis=0) + 25 + + face_pts_mean_personal = face_pts_mean_personal[INDEX_FACE_OVAL] + face_mask_pts = np.zeros([len(mat_list), len(face_pts_mean_personal), 2]) + for index_ in range(len(mat_list)): + rotationMatrix = mat_list[index_] + + keypoints = np.ones([4, len(face_pts_mean_personal)]) + keypoints[:3, :] = face_pts_mean_personal.T + driving_mask = rotationMatrix.dot(keypoints).T + face_mask_pts[index_] = driving_mask[:,:2] + + iteration = frames_num // len(pts_array_origin) + 1 + if iteration == 1: + pass + else: + pts_array_origin2 = copy.deepcopy(pts_array_origin) + mat_list2 = copy.deepcopy(mat_list) + face_mask_pts2 = copy.deepcopy(face_mask_pts) + for i in range(iteration - 1): + if i % 2 == 0: + pts_array_origin2 = np.concatenate( + [pts_array_origin2, pts_array_origin[::-1]], axis=0) + mat_list2 += mat_list[::-1] + face_mask_pts2 = np.concatenate( + [face_mask_pts2, face_mask_pts[::-1]], axis=0) + else: + pts_array_origin2 = np.concatenate( + [pts_array_origin2, pts_array_origin], axis=0) + mat_list2 += mat_list + face_mask_pts2 = np.concatenate( + [face_mask_pts2, face_mask_pts], axis=0) + pts_array_origin = pts_array_origin2 + mat_list = mat_list2 + face_mask_pts = face_mask_pts2 + + pts_array_origin, mat_list, face_mask_pts = pts_array_origin[:frames_num], mat_list[:frames_num], face_mask_pts[:frames_num] + return pts_array_origin, mat_list, face_mask_pts, face_personal, pts_normalized_list + + +def concat_output_2binfile(mat_list, pts_3d, face_pts_mean_personal, face_mask_pts_normalized): + face_stable_pts_2d = np.zeros([len(mat_list), len(INDEX_FACE_OVAL + INDEX_MUSCLE), 2]) # 法令纹和脸部外轮廓关键点 + face_mask_pts_2d = np.zeros([len(mat_list), face_mask_pts_normalized.shape[0], 2]) + for index_, i in enumerate(mat_list): + rotationMatrix = i + # 法令纹和脸部外轮廓关键点 + driving_mouth_pts = face_pts_mean_personal[INDEX_FACE_OVAL + INDEX_MUSCLE] + keypoints = np.ones([4, len(driving_mouth_pts)]) + keypoints[:3, :] = driving_mouth_pts.T + driving_mouth_pts = rotationMatrix.dot(keypoints).T + face_stable_pts_2d[index_] = driving_mouth_pts[:, :2] + + # 脸部mask关键点 + driving_mouth_pts = face_mask_pts_normalized + keypoints = np.ones([4, len(driving_mouth_pts)]) + keypoints[:3, :] = driving_mouth_pts.T + driving_mouth_pts = rotationMatrix.dot(keypoints).T + face_mask_pts_2d[index_] = driving_mouth_pts[:, :2] + + + pts_2d_main = pts_3d[:, main_keypoints_index, :2].reshape(len(pts_3d), -1) + smooth_array_ = np.array(mat_list).reshape(-1, 16) * 100 + face_mask_pts_2d = face_mask_pts_2d.reshape(len(face_mask_pts_2d), -1) + face_stable_pts_2d = face_stable_pts_2d.reshape(len(face_stable_pts_2d), -1) + + output = np.concatenate([smooth_array_, pts_2d_main, face_mask_pts_2d, face_stable_pts_2d], axis=1).astype(np.float32) + return output +from talkingface.data.few_shot_dataset import select_ref_index,get_ref_images_fromVideo +def prepare_video_data(video_path, Path_pkl, ref_img_index_list, ref_img = None,save_ref = None): + with open(Path_pkl, "rb") as f: + images_info = pickle.load(f)[:, main_keypoints_index, :] + + pts_driven = images_info.reshape(len(images_info), -1) + pts_driven = smooth_array(pts_driven).reshape(len(pts_driven), -1, 3) + cap_input = cv2.VideoCapture(video_path) + if ref_img is not None: + ref_img = cv2.imread(ref_img).reshape(256, -1, 256, 3).transpose(0, 2, 1, 3).reshape(256, 256, -1) + else: + if ref_img_index_list is None: + ref_img_index_list = select_ref_index(pts_driven, n_ref=5, ratio=1 / 2.) + ref_img = get_ref_images_fromVideo(cap_input, ref_img_index_list, pts_driven[:, :, :2]) + + mat_list, pts_normalized_list, face_pts_mean_personal, face_mask_pts = video_pts_process(pts_driven) + + if save_ref is not None: + h, w, c = ref_img.shape + ref_img_ = ref_img.reshape(h, w, -1, 3).transpose(0, 2, 1, 3).reshape(h, -1, 3) + # ref_path = "ref2.png" + cv2.imwrite(save_ref, ref_img_) + # logger.info("参考图片已存至{}.".format(ref_path)) + + return pts_driven, mat_list, pts_normalized_list, face_mask_pts, ref_img, cap_input \ No newline at end of file diff --git a/talkingface/util/__init__.py b/talkingface/util/__init__.py new file mode 100644 index 0000000..ae36f63 --- /dev/null +++ b/talkingface/util/__init__.py @@ -0,0 +1 @@ +"""This package includes a miscellaneous collection of useful helper functions.""" diff --git a/talkingface/util/get_data.py b/talkingface/util/get_data.py new file mode 100644 index 0000000..97edc3c --- /dev/null +++ b/talkingface/util/get_data.py @@ -0,0 +1,110 @@ +from __future__ import print_function +import os +import tarfile +import requests +from warnings import warn +from zipfile import ZipFile +from bs4 import BeautifulSoup +from os.path import abspath, isdir, join, basename + + +class GetData(object): + """A Python script for downloading CycleGAN or pix2pix datasets. + + Parameters: + technique (str) -- One of: 'cyclegan' or 'pix2pix'. + verbose (bool) -- If True, print additional information. + + Examples: + >>> from util.get_data import GetData + >>> gd = GetData(technique='cyclegan') + >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed. + + Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh' + and 'scripts/download_cyclegan_model.sh'. + """ + + def __init__(self, technique='cyclegan', verbose=True): + url_dict = { + 'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/', + 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets' + } + self.url = url_dict.get(technique.lower()) + self._verbose = verbose + + def _print(self, text): + if self._verbose: + print(text) + + @staticmethod + def _get_options(r): + soup = BeautifulSoup(r.text, 'lxml') + options = [h.text for h in soup.find_all('a', href=True) + if h.text.endswith(('.zip', 'tar.gz'))] + return options + + def _present_options(self): + r = requests.get(self.url) + options = self._get_options(r) + print('Options:\n') + for i, o in enumerate(options): + print("{0}: {1}".format(i, o)) + choice = input("\nPlease enter the number of the " + "dataset above you wish to download:") + return options[int(choice)] + + def _download_data(self, dataset_url, save_path): + if not isdir(save_path): + os.makedirs(save_path) + + base = basename(dataset_url) + temp_save_path = join(save_path, base) + + with open(temp_save_path, "wb") as f: + r = requests.get(dataset_url) + f.write(r.content) + + if base.endswith('.tar.gz'): + obj = tarfile.open(temp_save_path) + elif base.endswith('.zip'): + obj = ZipFile(temp_save_path, 'r') + else: + raise ValueError("Unknown File Type: {0}.".format(base)) + + self._print("Unpacking Data...") + obj.extractall(save_path) + obj.close() + os.remove(temp_save_path) + + def get(self, save_path, dataset=None): + """ + + Download a dataset. + + Parameters: + save_path (str) -- A directory to save the data to. + dataset (str) -- (optional). A specific dataset to download. + Note: this must include the file extension. + If None, options will be presented for you + to choose from. + + Returns: + save_path_full (str) -- the absolute path to the downloaded data. + + """ + if dataset is None: + selected_dataset = self._present_options() + else: + selected_dataset = dataset + + save_path_full = join(save_path, selected_dataset.split('.')[0]) + + if isdir(save_path_full): + warn("\n'{0}' already exists. Voiding Download.".format( + save_path_full)) + else: + self._print('Downloading Data...') + url = "{0}/{1}".format(self.url, selected_dataset) + self._download_data(url, save_path=save_path) + + return abspath(save_path_full) diff --git a/talkingface/util/html.py b/talkingface/util/html.py new file mode 100644 index 0000000..cc3262a --- /dev/null +++ b/talkingface/util/html.py @@ -0,0 +1,86 @@ +import dominate +from dominate.tags import meta, h3, table, tr, td, p, a, img, br +import os + + +class HTML: + """This HTML class allows us to save images and write texts into a single HTML file. + + It consists of functions such as (add a text header to the HTML file), + (add a row of images to the HTML file), and (save the HTML to the disk). + It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API. + """ + + def __init__(self, web_dir, title, refresh=0): + """Initialize the HTML classes + + Parameters: + web_dir (str) -- a directory that stores the webpage. HTML file will be created at /index.html; images will be saved at 0: + with self.doc.head: + meta(http_equiv="refresh", content=str(refresh)) + + def get_image_dir(self): + """Return the directory that stores images""" + return self.img_dir + + def add_header(self, text): + """Insert a header to the HTML file + + Parameters: + text (str) -- the header text + """ + with self.doc: + h3(text) + + def add_images(self, ims, txts, links, width=400): + """add images to the HTML file + + Parameters: + ims (str list) -- a list of image paths + txts (str list) -- a list of image names shown on the website + links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page + """ + self.t = table(border=1, style="table-layout: fixed;") # Insert a table + self.doc.add(self.t) + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % width, src=os.path.join('images', im)) + br() + p(txt) + + def save(self): + """save the current content to the HMTL file""" + html_file = '%s/index.html' % self.web_dir + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': # we show an example usage here. + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims, txts, links = [], [], [] + for n in range(4): + ims.append('image_%d.png' % n) + txts.append('text_%d' % n) + links.append('image_%d.png' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/talkingface/util/image_pool.py b/talkingface/util/image_pool.py new file mode 100644 index 0000000..6d086f8 --- /dev/null +++ b/talkingface/util/image_pool.py @@ -0,0 +1,54 @@ +import random +import torch + + +class ImagePool(): + """This class implements an image buffer that stores previously generated images. + + This buffer enables us to update discriminators using a history of generated images + rather than the ones produced by the latest generators. + """ + + def __init__(self, pool_size): + """Initialize the ImagePool class + + Parameters: + pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created + """ + self.pool_size = pool_size + if self.pool_size > 0: # create an empty pool + self.num_imgs = 0 + self.images = [] + + def query(self, images): + """Return an image from the pool. + + Parameters: + images: the latest generated images from the generator + + Returns images from the buffer. + + By 50/100, the buffer will return input images. + By 50/100, the buffer will return images previously stored in the buffer, + and insert the current images to the buffer. + """ + if self.pool_size == 0: # if the buffer size is 0, do nothing + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data, 0) + if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: # by another 50% chance, the buffer will return the current image + return_images.append(image) + return_images = torch.cat(return_images, 0) # collect all the images and return + return return_images diff --git a/talkingface/util/log_board.py b/talkingface/util/log_board.py new file mode 100644 index 0000000..dd528d8 --- /dev/null +++ b/talkingface/util/log_board.py @@ -0,0 +1,17 @@ +def log( + logger, step=None, losses=None, fig=None, audio=None, sampling_rate=22050, tag="" +): + if losses is not None: + logger.add_scalar("Loss/d_loss", losses[0], step) + logger.add_scalar("Loss/g_gan_loss", losses[1], step) + logger.add_scalar("Loss/g_l1_loss", losses[2], step) + + if fig is not None: + logger.add_image(tag, fig, 2, dataformats='HWC') + + if audio is not None: + logger.add_audio( + tag, + audio / max(abs(audio)), + sample_rate=sampling_rate, + ) \ No newline at end of file diff --git a/talkingface/util/smooth.py b/talkingface/util/smooth.py new file mode 100644 index 0000000..6a731db --- /dev/null +++ b/talkingface/util/smooth.py @@ -0,0 +1,44 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +def smooth_array(array, weight = [0.1,0.8,0.1]): + ''' + + Args: + array: [n_frames, n_values]， 需要转换为[n_values, 1, n_frames] + weight: Conv1d.weight, 一维卷积核权重 + Returns: + array: [n_frames, n_values]， 光滑后的array + ''' + input = torch.Tensor(np.transpose(array[:,np.newaxis,:], (2, 1, 0))) + smooth_length = len(weight) + assert smooth_length%2 == 1, "卷积核权重个数必须使用奇数" + pad = (smooth_length//2, smooth_length//2) # 当pad只有两个参数时，仅改变最后一个维度, 左边扩充1列，右边扩充1列 + input = F.pad(input, pad, "replicate") + + with torch.no_grad(): + conv1 = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=smooth_length) + # 卷积核的元素值初始化 + weight = torch.tensor(weight).view(1, 1, -1) + conv1.weight = torch.nn.Parameter(weight) + nn.init.constant_(conv1.bias, 0) # 偏置值为0 + # print(conv1.weight) + out = conv1(input) + return out.permute(2,1,0).squeeze().numpy() + +if __name__ == '__main__': + model_id = "new_case" + Path_output_pkl = "../preparation/{}/mouth_info.pkl".format(model_id + "/00001") + import pickle + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f) + pts_array_normalized = np.array(images_info[2]) + pts_array_normalized = pts_array_normalized.reshape(-1, 16) + smooth_array_ = smooth_array(pts_array_normalized) + print(smooth_array_, smooth_array_.shape) + smooth_array_ = smooth_array_.reshape(-1, 4, 4) + import pandas as pd + + pd.DataFrame(smooth_array_[:, :, 0]).to_csv("mat2.csv") \ No newline at end of file diff --git a/talkingface/util/util.py b/talkingface/util/util.py new file mode 100644 index 0000000..b050c13 --- /dev/null +++ b/talkingface/util/util.py @@ -0,0 +1,103 @@ +"""This module contains simple helper functions """ +from __future__ import print_function +import torch +import numpy as np +from PIL import Image +import os + + +def tensor2im(input_image, imtype=np.uint8): + """"Converts a Tensor array into a numpy image array. + + Parameters: + input_image (tensor) -- the input image tensor array + imtype (type) -- the desired type of the converted numpy array + """ + if not isinstance(input_image, np.ndarray): + if isinstance(input_image, torch.Tensor): # get the data from a variable + image_tensor = input_image.data + else: + return input_image + image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array + if image_numpy.shape[0] == 1: # grayscale to RGB + image_numpy = np.tile(image_numpy, (3, 1, 1)) + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling + else: # if it is a numpy array, do nothing + image_numpy = input_image + return image_numpy.astype(imtype) + + +def diagnose_network(net, name='network'): + """Calculate and print the mean of average absolute(gradients) + + Parameters: + net (torch network) -- Torch network + name (str) -- the name of the network + """ + mean = 0.0 + count = 0 + for param in net.parameters(): + if param.grad is not None: + mean += torch.mean(torch.abs(param.grad.data)) + count += 1 + if count > 0: + mean = mean / count + print(name) + print(mean) + + +def save_image(image_numpy, image_path, aspect_ratio=1.0): + """Save a numpy image to the disk + + Parameters: + image_numpy (numpy array) -- input numpy array + image_path (str) -- the path of the image + """ + + image_pil = Image.fromarray(image_numpy) + h, w, _ = image_numpy.shape + + if aspect_ratio > 1.0: + image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC) + if aspect_ratio < 1.0: + image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC) + image_pil.save(image_path) + + +def print_numpy(x, val=True, shp=False): + """Print the mean, min, max, median, std, and size of a numpy array + + Parameters: + val (bool) -- if print the values of the numpy array + shp (bool) -- if print the shape of the numpy array + """ + x = x.astype(np.float64) + if shp: + print('shape,', x.shape) + if val: + x = x.flatten() + print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % ( + np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x))) + + +def mkdirs(paths): + """create empty directories if they don't exist + + Parameters: + paths (str list) -- a list of directory paths + """ + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + """create a single empty directory if it didn't exist + + Parameters: + path (str) -- a single directory path + """ + if not os.path.exists(path): + os.makedirs(path) diff --git a/talkingface/util/utils.py b/talkingface/util/utils.py new file mode 100644 index 0000000..0a56885 --- /dev/null +++ b/talkingface/util/utils.py @@ -0,0 +1,132 @@ +from torch.optim import lr_scheduler + +import torch.nn as nn +import torch + +######################################################### training utils########################################################## + +def get_scheduler(optimizer, niter,niter_decay,lr_policy='lambda',lr_decay_iters=50): + ''' + scheduler in training stage + ''' + if lr_policy == 'lambda': + def lambda_rule(epoch): + lr_l = 1.0 - max(0, epoch - niter) / float(niter_decay + 1) + return lr_l + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) + elif lr_policy == 'step': + scheduler = lr_scheduler.StepLR(optimizer, step_size=lr_decay_iters, gamma=0.1) + elif lr_policy == 'plateau': + scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) + elif lr_policy == 'cosine': + scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=niter, eta_min=0) + else: + return NotImplementedError('learning rate policy [%s] is not implemented', lr_policy) + return scheduler + +def update_learning_rate(scheduler, optimizer): + scheduler.step() + lr = optimizer.param_groups[0]['lr'] + print('learning rate = %.7f' % lr) + +class GANLoss(nn.Module): + ''' + GAN loss + ''' + def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0): + super(GANLoss, self).__init__() + self.register_buffer('real_label', torch.tensor(target_real_label)) + self.register_buffer('fake_label', torch.tensor(target_fake_label)) + if use_lsgan: + self.loss = nn.MSELoss() + else: + self.loss = nn.BCELoss() + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + target_tensor = self.real_label + else: + target_tensor = self.fake_label + return target_tensor.expand_as(input) + + def forward(self, input, target_is_real): + target_tensor = self.get_target_tensor(input, target_is_real) + return self.loss(input, target_tensor) + + + +import tqdm +import numpy as np +import cv2 +import glob +import os +import math +import pickle +import mediapipe as mp +mp_face_mesh = mp.solutions.face_mesh +landmark_points_68 = [162,234,93,58,172,136,149,148,152,377,378,365,397,288,323,454,389, + 71,63,105,66,107,336,296,334,293,301, + 168,197,5,4,75,97,2,326,305, + 33,160,158,133,153,144,362,385,387,263,373, + 380,61,39,37,0,267,269,291,405,314,17,84,181,78,82,13,312,308,317,14,87] +def ExtractFaceFromFrameList(frames_list, vid_height, vid_width, out_size = 256): + pts_3d = np.zeros([len(frames_list), 478, 3]) + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5) as face_mesh: + + for index, frame in tqdm.tqdm(enumerate(frames_list)): + results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if not results.multi_face_landmarks: + print("****** WARNING! No face detected! ******") + pts_3d[index] = 0 + return + # continue + image_height, image_width = frame.shape[:2] + for face_landmarks in results.multi_face_landmarks: + for index_, i in enumerate(face_landmarks.landmark): + x_px = min(math.floor(i.x * image_width), image_width - 1) + y_px = min(math.floor(i.y * image_height), image_height - 1) + z_px = min(math.floor(i.z * image_height), image_height - 1) + pts_3d[index, index_] = np.array([x_px, y_px, z_px]) + + # 计算整个视频中人脸的范围 + + x_min, y_min, x_max, y_max = np.min(pts_3d[:, :, 0]), np.min( + pts_3d[:, :, 1]), np.max( + pts_3d[:, :, 0]), np.max(pts_3d[:, :, 1]) + new_w = int((x_max - x_min) * 0.55)*2 + new_h = int((y_max - y_min) * 0.6)*2 + center_x = int((x_max + x_min) / 2.) + center_y = int(y_min + (y_max - y_min) * 0.6) + size = max(new_h, new_w) + x_min, y_min, x_max, y_max = int(center_x - size // 2), int(center_y - size // 2), int( + center_x + size // 2), int(center_y + size // 2) + + # 确定裁剪区域上边top和左边left坐标 + top = y_min + left = x_min + # 裁剪区域与原图的重合区域 + top_coincidence = int(max(top, 0)) + bottom_coincidence = int(min(y_max, vid_height)) + left_coincidence = int(max(left, 0)) + right_coincidence = int(min(x_max, vid_width)) + + scale = out_size / size + pts_3d = (pts_3d - np.array([left, top, 0])) * scale + pts_3d = pts_3d + + face_rect = np.array([center_x, center_y, size]) + print(np.array([x_min, y_min, x_max, y_max])) + + img_array = np.zeros([len(frames_list), out_size, out_size, 3], dtype = np.uint8) + for index, frame in tqdm.tqdm(enumerate(frames_list)): + img_new = np.zeros([size, size, 3], dtype=np.uint8) + img_new[top_coincidence - top:bottom_coincidence - top, left_coincidence - left:right_coincidence - left,:] = \ + frame[top_coincidence:bottom_coincidence, left_coincidence:right_coincidence, :] + img_new = cv2.resize(img_new, (out_size, out_size)) + img_array[index] = img_new + return pts_3d,img_array, face_rect + diff --git a/talkingface/util/visualizer.py b/talkingface/util/visualizer.py new file mode 100644 index 0000000..b6bb884 --- /dev/null +++ b/talkingface/util/visualizer.py @@ -0,0 +1,257 @@ +import numpy as np +import os +import sys +import ntpath +import time +from . import util, html +from subprocess import Popen, PIPE + + +try: + import wandb +except ImportError: + print('Warning: wandb package cannot be found. The option "--use_wandb" will result in error.') + +if sys.version_info[0] == 2: + VisdomExceptionBase = Exception +else: + VisdomExceptionBase = ConnectionError + + +def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256, use_wandb=False): + """Save images to the disk. + + Parameters: + webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details) + visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs + image_path (str) -- the string is used to create image paths + aspect_ratio (float) -- the aspect ratio of saved images + width (int) -- the images will be resized to width x width + + This function will save images stored in 'visuals' to the HTML file specified by 'webpage'. + """ + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + webpage.add_header(name) + ims, txts, links = [], [], [] + ims_dict = {} + for label, im_data in visuals.items(): + im = util.tensor2im(im_data) + image_name = '%s_%s.png' % (name, label) + save_path = os.path.join(image_dir, image_name) + util.save_image(im, save_path, aspect_ratio=aspect_ratio) + ims.append(image_name) + txts.append(label) + links.append(image_name) + if use_wandb: + ims_dict[label] = wandb.Image(im) + webpage.add_images(ims, txts, links, width=width) + if use_wandb: + wandb.log(ims_dict) + + +class Visualizer(): + """This class includes several functions that can display/save images and print/save logging information. + + It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images. + """ + + def __init__(self, opt): + """Initialize the Visualizer class + + Parameters: + opt -- stores all the experiment flags; needs to be a subclass of BaseOptions + Step 1: Cache the training/test options + Step 2: connect to a visdom server + Step 3: create an HTML object for saveing HTML filters + Step 4: create a logging file to store training losses + """ + self.opt = opt # cache the option + self.display_id = opt.display_id + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + self.port = opt.display_port + self.saved = False + self.use_wandb = opt.use_wandb + self.wandb_project_name = opt.wandb_project_name + self.current_epoch = 0 + self.ncols = opt.display_ncols + + if self.display_id > 0: # connect to a visdom server given and + import visdom + self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env) + if not self.vis.check_connection(): + self.create_visdom_connections() + + if self.use_wandb: + self.wandb_run = wandb.init(project=self.wandb_project_name, name=opt.name, config=opt) if not wandb.run else wandb.run + self.wandb_run._label(repo='CycleGAN-and-pix2pix') + + if self.use_html: # create an HTML object at /web/; images will be saved under /web/images/ + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + # create a logging file to store training losses + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def reset(self): + """Reset the self.saved status""" + self.saved = False + + def create_visdom_connections(self): + """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """ + cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port + print('\n\nCould not connect to Visdom server. \n Trying to start a server....') + print('Command: %s' % cmd) + Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE) + + def display_current_results(self, visuals, epoch, save_result): + """Display current results on visdom; save current results to an HTML file. + + Parameters: + visuals (OrderedDict) - - dictionary of images to display or save + epoch (int) - - the current epoch + save_result (bool) - - if save the current results to an HTML file + """ + if self.display_id > 0: # show images in the browser using visdom + ncols = self.ncols + if ncols > 0: # show all the images in one visdom panel + ncols = min(ncols, len(visuals)) + h, w = next(iter(visuals.values())).shape[:2] + table_css = """""" % (w, h) # create a table css + # create a table of images. + title = self.name + label_html = '' + label_html_row = '' + images = [] + idx = 0 + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + label_html_row += '%s' % label + images.append(image_numpy.transpose([2, 0, 1])) + idx += 1 + if idx % ncols == 0: + label_html += '%s' % label_html_row + label_html_row = '' + white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255 + while idx % ncols != 0: + images.append(white_image) + label_html_row += '' + idx += 1 + if label_html_row != '': + label_html += '%s' % label_html_row + try: + self.vis.images(images, nrow=ncols, win=self.display_id + 1, + padding=2, opts=dict(title=title + ' images')) + label_html = '%s
' % label_html + self.vis.text(table_css + label_html, win=self.display_id + 2, + opts=dict(title=title + ' labels')) + except VisdomExceptionBase: + self.create_visdom_connections() + + else: # show each image in a separate visdom panel; + idx = 1 + try: + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label), + win=self.display_id + idx) + idx += 1 + except VisdomExceptionBase: + self.create_visdom_connections() + + if self.use_wandb: + columns = [key for key, _ in visuals.items()] + columns.insert(0, 'epoch') + result_table = wandb.Table(columns=columns) + table_row = [epoch] + ims_dict = {} + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + wandb_image = wandb.Image(image_numpy) + table_row.append(wandb_image) + ims_dict[label] = wandb_image + self.wandb_run.log(ims_dict) + if epoch != self.current_epoch: + self.current_epoch = epoch + result_table.add_data(*table_row) + self.wandb_run.log({"Result": result_table}) + + if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved. + self.saved = True + # save images to the disk + for label, image in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label)) + util.save_image(image_numpy, img_path) + + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims, txts, links = [], [], [] + + for label, image_numpy in visuals.items(): + image_numpy = util.tensor2im(image) + img_path = 'epoch%.3d_%s.png' % (n, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + webpage.add_images(ims, txts, links, width=self.win_size) + webpage.save() + + def plot_current_losses(self, epoch, counter_ratio, losses): + """display the current losses on visdom display: dictionary of error labels and values + + Parameters: + epoch (int) -- current epoch + counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1 + losses (OrderedDict) -- training losses stored in the format of (name, float) pairs + """ + if not hasattr(self, 'plot_data'): + self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())} + self.plot_data['X'].append(epoch + counter_ratio) + self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']]) + try: + self.vis.line( + X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1), + Y=np.array(self.plot_data['Y']), + opts={ + 'title': self.name + ' loss over time', + 'legend': self.plot_data['legend'], + 'xlabel': 'epoch', + 'ylabel': 'loss'}, + win=self.display_id) + except VisdomExceptionBase: + self.create_visdom_connections() + if self.use_wandb: + self.wandb_run.log(losses) + + # losses: same format as |losses| of plot_current_losses + def print_current_losses(self, epoch, iters, losses, t_comp, t_data): + """print current losses on console; also save the losses to the disk + + Parameters: + epoch (int) -- current epoch + iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch) + losses (OrderedDict) -- training losses stored in the format of (name, float) pairs + t_comp (float) -- computational time per data point (normalized by batch_size) + t_data (float) -- data loading time per data point (normalized by batch_size) + """ + message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data) + for k, v in losses.items(): + message += '%s: %.3f ' % (k, v) + + print(message) # print the message + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) # save the message diff --git a/talkingface/utils.py b/talkingface/utils.py new file mode 100644 index 0000000..60c23c5 --- /dev/null +++ b/talkingface/utils.py @@ -0,0 +1,329 @@ +import numpy as np +import random +import cv2 + +INDEX_LEFT_EYEBROW = [276, 283, 282, 295, 285, 336, 296, 334, 293, 300] +INDEX_RIGHT_EYEBROW = [46, 53, 52, 65, 55, 107, 66, 105, 63, 70] +INDEX_EYEBROW = INDEX_LEFT_EYEBROW + INDEX_RIGHT_EYEBROW + +INDEX_NOSE_EDGE = [343, 355, 358, 327, 326, 2, 97, 98, 129, 126, 114] +INDEX_NOSE_MID = [6, 197,195,5,4] +INDEX_NOSE = INDEX_NOSE_EDGE + INDEX_NOSE_MID + +INDEX_LIPS_INNER = [78, 95, 88, 178, 87, 14, 317, 402, 318, 324,308,415,310,311,312,13,82,81,80,191] +INDEX_LIPS_OUTER = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291,409,270,269,267,0,37,39,40,185,] +INDEX_LIPS = INDEX_LIPS_INNER + INDEX_LIPS_OUTER + +INDEX_LEFT_EYE = [263, 249, 390, 373, 374, 380, 381, 382, 362, 398, 384, 385, 386, 387, 388, 466] +INDEX_RIGHT_EYE = [33, 7, 163, 144, 145, 153, 154, 155, 133, 173, 157, 158, 159, 160, 161, 246] +INDEX_EYE = INDEX_LEFT_EYE + INDEX_RIGHT_EYE +# 下半边脸的轮廓 +INDEX_FACE_OVAL = [ + 454, 323, 361, 288, 397, 365, + 379, 378, 400, 377, 152, 148, 176, 149, 150, + 136, 172, 58, 132, 93, 234, + # 206, 426 +] + +INDEX_MUSCLE = [ + 371,266,425,427,434,394, + 169,214,207,205,36,142 +] + +main_keypoints_index = INDEX_EYEBROW + INDEX_NOSE + INDEX_LIPS + INDEX_EYE + INDEX_FACE_OVAL + INDEX_MUSCLE + +rotate_ref_index = INDEX_EYEBROW + INDEX_NOSE + INDEX_EYE + INDEX_FACE_OVAL + INDEX_MUSCLE +# print(len(main_keypoints_index)) +Normalized = True +if Normalized: + tmp = 0 + list_ = [] + for i in [INDEX_LEFT_EYEBROW, INDEX_RIGHT_EYEBROW, INDEX_NOSE_EDGE, INDEX_NOSE_MID, INDEX_LIPS_INNER, INDEX_LIPS_OUTER, INDEX_LEFT_EYE, INDEX_RIGHT_EYE, INDEX_FACE_OVAL, INDEX_MUSCLE]: + i = (tmp + np.arange(len(i))).tolist() + list_.append(i) + tmp += len(i) +[INDEX_LEFT_EYEBROW, INDEX_RIGHT_EYEBROW, INDEX_NOSE_EDGE, INDEX_NOSE_MID, INDEX_LIPS_INNER, INDEX_LIPS_OUTER, INDEX_LEFT_EYE, INDEX_RIGHT_EYE, INDEX_FACE_OVAL, INDEX_MUSCLE] = list_ +INDEX_EYEBROW = INDEX_LEFT_EYEBROW + INDEX_RIGHT_EYEBROW +INDEX_NOSE = INDEX_NOSE_EDGE + INDEX_NOSE_MID +INDEX_LIPS = INDEX_LIPS_INNER + INDEX_LIPS_OUTER +INDEX_EYE = INDEX_LEFT_EYE + INDEX_RIGHT_EYE + +INDEX_LIPS_LOWER = INDEX_LIPS_INNER[:11] + INDEX_LIPS_OUTER[:11][::-1] +INDEX_LIPS_UPPER = INDEX_LIPS_INNER[10:] + [INDEX_LIPS_INNER[0], INDEX_LIPS_OUTER[0]] + INDEX_LIPS_OUTER[10:][::-1] + +FACE_MASK_INDEX = INDEX_FACE_OVAL[2:-2] +def crop_face(keypoints, is_train = False, size = [512, 512]): + """ + x_ratio: 裁剪出一个正方形，边长根据keypoints的宽度 * x_ratio决定 + """ + x_min, y_min, x_max, y_max = np.min(keypoints[FACE_MASK_INDEX, 0]), np.min(keypoints[FACE_MASK_INDEX, 1]), np.max(keypoints[FACE_MASK_INDEX, 0]), np.max( + keypoints[FACE_MASK_INDEX, 1]) + y_min = keypoints[33, 1] # 两眼间的点开始y轴裁剪 + border_width_half = max(x_max - x_min, y_max - y_min) * 0.6 + center_x = int((x_min + x_max) / 2.0) + center_y = int((y_min + y_max) / 2.0) + if is_train: + w_offset = random.randint(-2, 2) + h_offset = random.randint(-2, 2) + center_x = center_x + w_offset + center_y = center_y + h_offset + x_min, y_min, x_max, y_max = int(center_x - border_width_half), int(center_y - border_width_half), int( + center_x + border_width_half), int(center_y + border_width_half) + x_min = max(0, x_min) + y_min = max(0, y_min) + x_max = min(size[1], x_max) + y_max = min(size[0], y_max) + return [x_min, y_min, x_max, y_max] + +def crop_mouth(pts_array_origin, img_w, img_h, is_train = False): + center_x = np.mean(pts_array_origin[INDEX_LIPS_OUTER, 0]) + center_y = np.mean(pts_array_origin[INDEX_LIPS_OUTER, 1]) + x_min, y_min, x_max, y_max = np.min(pts_array_origin[INDEX_FACE_OVAL[2:-2], 0]), np.min( + pts_array_origin[INDEX_FACE_OVAL[2:-2], 1]), np.max( + pts_array_origin[INDEX_FACE_OVAL[2:-2], 0]), np.max(pts_array_origin[INDEX_FACE_OVAL[2:-2], 1]) + x_min = max(0, x_min) + y_min = max(0, y_min) + x_max = min(x_max, img_w) + y_max = min(y_max, img_h) + new_size = max((x_max - x_min), (y_max - y_min))*0.46 + + if is_train: + h_offset = int(new_size * 0.04) + h_offset = random.randint(-h_offset, h_offset) + center_y = center_y + h_offset + + x_min, y_min, x_max, y_max = int(center_x - new_size), int(center_y - new_size*0.89), int( + center_x + new_size), int(center_y + new_size*1.11) + + x_min = max(0, x_min) + y_min = max(0, y_min) + x_max = min(img_w, x_max) + y_max = min(img_h, y_max) + return np.array([x_min, y_min, x_max, y_max]) + +def draw_mouth_maps(keypoints, size=(256, 256), im_edges = None): + w, h = size + # edge map for face region from keypoints + if im_edges is None: + im_edges = np.zeros((h, w, 3), np.uint8) # edge map for all edges + pts = keypoints[INDEX_LIPS_OUTER, :2] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(im_edges, [pts], color=(0, 0, 0)) + + pts = keypoints[INDEX_LIPS_UPPER, :2] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(im_edges, [pts], color=(255, 0, 0)) + pts = keypoints[INDEX_LIPS_LOWER, :2] + pts = pts.reshape((-1, 1, 2)).astype(np.int32) + cv2.fillPoly(im_edges, [pts], color=(127, 0, 0)) + return im_edges + +def draw_face_feature_maps(keypoints, mode = ["mouth", "nose", "eye", "oval"], size=(256, 256), im_edges = None, mouth_width = None, mouth_height = None): + w, h = size + # edge map for face region from keypoints + if im_edges is None: + im_edges = np.zeros((h, w, 3), np.uint8) # edge map for all edges + + if "mouth_bias" in mode: + + w0, w1, h0, h1 = (int(keypoints[INDEX_NOSE_EDGE[5], 0] - mouth_width / 2), + int(keypoints[INDEX_NOSE_EDGE[5], 0] + mouth_width / 2), + int(keypoints[INDEX_NOSE_EDGE[5], 1] + mouth_height / 4), + int(keypoints[INDEX_NOSE_EDGE[5], 1] + mouth_height / 4 + mouth_height)) + w0, h0 = max(0, w0), max(h0, 0) + # print(w0, w1, h0, h1) + mouth_mask = np.zeros((h, w, 3), np.uint8) # edge map for all edges + mouth_mask[h0:h1, w0:w1] = 255 + mouth_index = np.where(mouth_mask == 255) + blur = (10, 10) + img_mouth = cv2.cvtColor(im_edges, cv2.COLOR_BGR2GRAY) + img_mouth = cv2.blur(img_mouth, blur) + # print(mouth_index) + mean_ = int(np.mean(img_mouth[(mouth_index[0], mouth_index[1])])) + max_, min_ = random.randint(mean_ + 40, mean_ + 70), random.randint(mean_ - 70, mean_ - 40) + img_mouth = (img_mouth.astype(np.float32) - min_) / (max_ - min_) * 255. + img_mouth = img_mouth.clip(0, 255).astype(np.uint8) + # print(img_mouth.shape[0], img_mouth.shape[1]) + img_mouth = cv2.resize(img_mouth, (100, 50)) + + # 定义噪声的标准差 + sigma = 8 # 你可以根据需要调整这个值 + # 生成与图片相同大小和类型的噪声 + noise = sigma * np.random.randn(img_mouth.shape[0], img_mouth.shape[1]) + # 将噪声添加到图片上 + img_mouth = img_mouth + noise + img_mouth = cv2.resize(img_mouth.clip(0, 255).astype(np.uint8), (im_edges.shape[0], im_edges.shape[1])) + + img_mouth = np.concatenate( + [img_mouth[:, :, np.newaxis], img_mouth[:, :, np.newaxis], img_mouth[:, :, np.newaxis]], axis=2) + + # bias_x = int(min(size[0] - max(mouth_index[0]), min(mouth_index[0]))) + # bias_y = int(min(size[0] - max(mouth_index[1]), min(mouth_index[1]))) + # print(bias_x, bias_y) + # bias_x = random.randint(-bias_x // 10, bias_x // 10) + # bias_y = random.randint(-bias_y // 10, bias_y // 10) + # + # mouth_index2 = np.array(mouth_index) + # # print(mouth_index) + # mouth_index2[0] = mouth_index[0] + bias_x + # mouth_index2[1] = mouth_index[1] + bias_y + # mouth_index2 = (mouth_index2[0], mouth_index2[1], mouth_index2[2]) + # # print(mouth_index2.T, mouth_index2.shape) + # + output = np.zeros((h, w, 3), np.uint8) + # output[mouth_index2] = img_mouth[mouth_index] + # return im_edges + output[mouth_index] = img_mouth[mouth_index] + im_edges = output + if "nose" in mode: + for ii in range(len(INDEX_NOSE_EDGE) - 1): + pt1 = [int(flt) for flt in keypoints[INDEX_NOSE_EDGE[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_NOSE_EDGE[ii+1]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 255, 0), 2) + for ii in range(len(INDEX_NOSE_MID) -1): + pt1 = [int(flt) for flt in keypoints[INDEX_NOSE_MID[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_NOSE_MID[ii + 1]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 255, 0), 2) + if "eye" in mode: + for ii in range(len(INDEX_LEFT_EYE)): + pt1 = [int(flt) for flt in keypoints[INDEX_LEFT_EYE[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_LEFT_EYE[(ii + 1)%len(INDEX_LEFT_EYE)]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 255, 0), 2) + for ii in range(len(INDEX_RIGHT_EYE)): + pt1 = [int(flt) for flt in keypoints[INDEX_RIGHT_EYE[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_RIGHT_EYE[(ii + 1) % len(INDEX_RIGHT_EYE)]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 255, 0), 2) + if "oval" in mode: + tmp = INDEX_FACE_OVAL[:6] + for ii in range(len(tmp) -1): + pt1 = [int(flt) for flt in keypoints[tmp[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[tmp[ii + 1]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 0, 255), 2) + tmp = INDEX_FACE_OVAL[-6:] + for ii in range(len(tmp) - 1): + pt1 = [int(flt) for flt in keypoints[tmp[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[tmp[ii + 1]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 0, 255), 2) + + # if "mouth_outer" in mode: + # pts = keypoints[INDEX_LIPS_OUTER] + # pts = pts.reshape((-1, 1, 2)).astype(np.int32) + # cv2.fillPoly(im_edges, [pts], color=(255, 0, 0)) + # if "mouth" in mode: + # pts = keypoints[INDEX_LIPS_OUTER][:,2] + # pts = pts.reshape((-1, 1, 2)).astype(np.int32) + # cv2.fillPoly(im_edges, [pts], color=(255, 0, 0)) + # pts = keypoints[INDEX_LIPS_INNER][:,2] + # pts = pts.reshape((-1, 1, 2)).astype(np.int32) + # cv2.fillPoly(im_edges, [pts], color=(0, 0, 0)) + # if "mouth_outer" in mode: + # for ii in range(len(INDEX_LIPS_OUTER)): + # pt1 = [int(flt) for flt in keypoints[INDEX_LIPS_OUTER[ii]]][:2] + # pt2 = [int(flt) for flt in keypoints[INDEX_LIPS_OUTER[(ii + 1)%len(INDEX_LIPS_OUTER)]]][:2] + # cv2.line(im_edges, tuple(pt1), tuple(pt2), (255, 0, 0), 2) + + + + if "mouth" in mode: + for ii in range(len(INDEX_LIPS_OUTER)): + pt1 = [int(flt) for flt in keypoints[INDEX_LIPS_OUTER[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_LIPS_OUTER[(ii + 1)%len(INDEX_LIPS_OUTER)]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (255, 0, 0), 2) + for ii in range(len(INDEX_LIPS_INNER)): + pt1 = [int(flt) for flt in keypoints[INDEX_LIPS_INNER[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_LIPS_INNER[(ii + 1)%len(INDEX_LIPS_INNER)]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (255, 0, 0), 2) + if "muscle" in mode: + for ii in range(len(INDEX_MUSCLE) - 1): + pt1 = [int(flt) for flt in keypoints[INDEX_MUSCLE[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[INDEX_MUSCLE[(ii + 1) % len(INDEX_MUSCLE)]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (255, 255, 255), 2) + + if "oval_all" in mode: + tmp = INDEX_FACE_OVAL + for ii in range(len(tmp) -1): + pt1 = [int(flt) for flt in keypoints[tmp[ii]]][:2] + pt2 = [int(flt) for flt in keypoints[tmp[ii + 1]]][:2] + cv2.line(im_edges, tuple(pt1), tuple(pt2), (0, 0, 255), 2) + + return im_edges + +def smooth_array(array, weight = [0.1,0.8,0.1], mode = "numpy"): + ''' + + Args: + array: [n_frames, n_values]， 需要转换为[n_values, 1, n_frames] + weight: Conv1d.weight, 一维卷积核权重 + Returns: + array: [n_frames, n_values]， 光滑后的array + ''' + if mode == "torch": + import torch + import torch.nn as nn + import torch.nn.functional as F + input = torch.Tensor(np.transpose(array[:, np.newaxis, :], (2, 1, 0))) + smooth_length = len(weight) + assert smooth_length % 2 == 1, "卷积核权重个数必须使用奇数" + pad = (smooth_length // 2, smooth_length // 2) # 当pad只有两个参数时，仅改变最后一个维度, 左边扩充1列，右边扩充1列 + input = F.pad(input, pad, "replicate") + + with torch.no_grad(): + conv1 = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=smooth_length) + # 卷积核的元素值初始化 + weight = torch.tensor(weight).view(1, 1, -1) + conv1.weight = torch.nn.Parameter(weight) + nn.init.constant_(conv1.bias, 0) # 偏置值为0 + # print(conv1.weight) + out = conv1(input) + return out.permute(2, 1, 0).squeeze().numpy() + else: + # out = np.zeros([array.shape[0] + 2, array.shape[1]]) + # input = np.zeros([array.shape[0] + 2, array.shape[1]]) + # input[0] = array[0] + # input[-1] = array[-1] + # input[1:-1] = array + # for i in range(out.shape[1]): + # out[:, i] = np.convolve(input[:, i], weight, mode="same") + # out0 = out[1:-1] + smooth_length = len(weight) + assert smooth_length % 2 == 1, "卷积核权重个数必须使用奇数" + pad = smooth_length // 2 + fliter = np.array([weight]).T + x0 = array + fliter = np.repeat(fliter, x0.shape[1], axis=1) + out0 = np.zeros_like(x0) + for i in range(len(x0)): + if i < pad or i >= len(x0) - pad: + out0[i] = x0[i] + else: + tmp = x0[i - pad:i + pad + 1] * fliter + out0[i] = np.sum(tmp, axis=0) + return out0 + +def generate_face_mask(): + face_mask = np.zeros([256, 256], dtype=np.uint8) + for i in range(20): + ii = 19 - i + face_mask[ii, :] = 13 * i + face_mask[255 - ii, :] = 13 * i + face_mask[:, ii] = 13 * i + face_mask[:, 255 - ii] = 13 * i + face_mask = np.array([face_mask, face_mask, face_mask]).transpose(1, 2, 0).astype(float) / 255. + print(face_mask.shape) + return face_mask + +from math import cos,sin,radians +def RotateAngle2Matrix(tmp): #tmp为xyz的旋转角,角度值 + tmp = [radians(i) for i in tmp] + matX = np.array([[1.0, 0, 0], + [0.0, cos(tmp[0]), -sin(tmp[0])], + [0.0, sin(tmp[0]), cos(tmp[0])]]) + matY = np.array([[cos(tmp[1]), 0, sin(tmp[1])], + [0.0, 1, 0], + [-sin(tmp[1]), 0, cos(tmp[1])]]) + matZ = np.array([[cos(tmp[2]), -sin(tmp[2]), 0], + [sin(tmp[2]), cos(tmp[2]), 0], + [0, 0, 1]]) + matRotate = np.matmul(matZ, matY) + matRotate = np.matmul(matRotate, matX) + return matRotate \ No newline at end of file diff --git a/train/README.md b/train/README.md new file mode 100644 index 0000000..b1d2c90 --- /dev/null +++ b/train/README.md @@ -0,0 +1,60 @@ +## Overview +This project involves training two separate models for a face generation system: an audio model to extract mouth movement patterns and a rendering model to generate the final face. Each model requires its own data preparation and training process. +## Audio Model Training (LSTM) +The audio model is distilled from other models and is designed to extract mouth movement patterns from audio input. The code for this model will be released soon. +## Render Model Training (simplified DiNet) +### Original Data Structure +Please ensure that your data directory structure is organized as follows: +```bash +|--/dir_to_data +| |--/video0.mp4 +| |--/video1.mp4 +| |--/video2.mp4 +``` +### Data Preparation +Next, prepare your video using the data_preparation script. Replace YOUR_VIDEO_PATH with the path to your video: +```bash +python train/data_preparation_face.py dir_to_data +``` +After running the script, your data directory structure should be updated to: +```bash +|--/dir_to_data +| |--/video0 +| | |--/keypoint_rotate.pkl +| | |--/face_mat_mask.pkl +| | |--/image +| | |--/000000.png +| | |--/000001.png +| | |--/... +| |--/video1 +| | |--/keypoint_rotate.pkl +| | |--/face_mat_mask.pkl +| | |--/image +| | |--/000000.png +| | |--/000001.png +| | |--/... +``` +### Data Validation +Verify the prepared data using the following script: +```bash +python train/train_input_validation_render_model.py dir_to_data +``` +you will get the following image: +![](validation.jpg) +### Training +```bash +python train/train_render_model.py --train_data dir_to_data +``` +### Monitoring Training Progress +Monitor the training progress using TensorBoard. +```bash +tensorboard --logdir=checkpoint/Dinet_five_ref +``` +Then, open http://localhost:6006/ in your web browser to view the training metrics. + +### Downloading the Pre-trained Model +A pre-trained model can be accessed via both Baidu Netdisk and Google Drive. + + [Baidu Netdisk](https://pan.baidu.com/s/1oB7kJl6xf70t1P3rVkypRQ?pwd=ym7k) Extraction Code: ym7k + + [Google Drive](https://drive.google.com/file/d/108-cmzzDglGROMwi-CQ91Eijx1aBGur-/view?usp=drive_link) diff --git a/train/data_preparation_face.py b/train/data_preparation_face.py new file mode 100644 index 0000000..e1aea76 --- /dev/null +++ b/train/data_preparation_face.py @@ -0,0 +1,269 @@ +import os +os.environ["KMP_DUPLICATE_LIB_OK"] = "true" +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) +# sys.path.append("..") +import uuid +import tqdm +import numpy as np +import cv2 +import glob +import math +import pickle +from talkingface.util.smooth import smooth_array +from talkingface.run_utils import calc_face_mat +import tqdm +from talkingface.utils import * +import mediapipe as mp + +mp_face_mesh = mp.solutions.face_mesh +mp_face_detection = mp.solutions.face_detection +point_size = 1 +point_color = (0, 0, 255) # BGR +thickness = 4 # 0 、4、8 +def detect_face(frame): + # 剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80的 + with mp_face_detection.FaceDetection( + model_selection=1, min_detection_confidence=0.5) as face_detection: + results = face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + if not results.detections or len(results.detections) > 1: + return -1, None + rect = results.detections[0].location_data.relative_bounding_box + out_rect = [rect.xmin, rect.xmin + rect.width, rect.ymin, rect.ymin + rect.height] + nose_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.NOSE_TIP) + l_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.LEFT_EYE) + r_eye_ = mp_face_detection.get_key_point( + results.detections[0], mp_face_detection.FaceKeyPoint.RIGHT_EYE) + # print(nose_, l_eye_, r_eye_) + if nose_.x > l_eye_.x or nose_.x < r_eye_.x: + return -2, out_rect + + h, w = frame.shape[:2] + # print(frame.shape) + if rect.xmin < 0 or rect.ymin < 0 or rect.xmin + rect.width > w or rect.ymin + rect.height > h: + return -3, out_rect + if rect.width * w < 100 or rect.height * h < 100: + return -4, out_rect + return 1, out_rect + + +def calc_face_interact(face0, face1): + x_min = min(face0[0], face1[0]) + x_max = max(face0[1], face1[1]) + y_min = min(face0[2], face1[2]) + y_max = max(face0[3], face1[3]) + tmp0 = ((face0[1] - face0[0]) * (face0[3] - face0[2])) / ((x_max - x_min) * (y_max - y_min)) + tmp1 = ((face1[1] - face1[0]) * (face1[3] - face1[2])) / ((x_max - x_min) * (y_max - y_min)) + return min(tmp0, tmp1) + + +def detect_face_mesh(frame): + with mp_face_mesh.FaceMesh( + static_image_mode=True, + max_num_faces=1, + refine_landmarks=True, + min_detection_confidence=0.5) as face_mesh: + results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + pts_3d = np.zeros([478, 3]) + if not results.multi_face_landmarks: + print("****** WARNING! No face detected! ******") + else: + image_height, image_width = frame.shape[:2] + for face_landmarks in results.multi_face_landmarks: + for index_, i in enumerate(face_landmarks.landmark): + x_px = min(math.floor(i.x * image_width), image_width - 1) + y_px = min(math.floor(i.y * image_height), image_height - 1) + z_px = min(math.floor(i.z * image_width), image_width - 1) + pts_3d[index_] = np.array([x_px, y_px, z_px]) + return pts_3d + + +def ExtractFromVideo(video_path, circle = False): + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return 0 + + dir_path = os.path.dirname(video_path) + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + + totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 + totalFrames = int(totalFrames) + pts_3d = np.zeros([totalFrames, 478, 3]) + face_rect_list = [] + + # os.makedirs("../preparation/{}/image".format(model_name)) + for frame_index in tqdm.tqdm(range(totalFrames)): + ret, frame = cap.read() # 按帧读取视频 + # #到视频结尾时终止 + if ret is False: + break + # cv2.imwrite("../preparation/{}/image/{:0>6d}.png".format(model_name, frame_index), frame) + tag_, rect = detect_face(frame) + if frame_index == 0 and tag_ != 1: + print("第一帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80") + pts_3d = -1 + break + elif tag_ == -1: # 有时候人脸检测会失败，就用上一帧的结果替代这一帧的结果 + rect = face_rect_list[-1] + elif tag_ != 1: + print("第{}帧人脸检测异常，请剔除掉多个人脸、大角度侧脸（鼻子不在两个眼之间）、部分人脸框在画面外、人脸像素低于80*80, tag: {}".format(frame_index, tag_)) + # exit() + if len(face_rect_list) > 0: + face_area_inter = calc_face_interact(face_rect_list[-1], rect) + # print(frame_index, face_area_inter) + if face_area_inter < 0.6: + print("人脸区域变化幅度太大，请复查，超出值为{}, frame_num: {}".format(face_area_inter, frame_index)) + pts_3d = -2 + break + + face_rect_list.append(rect) + + x_min = rect[0] * vid_width + y_min = rect[2] * vid_height + x_max = rect[1] * vid_width + y_max = rect[3] * vid_height + seq_w, seq_h = x_max - x_min, y_max - y_min + x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2 + # x_min = int(max(0, x_mid - seq_w * 0.65)) + # y_min = int(max(0, y_mid - seq_h * 0.4)) + # x_max = int(min(vid_width, x_mid + seq_w * 0.65)) + # y_max = int(min(vid_height, y_mid + seq_h * 0.8)) + crop_size = int(max(seq_w * 1.35, seq_h * 1.35)) + x_min = int(max(0, x_mid - crop_size * 0.5)) + y_min = int(max(0, y_mid - crop_size * 0.45)) + x_max = int(min(vid_width, x_min + crop_size)) + y_max = int(min(vid_height, y_min + crop_size)) + + frame_face = frame[y_min:y_max, x_min:x_max] + # cv2.imshow("s", frame_face) + # cv2.waitKey(20) + frame_kps = detect_face_mesh(frame_face) + pts_3d[frame_index] = frame_kps + np.array([x_min, y_min, 0]) + cap.release() # 释放视频对象 + return pts_3d + + +def run(video_path, export_imgs = True): + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + return 0 + vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # 宽度 + vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # 高度 + + totalFrames = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 总帧数 + totalFrames = int(totalFrames) + cap.release() + pts_3d = ExtractFromVideo(video_path) + if type(pts_3d) is np.ndarray and len(pts_3d) == totalFrames: + print("关键点已提取") + else: + print("error in video: {}!!!".format(video_path)) + return + + video_name = os.path.basename(video_path).split(".")[0] + video_data_path = os.path.join(os.path.dirname(video_path), video_name) + os.makedirs(video_data_path, exist_ok=True) + + if export_imgs: + # 计算整个视频中人脸的范围 + x_min, y_min, x_max, y_max = np.min(pts_3d[:, :, 0]), np.min( + pts_3d[:, :, 1]), np.max( + pts_3d[:, :, 0]), np.max(pts_3d[:, :, 1]) + new_w = int((x_max - x_min) * 0.55) * 2 + new_h = int((y_max - y_min) * 0.6) * 2 + center_x = int((x_max + x_min) / 2.) + center_y = int(y_min + (y_max - y_min) * 0.6) + size = max(new_h, new_w) + x_min, y_min, x_max, y_max = int(center_x - size // 2), int(center_y - size // 2), int( + center_x + size // 2), int(center_y + size // 2) + + # 确定裁剪区域上边top和左边left坐标 + top = y_min + left = x_min + # 裁剪区域与原图的重合区域 + top_coincidence = int(max(top, 0)) + bottom_coincidence = int(min(y_max, vid_height)) + left_coincidence = int(max(left, 0)) + right_coincidence = int(min(x_max, vid_width)) + print("人脸活动范围：{}:{}, {}:{}".format(top_coincidence, bottom_coincidence, left_coincidence, right_coincidence)) + out_size = 512 + scale = 512. / size + pts_3d = (pts_3d - np.array([left_coincidence, top_coincidence, 0])) * scale + Path_output_pkl = "{}/keypoint_rotate.pkl".format(video_data_path) + with open(Path_output_pkl, "wb") as f: + pickle.dump(pts_3d, f) + os.makedirs("{}/image".format(video_data_path), exist_ok=True) + ffmpeg_cmd = "ffmpeg -i {} -vf crop={}:{}:{}:{},scale=512:512:flags=neighbor -loglevel quiet -y {}/image/%06d.png".format( + video_path, + right_coincidence - left_coincidence, + bottom_coincidence - top_coincidence, + left_coincidence, + top_coincidence, + video_data_path + ) + os.system(ffmpeg_cmd) + + + img_filelist = glob.glob("{}/image/*.png".format(video_data_path)) + img_filelist.sort() + + Path_output_pkl = "{}/keypoint_rotate.pkl".format(video_data_path) + + with open(Path_output_pkl, "rb") as f: + images_info = pickle.load(f)[:, main_keypoints_index, :] + pts_driven = images_info.reshape(len(images_info), -1) + pts_driven = smooth_array(pts_driven).reshape(len(pts_driven), -1, 3) + + + face_pts_mean = np.loadtxt(os.path.join(current_dir, "../data/face_pts_mean_mainKps.txt")) + mat_list, pts_normalized_list, face_pts_mean_personal = calc_face_mat(pts_driven, face_pts_mean) + pts_normalized_list = np.array(pts_normalized_list) + # print(face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1]) + # print(np.max(pts_normalized_list[:,INDEX_FACE_OVAL[:10], 1], axis = 1)) + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 1], + axis=0) + np.arange(5, 25, 2) + face_pts_mean_personal[INDEX_FACE_OVAL[:10], 0] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[:10], 0], + axis=0) - (9 - np.arange(0, 10)) + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 1], + axis=0) - np.arange(5, 25, 2) + 28 + face_pts_mean_personal[INDEX_FACE_OVAL[-10:], 0] = np.min(pts_normalized_list[:, INDEX_FACE_OVAL[-10:], 0], + axis=0) + np.arange(0, 10) + + face_pts_mean_personal[INDEX_FACE_OVAL[10], 1] = np.max(pts_normalized_list[:, INDEX_FACE_OVAL[10], 1], axis=0) + 25 + + # for keypoints_normalized in pts_normalized_list: + # img = np.zeros([1000,1000,3], dtype=np.uint8) + # for coor in face_pts_mean_personal: + # # coor = (coor +1 )/2. + # cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, (255, 0, 0), thickness) + # for coor in keypoints_normalized: + # # coor = (coor +1 )/2. + # cv2.circle(img, (int(coor[0]), int(coor[1])), point_size, point_color, thickness) + # cv2.imshow("a", img) + # cv2.waitKey(30) + + with open("{}/face_mat_mask.pkl".format(video_data_path), "wb") as f: + pickle.dump([mat_list, face_pts_mean_personal], f) + + + +def main(): + # 检查命令行参数的数量 + if len(sys.argv) != 2: + print("Usage: python data_preparation.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + data_dir = sys.argv[1] + print(f"Video dir is set to: {data_dir}") + # data_dir = r"F:\C\AI\CV\88" + video_files = glob.glob("{}/*.mp4".format(data_dir)) + for video_path in tqdm.tqdm(video_files): + run(video_path) + +if __name__ == "__main__": + main() diff --git a/train/train_input_validation_render_model.py b/train/train_input_validation_render_model.py new file mode 100644 index 0000000..0199071 --- /dev/null +++ b/train/train_input_validation_render_model.py @@ -0,0 +1,59 @@ +import os +os.environ["KMP_DUPLICATE_LIB_OK"] = "true" +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) +import pickle +import cv2 +import numpy as np +import random + +import glob +import sys +import torch +from torch.utils.data import DataLoader +from talkingface.data.few_shot_dataset import Few_Shot_Dataset,data_preparation +from talkingface.utils import * + +def main(): + # 检查命令行参数的数量 + if len(sys.argv) != 2: + print("Usage: python train_input_validation_render_model.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + data_dir = sys.argv[1] + # data_dir = r"F:\C\AI\CV\88" + print(f"Video dir is set to: {data_dir}") + + video_list = [os.path.join(data_dir, i) for i in os.listdir(data_dir)] + dict_info = data_preparation(video_list) + device = torch.device("cuda:0") + test_set = Few_Shot_Dataset(dict_info, is_train=True, n_ref=1) + testing_data_loader = DataLoader(dataset=test_set, num_workers=0, batch_size=1, shuffle=False) + + def Tensor2img(tensor_, channel_index): + frame = tensor_[channel_index:channel_index + 3, :, :].detach().squeeze(0).cpu().float().numpy() + frame = np.transpose(frame, (1, 2, 0)) * 255.0 + frame = frame.clip(0, 255) + return frame.astype(np.uint8) + + for iteration, batch in enumerate(testing_data_loader): + # source_tensor, source_prompt_tensor, ref_tensor, ref_prompt_tensor, target_tensor = [iii.to(device) for iii in batch] + source_tensor, ref_tensor, target_tensor = [iii.to(device) for iii in batch] + print(source_tensor.size(), ref_tensor.size(), target_tensor.size()) + + frame0 = Tensor2img(source_tensor[0], 0) + frame1 = Tensor2img(source_tensor[0], 3) + frame2 = Tensor2img(ref_tensor[0], 0) + frame3 = Tensor2img(ref_tensor[0], 3) + frame4 = Tensor2img(target_tensor[0], 0) + frame = np.concatenate([frame0, frame1, frame2, frame3, frame4], axis=1) + + cv2.imshow("ss", frame) + cv2.waitKey(-1) + # break + cv2.destroyAllWindows() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train/train_render_model.py b/train/train_render_model.py new file mode 100644 index 0000000..ecaa7a1 --- /dev/null +++ b/train/train_render_model.py @@ -0,0 +1,197 @@ +import os +os.environ["KMP_DUPLICATE_LIB_OK"] = "true" +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) +from talkingface.models.common.Discriminator import Discriminator +from talkingface.models.common.VGG19 import Vgg19 +from talkingface.models.DINet import DINet_five_Ref +from talkingface.util.utils import GANLoss,get_scheduler, update_learning_rate +from talkingface.config.config import DINetTrainingOptions +from torch.utils.tensorboard import SummaryWriter +from talkingface.util.log_board import log +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader +import random +import numpy as np +import os +import sys +import torch.nn.functional as F +import cv2 +from talkingface.data.few_shot_dataset import Few_Shot_Dataset,data_preparation + +def Tensor2img(tensor_, channel_index): + frame = tensor_[channel_index:channel_index + 3, :, :].detach().squeeze(0).cpu().float().numpy() + frame = np.transpose(frame, (1, 2, 0)) * 255.0 + frame = frame.clip(0, 255) + return frame.astype(np.uint8) + +if __name__ == "__main__": + ''' + training code of person image generation + ''' + # load config + opt = DINetTrainingOptions().parse_args() + n_ref = 5 + opt.source_channel = 3 * 2 + opt.target_channel = 3 + opt.ref_channel = n_ref * 3 * 2 + opt.batch_size = 4 + opt.result_path = "checkpoint/Dinet_five_ref" + opt.resume = False + opt.resume_path = None + + # set seed + random.seed(opt.seed) + np.random.seed(opt.seed) + torch.cuda.manual_seed(opt.seed) + + + video_list = [] + # path_ = r"../preparation_bilibili" + path_ = opt.train_data + video_list += [os.path.join(path_, i) for i in os.listdir(path_)] + + print("video_selected final: ", len(video_list)) + video_list.sort() + train_dict_info = data_preparation(video_list[:]) + train_set = Few_Shot_Dataset(train_dict_info, n_ref=n_ref, is_train=True) + training_data_loader = DataLoader(dataset=train_set, num_workers=0, batch_size=opt.batch_size, shuffle=True) + train_log_path = "train_log.txt" + train_data_length = len(training_data_loader) + # init network + net_g = DINet_five_Ref(opt.source_channel,opt.ref_channel).cuda() + net_d = Discriminator(opt.target_channel, opt.D_block_expansion, opt.D_num_blocks, opt.D_max_features).cuda() + net_vgg = Vgg19().cuda() + + # set optimizer + optimizer_g = optim.Adam(net_g.parameters(), lr=opt.lr_g) + optimizer_d = optim.Adam(net_d.parameters(), lr=opt.lr_d) + + if opt.resume: + print('loading checkpoint {}'.format(opt.resume_path)) + checkpoint = torch.load(opt.resume_path) + # opt.start_epoch = checkpoint['epoch'] + # opt.start_epoch = 200 + net_g_static = checkpoint['state_dict']['net_g'] + net_g.load_state_dict(net_g_static) + net_d.load_state_dict(checkpoint['state_dict']['net_d']) + optimizer_g.load_state_dict(checkpoint['optimizer']['net_g']) + optimizer_d.load_state_dict(checkpoint['optimizer']['net_d']) + + # set criterion + criterionGAN = GANLoss().cuda() + criterionL1 = nn.L1Loss().cuda() + # set scheduler + net_g_scheduler = get_scheduler(optimizer_g, opt.non_decay, opt.decay) + net_d_scheduler = get_scheduler(optimizer_d, opt.non_decay, opt.decay) + + + + train_log_path = os.path.join("checkpoint/{}/log".format("DiNet_five_ref"), "train") + os.makedirs(train_log_path, exist_ok=True) + train_logger = SummaryWriter(train_log_path) + + # start train + for epoch in range(opt.start_epoch, opt.non_decay + opt.decay + 1): + net_g.train() + avg_loss_g_perception = 0 + avg_Loss_DI = 0 + avg_Loss_GI = 0 + for iteration, data in enumerate(training_data_loader): + # read data + source_tensor, ref_tensor, target_tensor = data + source_tensor = source_tensor.float().cuda() + ref_tensor = ref_tensor.float().cuda() + target_tensor = target_tensor.float().cuda() + + source_tensor, source_prompt_tensor = source_tensor[:, :3], source_tensor[:, 3:] + # network forward + fake_out = net_g(source_tensor, source_prompt_tensor, ref_tensor) + # down sample output image and real image + fake_out_half = F.avg_pool2d(fake_out, 3, 2, 1, count_include_pad=False) + target_tensor_half = F.interpolate(target_tensor, scale_factor=0.5, mode='bilinear') + # (1) Update D network + optimizer_d.zero_grad() + # compute fake loss + _,pred_fake_d = net_d(fake_out) + loss_d_fake = criterionGAN(pred_fake_d, False) + # compute real loss + _,pred_real_d = net_d(target_tensor) + loss_d_real = criterionGAN(pred_real_d, True) + # Combine D loss + loss_dI = (loss_d_fake + loss_d_real) * 0.5 + loss_dI.backward(retain_graph=True) + optimizer_d.step() + # (2) Update G network + _, pred_fake_dI = net_d(fake_out) + optimizer_g.zero_grad() + # compute perception loss + perception_real = net_vgg(target_tensor) + perception_fake = net_vgg(fake_out) + perception_real_half = net_vgg(target_tensor_half) + perception_fake_half = net_vgg(fake_out_half) + loss_g_perception = 0 + for i in range(len(perception_real)): + loss_g_perception += criterionL1(perception_fake[i], perception_real[i]) + loss_g_perception += criterionL1(perception_fake_half[i], perception_real_half[i]) + loss_g_perception = (loss_g_perception / (len(perception_real) * 2)) * opt.lamb_perception + # gan dI loss + loss_g_dI = criterionGAN(pred_fake_dI, True) + # combine perception loss and gan loss + loss_g = loss_g_perception + loss_g_dI + loss_g.backward() + optimizer_g.step() + message = "===> Epoch[{}]({}/{}): Loss_DI: {:.4f} Loss_GI: {:.4f} Loss_perception: {:.4f} lr_g = {:.7f} lr_d = {:.7f}".format( + epoch, iteration, len(training_data_loader), float(loss_dI), float(loss_g_dI), + float(loss_g_perception), optimizer_g.param_groups[0]['lr'], optimizer_d.param_groups[0]['lr']) + print(message) + # with open("train_log.txt", "a") as f: + # f.write(message + "\n") + + if iteration%200 == 0: + inference_out = fake_out * 255 + inference_out = inference_out[0].cpu().permute(1, 2, 0).float().detach().numpy().astype(np.uint8) + inference_in = (target_tensor[0, :3]* 255).cpu().permute(1, 2, 0).float().detach().numpy().astype(np.uint8) + inference_in_prompt = (source_prompt_tensor[0, :3] * 255).cpu().permute(1, 2, 0).float().detach().numpy().astype( + np.uint8) + frame2 = Tensor2img(ref_tensor[0], 0) + frame3 = Tensor2img(ref_tensor[0], 3) + inference_out = np.concatenate([inference_in, inference_in_prompt, inference_out, frame2, frame3], axis=1) + inference_out = cv2.cvtColor(inference_out, cv2.COLOR_RGB2BGR) + + log(train_logger, fig=inference_out, tag="Training/epoch_{}_{}".format(epoch, iteration)) + + real_iteration = epoch * len(training_data_loader) + iteration + message1 = "Step {}/{}, ".format(real_iteration, (epoch + 1) * len(training_data_loader)) + message2 = "" + losses = [loss_dI.item(), loss_g_perception.item(), loss_g_dI.item()] + train_logger.add_scalar("Loss/loss_dI", losses[0], real_iteration) + train_logger.add_scalar("Loss/loss_g_perception", losses[1], real_iteration) + train_logger.add_scalar("Loss/loss_g_dI", losses[2], real_iteration) + + avg_loss_g_perception += loss_g_perception.item() + avg_Loss_DI += loss_dI.item() + avg_Loss_GI += loss_g_dI.item() + train_logger.add_scalar("Loss/{}".format("epoch_g_perception"), avg_loss_g_perception / len(training_data_loader), epoch) + train_logger.add_scalar("Loss/{}".format("epoch_DI"), + avg_Loss_DI / len(training_data_loader), epoch) + train_logger.add_scalar("Loss/{}".format("epoch_GI"), + avg_Loss_GI / len(training_data_loader), epoch) + update_learning_rate(net_g_scheduler, optimizer_g) + update_learning_rate(net_d_scheduler, optimizer_d) + + # checkpoint + if epoch % opt.checkpoint == 0: + if not os.path.exists(opt.result_path): + os.mkdir(opt.result_path) + model_out_path = os.path.join(opt.result_path, 'epoch_{}.pth'.format(epoch)) + states = { + 'epoch': epoch + 1, + 'state_dict': {'net_g': net_g.state_dict(), 'net_d': net_d.state_dict()}, + 'optimizer': {'net_g': optimizer_g.state_dict(), 'net_d': optimizer_d.state_dict()} + } + torch.save(states, model_out_path) + print("Checkpoint saved to {}".format(epoch)) \ No newline at end of file diff --git a/train/validation.jpg b/train/validation.jpg new file mode 100644 index 0000000..5986c8a Binary files /dev/null and b/train/validation.jpg differ diff --git a/train_audio/README.md b/train_audio/README.md new file mode 100644 index 0000000..e56cbb7 --- /dev/null +++ b/train_audio/README.md @@ -0,0 +1,56 @@ +# LSTM Training Notes + +## Data Preparation +- Use over 10,000 short audio clips with diverse speakers, each at least 5 seconds long, in 16k single-channel wav format. +- Place these files in the `train_audio/train_data` folder. +- Download `wav2lip.pth` from the wav2lip project (https://github.com/Rudrabha/Wav2Lip) and place it in the `train_audio/checkpoints` folder. + +#### Notes +Audio files must be in 16k single-channel wav format. + +## Steps + +### 1. Face Video Generation +```bash +python preparation_step0.py +# Example: preparation_step0.py face.jpg train_data +``` + +### 2. Mouth Region Extraction and PCA Modeling +```bash +python preparation_step1.py +# Example: preparation_step1.py train_data +``` + +Now ensure the file directory is as follows: +```bash +|--/train_audio +| |--/checkpoints +| | |--/wav2lip.pth +| | |--/pca.pkl +| | |--/wav2lip_pca_all.gif +| |--/train_data +| | |--/000001.wav +| | |--/000001.avi +| | |--/000001.txt +| | |--/000002.wav +| | |--/000002.avi +| | |--/000002.txt +| | |--/000003.wav +| | |--/000003.avi +| | |--/000003.txt +``` + +### 3. Training LSTM Model +```bash +python train_lstm.py +# Example: train_lstm.py train_data +``` + +### 4. Testing Audio Accuracy +```bash +python test.py +# Example: python test.py D:/Code/py/test_wav/0013.wav checkpoints/audio.pkl +``` + + diff --git a/train_audio/audio.py b/train_audio/audio.py new file mode 100644 index 0000000..d7b0e8c --- /dev/null +++ b/train_audio/audio.py @@ -0,0 +1,137 @@ +import librosa +import librosa.filters +import numpy as np +# import tensorflow as tf +from scipy import signal +from scipy.io import wavfile +from hparams import hparams as hp +import os +os.environ['KMP_DUPLICATE_LIB_OK']='True' +def load_wav(path, sr): + return librosa.core.load(path, sr=sr)[0] + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + #proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + +def get_hop_size(): + hop_size = hp.hop_size + if hop_size is None: + assert hp.frame_shift_ms is not None + hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) + return hop_size + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(np.abs(D)) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def melspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def _lws_processor(): + import lws + return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") + +def _stft(y): + if hp.use_lws: + return _lws_processor(hp).stft(y).T + else: + return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) + +########################################################## +#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram + """ + pad = (fsize - fshift) + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding + """ + M = num_frames(len(x), fsize, fshift) + pad = (fsize - fshift) + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r +########################################################## +#Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + +# Conversions +_mel_basis = None + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + +def _build_mel_basis(): + assert hp.fmax <= hp.sample_rate // 2 + return librosa.filters.mel(sr = hp.sample_rate, n_fft = hp.n_fft, n_mels=hp.num_mels, + fmin=hp.fmin, fmax=hp.fmax) + +def _amp_to_db(x): + min_level = np.exp(hp.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + +def _normalize(S): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, + -hp.max_abs_value, hp.max_abs_value) + else: + return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) + + assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 + if hp.symmetric_mels: + return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value + else: + return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) + +def _denormalize(D): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return (((np.clip(D, -hp.max_abs_value, + hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + + hp.min_level_db) + else: + return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) + + if hp.symmetric_mels: + return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) + else: + return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/train_audio/face.jpg b/train_audio/face.jpg new file mode 100644 index 0000000..9168d36 Binary files /dev/null and b/train_audio/face.jpg differ diff --git a/train_audio/hparams.py b/train_audio/hparams.py new file mode 100644 index 0000000..1c01904 --- /dev/null +++ b/train_audio/hparams.py @@ -0,0 +1,101 @@ +from glob import glob +import os + +def get_image_list(data_root, split): + filelist = [] + + with open('filelists/{}.txt'.format(split)) as f: + for line in f: + line = line.strip() + if ' ' in line: line = line.split()[0] + filelist.append(os.path.join(data_root, line)) + + return filelist + +class HParams: + def __init__(self, **kwargs): + self.data = {} + + for key, value in kwargs.items(): + self.data[key] = value + + def __getattr__(self, key): + if key not in self.data: + raise AttributeError("'HParams' object has no attribute %s" % key) + return self.data[key] + + def set_hparam(self, key, value): + self.data[key] = value + + +# Default hyperparameters +hparams = HParams( + num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality + # network + rescale=True, # Whether to rescale audio prior to preprocessing + rescaling_max=0.9, # Rescaling value + + # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction + # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder + # Does not work if n_ffit is not multiple of hop_size!! + use_lws=False, + + n_fft=800, # Extra window size is filled with 0 paddings to match this parameter + hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) + win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) + sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i ) + + frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) + + # Mel and Linear spectrograms normalization/scaling and clipping + signal_normalization=True, + # Whether to normalize mel spectrograms to some predefined range (following below parameters) + allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True + symmetric_mels=True, + # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, + # faster and cleaner convergence) + max_abs_value=4., + # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not + # be too big to avoid gradient explosion, + # not too small for fast convergence) + # Contribution by @begeekmyfriend + # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude + # levels. Also allows for better G&L phase reconstruction) + preemphasize=True, # whether to apply filter + preemphasis=0.97, # filter coefficient. + + # Limits + min_level_db=-100, + ref_level_db=20, + fmin=55, + # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To + # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) + fmax=7600, # To be increased/reduced depending on data. + + ###################### Our training parameters ################################# + img_size=96, + fps=25, + + batch_size=16, + initial_learning_rate=1e-4, + nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs + num_workers=16, + checkpoint_interval=3000, + eval_interval=3000, + save_optimizer_state=True, + + syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. + syncnet_batch_size=64, + syncnet_lr=1e-4, + syncnet_eval_interval=10000, + syncnet_checkpoint_interval=10000, + + disc_wt=0.07, + disc_initial_learning_rate=1e-4, +) + + +def hparams_debug_string(): + values = hparams.values() + hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] + return "Hyperparameters:\n" + "\n".join(hp) diff --git a/train_audio/models/__init__.py b/train_audio/models/__init__.py new file mode 100644 index 0000000..4374370 --- /dev/null +++ b/train_audio/models/__init__.py @@ -0,0 +1,2 @@ +from .wav2lip import Wav2Lip, Wav2Lip_disc_qual +from .syncnet import SyncNet_color \ No newline at end of file diff --git a/train_audio/models/conv.py b/train_audio/models/conv.py new file mode 100644 index 0000000..ed83da0 --- /dev/null +++ b/train_audio/models/conv.py @@ -0,0 +1,44 @@ +import torch +from torch import nn +from torch.nn import functional as F + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act(out) + +class nonorm_Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + ) + self.act = nn.LeakyReLU(0.01, inplace=True) + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2dTranspose(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) diff --git a/train_audio/models/syncnet.py b/train_audio/models/syncnet.py new file mode 100644 index 0000000..e773cdc --- /dev/null +++ b/train_audio/models/syncnet.py @@ -0,0 +1,66 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from .conv import Conv2d + +class SyncNet_color(nn.Module): + def __init__(self): + super(SyncNet_color, self).__init__() + + self.face_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3), + + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=2, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=2, padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(512, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T) + face_embedding = self.face_encoder(face_sequences) + audio_embedding = self.audio_encoder(audio_sequences) + + audio_embedding = audio_embedding.view(audio_embedding.size(0), -1) + face_embedding = face_embedding.view(face_embedding.size(0), -1) + + audio_embedding = F.normalize(audio_embedding, p=2, dim=1) + face_embedding = F.normalize(face_embedding, p=2, dim=1) + + + return audio_embedding, face_embedding diff --git a/train_audio/models/wav2lip.py b/train_audio/models/wav2lip.py new file mode 100644 index 0000000..ae5d691 --- /dev/null +++ b/train_audio/models/wav2lip.py @@ -0,0 +1,184 @@ +import torch +from torch import nn +from torch.nn import functional as F +import math + +from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d + +class Wav2Lip(nn.Module): + def __init__(self): + super(Wav2Lip, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 + + nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 24,24 + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # 12,12 + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 6,6 + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.face_decoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6 + + nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12 + + nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24 + + nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48 + + nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96 + + self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), + nn.Sigmoid()) + + def forward(self, audio_sequences, face_sequences): + # audio_sequences = (B, T, 1, 80, 16) + B = audio_sequences.size(0) + + input_dim_size = len(face_sequences.size()) + if input_dim_size > 4: + audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + + audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 + + feats = [] + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + feats.append(x) + + x = audio_embedding + for f in self.face_decoder_blocks: + x = f(x) + try: + x = torch.cat((x, feats[-1]), dim=1) + except Exception as e: + print(x.size()) + print(feats[-1].size()) + raise e + + feats.pop() + + x = self.output_block(x) + + if input_dim_size > 4: + x = torch.split(x, B, dim=0) # [(B, C, H, W)] + outputs = torch.stack(x, dim=2) # (B, C, T, H, W) + + else: + outputs = x + + return outputs + +class Wav2Lip_disc_qual(nn.Module): + def __init__(self): + super(Wav2Lip_disc_qual, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), # 48,96 + + nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48 + nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), # 24,24 + nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), # 12,12 + nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 6,6 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), # 3,3 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) + self.label_noise = .0 + + def get_lower_half(self, face_sequences): + return face_sequences[:, :, face_sequences.size(2)//2:] + + def to_2d(self, face_sequences): + B = face_sequences.size(0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + return face_sequences + + def perceptual_forward(self, false_face_sequences): + false_face_sequences = self.to_2d(false_face_sequences) + false_face_sequences = self.get_lower_half(false_face_sequences) + + false_feats = false_face_sequences + for f in self.face_encoder_blocks: + false_feats = f(false_feats) + + false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), + torch.ones((len(false_feats), 1)).cuda()) + + return false_pred_loss + + def forward(self, face_sequences): + face_sequences = self.to_2d(face_sequences) + face_sequences = self.get_lower_half(face_sequences) + + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + + return self.binary_pred(x).view(len(x), -1) diff --git a/train_audio/preparation_step0.py b/train_audio/preparation_step0.py new file mode 100644 index 0000000..a53cf97 --- /dev/null +++ b/train_audio/preparation_step0.py @@ -0,0 +1,124 @@ +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) + +import sys, audio +from tqdm import tqdm +import glob +from models import Wav2Lip +import cv2 +import numpy as np +import torch +device = 'cuda' if torch.cuda.is_available() else 'cpu' +checkpoint_path = "checkpoints/wav2lip.pth" + +mel_step_size = 16 +wav2lip_batch_size = 8 +img_size = 96 +fps = 25 +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} for inference.'.format(device)) +Wav2Lip_model = None +def load_model(path): + model = Wav2Lip() + print("Load checkpoint from: {}".format(path)) + checkpoint = torch.load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + + model = model.to(device) + return model.eval() + +def face_detect(image): + from talkingface.mediapipe_utils import detect_face_mesh + keypoints = detect_face_mesh([image])[0] + x_min, y_min, x_max, y_max = np.min(keypoints[:, 0]), np.min(keypoints[:, 1]), np.max(keypoints[:, 0]), np.max( + keypoints[:, 1]) + y_min = y_min - (y_max - y_min) * 0.1 + (x1, y1, x2, y2) = np.array([x_min, y_min, x_max, y_max], dtype = int) + face = image[y1: y2, x1:x2] + face = cv2.resize(face, (96, 96)) + return face + +def datagen(image, mels): + img_batch = [] + mel_batch = [] + for i, m in enumerate(mels): + img_batch.append(image) + mel_batch.append(m) + if len(img_batch) >= wav2lip_batch_size: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, img_size // 2:] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. + mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch + img_batch, mel_batch = [], [] + + +def main(face_image, wav_path, outfile): + global Wav2Lip_model + if Wav2Lip_model is None: + Wav2Lip_model = load_model(checkpoint_path) + print(wav_path) + wav = audio.load_wav(wav_path, 16000) + mel = audio.melspectrogram(wav) + print(mel.shape) + + mel_chunks = [] + mel_idx_multiplier = 80. / fps + i = 0 + while 1: + start_idx = int(i * mel_idx_multiplier) + if start_idx + mel_step_size > len(mel[0]): + mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:]) + break + mel_chunks.append(mel[:, start_idx: start_idx + mel_step_size]) + i += 1 + + print("Length of mel chunks: {}".format(len(mel_chunks))) + + batch_size = 8 + gen = datagen(face_image, mel_chunks) + + for i, (img_batch, mel_batch) in enumerate(tqdm(gen, total=int(np.ceil(float(len(mel_chunks)) / batch_size)))): + if i == 0: + out = cv2.VideoWriter(outfile, cv2.VideoWriter_fourcc(*'DIVX'), fps, (96, 96)) + + img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) + mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) + + with torch.no_grad(): + pred = Wav2Lip_model(mel_batch, img_batch) + + pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. + + for p in pred: + f = p.astype(np.uint8) + out.write(f) + out.release() + +if __name__ == '__main__': + if len(sys.argv) < 3: + print("Usage: python preparation_step0.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + face_path = sys.argv[1] + print(f"Face path is set to: {face_path}") + wav_16K_path = sys.argv[2] + print(f"Wav 16K path is set to: {wav_16K_path}, please make sure all wav files duration not less than 2s.") + + image = cv2.imread(face_path) + face_pix96 = face_detect(image) + wav_files = glob.glob(os.path.join(wav_16K_path, "*.wav")) + for index_, wav_path in enumerate(wav_files): + outfile = wav_path.replace(".wav", ".avi") + main(face_pix96, wav_path, outfile) diff --git a/train_audio/preparation_step1.py b/train_audio/preparation_step1.py new file mode 100644 index 0000000..295c0ef --- /dev/null +++ b/train_audio/preparation_step1.py @@ -0,0 +1,123 @@ +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) +from sklearn import decomposition +import imageio +import numpy as np +import cv2 +import pickle +import tqdm +import glob +from talkingface.utils import INDEX_LIPS_OUTER,main_keypoints_index +from talkingface.mediapipe_utils import detect_face_mesh + +def main(video_path_list): + cap = cv2.VideoCapture(video_path_list[0]) + vid_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + frames = [] + for i in range(min(500, vid_frame_count)): + frames.append(cap.read()[1]) + cap.release() + keypoints = detect_face_mesh(frames) + keypoints = keypoints[:, main_keypoints_index] + print(keypoints.shape, len(main_keypoints_index)) + # 先根据一个视频的前500帧估计嘴巴范围 + x_min, x_max = np.min(keypoints[:, INDEX_LIPS_OUTER, 0]), np.max(keypoints[:, INDEX_LIPS_OUTER, 0]) + y_min, y_max = np.min(keypoints[:, INDEX_LIPS_OUTER, 1]), np.max(keypoints[:, INDEX_LIPS_OUTER, 1]) + x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2 + x_len, y_len = (x_max - x_min) / 2, (y_max - y_min) / 2 + x_min, x_max = x_mid - x_len * 0.9, x_mid + x_len * 0.9 + y_min, y_max = y_mid - y_len * 0.9, y_mid + y_len * 0.9 + print("嘴部区域:", x_min, x_max, y_min, y_max) + frames = [] + num_sum = 0 + for video_path in video_path_list: + cap = cv2.VideoCapture(video_path) + vid_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + for i in range(vid_frame_count): + frame = cv2.resize(cap.read()[1][int(y_min):int(y_max), int(x_min):int(x_max)], (30, 15)).astype(np.float32) + # frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY).astype(np.float32) + tmp = (frame[:, :15] + frame[:, 15:][:, ::-1]) / 2 + frame = np.concatenate([tmp, tmp[:, ::-1]], axis=1) + # max_, min_ = np.max(frame), np.min(frame) + # print(max_, min_) + frame = (frame - 60) / (180 - 60.) * 255 + frame = frame.clip(0, 255).astype(np.uint8) + # cv2.imshow("s", cv2.resize(frame, (400,200))) + # cv2.waitKey(40) + frames.append(frame) + cap.release() + num_sum += vid_frame_count + if num_sum > 100000: + break + # exit() + + out_size = [30, 15] + + x = np.array([frame.flatten() for frame in frames]) + del frames + n_components = 7 + pca = decomposition.PCA(n_components=n_components) + pca.fit(x[:150000]) + y = pca.transform(x[:30000]) + print("PCA建模完成") + frames = [] + for index_ in range(n_components): + bs_sort = np.sort(y[:, index_]) + y_min_bs = bs_sort[int(0. * len(bs_sort))] + y_max_bs = bs_sort[int(1 * len(bs_sort)) - 1] + frames_ = [] + # 从-0.5到1.5，均分为30份 + list_ = [0 + jj * 1. / 30 for jj in range(30)] + for index__ in list_: + weight = y_min_bs + index__ * (y_max_bs - y_min_bs) + frame = pca.mean_ + pca.components_[index_] * weight + frame = frame.reshape(out_size[1], out_size[0], 3) + frame = frame.clip(0, 255).astype(np.uint8) + frame = cv2.resize(frame, (frame.shape[1] * 20, frame.shape[0] * 20)) + # cv2.imshow("s", frame) + # cv2.waitKey(30) + + frames_.append(frame) + frames.append(frames_) + frames_final = [] + for i in range(30): + img0 = np.concatenate([frames[0][i], frames[1][i], frames[2][i]], axis=1) + img1 = np.concatenate([frames[3][i], frames[4][i], frames[5][i]], axis=1) + frame = np.concatenate([img0, img1]) + frames_final.append(frame[:, :, ::-1]) + + imageio.mimsave("checkpoints/wav2lip_pca_all.gif", frames_final, 'GIF', duration=0.15) + + Path_output_pkl = r"checkpoints/pca.pkl" + with open(Path_output_pkl, "wb") as f: + pickle.dump(pca, f) + + for video_path in tqdm.tqdm(video_path_list[:]): + frames = [] + + cap = cv2.VideoCapture(video_path) + vid_frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + for i in range(vid_frame_count): + frame = cv2.resize(cap.read()[1][int(y_min):int(y_max), int(x_min):int(x_max)], (30, 15)) + # frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) + frames.append(frame) + cap.release() + + x = np.array([frame.flatten() for frame in frames]) + y = pca.transform(x) + pca_coefs_path = video_path.replace(".avi", ".txt") + np.savetxt(pca_coefs_path, y, fmt="%.02f") + print("Done!") + + +if __name__ == '__main__': + if len(sys.argv) < 2: + print("Usage: python preparation_step1.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + data_path = sys.argv[1] + video_path_list = glob.glob(os.path.join(data_path, "*.avi")) + main(video_path_list) \ No newline at end of file diff --git a/train_audio/test.py b/train_audio/test.py new file mode 100644 index 0000000..d60c7f6 --- /dev/null +++ b/train_audio/test.py @@ -0,0 +1,89 @@ +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) + +import pickle +import numpy as np +import kaldi_native_fbank as knf +from scipy.io import wavfile +import torch +import glob +import cv2 +import os +from talkingface.models.audio2bs_lstm import Audio2Feature + +def main(wavpath, ckpt_path): + device = "cuda" if torch.cuda.is_available() else "cpu" + # 加载模型 + Audio2FeatureModel = Audio2Feature().to(device) + Audio2FeatureModel.load_state_dict(torch.load(ckpt_path)) + Audio2FeatureModel.eval() + Path_output_pkl = "checkpoints/pca.pkl" + with open(Path_output_pkl, "rb") as f: + pca = pickle.load(f) + rate, wav = wavfile.read(wavpath, mmap=False) + augmented_samples = wav + augmented_samples2 = augmented_samples.astype(np.float32, order='C') / 32768.0 + print(augmented_samples2.shape, augmented_samples2.shape[0] / 16000) + + opts = knf.FbankOptions() + opts.frame_opts.dither = 0 + opts.frame_opts.frame_length_ms = 50 + opts.frame_opts.frame_shift_ms = 20 + opts.mel_opts.num_bins = 80 + opts.frame_opts.snip_edges = False + opts.mel_opts.debug_mel = False + fbank = knf.OnlineFbank(opts) + fbank.accept_waveform(16000, augmented_samples2.tolist()) + seq_len = fbank.num_frames_ready // 2 + A2Lsamples = np.zeros([2 * seq_len, 80]) + for i in range(2 * seq_len): + print(i) + f2 = fbank.get_frame(i) + A2Lsamples[i] = f2 + + orig_mel = A2Lsamples + print(orig_mel.shape) + input = torch.from_numpy(orig_mel).unsqueeze(0).float().to(device) + print(input.shape) + h0 = torch.zeros(2, 1, 192).to(device) + c0 = torch.zeros(2, 1, 192).to(device) + bs_array, hn, cn = Audio2FeatureModel(input, h0, c0) + print(bs_array.shape) + bs_array = bs_array[0].detach().cpu().float().numpy() + print(bs_array.shape) + bs_array = bs_array[4:] + frame_num = len(bs_array) + import uuid + + task_id = str(uuid.uuid1()) + for frame_index in range(frame_num): + bs_real = bs_array[frame_index] + pts = np.dot(bs_real[:2], pca.components_[:2]) + pca.mean_ + ref_img_ = pts.reshape(15, -1, 3).astype(np.uint8) + frame = cv2.resize(ref_img_, (ref_img_.shape[1] * 4, ref_img_.shape[0] * 4)) + + # for point in pts: + # cv2.circle(frame, (int(point[0]), int(point[1])), point_size, point_color, thickness) + + os.makedirs("output/{}".format(task_id), exist_ok=True) + cv2.imwrite("output/{}/{:>06d}.png".format(task_id, frame_index), frame) + # cv2.imshow("aa", frame) + # cv2.waitKey(16) + + cv2.destroyAllWindows() + val_video = "output/{}.mp4".format(task_id) + os.system( + "ffmpeg -r 25 -i output/{}/%06d.png -i {} -c:v libx264 -pix_fmt yuv420p {}".format(task_id, wavpath, val_video)) + +if __name__ == '__main__': + if len(sys.argv) < 3: + print("Usage: python test.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + wav_path = sys.argv[1] + ckpt_path = sys.argv[2] + # model_path = 'checkpoints/audio.pkl' + main(wav_path, ckpt_path) \ No newline at end of file diff --git a/train_audio/train_lstm.py b/train_audio/train_lstm.py new file mode 100644 index 0000000..dec5aa3 --- /dev/null +++ b/train_audio/train_lstm.py @@ -0,0 +1,244 @@ +import os +current_dir = os.path.dirname(os.path.abspath(__file__)) +import sys +sys.path.append(os.path.join(current_dir, "..")) +import glob + +model_name = "lstm" +seq_len = 26 +from talkingface.models.audio2bs_lstm import Audio2Feature +import torch +from torch.utils.data import DataLoader +from talkingface.data.dataset_wav import AudioVisualDataset +device = "cuda" if torch.cuda.is_available() else "cpu" +from scipy.io import wavfile +import pickle +import cv2 +import tqdm +import numpy as np +import os + + +def main(data_path): + pcm_16k_list = [] + pca_info_list = [] + print("Loading all data into memory. Make sure your memory is enough.") + wav2_dir = glob.glob(os.path.join(data_path, "*.wav")) + for index_, wav_file in enumerate(wav2_dir): + coef_path = wav_file.replace(".wav", ".txt") + pca_coef = np.loadtxt(coef_path) + rate, wav = wavfile.read(wav_file, mmap=False) + pca_info_list.append(pca_coef[:, :6]) + pcm_16k_list.append(wav) + print("All data Loaded. Total wav files number: {}. Total pca files number: {}.".format(len(pcm_16k_list), + len(pca_info_list))) + Path_output_pkl = "checkpoints/pca.pkl" + with open(Path_output_pkl, "rb") as f: + pca = pickle.load(f) + + def generate_ref_image(bs_real, bs_pred): + # print(bs_real.shape, bs_pred.shape) + pts = np.dot(bs_real[:8], pca.components_[:6]) + pca.mean_ + ref_img_ = pts.reshape(8, 15, 30, 3).transpose(1, 0, 2, 3).reshape(15, -1, 3).astype(np.uint8) + ref_img_ = cv2.resize(ref_img_, (ref_img_.shape[1] * 5, ref_img_.shape[0] * 5)) + pts = np.dot(bs_pred[:8], pca.components_[:6]) + pca.mean_ + ref_img_2 = pts.reshape(8, 15, 30, 3).transpose(1, 0, 2, 3).reshape(15, -1, 3).astype(np.uint8) + ref_img_2 = cv2.resize(ref_img_2, (ref_img_2.shape[1] * 5, ref_img_2.shape[0] * 5)) + ref_img_ = np.concatenate([ref_img_, ref_img_2], axis=0) + return ref_img_ + + from sklearn.model_selection import train_test_split + import random + random_st = random.choice(range(10000)) + print("random_st:", random_st) + random_st = 777 + train_pcm_16k_list, test_pcm_16k_list, train_pca_info_list, test_pca_info_list = train_test_split(pcm_16k_list, + pca_info_list, + test_size=0.2, + random_state=random_st) + + train_audioVisualDataset = AudioVisualDataset(train_pcm_16k_list, train_pca_info_list, seq_len=seq_len) + test_audioVisualDataset = AudioVisualDataset(test_pcm_16k_list, test_pca_info_list, seq_len=seq_len) + + training_data_loader = DataLoader(dataset=train_audioVisualDataset, num_workers=0, batch_size=32, shuffle=True) + test_data_loader = DataLoader(dataset=test_audioVisualDataset, num_workers=0, batch_size=8, shuffle=True) + + # 加载模型 + Audio2FeatureModel = Audio2Feature().to(device) + criterionL1 = torch.nn.L1Loss().to(device) + + # setup optimizer + optimizer = torch.optim.Adam(Audio2FeatureModel.parameters(), lr=0.0004, betas=(0.9, 0.99)) + + # tensorboard设置 + train_log_path = os.path.join("checkpoints/log", model_name, "train") + val_log_path = os.path.join("checkpoints/log", model_name, "val") + os.makedirs(train_log_path, exist_ok=True) + os.makedirs(val_log_path, exist_ok=True) + from torch.utils.tensorboard import SummaryWriter + train_logger = SummaryWriter(train_log_path) + val_logger = SummaryWriter(val_log_path) + + def log( + logger, step=None, losses=None, fig=None, audio=None, sampling_rate=22050, tag="", video=None + ): + if losses is not None: + logger.add_scalar("Loss/loss", losses[0], step) + + if fig is not None: + logger.add_image(tag, fig, 2, dataformats='HWC') + if video is not None: + logger.add_video(tag, video, fps=60) + + if audio is not None: + logger.add_audio( + tag, + audio / max(abs(audio)), + sample_rate=sampling_rate, + ) + + point_size = 3 + point_color = (0, 0, 255) # BGR + thickness = 3 # 0 、4、8 + loss_iteration_100_train = 0 + loss_iteration_100_test = 0 + for epoch in range(0, 1001): + Audio2FeatureModel.train() + for iteration, batch in enumerate(training_data_loader): + A2Lsamples, target_pts2d = batch + A2Lsamples, target_pts2d = A2Lsamples.to(device), target_pts2d.to(device) + # print(len(batch), batch[0].size()) + # # A2Lsamples, target_pts2d = batch[0].to(device), batch[1].to(device) + # print(iteration, A2Lsamples.shape, target_pts2d.shape) + bs = A2Lsamples.size()[0] + h0 = torch.zeros(2, bs, 192).to(device) + c0 = torch.zeros(2, bs, 192).to(device) + pred_pts2d, _, _ = Audio2FeatureModel(A2Lsamples, h0, c0) + + # print(iteration, A2Lsamples.shape, target_pts2d.shape, pred_pts2d.shape) + + # 25帧的序列，生成25帧的关键点，只使用第5-24帧的结果，和目标关键点序列target_pts2d的第0-19帧对应 + output_dim_size = pred_pts2d.size() + if len(output_dim_size) > 2: + pred_pts2d = pred_pts2d[:, 4:] + target_pts2d = target_pts2d[:, :-4] + else: + pred_pts2d = pred_pts2d.view(output_dim_size[0], 1, output_dim_size[1]) + target_pts2d = target_pts2d[:, seq_len // 2:seq_len // 2 + 1] + + # print(pred_pts2d.shape, target_pts2d.shape) + # exit() + + # Backward and optimize + optimizer.zero_grad() + # print(target_pts2d.size(), pred_pts2d.size()) + # pred_pts2d = torch.ones_like(pred_pts2d).cuda() * 0.5 + loss = criterionL1(target_pts2d, pred_pts2d) + # loss = cosine_loss(target_pts2d, pred_pts2d, bs) + loss.requires_grad_(True) + loss.backward() + optimizer.step() + + print("===> Epoch[{}]({}/{}): Loss: {:.4f}".format( + epoch, iteration, len(training_data_loader), loss.item())) + loss_iteration_100_train += loss.item() + + real_iteration = epoch * len(training_data_loader) + iteration + + if real_iteration % 100 == 0 and real_iteration > 0: + losses = [loss_iteration_100_train / 100] + message1 = "Step {}/{}, ".format(real_iteration, epoch * len(training_data_loader)) + message2 = "Loss: {:.4f},".format( + *losses + ) + + with open(os.path.join(train_log_path, "log.txt"), "a") as f: + f.write(message1 + message2 + "\n") + + # outer_bar.write(message1 + message2) + + log(train_logger, real_iteration, losses=losses) + loss_iteration_100_train = 0 + + if real_iteration % 300 == 0: + bs_pred = pred_pts2d[0].detach().cpu().float().numpy() + bs_real = target_pts2d[0].detach().cpu().float().numpy() + frame = generate_ref_image(bs_real, bs_pred) + log( + train_logger, + fig=frame, + tag="Training/epoch_{}_{}_predict".format(epoch, iteration), + ) + + Audio2FeatureModel.eval() + + for iteration, batch in enumerate(test_data_loader): + A2Lsamples, target_pts2d = batch[0].to(device), batch[1].to(device) + bs = A2Lsamples.size()[0] + h0 = torch.zeros(2, bs, 192).to(device) + c0 = torch.zeros(2, bs, 192).to(device) + pred_pts2d, _, _ = Audio2FeatureModel(A2Lsamples, h0, c0) + + output_dim_size = pred_pts2d.size() + if len(output_dim_size) > 2: + pred_pts2d = pred_pts2d[:, 4:] + target_pts2d = target_pts2d[:, :-4] + else: + pred_pts2d = pred_pts2d.view(output_dim_size[0], 1, output_dim_size[1]) + target_pts2d = target_pts2d[:, seq_len // 2:seq_len // 2 + 1] + + # print(iteration, A2Lsamples.shape, target_pts2d.shape, pred_pts2d.shape) + # pred_pts2d = torch.ones_like(pred_pts2d).cuda() * 0.5 + loss = criterionL1(target_pts2d, pred_pts2d) + # loss = cosine_loss(target_pts2d, pred_pts2d, bs) + print("===> Epoch[{}]({}/{}): Loss: {:.4f}".format( + epoch, iteration, len(test_data_loader), loss.item())) + + real_iteration = epoch * len(test_data_loader) + iteration + loss_iteration_100_test += loss.item() + + if real_iteration % 100 == 0 and real_iteration > 0: + losses = [loss_iteration_100_test / 100] + message1 = "Step {}/{}, ".format(real_iteration, epoch * len(test_data_loader)) + message2 = "Loss: {:.4f},".format( + *losses + ) + + with open(os.path.join(val_log_path, "log.txt"), "a") as f: + f.write(message1 + message2 + "\n") + + # outer_bar.write(message1 + message2) + + log(val_logger, real_iteration, losses=losses) + loss_iteration_100_test = 0 + + if real_iteration % 300 == 0: + bs_pred = pred_pts2d[0].detach().cpu().float().numpy() + bs_real = target_pts2d[0].detach().cpu().float().numpy() + frame = generate_ref_image(bs_real, bs_pred) + log( + train_logger, + fig=frame, + tag="Testing/epoch_{}_{}_predict".format(epoch, iteration), + ) + # checkpoint + if epoch % 5 == 0: + if not os.path.exists("checkpoints"): + os.mkdir("checkpoints") + if not os.path.exists(os.path.join("checkpoints", model_name)): + os.mkdir(os.path.join("checkpoints", model_name)) + model_out_path = "checkpoints/{}/epoch_{}.pth".format(model_name, epoch) + # states = { 'epoch': epoch + 1, 'state_dict': Audio2FeatureModel.state_dict(), 'optimizer': optimizer.state_dict() } + states = Audio2FeatureModel.state_dict() + torch.save(states, model_out_path) + print("Checkpoint saved to {}".format(epoch)) + torch.save(Audio2FeatureModel.state_dict(), "checkpoints/audio.pkl") + +if __name__ == '__main__': + if len(sys.argv) < 2: + print("Usage: python train_lstm.py ") + sys.exit(1) # 参数数量不正确时退出程序 + + # 获取video_name参数 + data_path = sys.argv[1] + main(data_path) \ No newline at end of file diff --git a/video_concat.txt b/video_concat.txt new file mode 100644 index 0000000..dcdaac4 --- /dev/null +++ b/video_concat.txt @@ -0,0 +1,2 @@ +file 'front.mp4' +file 'back.mp4' \ No newline at end of file diff --git a/video_data/audio0.wav b/video_data/audio0.wav new file mode 100644 index 0000000..d879630 Binary files /dev/null and b/video_data/audio0.wav differ diff --git a/video_data/audio1.wav b/video_data/audio1.wav new file mode 100644 index 0000000..a6130f5 Binary files /dev/null and b/video_data/audio1.wav differ diff --git a/video_data/demo.mp4 b/video_data/demo.mp4 new file mode 100644 index 0000000..ba018f5 Binary files /dev/null and b/video_data/demo.mp4 differ diff --git a/video_data/test/circle.mp4 b/video_data/test/circle.mp4 new file mode 100644 index 0000000..8ee4930 Binary files /dev/null and b/video_data/test/circle.mp4 differ diff --git a/video_data/test/keypoint_rotate.pkl b/video_data/test/keypoint_rotate.pkl new file mode 100644 index 0000000..1719918 Binary files /dev/null and b/video_data/test/keypoint_rotate.pkl differ diff --git a/web_demo/server.py b/web_demo/server.py new file mode 100644 index 0000000..08f0f78 --- /dev/null +++ b/web_demo/server.py @@ -0,0 +1,27 @@ +import json +import requests +import time +from starlette.staticfiles import StaticFiles +from fastapi import FastAPI, Request +from fastapi.responses import JSONResponse +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import StreamingResponse +import uuid +import re +app = FastAPI() + +# 允许前端跨域调用 +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) +app.mount("/static", StaticFiles(directory="static"), name="static") + +# 启动Uvicorn服务器 +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8888) \ No newline at end of file diff --git a/web_demo/static/DHLiveMini.wasm b/web_demo/static/DHLiveMini.wasm new file mode 100644 index 0000000..b8ae5c8 Binary files /dev/null and b/web_demo/static/DHLiveMini.wasm differ diff --git a/web_demo/static/MiniLive.html b/web_demo/static/MiniLive.html new file mode 100644 index 0000000..da40bd8 --- /dev/null +++ b/web_demo/static/MiniLive.html @@ -0,0 +1,89 @@ + + + + + + + + MiniLive + + + + +
+ MiniMates: loading... +
+ + + + + + + + + + + +
+ + + + + + + \ No newline at end of file diff --git a/web_demo/static/assets/01.mp4 b/web_demo/static/assets/01.mp4 new file mode 100644 index 0000000..04452b4 Binary files /dev/null and b/web_demo/static/assets/01.mp4 differ diff --git a/web_demo/static/assets/bs_texture_halfFace.png b/web_demo/static/assets/bs_texture_halfFace.png new file mode 100644 index 0000000..f28a247 Binary files /dev/null and b/web_demo/static/assets/bs_texture_halfFace.png differ diff --git a/web_demo/static/assets/face3D.obj b/web_demo/static/assets/face3D.obj new file mode 100644 index 0000000..1831cf9 --- /dev/null +++ b/web_demo/static/assets/face3D.obj @@ -0,0 +1,696 @@ +v 498.817 737.983 -238.767 2.00 0 +v 503.928 697.292 -236.049 -1.00 1 +v 499.195 746.284 -236.865 2.00 2 +v 499.807 755.259 -230.745 2.00 3 +v 500.646 761.073 -222.339 2.00 4 +v 501.051 778.732 -217.698 2.01 5 +v 501.303 788.041 -219.775 2.01 6 +v 501.056 799.634 -221.794 2.01 7 +v 501.775 811.280 -216.869 2.01 8 +v 505.085 827.477 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i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift("..")}}return parts},normalize:path=>{var isAbsolute=PATH.isAbs(path),trailingSlash=path.substr(-1)==="/";path=PATH.normalizeArray(path.split("/").filter(p=>!!p),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path},dirname:path=>{var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:path=>{if(path==="/")return"/";path=PATH.normalize(path);path=path.replace(/\/$/,"");var lastSlash=path.lastIndexOf("/");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},join:function(){var paths=Array.prototype.slice.call(arguments);return PATH.normalize(paths.join("/"))},join2:(l,r)=>{return PATH.normalize(l+"/"+r)}};function getRandomDevice(){if(typeof crypto=="object"&&typeof crypto["getRandomValues"]=="function"){var randomBuffer=new Uint8Array(1);return()=>{crypto.getRandomValues(randomBuffer);return randomBuffer[0]}}else if(ENVIRONMENT_IS_NODE){try{var crypto_module=require("crypto");return()=>crypto_module["randomBytes"](1)[0]}catch(e){}}return()=>abort("randomDevice")}var PATH_FS={resolve:function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:FS.cwd();if(typeof path!="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){return""}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=PATH.isAbs(path)}resolvedPath=PATH.normalizeArray(resolvedPath.split("/").filter(p=>!!p),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."},relative:(from,to)=>{from=PATH_FS.resolve(from).substr(1);to=PATH_FS.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i0?length:lengthBytesUTF8(stringy)+1;var u8array=new Array(len);var numBytesWritten=stringToUTF8Array(stringy,u8array,0,u8array.length);if(dontAddNull)u8array.length=numBytesWritten;return u8array}var TTY={ttys:[],init:function(){},shutdown:function(){},register:function(dev,ops){TTY.ttys[dev]={input:[],output:[],ops:ops};FS.registerDevice(dev,TTY.stream_ops)},stream_ops:{open:function(stream){var tty=TTY.ttys[stream.node.rdev];if(!tty){throw new FS.ErrnoError(43)}stream.tty=tty;stream.seekable=false},close:function(stream){stream.tty.ops.fsync(stream.tty)},fsync:function(stream){stream.tty.ops.fsync(stream.tty)},read:function(stream,buffer,offset,length,pos){if(!stream.tty||!stream.tty.ops.get_char){throw new FS.ErrnoError(60)}var bytesRead=0;for(var i=0;i0){result=buf.slice(0,bytesRead).toString("utf-8")}else{result=null}}else if(typeof window!="undefined"&&typeof window.prompt=="function"){result=window.prompt("Input: ");if(result!==null){result+="\n"}}else if(typeof readline=="function"){result=readline();if(result!==null){result+="\n"}}if(!result){return null}tty.input=intArrayFromString(result,true)}return tty.input.shift()},put_char:function(tty,val){if(val===null||val===10){out(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},fsync:function(tty){if(tty.output&&tty.output.length>0){out(UTF8ArrayToString(tty.output,0));tty.output=[]}}},default_tty1_ops:{put_char:function(tty,val){if(val===null||val===10){err(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},fsync:function(tty){if(tty.output&&tty.output.length>0){err(UTF8ArrayToString(tty.output,0));tty.output=[]}}}};function zeroMemory(address,size){HEAPU8.fill(0,address,address+size);return address}function alignMemory(size,alignment){return Math.ceil(size/alignment)*alignment}function mmapAlloc(size){size=alignMemory(size,65536);var ptr=_emscripten_builtin_memalign(65536,size);if(!ptr)return 0;return zeroMemory(ptr,size)}var MEMFS={ops_table:null,mount:function(mount){return MEMFS.createNode(null,"/",16384|511,0)},createNode:function(parent,name,mode,dev){if(FS.isBlkdev(mode)||FS.isFIFO(mode)){throw new FS.ErrnoError(63)}if(!MEMFS.ops_table){MEMFS.ops_table={dir:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,lookup:MEMFS.node_ops.lookup,mknod:MEMFS.node_ops.mknod,rename:MEMFS.node_ops.rename,unlink:MEMFS.node_ops.unlink,rmdir:MEMFS.node_ops.rmdir,readdir:MEMFS.node_ops.readdir,symlink:MEMFS.node_ops.symlink},stream:{llseek:MEMFS.stream_ops.llseek}},file:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:{llseek:MEMFS.stream_ops.llseek,read:MEMFS.stream_ops.read,write:MEMFS.stream_ops.write,allocate:MEMFS.stream_ops.allocate,mmap:MEMFS.stream_ops.mmap,msync:MEMFS.stream_ops.msync}},link:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,readlink:MEMFS.node_ops.readlink},stream:{}},chrdev:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:FS.chrdev_stream_ops}}}var node=FS.createNode(parent,name,mode,dev);if(FS.isDir(node.mode)){node.node_ops=MEMFS.ops_table.dir.node;node.stream_ops=MEMFS.ops_table.dir.stream;node.contents={}}else if(FS.isFile(node.mode)){node.node_ops=MEMFS.ops_table.file.node;node.stream_ops=MEMFS.ops_table.file.stream;node.usedBytes=0;node.contents=null}else if(FS.isLink(node.mode)){node.node_ops=MEMFS.ops_table.link.node;node.stream_ops=MEMFS.ops_table.link.stream}else if(FS.isChrdev(node.mode)){node.node_ops=MEMFS.ops_table.chrdev.node;node.stream_ops=MEMFS.ops_table.chrdev.stream}node.timestamp=Date.now();if(parent){parent.contents[name]=node;parent.timestamp=node.timestamp}return node},getFileDataAsTypedArray:function(node){if(!node.contents)return new Uint8Array(0);if(node.contents.subarray)return node.contents.subarray(0,node.usedBytes);return new Uint8Array(node.contents)},expandFileStorage:function(node,newCapacity){var prevCapacity=node.contents?node.contents.length:0;if(prevCapacity>=newCapacity)return;var CAPACITY_DOUBLING_MAX=1024*1024;newCapacity=Math.max(newCapacity,prevCapacity*(prevCapacity>>0);if(prevCapacity!=0)newCapacity=Math.max(newCapacity,256);var oldContents=node.contents;node.contents=new Uint8Array(newCapacity);if(node.usedBytes>0)node.contents.set(oldContents.subarray(0,node.usedBytes),0)},resizeFileStorage:function(node,newSize){if(node.usedBytes==newSize)return;if(newSize==0){node.contents=null;node.usedBytes=0}else{var oldContents=node.contents;node.contents=new Uint8Array(newSize);if(oldContents){node.contents.set(oldContents.subarray(0,Math.min(newSize,node.usedBytes)))}node.usedBytes=newSize}},node_ops:{getattr:function(node){var attr={};attr.dev=FS.isChrdev(node.mode)?node.id:1;attr.ino=node.id;attr.mode=node.mode;attr.nlink=1;attr.uid=0;attr.gid=0;attr.rdev=node.rdev;if(FS.isDir(node.mode)){attr.size=4096}else if(FS.isFile(node.mode)){attr.size=node.usedBytes}else if(FS.isLink(node.mode)){attr.size=node.link.length}else{attr.size=0}attr.atime=new Date(node.timestamp);attr.mtime=new Date(node.timestamp);attr.ctime=new Date(node.timestamp);attr.blksize=4096;attr.blocks=Math.ceil(attr.size/attr.blksize);return attr},setattr:function(node,attr){if(attr.mode!==undefined){node.mode=attr.mode}if(attr.timestamp!==undefined){node.timestamp=attr.timestamp}if(attr.size!==undefined){MEMFS.resizeFileStorage(node,attr.size)}},lookup:function(parent,name){throw FS.genericErrors[44]},mknod:function(parent,name,mode,dev){return MEMFS.createNode(parent,name,mode,dev)},rename:function(old_node,new_dir,new_name){if(FS.isDir(old_node.mode)){var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(new_node){for(var i in new_node.contents){throw new FS.ErrnoError(55)}}}delete old_node.parent.contents[old_node.name];old_node.parent.timestamp=Date.now();old_node.name=new_name;new_dir.contents[new_name]=old_node;new_dir.timestamp=old_node.parent.timestamp;old_node.parent=new_dir},unlink:function(parent,name){delete parent.contents[name];parent.timestamp=Date.now()},rmdir:function(parent,name){var node=FS.lookupNode(parent,name);for(var i in node.contents){throw new FS.ErrnoError(55)}delete parent.contents[name];parent.timestamp=Date.now()},readdir:function(node){var entries=[".",".."];for(var key in node.contents){if(!node.contents.hasOwnProperty(key)){continue}entries.push(key)}return entries},symlink:function(parent,newname,oldpath){var node=MEMFS.createNode(parent,newname,511|40960,0);node.link=oldpath;return node},readlink:function(node){if(!FS.isLink(node.mode)){throw new FS.ErrnoError(28)}return node.link}},stream_ops:{read:function(stream,buffer,offset,length,position){var contents=stream.node.contents;if(position>=stream.node.usedBytes)return 0;var size=Math.min(stream.node.usedBytes-position,length);if(size>8&&contents.subarray){buffer.set(contents.subarray(position,position+size),offset)}else{for(var i=0;i0||position+length{assert(arrayBuffer,'Loading data file "'+url+'" failed (no arrayBuffer).');onload(new Uint8Array(arrayBuffer));if(dep)removeRunDependency(dep)},event=>{if(onerror){onerror()}else{throw'Loading data file "'+url+'" failed.'}});if(dep)addRunDependency(dep)}var FS={root:null,mounts:[],devices:{},streams:[],nextInode:1,nameTable:null,currentPath:"/",initialized:false,ignorePermissions:true,ErrnoError:null,genericErrors:{},filesystems:null,syncFSRequests:0,lookupPath:(path,opts={})=>{path=PATH_FS.resolve(path);if(!path)return{path:"",node:null};var defaults={follow_mount:true,recurse_count:0};opts=Object.assign(defaults,opts);if(opts.recurse_count>8){throw new FS.ErrnoError(32)}var parts=path.split("/").filter(p=>!!p);var current=FS.root;var current_path="/";for(var i=0;i40){throw new FS.ErrnoError(32)}}}}return{path:current_path,node:current}},getPath:node=>{var path;while(true){if(FS.isRoot(node)){var mount=node.mount.mountpoint;if(!path)return mount;return mount[mount.length-1]!=="/"?mount+"/"+path:mount+path}path=path?node.name+"/"+path:node.name;node=node.parent}},hashName:(parentid,name)=>{var hash=0;for(var i=0;i>>0)%FS.nameTable.length},hashAddNode:node=>{var hash=FS.hashName(node.parent.id,node.name);node.name_next=FS.nameTable[hash];FS.nameTable[hash]=node},hashRemoveNode:node=>{var hash=FS.hashName(node.parent.id,node.name);if(FS.nameTable[hash]===node){FS.nameTable[hash]=node.name_next}else{var current=FS.nameTable[hash];while(current){if(current.name_next===node){current.name_next=node.name_next;break}current=current.name_next}}},lookupNode:(parent,name)=>{var errCode=FS.mayLookup(parent);if(errCode){throw new FS.ErrnoError(errCode,parent)}var hash=FS.hashName(parent.id,name);for(var node=FS.nameTable[hash];node;node=node.name_next){var nodeName=node.name;if(node.parent.id===parent.id&&nodeName===name){return node}}return FS.lookup(parent,name)},createNode:(parent,name,mode,rdev)=>{var node=new FS.FSNode(parent,name,mode,rdev);FS.hashAddNode(node);return node},destroyNode:node=>{FS.hashRemoveNode(node)},isRoot:node=>{return node===node.parent},isMountpoint:node=>{return!!node.mounted},isFile:mode=>{return(mode&61440)===32768},isDir:mode=>{return(mode&61440)===16384},isLink:mode=>{return(mode&61440)===40960},isChrdev:mode=>{return(mode&61440)===8192},isBlkdev:mode=>{return(mode&61440)===24576},isFIFO:mode=>{return(mode&61440)===4096},isSocket:mode=>{return(mode&49152)===49152},flagModes:{"r":0,"r+":2,"w":577,"w+":578,"a":1089,"a+":1090},modeStringToFlags:str=>{var flags=FS.flagModes[str];if(typeof flags=="undefined"){throw new Error("Unknown file open mode: "+str)}return flags},flagsToPermissionString:flag=>{var perms=["r","w","rw"][flag&3];if(flag&512){perms+="w"}return perms},nodePermissions:(node,perms)=>{if(FS.ignorePermissions){return 0}if(perms.includes("r")&&!(node.mode&292)){return 2}else if(perms.includes("w")&&!(node.mode&146)){return 2}else if(perms.includes("x")&&!(node.mode&73)){return 2}return 0},mayLookup:dir=>{var errCode=FS.nodePermissions(dir,"x");if(errCode)return errCode;if(!dir.node_ops.lookup)return 2;return 0},mayCreate:(dir,name)=>{try{var node=FS.lookupNode(dir,name);return 20}catch(e){}return FS.nodePermissions(dir,"wx")},mayDelete:(dir,name,isdir)=>{var node;try{node=FS.lookupNode(dir,name)}catch(e){return e.errno}var errCode=FS.nodePermissions(dir,"wx");if(errCode){return errCode}if(isdir){if(!FS.isDir(node.mode)){return 54}if(FS.isRoot(node)||FS.getPath(node)===FS.cwd()){return 10}}else{if(FS.isDir(node.mode)){return 31}}return 0},mayOpen:(node,flags)=>{if(!node){return 44}if(FS.isLink(node.mode)){return 32}else if(FS.isDir(node.mode)){if(FS.flagsToPermissionString(flags)!=="r"||flags&512){return 31}}return FS.nodePermissions(node,FS.flagsToPermissionString(flags))},MAX_OPEN_FDS:4096,nextfd:(fd_start=0,fd_end=FS.MAX_OPEN_FDS)=>{for(var fd=fd_start;fd<=fd_end;fd++){if(!FS.streams[fd]){return fd}}throw new FS.ErrnoError(33)},getStream:fd=>FS.streams[fd],createStream:(stream,fd_start,fd_end)=>{if(!FS.FSStream){FS.FSStream=function(){this.shared={}};FS.FSStream.prototype={};Object.defineProperties(FS.FSStream.prototype,{object:{get:function(){return this.node},set:function(val){this.node=val}},isRead:{get:function(){return(this.flags&2097155)!==1}},isWrite:{get:function(){return(this.flags&2097155)!==0}},isAppend:{get:function(){return this.flags&1024}},flags:{get:function(){return this.shared.flags},set:function(val){this.shared.flags=val}},position:{get:function(){return this.shared.position},set:function(val){this.shared.position=val}}})}stream=Object.assign(new FS.FSStream,stream);var fd=FS.nextfd(fd_start,fd_end);stream.fd=fd;FS.streams[fd]=stream;return stream},closeStream:fd=>{FS.streams[fd]=null},chrdev_stream_ops:{open:stream=>{var device=FS.getDevice(stream.node.rdev);stream.stream_ops=device.stream_ops;if(stream.stream_ops.open){stream.stream_ops.open(stream)}},llseek:()=>{throw new FS.ErrnoError(70)}},major:dev=>dev>>8,minor:dev=>dev&255,makedev:(ma,mi)=>ma<<8|mi,registerDevice:(dev,ops)=>{FS.devices[dev]={stream_ops:ops}},getDevice:dev=>FS.devices[dev],getMounts:mount=>{var mounts=[];var check=[mount];while(check.length){var m=check.pop();mounts.push(m);check.push.apply(check,m.mounts)}return mounts},syncfs:(populate,callback)=>{if(typeof populate=="function"){callback=populate;populate=false}FS.syncFSRequests++;if(FS.syncFSRequests>1){err("warning: "+FS.syncFSRequests+" FS.syncfs operations in flight at once, probably just doing extra work")}var mounts=FS.getMounts(FS.root.mount);var completed=0;function doCallback(errCode){FS.syncFSRequests--;return callback(errCode)}function done(errCode){if(errCode){if(!done.errored){done.errored=true;return doCallback(errCode)}return}if(++completed>=mounts.length){doCallback(null)}}mounts.forEach(mount=>{if(!mount.type.syncfs){return done(null)}mount.type.syncfs(mount,populate,done)})},mount:(type,opts,mountpoint)=>{var root=mountpoint==="/";var pseudo=!mountpoint;var node;if(root&&FS.root){throw new FS.ErrnoError(10)}else if(!root&&!pseudo){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});mountpoint=lookup.path;node=lookup.node;if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}if(!FS.isDir(node.mode)){throw new FS.ErrnoError(54)}}var mount={type:type,opts:opts,mountpoint:mountpoint,mounts:[]};var mountRoot=type.mount(mount);mountRoot.mount=mount;mount.root=mountRoot;if(root){FS.root=mountRoot}else if(node){node.mounted=mount;if(node.mount){node.mount.mounts.push(mount)}}return mountRoot},unmount:mountpoint=>{var lookup=FS.lookupPath(mountpoint,{follow_mount:false});if(!FS.isMountpoint(lookup.node)){throw new FS.ErrnoError(28)}var node=lookup.node;var mount=node.mounted;var mounts=FS.getMounts(mount);Object.keys(FS.nameTable).forEach(hash=>{var current=FS.nameTable[hash];while(current){var next=current.name_next;if(mounts.includes(current.mount)){FS.destroyNode(current)}current=next}});node.mounted=null;var idx=node.mount.mounts.indexOf(mount);node.mount.mounts.splice(idx,1)},lookup:(parent,name)=>{return parent.node_ops.lookup(parent,name)},mknod:(path,mode,dev)=>{var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);if(!name||name==="."||name===".."){throw new FS.ErrnoError(28)}var errCode=FS.mayCreate(parent,name);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.mknod){throw new FS.ErrnoError(63)}return parent.node_ops.mknod(parent,name,mode,dev)},create:(path,mode)=>{mode=mode!==undefined?mode:438;mode&=4095;mode|=32768;return FS.mknod(path,mode,0)},mkdir:(path,mode)=>{mode=mode!==undefined?mode:511;mode&=511|512;mode|=16384;return FS.mknod(path,mode,0)},mkdirTree:(path,mode)=>{var dirs=path.split("/");var d="";for(var i=0;i{if(typeof dev=="undefined"){dev=mode;mode=438}mode|=8192;return FS.mknod(path,mode,dev)},symlink:(oldpath,newpath)=>{if(!PATH_FS.resolve(oldpath)){throw new FS.ErrnoError(44)}var lookup=FS.lookupPath(newpath,{parent:true});var parent=lookup.node;if(!parent){throw new FS.ErrnoError(44)}var newname=PATH.basename(newpath);var errCode=FS.mayCreate(parent,newname);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.symlink){throw new FS.ErrnoError(63)}return parent.node_ops.symlink(parent,newname,oldpath)},rename:(old_path,new_path)=>{var old_dirname=PATH.dirname(old_path);var new_dirname=PATH.dirname(new_path);var old_name=PATH.basename(old_path);var new_name=PATH.basename(new_path);var lookup,old_dir,new_dir;lookup=FS.lookupPath(old_path,{parent:true});old_dir=lookup.node;lookup=FS.lookupPath(new_path,{parent:true});new_dir=lookup.node;if(!old_dir||!new_dir)throw new FS.ErrnoError(44);if(old_dir.mount!==new_dir.mount){throw new FS.ErrnoError(75)}var old_node=FS.lookupNode(old_dir,old_name);var relative=PATH_FS.relative(old_path,new_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(28)}relative=PATH_FS.relative(new_path,old_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(55)}var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(old_node===new_node){return}var isdir=FS.isDir(old_node.mode);var errCode=FS.mayDelete(old_dir,old_name,isdir);if(errCode){throw new FS.ErrnoError(errCode)}errCode=new_node?FS.mayDelete(new_dir,new_name,isdir):FS.mayCreate(new_dir,new_name);if(errCode){throw new FS.ErrnoError(errCode)}if(!old_dir.node_ops.rename){throw new FS.ErrnoError(63)}if(FS.isMountpoint(old_node)||new_node&&FS.isMountpoint(new_node)){throw new FS.ErrnoError(10)}if(new_dir!==old_dir){errCode=FS.nodePermissions(old_dir,"w");if(errCode){throw new FS.ErrnoError(errCode)}}FS.hashRemoveNode(old_node);try{old_dir.node_ops.rename(old_node,new_dir,new_name)}catch(e){throw e}finally{FS.hashAddNode(old_node)}},rmdir:path=>{var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var errCode=FS.mayDelete(parent,name,true);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.rmdir){throw new FS.ErrnoError(63)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}parent.node_ops.rmdir(parent,name);FS.destroyNode(node)},readdir:path=>{var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;if(!node.node_ops.readdir){throw new FS.ErrnoError(54)}return node.node_ops.readdir(node)},unlink:path=>{var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;if(!parent){throw new FS.ErrnoError(44)}var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var errCode=FS.mayDelete(parent,name,false);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.unlink){throw new FS.ErrnoError(63)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}parent.node_ops.unlink(parent,name);FS.destroyNode(node)},readlink:path=>{var lookup=FS.lookupPath(path);var link=lookup.node;if(!link){throw new FS.ErrnoError(44)}if(!link.node_ops.readlink){throw new FS.ErrnoError(28)}return PATH_FS.resolve(FS.getPath(link.parent),link.node_ops.readlink(link))},stat:(path,dontFollow)=>{var lookup=FS.lookupPath(path,{follow:!dontFollow});var node=lookup.node;if(!node){throw new FS.ErrnoError(44)}if(!node.node_ops.getattr){throw new FS.ErrnoError(63)}return node.node_ops.getattr(node)},lstat:path=>{return FS.stat(path,true)},chmod:(path,mode,dontFollow)=>{var node;if(typeof path=="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}node.node_ops.setattr(node,{mode:mode&4095|node.mode&~4095,timestamp:Date.now()})},lchmod:(path,mode)=>{FS.chmod(path,mode,true)},fchmod:(fd,mode)=>{var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}FS.chmod(stream.node,mode)},chown:(path,uid,gid,dontFollow)=>{var node;if(typeof path=="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}node.node_ops.setattr(node,{timestamp:Date.now()})},lchown:(path,uid,gid)=>{FS.chown(path,uid,gid,true)},fchown:(fd,uid,gid)=>{var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}FS.chown(stream.node,uid,gid)},truncate:(path,len)=>{if(len<0){throw new FS.ErrnoError(28)}var node;if(typeof path=="string"){var lookup=FS.lookupPath(path,{follow:true});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}if(FS.isDir(node.mode)){throw new FS.ErrnoError(31)}if(!FS.isFile(node.mode)){throw new FS.ErrnoError(28)}var errCode=FS.nodePermissions(node,"w");if(errCode){throw new FS.ErrnoError(errCode)}node.node_ops.setattr(node,{size:len,timestamp:Date.now()})},ftruncate:(fd,len)=>{var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(28)}FS.truncate(stream.node,len)},utime:(path,atime,mtime)=>{var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;node.node_ops.setattr(node,{timestamp:Math.max(atime,mtime)})},open:(path,flags,mode)=>{if(path===""){throw new FS.ErrnoError(44)}flags=typeof flags=="string"?FS.modeStringToFlags(flags):flags;mode=typeof mode=="undefined"?438:mode;if(flags&64){mode=mode&4095|32768}else{mode=0}var node;if(typeof path=="object"){node=path}else{path=PATH.normalize(path);try{var lookup=FS.lookupPath(path,{follow:!(flags&131072)});node=lookup.node}catch(e){}}var created=false;if(flags&64){if(node){if(flags&128){throw new FS.ErrnoError(20)}}else{node=FS.mknod(path,mode,0);created=true}}if(!node){throw new FS.ErrnoError(44)}if(FS.isChrdev(node.mode)){flags&=~512}if(flags&65536&&!FS.isDir(node.mode)){throw new FS.ErrnoError(54)}if(!created){var errCode=FS.mayOpen(node,flags);if(errCode){throw new FS.ErrnoError(errCode)}}if(flags&512&&!created){FS.truncate(node,0)}flags&=~(128|512|131072);var stream=FS.createStream({node:node,path:FS.getPath(node),flags:flags,seekable:true,position:0,stream_ops:node.stream_ops,ungotten:[],error:false});if(stream.stream_ops.open){stream.stream_ops.open(stream)}if(Module["logReadFiles"]&&!(flags&1)){if(!FS.readFiles)FS.readFiles={};if(!(path in FS.readFiles)){FS.readFiles[path]=1}}return stream},close:stream=>{if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(stream.getdents)stream.getdents=null;try{if(stream.stream_ops.close){stream.stream_ops.close(stream)}}catch(e){throw e}finally{FS.closeStream(stream.fd)}stream.fd=null},isClosed:stream=>{return stream.fd===null},llseek:(stream,offset,whence)=>{if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(!stream.seekable||!stream.stream_ops.llseek){throw new FS.ErrnoError(70)}if(whence!=0&&whence!=1&&whence!=2){throw new FS.ErrnoError(28)}stream.position=stream.stream_ops.llseek(stream,offset,whence);stream.ungotten=[];return stream.position},read:(stream,buffer,offset,length,position)=>{if(length<0||position<0){throw new FS.ErrnoError(28)}if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===1){throw new FS.ErrnoError(8)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(31)}if(!stream.stream_ops.read){throw new FS.ErrnoError(28)}var seeking=typeof position!="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(70)}var bytesRead=stream.stream_ops.read(stream,buffer,offset,length,position);if(!seeking)stream.position+=bytesRead;return bytesRead},write:(stream,buffer,offset,length,position,canOwn)=>{if(length<0||position<0){throw new FS.ErrnoError(28)}if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(8)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(31)}if(!stream.stream_ops.write){throw new FS.ErrnoError(28)}if(stream.seekable&&stream.flags&1024){FS.llseek(stream,0,2)}var seeking=typeof position!="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(70)}var bytesWritten=stream.stream_ops.write(stream,buffer,offset,length,position,canOwn);if(!seeking)stream.position+=bytesWritten;return bytesWritten},allocate:(stream,offset,length)=>{if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(offset<0||length<=0){throw new FS.ErrnoError(28)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(8)}if(!FS.isFile(stream.node.mode)&&!FS.isDir(stream.node.mode)){throw new FS.ErrnoError(43)}if(!stream.stream_ops.allocate){throw new FS.ErrnoError(138)}stream.stream_ops.allocate(stream,offset,length)},mmap:(stream,length,position,prot,flags)=>{if((prot&2)!==0&&(flags&2)===0&&(stream.flags&2097155)!==2){throw new FS.ErrnoError(2)}if((stream.flags&2097155)===1){throw new FS.ErrnoError(2)}if(!stream.stream_ops.mmap){throw new FS.ErrnoError(43)}return stream.stream_ops.mmap(stream,length,position,prot,flags)},msync:(stream,buffer,offset,length,mmapFlags)=>{if(!stream.stream_ops.msync){return 0}return stream.stream_ops.msync(stream,buffer,offset,length,mmapFlags)},munmap:stream=>0,ioctl:(stream,cmd,arg)=>{if(!stream.stream_ops.ioctl){throw new FS.ErrnoError(59)}return stream.stream_ops.ioctl(stream,cmd,arg)},readFile:(path,opts={})=>{opts.flags=opts.flags||0;opts.encoding=opts.encoding||"binary";if(opts.encoding!=="utf8"&&opts.encoding!=="binary"){throw new Error('Invalid encoding type "'+opts.encoding+'"')}var ret;var stream=FS.open(path,opts.flags);var stat=FS.stat(path);var length=stat.size;var buf=new Uint8Array(length);FS.read(stream,buf,0,length,0);if(opts.encoding==="utf8"){ret=UTF8ArrayToString(buf,0)}else if(opts.encoding==="binary"){ret=buf}FS.close(stream);return ret},writeFile:(path,data,opts={})=>{opts.flags=opts.flags||577;var stream=FS.open(path,opts.flags,opts.mode);if(typeof data=="string"){var buf=new Uint8Array(lengthBytesUTF8(data)+1);var actualNumBytes=stringToUTF8Array(data,buf,0,buf.length);FS.write(stream,buf,0,actualNumBytes,undefined,opts.canOwn)}else if(ArrayBuffer.isView(data)){FS.write(stream,data,0,data.byteLength,undefined,opts.canOwn)}else{throw new Error("Unsupported data type")}FS.close(stream)},cwd:()=>FS.currentPath,chdir:path=>{var lookup=FS.lookupPath(path,{follow:true});if(lookup.node===null){throw new FS.ErrnoError(44)}if(!FS.isDir(lookup.node.mode)){throw new FS.ErrnoError(54)}var errCode=FS.nodePermissions(lookup.node,"x");if(errCode){throw new FS.ErrnoError(errCode)}FS.currentPath=lookup.path},createDefaultDirectories:()=>{FS.mkdir("/tmp");FS.mkdir("/home");FS.mkdir("/home/web_user")},createDefaultDevices:()=>{FS.mkdir("/dev");FS.registerDevice(FS.makedev(1,3),{read:()=>0,write:(stream,buffer,offset,length,pos)=>length});FS.mkdev("/dev/null",FS.makedev(1,3));TTY.register(FS.makedev(5,0),TTY.default_tty_ops);TTY.register(FS.makedev(6,0),TTY.default_tty1_ops);FS.mkdev("/dev/tty",FS.makedev(5,0));FS.mkdev("/dev/tty1",FS.makedev(6,0));var random_device=getRandomDevice();FS.createDevice("/dev","random",random_device);FS.createDevice("/dev","urandom",random_device);FS.mkdir("/dev/shm");FS.mkdir("/dev/shm/tmp")},createSpecialDirectories:()=>{FS.mkdir("/proc");var proc_self=FS.mkdir("/proc/self");FS.mkdir("/proc/self/fd");FS.mount({mount:()=>{var node=FS.createNode(proc_self,"fd",16384|511,73);node.node_ops={lookup:(parent,name)=>{var fd=+name;var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);var ret={parent:null,mount:{mountpoint:"fake"},node_ops:{readlink:()=>stream.path}};ret.parent=ret;return ret}};return node}},{},"/proc/self/fd")},createStandardStreams:()=>{if(Module["stdin"]){FS.createDevice("/dev","stdin",Module["stdin"])}else{FS.symlink("/dev/tty","/dev/stdin")}if(Module["stdout"]){FS.createDevice("/dev","stdout",null,Module["stdout"])}else{FS.symlink("/dev/tty","/dev/stdout")}if(Module["stderr"]){FS.createDevice("/dev","stderr",null,Module["stderr"])}else{FS.symlink("/dev/tty1","/dev/stderr")}var stdin=FS.open("/dev/stdin",0);var stdout=FS.open("/dev/stdout",1);var stderr=FS.open("/dev/stderr",1)},ensureErrnoError:()=>{if(FS.ErrnoError)return;FS.ErrnoError=function ErrnoError(errno,node){this.node=node;this.setErrno=function(errno){this.errno=errno};this.setErrno(errno);this.message="FS error"};FS.ErrnoError.prototype=new Error;FS.ErrnoError.prototype.constructor=FS.ErrnoError;[44].forEach(code=>{FS.genericErrors[code]=new FS.ErrnoError(code);FS.genericErrors[code].stack=""})},staticInit:()=>{FS.ensureErrnoError();FS.nameTable=new Array(4096);FS.mount(MEMFS,{},"/");FS.createDefaultDirectories();FS.createDefaultDevices();FS.createSpecialDirectories();FS.filesystems={"MEMFS":MEMFS}},init:(input,output,error)=>{FS.init.initialized=true;FS.ensureErrnoError();Module["stdin"]=input||Module["stdin"];Module["stdout"]=output||Module["stdout"];Module["stderr"]=error||Module["stderr"];FS.createStandardStreams()},quit:()=>{FS.init.initialized=false;for(var i=0;i{var mode=0;if(canRead)mode|=292|73;if(canWrite)mode|=146;return mode},findObject:(path,dontResolveLastLink)=>{var ret=FS.analyzePath(path,dontResolveLastLink);if(!ret.exists){return null}return ret.object},analyzePath:(path,dontResolveLastLink)=>{try{var lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});path=lookup.path}catch(e){}var ret={isRoot:false,exists:false,error:0,name:null,path:null,object:null,parentExists:false,parentPath:null,parentObject:null};try{var lookup=FS.lookupPath(path,{parent:true});ret.parentExists=true;ret.parentPath=lookup.path;ret.parentObject=lookup.node;ret.name=PATH.basename(path);lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});ret.exists=true;ret.path=lookup.path;ret.object=lookup.node;ret.name=lookup.node.name;ret.isRoot=lookup.path==="/"}catch(e){ret.error=e.errno}return ret},createPath:(parent,path,canRead,canWrite)=>{parent=typeof parent=="string"?parent:FS.getPath(parent);var parts=path.split("/").reverse();while(parts.length){var part=parts.pop();if(!part)continue;var current=PATH.join2(parent,part);try{FS.mkdir(current)}catch(e){}parent=current}return current},createFile:(parent,name,properties,canRead,canWrite)=>{var path=PATH.join2(typeof parent=="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(canRead,canWrite);return FS.create(path,mode)},createDataFile:(parent,name,data,canRead,canWrite,canOwn)=>{var path=name;if(parent){parent=typeof parent=="string"?parent:FS.getPath(parent);path=name?PATH.join2(parent,name):parent}var mode=FS.getMode(canRead,canWrite);var node=FS.create(path,mode);if(data){if(typeof data=="string"){var arr=new Array(data.length);for(var i=0,len=data.length;i{var path=PATH.join2(typeof parent=="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(!!input,!!output);if(!FS.createDevice.major)FS.createDevice.major=64;var dev=FS.makedev(FS.createDevice.major++,0);FS.registerDevice(dev,{open:stream=>{stream.seekable=false},close:stream=>{if(output&&output.buffer&&output.buffer.length){output(10)}},read:(stream,buffer,offset,length,pos)=>{var bytesRead=0;for(var i=0;i{for(var i=0;i{if(obj.isDevice||obj.isFolder||obj.link||obj.contents)return true;if(typeof XMLHttpRequest!="undefined"){throw new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread.")}else if(read_){try{obj.contents=intArrayFromString(read_(obj.url),true);obj.usedBytes=obj.contents.length}catch(e){throw new FS.ErrnoError(29)}}else{throw new Error("Cannot load without read() or XMLHttpRequest.")}},createLazyFile:(parent,name,url,canRead,canWrite)=>{function LazyUint8Array(){this.lengthKnown=false;this.chunks=[]}LazyUint8Array.prototype.get=function LazyUint8Array_get(idx){if(idx>this.length-1||idx<0){return undefined}var chunkOffset=idx%this.chunkSize;var chunkNum=idx/this.chunkSize|0;return this.getter(chunkNum)[chunkOffset]};LazyUint8Array.prototype.setDataGetter=function LazyUint8Array_setDataGetter(getter){this.getter=getter};LazyUint8Array.prototype.cacheLength=function LazyUint8Array_cacheLength(){var xhr=new XMLHttpRequest;xhr.open("HEAD",url,false);xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);var datalength=Number(xhr.getResponseHeader("Content-length"));var header;var hasByteServing=(header=xhr.getResponseHeader("Accept-Ranges"))&&header==="bytes";var usesGzip=(header=xhr.getResponseHeader("Content-Encoding"))&&header==="gzip";var chunkSize=1024*1024;if(!hasByteServing)chunkSize=datalength;var doXHR=(from,to)=>{if(from>to)throw new Error("invalid range ("+from+", "+to+") or no bytes requested!");if(to>datalength-1)throw new Error("only "+datalength+" bytes available! programmer error!");var xhr=new XMLHttpRequest;xhr.open("GET",url,false);if(datalength!==chunkSize)xhr.setRequestHeader("Range","bytes="+from+"-"+to);xhr.responseType="arraybuffer";if(xhr.overrideMimeType){xhr.overrideMimeType("text/plain; charset=x-user-defined")}xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);if(xhr.response!==undefined){return new Uint8Array(xhr.response||[])}return intArrayFromString(xhr.responseText||"",true)};var lazyArray=this;lazyArray.setDataGetter(chunkNum=>{var start=chunkNum*chunkSize;var end=(chunkNum+1)*chunkSize-1;end=Math.min(end,datalength-1);if(typeof lazyArray.chunks[chunkNum]=="undefined"){lazyArray.chunks[chunkNum]=doXHR(start,end)}if(typeof lazyArray.chunks[chunkNum]=="undefined")throw new Error("doXHR failed!");return lazyArray.chunks[chunkNum]});if(usesGzip||!datalength){chunkSize=datalength=1;datalength=this.getter(0).length;chunkSize=datalength;out("LazyFiles on gzip forces download of the whole file when length is accessed")}this._length=datalength;this._chunkSize=chunkSize;this.lengthKnown=true};if(typeof XMLHttpRequest!="undefined"){if(!ENVIRONMENT_IS_WORKER)throw"Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";var lazyArray=new LazyUint8Array;Object.defineProperties(lazyArray,{length:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._length}},chunkSize:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._chunkSize}}});var properties={isDevice:false,contents:lazyArray}}else{var properties={isDevice:false,url:url}}var node=FS.createFile(parent,name,properties,canRead,canWrite);if(properties.contents){node.contents=properties.contents}else if(properties.url){node.contents=null;node.url=properties.url}Object.defineProperties(node,{usedBytes:{get:function(){return this.contents.length}}});var stream_ops={};var keys=Object.keys(node.stream_ops);keys.forEach(key=>{var fn=node.stream_ops[key];stream_ops[key]=function forceLoadLazyFile(){FS.forceLoadFile(node);return fn.apply(null,arguments)}});function writeChunks(stream,buffer,offset,length,position){var contents=stream.node.contents;if(position>=contents.length)return 0;var size=Math.min(contents.length-position,length);if(contents.slice){for(var i=0;i{FS.forceLoadFile(node);return writeChunks(stream,buffer,offset,length,position)};stream_ops.mmap=(stream,length,position,prot,flags)=>{FS.forceLoadFile(node);var ptr=mmapAlloc(length);if(!ptr){throw new FS.ErrnoError(48)}writeChunks(stream,HEAP8,ptr,length,position);return{ptr:ptr,allocated:true}};node.stream_ops=stream_ops;return node},createPreloadedFile:(parent,name,url,canRead,canWrite,onload,onerror,dontCreateFile,canOwn,preFinish)=>{var fullname=name?PATH_FS.resolve(PATH.join2(parent,name)):parent;var dep=getUniqueRunDependency("cp "+fullname);function processData(byteArray){function finish(byteArray){if(preFinish)preFinish();if(!dontCreateFile){FS.createDataFile(parent,name,byteArray,canRead,canWrite,canOwn)}if(onload)onload();removeRunDependency(dep)}if(Browser.handledByPreloadPlugin(byteArray,fullname,finish,()=>{if(onerror)onerror();removeRunDependency(dep)})){return}finish(byteArray)}addRunDependency(dep);if(typeof url=="string"){asyncLoad(url,byteArray=>processData(byteArray),onerror)}else{processData(url)}},indexedDB:()=>{return window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB},DB_NAME:()=>{return"EM_FS_"+window.location.pathname},DB_VERSION:20,DB_STORE_NAME:"FILE_DATA",saveFilesToDB:(paths,onload,onerror)=>{onload=onload||(()=>{});onerror=onerror||(()=>{});var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=()=>{out("creating db");var db=openRequest.result;db.createObjectStore(FS.DB_STORE_NAME)};openRequest.onsuccess=()=>{var db=openRequest.result;var transaction=db.transaction([FS.DB_STORE_NAME],"readwrite");var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach(path=>{var putRequest=files.put(FS.analyzePath(path).object.contents,path);putRequest.onsuccess=()=>{ok++;if(ok+fail==total)finish()};putRequest.onerror=()=>{fail++;if(ok+fail==total)finish()}});transaction.onerror=onerror};openRequest.onerror=onerror},loadFilesFromDB:(paths,onload,onerror)=>{onload=onload||(()=>{});onerror=onerror||(()=>{});var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=onerror;openRequest.onsuccess=()=>{var db=openRequest.result;try{var transaction=db.transaction([FS.DB_STORE_NAME],"readonly")}catch(e){onerror(e);return}var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach(path=>{var getRequest=files.get(path);getRequest.onsuccess=()=>{if(FS.analyzePath(path).exists){FS.unlink(path)}FS.createDataFile(PATH.dirname(path),PATH.basename(path),getRequest.result,true,true,true);ok++;if(ok+fail==total)finish()};getRequest.onerror=()=>{fail++;if(ok+fail==total)finish()}});transaction.onerror=onerror};openRequest.onerror=onerror}};var SYSCALLS={DEFAULT_POLLMASK:5,calculateAt:function(dirfd,path,allowEmpty){if(PATH.isAbs(path)){return path}var dir;if(dirfd===-100){dir=FS.cwd()}else{var dirstream=SYSCALLS.getStreamFromFD(dirfd);dir=dirstream.path}if(path.length==0){if(!allowEmpty){throw new FS.ErrnoError(44)}return dir}return PATH.join2(dir,path)},doStat:function(func,path,buf){try{var stat=func(path)}catch(e){if(e&&e.node&&PATH.normalize(path)!==PATH.normalize(FS.getPath(e.node))){return-54}throw e}HEAP32[buf>>2]=stat.dev;HEAP32[buf+8>>2]=stat.ino;HEAP32[buf+12>>2]=stat.mode;HEAPU32[buf+16>>2]=stat.nlink;HEAP32[buf+20>>2]=stat.uid;HEAP32[buf+24>>2]=stat.gid;HEAP32[buf+28>>2]=stat.rdev;tempI64=[stat.size>>>0,(tempDouble=stat.size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+40>>2]=tempI64[0],HEAP32[buf+44>>2]=tempI64[1];HEAP32[buf+48>>2]=4096;HEAP32[buf+52>>2]=stat.blocks;var atime=stat.atime.getTime();var mtime=stat.mtime.getTime();var ctime=stat.ctime.getTime();tempI64=[Math.floor(atime/1e3)>>>0,(tempDouble=Math.floor(atime/1e3),+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+56>>2]=tempI64[0],HEAP32[buf+60>>2]=tempI64[1];HEAPU32[buf+64>>2]=atime%1e3*1e3;tempI64=[Math.floor(mtime/1e3)>>>0,(tempDouble=Math.floor(mtime/1e3),+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+72>>2]=tempI64[0],HEAP32[buf+76>>2]=tempI64[1];HEAPU32[buf+80>>2]=mtime%1e3*1e3;tempI64=[Math.floor(ctime/1e3)>>>0,(tempDouble=Math.floor(ctime/1e3),+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+88>>2]=tempI64[0],HEAP32[buf+92>>2]=tempI64[1];HEAPU32[buf+96>>2]=ctime%1e3*1e3;tempI64=[stat.ino>>>0,(tempDouble=stat.ino,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+104>>2]=tempI64[0],HEAP32[buf+108>>2]=tempI64[1];return 0},doMsync:function(addr,stream,len,flags,offset){if(!FS.isFile(stream.node.mode)){throw new FS.ErrnoError(43)}if(flags&2){return 0}var buffer=HEAPU8.slice(addr,addr+len);FS.msync(stream,buffer,offset,len,flags)},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},getStreamFromFD:function(fd){var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);return stream}};function ___syscall_chmod(path,mode){try{path=SYSCALLS.getStr(path);FS.chmod(path,mode);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_faccessat(dirfd,path,amode,flags){try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path);if(amode&~7){return-28}var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;if(!node){return-44}var perms="";if(amode&4)perms+="r";if(amode&2)perms+="w";if(amode&1)perms+="x";if(perms&&FS.nodePermissions(node,perms)){return-2}return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_fchmod(fd,mode){try{FS.fchmod(fd,mode);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function setErrNo(value){HEAP32[___errno_location()>>2]=value;return value}function ___syscall_fcntl64(fd,cmd,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(fd);switch(cmd){case 0:{var arg=SYSCALLS.get();if(arg<0){return-28}var newStream;newStream=FS.createStream(stream,arg);return newStream.fd}case 1:case 2:return 0;case 3:return stream.flags;case 4:{var arg=SYSCALLS.get();stream.flags|=arg;return 0}case 5:{var arg=SYSCALLS.get();var offset=0;HEAP16[arg+offset>>1]=2;return 0}case 6:case 7:return 0;case 16:case 8:return-28;case 9:setErrNo(28);return-1;default:{return-28}}}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_fstat64(fd,buf){try{var stream=SYSCALLS.getStreamFromFD(fd);return SYSCALLS.doStat(FS.stat,stream.path,buf)}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}var MAX_INT53=9007199254740992;var MIN_INT53=-9007199254740992;function bigintToI53Checked(num){return numMAX_INT53?NaN:Number(num)}function ___syscall_ftruncate64(fd,length){try{length=bigintToI53Checked(length);if(isNaN(length))return-61;FS.ftruncate(fd,length);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_getcwd(buf,size){try{if(size===0)return-28;var cwd=FS.cwd();var cwdLengthInBytes=lengthBytesUTF8(cwd)+1;if(size>>0,(tempDouble=id,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos>>2]=tempI64[0],HEAP32[dirp+pos+4>>2]=tempI64[1];tempI64=[(idx+1)*struct_size>>>0,(tempDouble=(idx+1)*struct_size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos+8>>2]=tempI64[0],HEAP32[dirp+pos+12>>2]=tempI64[1];HEAP16[dirp+pos+16>>1]=280;HEAP8[dirp+pos+18>>0]=type;stringToUTF8(name,dirp+pos+19,256);pos+=struct_size;idx+=1}FS.llseek(stream,idx*struct_size,0);return pos}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_lstat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.lstat,path,buf)}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_mkdirat(dirfd,path,mode){try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path);path=PATH.normalize(path);if(path[path.length-1]==="/")path=path.substr(0,path.length-1);FS.mkdir(path,mode,0);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_newfstatat(dirfd,path,buf,flags){try{path=SYSCALLS.getStr(path);var nofollow=flags&256;var allowEmpty=flags&4096;flags=flags&~6400;path=SYSCALLS.calculateAt(dirfd,path,allowEmpty);return SYSCALLS.doStat(nofollow?FS.lstat:FS.stat,path,buf)}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_openat(dirfd,path,flags,varargs){SYSCALLS.varargs=varargs;try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path);var mode=varargs?SYSCALLS.get():0;return FS.open(path,flags,mode).fd}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_readlinkat(dirfd,path,buf,bufsize){try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path);if(bufsize<=0)return-28;var ret=FS.readlink(path);var len=Math.min(bufsize,lengthBytesUTF8(ret));var endChar=HEAP8[buf+len];stringToUTF8(ret,buf,bufsize+1);HEAP8[buf+len]=endChar;return len}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_renameat(olddirfd,oldpath,newdirfd,newpath){try{oldpath=SYSCALLS.getStr(oldpath);newpath=SYSCALLS.getStr(newpath);oldpath=SYSCALLS.calculateAt(olddirfd,oldpath);newpath=SYSCALLS.calculateAt(newdirfd,newpath);FS.rename(oldpath,newpath);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_rmdir(path){try{path=SYSCALLS.getStr(path);FS.rmdir(path);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_stat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.stat,path,buf)}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_symlink(target,linkpath){try{target=SYSCALLS.getStr(target);linkpath=SYSCALLS.getStr(linkpath);FS.symlink(target,linkpath);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_truncate64(path,length){try{length=bigintToI53Checked(length);if(isNaN(length))return-61;path=SYSCALLS.getStr(path);FS.truncate(path,length);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function ___syscall_unlinkat(dirfd,path,flags){try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path);if(flags===0){FS.unlink(path)}else if(flags===512){FS.rmdir(path)}else{abort("Invalid flags passed to unlinkat")}return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function readI53FromI64(ptr){return HEAPU32[ptr>>2]+HEAP32[ptr+4>>2]*4294967296}function ___syscall_utimensat(dirfd,path,times,flags){try{path=SYSCALLS.getStr(path);path=SYSCALLS.calculateAt(dirfd,path,true);if(!times){var atime=Date.now();var mtime=atime}else{var seconds=readI53FromI64(times);var nanoseconds=HEAP32[times+8>>2];atime=seconds*1e3+nanoseconds/(1e3*1e3);times+=16;seconds=readI53FromI64(times);nanoseconds=HEAP32[times+8>>2];mtime=seconds*1e3+nanoseconds/(1e3*1e3)}FS.utime(path,atime,mtime);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function embindRepr(v){if(v===null){return"null"}var t=typeof v;if(t==="object"||t==="array"||t==="function"){return v.toString()}else{return""+v}}function embind_init_charCodes(){var codes=new Array(256);for(var i=0;i<256;++i){codes[i]=String.fromCharCode(i)}embind_charCodes=codes}var embind_charCodes=undefined;function readLatin1String(ptr){var ret="";var c=ptr;while(HEAPU8[c]){ret+=embind_charCodes[HEAPU8[c++]]}return ret}var awaitingDependencies={};var registeredTypes={};var typeDependencies={};var char_0=48;var char_9=57;function makeLegalFunctionName(name){if(undefined===name){return"_unknown"}name=name.replace(/[^a-zA-Z0-9_]/g,"$");var f=name.charCodeAt(0);if(f>=char_0&&f<=char_9){return"_"+name}return name}function createNamedFunction(name,body){name=makeLegalFunctionName(name);return new Function("body","return function "+name+"() {\n"+' "use strict";'+" return body.apply(this, arguments);\n"+"};\n")(body)}function extendError(baseErrorType,errorName){var errorClass=createNamedFunction(errorName,function(message){this.name=errorName;this.message=message;var stack=new Error(message).stack;if(stack!==undefined){this.stack=this.toString()+"\n"+stack.replace(/^Error(:[^\n]*)?\n/,"")}});errorClass.prototype=Object.create(baseErrorType.prototype);errorClass.prototype.constructor=errorClass;errorClass.prototype.toString=function(){if(this.message===undefined){return this.name}else{return this.name+": "+this.message}};return errorClass}var BindingError=undefined;function throwBindingError(message){throw new BindingError(message)}var InternalError=undefined;function throwInternalError(message){throw new InternalError(message)}function whenDependentTypesAreResolved(myTypes,dependentTypes,getTypeConverters){myTypes.forEach(function(type){typeDependencies[type]=dependentTypes});function onComplete(typeConverters){var myTypeConverters=getTypeConverters(typeConverters);if(myTypeConverters.length!==myTypes.length){throwInternalError("Mismatched type converter count")}for(var i=0;i{if(registeredTypes.hasOwnProperty(dt)){typeConverters[i]=registeredTypes[dt]}else{unregisteredTypes.push(dt);if(!awaitingDependencies.hasOwnProperty(dt)){awaitingDependencies[dt]=[]}awaitingDependencies[dt].push(()=>{typeConverters[i]=registeredTypes[dt];++registered;if(registered===unregisteredTypes.length){onComplete(typeConverters)}})}});if(0===unregisteredTypes.length){onComplete(typeConverters)}}function registerType(rawType,registeredInstance,options={}){if(!("argPackAdvance"in registeredInstance)){throw new TypeError("registerType registeredInstance requires argPackAdvance")}var name=registeredInstance.name;if(!rawType){throwBindingError('type "'+name+'" must have a positive integer typeid pointer')}if(registeredTypes.hasOwnProperty(rawType)){if(options.ignoreDuplicateRegistrations){return}else{throwBindingError("Cannot register type '"+name+"' twice")}}registeredTypes[rawType]=registeredInstance;delete typeDependencies[rawType];if(awaitingDependencies.hasOwnProperty(rawType)){var callbacks=awaitingDependencies[rawType];delete awaitingDependencies[rawType];callbacks.forEach(cb=>cb())}}function integerReadValueFromPointer(name,shift,signed){switch(shift){case 0:return signed?function readS8FromPointer(pointer){return HEAP8[pointer]}:function readU8FromPointer(pointer){return HEAPU8[pointer]};case 1:return signed?function readS16FromPointer(pointer){return HEAP16[pointer>>1]}:function readU16FromPointer(pointer){return HEAPU16[pointer>>1]};case 2:return signed?function readS32FromPointer(pointer){return HEAP32[pointer>>2]}:function readU32FromPointer(pointer){return HEAPU32[pointer>>2]};case 3:return signed?function readS64FromPointer(pointer){return HEAP64[pointer>>3]}:function readU64FromPointer(pointer){return HEAPU64[pointer>>3]};default:throw new TypeError("Unknown integer type: "+name)}}function __embind_register_bigint(primitiveType,name,size,minRange,maxRange){name=readLatin1String(name);var shift=getShiftFromSize(size);var isUnsignedType=name.indexOf("u")!=-1;if(isUnsignedType){maxRange=(1n<<64n)-1n}registerType(primitiveType,{name:name,"fromWireType":function(value){return value},"toWireType":function(destructors,value){if(typeof value!="bigint"&&typeof value!="number"){throw new TypeError('Cannot convert "'+embindRepr(value)+'" to '+this.name)}if(valuemaxRange){throw new TypeError('Passing a number "'+embindRepr(value)+'" from JS side to C/C++ side to an argument of type "'+name+'", which is outside the valid range ['+minRange+", "+maxRange+"]!")}return value},"argPackAdvance":8,"readValueFromPointer":integerReadValueFromPointer(name,shift,!isUnsignedType),destructorFunction:null})}function getShiftFromSize(size){switch(size){case 1:return 0;case 2:return 1;case 4:return 2;case 8:return 3;default:throw new TypeError("Unknown type size: "+size)}}function __embind_register_bool(rawType,name,size,trueValue,falseValue){var shift=getShiftFromSize(size);name=readLatin1String(name);registerType(rawType,{name:name,"fromWireType":function(wt){return!!wt},"toWireType":function(destructors,o){return o?trueValue:falseValue},"argPackAdvance":8,"readValueFromPointer":function(pointer){var heap;if(size===1){heap=HEAP8}else if(size===2){heap=HEAP16}else if(size===4){heap=HEAP32}else{throw new TypeError("Unknown boolean type size: "+name)}return this["fromWireType"](heap[pointer>>shift])},destructorFunction:null})}var emval_free_list=[];var emval_handle_array=[{},{value:undefined},{value:null},{value:true},{value:false}];function __emval_decref(handle){if(handle>4&&0===--emval_handle_array[handle].refcount){emval_handle_array[handle]=undefined;emval_free_list.push(handle)}}function count_emval_handles(){var count=0;for(var i=5;i{if(!handle){throwBindingError("Cannot use deleted val. handle = "+handle)}return emval_handle_array[handle].value},toHandle:value=>{switch(value){case undefined:return 1;case null:return 2;case true:return 3;case false:return 4;default:{var handle=emval_free_list.length?emval_free_list.pop():emval_handle_array.length;emval_handle_array[handle]={refcount:1,value:value};return handle}}}};function simpleReadValueFromPointer(pointer){return this["fromWireType"](HEAP32[pointer>>2])}function __embind_register_emval(rawType,name){name=readLatin1String(name);registerType(rawType,{name:name,"fromWireType":function(handle){var rv=Emval.toValue(handle);__emval_decref(handle);return rv},"toWireType":function(destructors,value){return Emval.toHandle(value)},"argPackAdvance":8,"readValueFromPointer":simpleReadValueFromPointer,destructorFunction:null})}function floatReadValueFromPointer(name,shift){switch(shift){case 2:return function(pointer){return this["fromWireType"](HEAPF32[pointer>>2])};case 3:return function(pointer){return this["fromWireType"](HEAPF64[pointer>>3])};default:throw new TypeError("Unknown float type: "+name)}}function __embind_register_float(rawType,name,size){var shift=getShiftFromSize(size);name=readLatin1String(name);registerType(rawType,{name:name,"fromWireType":function(value){return value},"toWireType":function(destructors,value){return value},"argPackAdvance":8,"readValueFromPointer":floatReadValueFromPointer(name,shift),destructorFunction:null})}function new_(constructor,argumentList){if(!(constructor instanceof Function)){throw new TypeError("new_ called with constructor type "+typeof constructor+" which is not a function")}var dummy=createNamedFunction(constructor.name||"unknownFunctionName",function(){});dummy.prototype=constructor.prototype;var obj=new dummy;var r=constructor.apply(obj,argumentList);return r instanceof Object?r:obj}function runDestructors(destructors){while(destructors.length){var ptr=destructors.pop();var del=destructors.pop();del(ptr)}}function craftInvokerFunction(humanName,argTypes,classType,cppInvokerFunc,cppTargetFunc){var argCount=argTypes.length;if(argCount<2){throwBindingError("argTypes array size mismatch! Must at least get return value and 'this' types!")}var isClassMethodFunc=argTypes[1]!==null&&classType!==null;var needsDestructorStack=false;for(var i=1;i0?", ":"")+argsListWired}invokerFnBody+=(returns?"var rv = ":"")+"invoker(fn"+(argsListWired.length>0?", ":"")+argsListWired+");\n";if(needsDestructorStack){invokerFnBody+="runDestructors(destructors);\n"}else{for(var i=isClassMethodFunc?1:2;i>2])}return array}function replacePublicSymbol(name,value,numArguments){if(!Module.hasOwnProperty(name)){throwInternalError("Replacing nonexistant public symbol")}if(undefined!==Module[name].overloadTable&&undefined!==numArguments){Module[name].overloadTable[numArguments]=value}else{Module[name]=value;Module[name].argCount=numArguments}}function embind__requireFunction(signature,rawFunction){signature=readLatin1String(signature);function makeDynCaller(){return getWasmTableEntry(rawFunction)}var fp=makeDynCaller();if(typeof fp!="function"){throwBindingError("unknown function pointer with signature "+signature+": "+rawFunction)}return fp}var UnboundTypeError=undefined;function getTypeName(type){var ptr=___getTypeName(type);var rv=readLatin1String(ptr);_free(ptr);return rv}function throwUnboundTypeError(message,types){var unboundTypes=[];var seen={};function visit(type){if(seen[type]){return}if(registeredTypes[type]){return}if(typeDependencies[type]){typeDependencies[type].forEach(visit);return}unboundTypes.push(type);seen[type]=true}types.forEach(visit);throw new UnboundTypeError(message+": "+unboundTypes.map(getTypeName).join([", "]))}function __embind_register_function(name,argCount,rawArgTypesAddr,signature,rawInvoker,fn){var argTypes=heap32VectorToArray(argCount,rawArgTypesAddr);name=readLatin1String(name);rawInvoker=embind__requireFunction(signature,rawInvoker);exposePublicSymbol(name,function(){throwUnboundTypeError("Cannot call "+name+" due to unbound types",argTypes)},argCount-1);whenDependentTypesAreResolved([],argTypes,function(argTypes){var invokerArgsArray=[argTypes[0],null].concat(argTypes.slice(1));replacePublicSymbol(name,craftInvokerFunction(name,invokerArgsArray,null,rawInvoker,fn),argCount-1);return[]})}function __embind_register_integer(primitiveType,name,size,minRange,maxRange){name=readLatin1String(name);if(maxRange===-1){maxRange=4294967295}var shift=getShiftFromSize(size);var fromWireType=value=>value;if(minRange===0){var bitshift=32-8*size;fromWireType=value=>value<>>bitshift}var isUnsignedType=name.includes("unsigned");var checkAssertions=(value,toTypeName)=>{};var toWireType;if(isUnsignedType){toWireType=function(destructors,value){checkAssertions(value,this.name);return value>>>0}}else{toWireType=function(destructors,value){checkAssertions(value,this.name);return value}}registerType(primitiveType,{name:name,"fromWireType":fromWireType,"toWireType":toWireType,"argPackAdvance":8,"readValueFromPointer":integerReadValueFromPointer(name,shift,minRange!==0),destructorFunction:null})}function __embind_register_memory_view(rawType,dataTypeIndex,name){var typeMapping=[Int8Array,Uint8Array,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array,BigInt64Array,BigUint64Array];var TA=typeMapping[dataTypeIndex];function decodeMemoryView(handle){handle=handle>>2;var heap=HEAPU32;var size=heap[handle];var data=heap[handle+1];return new TA(buffer,data,size)}name=readLatin1String(name);registerType(rawType,{name:name,"fromWireType":decodeMemoryView,"argPackAdvance":8,"readValueFromPointer":decodeMemoryView},{ignoreDuplicateRegistrations:true})}function __embind_register_std_string(rawType,name){name=readLatin1String(name);var stdStringIsUTF8=name==="std::string";registerType(rawType,{name:name,"fromWireType":function(value){var length=HEAPU32[value>>2];var payload=value+4;var str;if(stdStringIsUTF8){var decodeStartPtr=payload;for(var i=0;i<=length;++i){var currentBytePtr=payload+i;if(i==length||HEAPU8[currentBytePtr]==0){var maxRead=currentBytePtr-decodeStartPtr;var stringSegment=UTF8ToString(decodeStartPtr,maxRead);if(str===undefined){str=stringSegment}else{str+=String.fromCharCode(0);str+=stringSegment}decodeStartPtr=currentBytePtr+1}}}else{var a=new Array(length);for(var i=0;i>2]=length;if(stdStringIsUTF8&&valueIsOfTypeString){stringToUTF8(value,ptr,length+1)}else{if(valueIsOfTypeString){for(var i=0;i255){_free(ptr);throwBindingError("String has UTF-16 code units that do not fit in 8 bits")}HEAPU8[ptr+i]=charCode}}else{for(var i=0;i>1;var maxIdx=idx+maxBytesToRead/2;while(!(idx>=maxIdx)&&HEAPU16[idx])++idx;endPtr=idx<<1;if(endPtr-ptr>32&&UTF16Decoder)return UTF16Decoder.decode(HEAPU8.subarray(ptr,endPtr));var str="";for(var i=0;!(i>=maxBytesToRead/2);++i){var codeUnit=HEAP16[ptr+i*2>>1];if(codeUnit==0)break;str+=String.fromCharCode(codeUnit)}return str}function stringToUTF16(str,outPtr,maxBytesToWrite){if(maxBytesToWrite===undefined){maxBytesToWrite=2147483647}if(maxBytesToWrite<2)return 0;maxBytesToWrite-=2;var startPtr=outPtr;var numCharsToWrite=maxBytesToWrite>1]=codeUnit;outPtr+=2}HEAP16[outPtr>>1]=0;return outPtr-startPtr}function lengthBytesUTF16(str){return str.length*2}function UTF32ToString(ptr,maxBytesToRead){var i=0;var str="";while(!(i>=maxBytesToRead/4)){var utf32=HEAP32[ptr+i*4>>2];if(utf32==0)break;++i;if(utf32>=65536){var ch=utf32-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}else{str+=String.fromCharCode(utf32)}}return str}function stringToUTF32(str,outPtr,maxBytesToWrite){if(maxBytesToWrite===undefined){maxBytesToWrite=2147483647}if(maxBytesToWrite<4)return 0;var startPtr=outPtr;var endPtr=startPtr+maxBytesToWrite-4;for(var i=0;i=55296&&codeUnit<=57343){var trailSurrogate=str.charCodeAt(++i);codeUnit=65536+((codeUnit&1023)<<10)|trailSurrogate&1023}HEAP32[outPtr>>2]=codeUnit;outPtr+=4;if(outPtr+4>endPtr)break}HEAP32[outPtr>>2]=0;return outPtr-startPtr}function lengthBytesUTF32(str){var len=0;for(var i=0;i=55296&&codeUnit<=57343)++i;len+=4}return len}function __embind_register_std_wstring(rawType,charSize,name){name=readLatin1String(name);var decodeString,encodeString,getHeap,lengthBytesUTF,shift;if(charSize===2){decodeString=UTF16ToString;encodeString=stringToUTF16;lengthBytesUTF=lengthBytesUTF16;getHeap=()=>HEAPU16;shift=1}else if(charSize===4){decodeString=UTF32ToString;encodeString=stringToUTF32;lengthBytesUTF=lengthBytesUTF32;getHeap=()=>HEAPU32;shift=2}registerType(rawType,{name:name,"fromWireType":function(value){var length=HEAPU32[value>>2];var HEAP=getHeap();var str;var decodeStartPtr=value+4;for(var i=0;i<=length;++i){var currentBytePtr=value+4+i*charSize;if(i==length||HEAP[currentBytePtr>>shift]==0){var maxReadBytes=currentBytePtr-decodeStartPtr;var stringSegment=decodeString(decodeStartPtr,maxReadBytes);if(str===undefined){str=stringSegment}else{str+=String.fromCharCode(0);str+=stringSegment}decodeStartPtr=currentBytePtr+charSize}}_free(value);return str},"toWireType":function(destructors,value){if(!(typeof value=="string")){throwBindingError("Cannot pass non-string to C++ string type "+name)}var length=lengthBytesUTF(value);var ptr=_malloc(4+length+charSize);HEAPU32[ptr>>2]=length>>shift;encodeString(value,ptr+4,length+charSize);if(destructors!==null){destructors.push(_free,ptr)}return ptr},"argPackAdvance":8,"readValueFromPointer":simpleReadValueFromPointer,destructorFunction:function(ptr){_free(ptr)}})}function __embind_register_void(rawType,name){name=readLatin1String(name);registerType(rawType,{isVoid:true,name:name,"argPackAdvance":0,"fromWireType":function(){return undefined},"toWireType":function(destructors,o){return undefined}})}var nowIsMonotonic=true;function __emscripten_get_now_is_monotonic(){return nowIsMonotonic}function requireRegisteredType(rawType,humanName){var impl=registeredTypes[rawType];if(undefined===impl){throwBindingError(humanName+" has unknown type "+getTypeName(rawType))}return impl}function __emval_as(handle,returnType,destructorsRef){handle=Emval.toValue(handle);returnType=requireRegisteredType(returnType,"emval::as");var destructors=[];var rd=Emval.toHandle(destructors);HEAPU32[destructorsRef>>2]=rd;return returnType["toWireType"](destructors,handle)}function emval_allocateDestructors(destructorsRef){var destructors=[];HEAPU32[destructorsRef>>2]=Emval.toHandle(destructors);return destructors}var emval_symbols={};function getStringOrSymbol(address){var symbol=emval_symbols[address];if(symbol===undefined){return readLatin1String(address)}return symbol}var emval_methodCallers=[];function __emval_call_method(caller,handle,methodName,destructorsRef,args){caller=emval_methodCallers[caller];handle=Emval.toValue(handle);methodName=getStringOrSymbol(methodName);return caller(handle,methodName,emval_allocateDestructors(destructorsRef),args)}function __emval_call_void_method(caller,handle,methodName,args){caller=emval_methodCallers[caller];handle=Emval.toValue(handle);methodName=getStringOrSymbol(methodName);caller(handle,methodName,null,args)}function emval_get_global(){if(typeof globalThis=="object"){return globalThis}return function(){return Function}()("return this")()}function __emval_get_global(name){if(name===0){return Emval.toHandle(emval_get_global())}else{name=getStringOrSymbol(name);return Emval.toHandle(emval_get_global()[name])}}function emval_addMethodCaller(caller){var id=emval_methodCallers.length;emval_methodCallers.push(caller);return id}function emval_lookupTypes(argCount,argTypes){var a=new Array(argCount);for(var i=0;i>2],"parameter "+i)}return a}var emval_registeredMethods=[];function __emval_get_method_caller(argCount,argTypes){var types=emval_lookupTypes(argCount,argTypes);var retType=types[0];var signatureName=retType.name+"_$"+types.slice(1).map(function(t){return t.name}).join("_")+"$";var returnId=emval_registeredMethods[signatureName];if(returnId!==undefined){return returnId}var params=["retType"];var args=[retType];var argsList="";for(var i=0;i4){emval_handle_array[handle].refcount+=1}}function __emval_new_cstring(v){return Emval.toHandle(getStringOrSymbol(v))}function __emval_new_object(){return Emval.toHandle({})}function __emval_run_destructors(handle){var destructors=Emval.toValue(handle);runDestructors(destructors);__emval_decref(handle)}function __emval_set_property(handle,key,value){handle=Emval.toValue(handle);key=Emval.toValue(key);value=Emval.toValue(value);handle[key]=value}function __emval_take_value(type,arg){type=requireRegisteredType(type,"_emval_take_value");var v=type["readValueFromPointer"](arg);return Emval.toHandle(v)}function __isLeapYear(year){return year%4===0&&(year%100!==0||year%400===0)}var __MONTH_DAYS_LEAP_CUMULATIVE=[0,31,60,91,121,152,182,213,244,274,305,335];var __MONTH_DAYS_REGULAR_CUMULATIVE=[0,31,59,90,120,151,181,212,243,273,304,334];function __yday_from_date(date){var isLeapYear=__isLeapYear(date.getFullYear());var monthDaysCumulative=isLeapYear?__MONTH_DAYS_LEAP_CUMULATIVE:__MONTH_DAYS_REGULAR_CUMULATIVE;var yday=monthDaysCumulative[date.getMonth()]+date.getDate()-1;return yday}function __localtime_js(time,tmPtr){var date=new Date(readI53FromI64(time)*1e3);HEAP32[tmPtr>>2]=date.getSeconds();HEAP32[tmPtr+4>>2]=date.getMinutes();HEAP32[tmPtr+8>>2]=date.getHours();HEAP32[tmPtr+12>>2]=date.getDate();HEAP32[tmPtr+16>>2]=date.getMonth();HEAP32[tmPtr+20>>2]=date.getFullYear()-1900;HEAP32[tmPtr+24>>2]=date.getDay();var yday=__yday_from_date(date)|0;HEAP32[tmPtr+28>>2]=yday;HEAP32[tmPtr+36>>2]=-(date.getTimezoneOffset()*60);var start=new Date(date.getFullYear(),0,1);var summerOffset=new Date(date.getFullYear(),6,1).getTimezoneOffset();var winterOffset=start.getTimezoneOffset();var dst=(summerOffset!=winterOffset&&date.getTimezoneOffset()==Math.min(winterOffset,summerOffset))|0;HEAP32[tmPtr+32>>2]=dst}function __mktime_js(tmPtr){var date=new Date(HEAP32[tmPtr+20>>2]+1900,HEAP32[tmPtr+16>>2],HEAP32[tmPtr+12>>2],HEAP32[tmPtr+8>>2],HEAP32[tmPtr+4>>2],HEAP32[tmPtr>>2],0);var dst=HEAP32[tmPtr+32>>2];var guessedOffset=date.getTimezoneOffset();var start=new Date(date.getFullYear(),0,1);var summerOffset=new Date(date.getFullYear(),6,1).getTimezoneOffset();var winterOffset=start.getTimezoneOffset();var dstOffset=Math.min(winterOffset,summerOffset);if(dst<0){HEAP32[tmPtr+32>>2]=Number(summerOffset!=winterOffset&&dstOffset==guessedOffset)}else if(dst>0!=(dstOffset==guessedOffset)){var nonDstOffset=Math.max(winterOffset,summerOffset);var trueOffset=dst>0?dstOffset:nonDstOffset;date.setTime(date.getTime()+(trueOffset-guessedOffset)*6e4)}HEAP32[tmPtr+24>>2]=date.getDay();var yday=__yday_from_date(date)|0;HEAP32[tmPtr+28>>2]=yday;HEAP32[tmPtr>>2]=date.getSeconds();HEAP32[tmPtr+4>>2]=date.getMinutes();HEAP32[tmPtr+8>>2]=date.getHours();HEAP32[tmPtr+12>>2]=date.getDate();HEAP32[tmPtr+16>>2]=date.getMonth();HEAP32[tmPtr+20>>2]=date.getYear();return date.getTime()/1e3|0}function __mmap_js(len,prot,flags,fd,off,allocated,addr){try{var stream=SYSCALLS.getStreamFromFD(fd);var res=FS.mmap(stream,len,off,prot,flags);var ptr=res.ptr;HEAP32[allocated>>2]=res.allocated;HEAPU32[addr>>2]=ptr;return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function __munmap_js(addr,len,prot,flags,fd,offset){try{var stream=SYSCALLS.getStreamFromFD(fd);if(prot&2){SYSCALLS.doMsync(addr,stream,len,flags,offset)}FS.munmap(stream)}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return-e.errno}}function allocateUTF8(str){var size=lengthBytesUTF8(str)+1;var ret=_malloc(size);if(ret)stringToUTF8Array(str,HEAP8,ret,size);return ret}function __tzset_js(timezone,daylight,tzname){var currentYear=(new Date).getFullYear();var winter=new Date(currentYear,0,1);var summer=new Date(currentYear,6,1);var winterOffset=winter.getTimezoneOffset();var summerOffset=summer.getTimezoneOffset();var stdTimezoneOffset=Math.max(winterOffset,summerOffset);HEAPU32[timezone>>2]=stdTimezoneOffset*60;HEAP32[daylight>>2]=Number(winterOffset!=summerOffset);function extractZone(date){var match=date.toTimeString().match(/\(([A-Za-z ]+)\)$/);return match?match[1]:"GMT"}var winterName=extractZone(winter);var summerName=extractZone(summer);var winterNamePtr=allocateUTF8(winterName);var summerNamePtr=allocateUTF8(summerName);if(summerOffset>2]=winterNamePtr;HEAPU32[tzname+4>>2]=summerNamePtr}else{HEAPU32[tzname>>2]=summerNamePtr;HEAPU32[tzname+4>>2]=winterNamePtr}}function _abort(){abort("")}function _emscripten_date_now(){return Date.now()}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=()=>{var t=process["hrtime"]();return t[0]*1e3+t[1]/1e6}}else _emscripten_get_now=()=>performance.now();var IDBStore={indexedDB:function(){if(typeof indexedDB!="undefined")return indexedDB;var ret=null;if(typeof window=="object")ret=window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB;assert(ret,"IDBStore used, but indexedDB not supported");return ret},DB_VERSION:22,DB_STORE_NAME:"FILE_DATA",dbs:{},blobs:[0],getDB:function(name,callback){var db=IDBStore.dbs[name];if(db){return callback(null,db)}var req;try{req=IDBStore.indexedDB().open(name,IDBStore.DB_VERSION)}catch(e){return callback(e)}req.onupgradeneeded=function(e){var db=e.target.result;var transaction=e.target.transaction;var fileStore;if(db.objectStoreNames.contains(IDBStore.DB_STORE_NAME)){fileStore=transaction.objectStore(IDBStore.DB_STORE_NAME)}else{fileStore=db.createObjectStore(IDBStore.DB_STORE_NAME)}};req.onsuccess=function(){db=req.result;IDBStore.dbs[name]=db;callback(null,db)};req.onerror=function(e){callback(this.error);e.preventDefault()}},getStore:function(dbName,type,callback){IDBStore.getDB(dbName,function(error,db){if(error)return callback(error);var transaction=db.transaction([IDBStore.DB_STORE_NAME],type);transaction.onerror=function(e){callback(this.error||"unknown error");e.preventDefault()};var store=transaction.objectStore(IDBStore.DB_STORE_NAME);callback(null,store)})},getFile:function(dbName,id,callback){IDBStore.getStore(dbName,"readonly",function(err,store){if(err)return callback(err);var req=store.get(id);req.onsuccess=function(event){var result=event.target.result;if(!result){return callback("file "+id+" not found")}return callback(null,result)};req.onerror=function(error){callback(error)}})},setFile:function(dbName,id,data,callback){IDBStore.getStore(dbName,"readwrite",function(err,store){if(err)return callback(err);var req=store.put(data,id);req.onsuccess=function(event){callback()};req.onerror=function(error){callback(error)}})},deleteFile:function(dbName,id,callback){IDBStore.getStore(dbName,"readwrite",function(err,store){if(err)return callback(err);var req=store.delete(id);req.onsuccess=function(event){callback()};req.onerror=function(error){callback(error)}})},existsFile:function(dbName,id,callback){IDBStore.getStore(dbName,"readonly",function(err,store){if(err)return callback(err);var req=store.count(id);req.onsuccess=function(event){callback(null,event.target.result>0)};req.onerror=function(error){callback(error)}})}};function _emscripten_idb_async_delete(db,id,arg,ondelete,onerror){IDBStore.deleteFile(UTF8ToString(db),UTF8ToString(id),function(error){if(error){if(onerror)getWasmTableEntry(onerror)(arg);return}if(ondelete)getWasmTableEntry(ondelete)(arg)})}function _emscripten_idb_async_exists(db,id,arg,oncheck,onerror){IDBStore.existsFile(UTF8ToString(db),UTF8ToString(id),function(error,exists){if(error){if(onerror)getWasmTableEntry(onerror)(arg);return}if(oncheck)getWasmTableEntry(oncheck)(arg,exists)})}function _emscripten_idb_async_load(db,id,arg,onload,onerror){IDBStore.getFile(UTF8ToString(db),UTF8ToString(id),function(error,byteArray){if(error){if(onerror)getWasmTableEntry(onerror)(arg);return}var buffer=_malloc(byteArray.length);HEAPU8.set(byteArray,buffer);getWasmTableEntry(onload)(arg,buffer,byteArray.length);_free(buffer)})}function _emscripten_idb_async_store(db,id,ptr,num,arg,onstore,onerror){IDBStore.setFile(UTF8ToString(db),UTF8ToString(id),new Uint8Array(HEAPU8.subarray(ptr,ptr+num)),function(error){if(error){if(onerror)getWasmTableEntry(onerror)(arg);return}if(onstore)getWasmTableEntry(onstore)(arg)})}function reallyNegative(x){return x<0||x===0&&1/x===-Infinity}function convertI32PairToI53(lo,hi){return(lo>>>0)+hi*4294967296}function convertU32PairToI53(lo,hi){return(lo>>>0)+(hi>>>0)*4294967296}function reSign(value,bits){if(value<=0){return value}var half=bits<=32?Math.abs(1<=half&&(bits<=32||value>half)){value=-2*half+value}return value}function unSign(value,bits){if(value>=0){return value}return bits<=32?2*Math.abs(1<>3];argIndex+=8}else if(type=="i64"){ret=[HEAP32[argIndex>>2],HEAP32[argIndex+4>>2]];argIndex+=8}else{type="i32";ret=HEAP32[argIndex>>2];argIndex+=4}return ret}var ret=[];var curr,next,currArg;while(1){var startTextIndex=textIndex;curr=HEAP8[textIndex>>0];if(curr===0)break;next=HEAP8[textIndex+1>>0];if(curr==37){var flagAlwaysSigned=false;var flagLeftAlign=false;var flagAlternative=false;var flagZeroPad=false;var flagPadSign=false;flagsLoop:while(1){switch(next){case 43:flagAlwaysSigned=true;break;case 45:flagLeftAlign=true;break;case 35:flagAlternative=true;break;case 48:if(flagZeroPad){break flagsLoop}else{flagZeroPad=true;break}case 32:flagPadSign=true;break;default:break flagsLoop}textIndex++;next=HEAP8[textIndex+1>>0]}var width=0;if(next==42){width=getNextArg("i32");textIndex++;next=HEAP8[textIndex+1>>0]}else{while(next>=48&&next<=57){width=width*10+(next-48);textIndex++;next=HEAP8[textIndex+1>>0]}}var precisionSet=false,precision=-1;if(next==46){precision=0;precisionSet=true;textIndex++;next=HEAP8[textIndex+1>>0];if(next==42){precision=getNextArg("i32");textIndex++}else{while(1){var precisionChr=HEAP8[textIndex+1>>0];if(precisionChr<48||precisionChr>57)break;precision=precision*10+(precisionChr-48);textIndex++}}next=HEAP8[textIndex+1>>0]}if(precision<0){precision=6;precisionSet=false}var argSize;switch(String.fromCharCode(next)){case"h":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==104){textIndex++;argSize=1}else{argSize=2}break;case"l":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==108){textIndex++;argSize=8}else{argSize=4}break;case"L":case"q":case"j":argSize=8;break;case"z":case"t":case"I":argSize=4;break;default:argSize=null}if(argSize)textIndex++;next=HEAP8[textIndex+1>>0];switch(String.fromCharCode(next)){case"d":case"i":case"u":case"o":case"x":case"X":case"p":{var signed=next==100||next==105;argSize=argSize||4;currArg=getNextArg("i"+argSize*8);var argText;if(argSize==8){currArg=next==117?convertU32PairToI53(currArg[0],currArg[1]):convertI32PairToI53(currArg[0],currArg[1])}if(argSize<=4){var limit=Math.pow(256,argSize)-1;currArg=(signed?reSign:unSign)(currArg&limit,argSize*8)}var currAbsArg=Math.abs(currArg);var prefix="";if(next==100||next==105){argText=reSign(currArg,8*argSize).toString(10)}else if(next==117){argText=unSign(currArg,8*argSize).toString(10);currArg=Math.abs(currArg)}else if(next==111){argText=(flagAlternative?"0":"")+currAbsArg.toString(8)}else if(next==120||next==88){prefix=flagAlternative&&currArg!=0?"0x":"";if(currArg<0){currArg=-currArg;argText=(currAbsArg-1).toString(16);var buffer=[];for(var i=0;i=0){if(flagAlwaysSigned){prefix="+"+prefix}else if(flagPadSign){prefix=" "+prefix}}if(argText.charAt(0)=="-"){prefix="-"+prefix;argText=argText.substr(1)}while(prefix.length+argText.lengthexponent&&exponent>=-4){next=(next==103?"f":"F").charCodeAt(0);precision-=exponent+1}else{next=(next==103?"e":"E").charCodeAt(0);precision--}effectivePrecision=Math.min(precision,20)}if(next==101||next==69){argText=currArg.toExponential(effectivePrecision);if(/[eE][-+]\d$/.test(argText)){argText=argText.slice(0,-1)+"0"+argText.slice(-1)}}else if(next==102||next==70){argText=currArg.toFixed(effectivePrecision);if(currArg===0&&reallyNegative(currArg)){argText="-"+argText}}var parts=argText.split("e");if(isGeneral&&!flagAlternative){while(parts[0].length>1&&parts[0].includes(".")&&(parts[0].slice(-1)=="0"||parts[0].slice(-1)==".")){parts[0]=parts[0].slice(0,-1)}}else{if(flagAlternative&&argText.indexOf(".")==-1)parts[0]+=".";while(precision>effectivePrecision++)parts[0]+="0"}argText=parts[0]+(parts.length>1?"e"+parts[1]:"");if(next==69)argText=argText.toUpperCase();if(currArg>=0){if(flagAlwaysSigned){argText="+"+argText}else if(flagPadSign){argText=" "+argText}}}while(argText.length>0])}}else{ret=ret.concat(intArrayFromString("(null)".substr(0,argLength),true))}if(flagLeftAlign){while(argLength0){ret.push(32)}if(!flagLeftAlign)ret.push(getNextArg("i8"));break}case"n":{var ptr=getNextArg("i32*");HEAP32[ptr>>2]=ret.length;break}case"%":{ret.push(curr);break}default:{for(var i=startTextIndex;i>0])}}}textIndex+=2}else{ret.push(curr);textIndex+=1}}return ret}function traverseStack(args){if(!args||!args.callee||!args.callee.name){return[null,"",""]}var funstr=args.callee.toString();var funcname=args.callee.name;var str="(";var first=true;for(var i in args){var a=args[i];if(!first){str+=", "}first=false;if(typeof a=="number"||typeof a=="string"){str+=a}else{str+="("+typeof a+")"}}str+=")";var caller=args.callee.caller;args=caller?caller.arguments:[];if(first)str="";return[args,funcname,str]}function jsStackTrace(){var error=new Error;if(!error.stack){try{throw new Error}catch(e){error=e}if(!error.stack){return"(no stack trace available)"}}return error.stack.toString()}function warnOnce(text){if(!warnOnce.shown)warnOnce.shown={};if(!warnOnce.shown[text]){warnOnce.shown[text]=1;if(ENVIRONMENT_IS_NODE)text="warning: "+text;err(text)}}function _emscripten_get_callstack_js(flags){var callstack=jsStackTrace();var iThisFunc=callstack.lastIndexOf("_emscripten_log");var iThisFunc2=callstack.lastIndexOf("_emscripten_get_callstack");var iNextLine=callstack.indexOf("\n",Math.max(iThisFunc,iThisFunc2))+1;callstack=callstack.slice(iNextLine);if(flags&32){warnOnce("EM_LOG_DEMANGLE is deprecated; ignoring")}if(flags&8&&typeof emscripten_source_map=="undefined"){warnOnce('Source map information is not available, emscripten_log with EM_LOG_C_STACK will be ignored. Build with "--pre-js $EMSCRIPTEN/src/emscripten-source-map.min.js" linker flag to add source map loading to code.');flags^=8;flags|=16}var stack_args=null;if(flags&128){stack_args=traverseStack(arguments);while(stack_args[1].includes("_emscripten_"))stack_args=traverseStack(stack_args[0])}var lines=callstack.split("\n");callstack="";var newFirefoxRe=new RegExp("\\s*(.*?)@(.*?):([0-9]+):([0-9]+)");var firefoxRe=new RegExp("\\s*(.*?)@(.*):(.*)(:(.*))?");var chromeRe=new RegExp("\\s*at (.*?) \\((.*):(.*):(.*)\\)");for(var l in lines){var line=lines[l];var symbolName="";var file="";var lineno=0;var column=0;var parts=chromeRe.exec(line);if(parts&&parts.length==5){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]}else{parts=newFirefoxRe.exec(line);if(!parts)parts=firefoxRe.exec(line);if(parts&&parts.length>=4){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]|0}else{callstack+=line+"\n";continue}}var haveSourceMap=false;if(flags&8){var orig=emscripten_source_map.originalPositionFor({line:lineno,column:column});haveSourceMap=orig&&orig.source;if(haveSourceMap){if(flags&64){orig.source=orig.source.substring(orig.source.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+=" at "+symbolName+" ("+orig.source+":"+orig.line+":"+orig.column+")\n"}}if(flags&16||!haveSourceMap){if(flags&64){file=file.substring(file.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+=(haveSourceMap?" = "+symbolName:" at "+symbolName)+" ("+file+":"+lineno+":"+column+")\n"}if(flags&128&&stack_args[0]){if(stack_args[1]==symbolName&&stack_args[2].length>0){callstack=callstack.replace(/\s+$/,"");callstack+=" with values: "+stack_args[1]+stack_args[2]+"\n"}stack_args=traverseStack(stack_args[0])}}callstack=callstack.replace(/\s+$/,"");return callstack}function _emscripten_log_js(flags,str){if(flags&24){str=str.replace(/\s+$/,"");str+=(str.length>0?"\n":"")+_emscripten_get_callstack_js(flags)}if(flags&1){if(flags&4){console.error(str)}else if(flags&2){console.warn(str)}else if(flags&512){console.info(str)}else if(flags&256){console.debug(str)}else{console.log(str)}}else if(flags&6){err(str)}else{out(str)}}function _emscripten_log(flags,format,varargs){var result=formatString(format,varargs);var str=UTF8ArrayToString(result,0);_emscripten_log_js(flags,str)}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function getHeapMax(){return 2147483648}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=HEAPU8.length;requestedSize=requestedSize>>>0;var maxHeapSize=getHeapMax();if(requestedSize>maxHeapSize){return false}let alignUp=(x,multiple)=>x+(multiple-x%multiple)%multiple;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>3]=e.timeStamp;var idx=eventStruct>>2;HEAP32[idx+2]=e.screenX;HEAP32[idx+3]=e.screenY;HEAP32[idx+4]=e.clientX;HEAP32[idx+5]=e.clientY;HEAP32[idx+6]=e.ctrlKey;HEAP32[idx+7]=e.shiftKey;HEAP32[idx+8]=e.altKey;HEAP32[idx+9]=e.metaKey;HEAP16[idx*2+20]=e.button;HEAP16[idx*2+21]=e.buttons;HEAP32[idx+11]=e["movementX"];HEAP32[idx+12]=e["movementY"];var rect=getBoundingClientRect(target);HEAP32[idx+13]=e.clientX-rect.left;HEAP32[idx+14]=e.clientY-rect.top}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):undefined);return domElement}function registerWheelEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.wheelEvent)JSEvents.wheelEvent=_malloc(104);var wheelHandlerFunc=function(ev){var e=ev||event;var wheelEvent=JSEvents.wheelEvent;fillMouseEventData(wheelEvent,e,target);HEAPF64[wheelEvent+72>>3]=e["deltaX"];HEAPF64[wheelEvent+80>>3]=e["deltaY"];HEAPF64[wheelEvent+88>>3]=e["deltaZ"];HEAP32[wheelEvent+96>>2]=e["deltaMode"];if(getWasmTableEntry(callbackfunc)(eventTypeId,wheelEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:wheelHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_wheel_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){target=findEventTarget(target);if(typeof target.onwheel!="undefined"){registerWheelEventCallback(target,userData,useCapture,callbackfunc,9,"wheel",targetThread);return 0}else{return-1}}var ENV={};function getExecutableName(){return thisProgram||"./this.program"}function getEnvStrings(){if(!getEnvStrings.strings){var lang=(typeof navigator=="object"&&navigator.languages&&navigator.languages[0]||"C").replace("-","_")+".UTF-8";var env={"USER":"web_user","LOGNAME":"web_user","PATH":"/","PWD":"/","HOME":"/home/web_user","LANG":lang,"_":getExecutableName()};for(var x in ENV){if(ENV[x]===undefined)delete env[x];else env[x]=ENV[x]}var strings=[];for(var x in env){strings.push(x+"="+env[x])}getEnvStrings.strings=strings}return getEnvStrings.strings}function writeAsciiToMemory(str,buffer,dontAddNull){for(var i=0;i>0]=str.charCodeAt(i)}if(!dontAddNull)HEAP8[buffer>>0]=0}function _environ_get(__environ,environ_buf){var bufSize=0;getEnvStrings().forEach(function(string,i){var ptr=environ_buf+bufSize;HEAPU32[__environ+i*4>>2]=ptr;writeAsciiToMemory(string,ptr);bufSize+=string.length+1});return 0}function _environ_sizes_get(penviron_count,penviron_buf_size){var strings=getEnvStrings();HEAPU32[penviron_count>>2]=strings.length;var bufSize=0;strings.forEach(function(string){bufSize+=string.length+1});HEAPU32[penviron_buf_size>>2]=bufSize;return 0}function _proc_exit(code){EXITSTATUS=code;if(!keepRuntimeAlive()){if(Module["onExit"])Module["onExit"](code);ABORT=true}quit_(code,new ExitStatus(code))}function exitJS(status,implicit){EXITSTATUS=status;_proc_exit(status)}var _exit=exitJS;function _fd_close(fd){try{var stream=SYSCALLS.getStreamFromFD(fd);FS.close(stream);return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function _fd_fdstat_get(fd,pbuf){try{var stream=SYSCALLS.getStreamFromFD(fd);var type=stream.tty?2:FS.isDir(stream.mode)?3:FS.isLink(stream.mode)?7:4;HEAP8[pbuf>>0]=type;return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function doReadv(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i>2];var len=HEAPU32[iov+4>>2];iov+=8;var curr=FS.read(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr;if(curr>2]=num;return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function _fd_seek(fd,offset,whence,newOffset){try{offset=bigintToI53Checked(offset);if(isNaN(offset))return 61;var stream=SYSCALLS.getStreamFromFD(fd);FS.llseek(stream,offset,whence);tempI64=[stream.position>>>0,(tempDouble=stream.position,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[newOffset>>2]=tempI64[0],HEAP32[newOffset+4>>2]=tempI64[1];if(stream.getdents&&offset===0&&whence===0)stream.getdents=null;return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function _fd_sync(fd){try{var stream=SYSCALLS.getStreamFromFD(fd);if(stream.stream_ops&&stream.stream_ops.fsync){return stream.stream_ops.fsync(stream)}return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function doWritev(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i>2];var len=HEAPU32[iov+4>>2];iov+=8;var curr=FS.write(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr}return ret}function _fd_write(fd,iov,iovcnt,pnum){try{var stream=SYSCALLS.getStreamFromFD(fd);var num=doWritev(stream,iov,iovcnt);HEAPU32[pnum>>2]=num;return 0}catch(e){if(typeof FS=="undefined"||!(e instanceof FS.ErrnoError))throw e;return e.errno}}function _getentropy(buffer,size){if(!_getentropy.randomDevice){_getentropy.randomDevice=getRandomDevice()}for(var i=0;i>0]=_getentropy.randomDevice()}return 0}function __arraySum(array,index){var sum=0;for(var i=0;i<=index;sum+=array[i++]){}return sum}var 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createQtAppInstance.ready +} +); +})(); +if (typeof exports === 'object' && typeof module === 'object') + module.exports = createQtAppInstance; +else if (typeof define === 'function' && define['amd']) + define([], function() { return createQtAppInstance; }); +else if (typeof exports === 'object') + exports["createQtAppInstance"] = createQtAppInstance; diff --git a/web_demo/static/js/MiniLive2.js b/web_demo/static/js/MiniLive2.js new file mode 100644 index 0000000..fbeb7be --- /dev/null +++ b/web_demo/static/js/MiniLive2.js @@ -0,0 +1,565 @@ +const audioSource = document.getElementById('audioSource'); +const audio = document.getElementById('audio'); + + const video = document.getElementById('video'); + const canvas_gl = document.getElementById('canvas_gl'); + const gl = canvas_gl.getContext('webgl2',{ antialias: false }); + + const canvas_video = document.getElementById('canvas_video'); + const ctx_video = canvas_video.getContext('2d'); + + // 缩放到128x128 + const resizedCanvas = document.createElement('canvas'); + const resizedCtx = resizedCanvas.getContext('2d', {willReadFrequently: true}); + + // 创建一个像素缓冲区来存储读取的像素数据 + const pixels_fbo = new Uint8Array(128 * 128 * 4); + + + let objData; + let dataSets = []; + + let program; + let indexBuffer; + const texture_bs = gl.createTexture();; + var bs_array = new Float32Array(12); + + const mat4 = glMatrix.mat4; + +document.getElementById('playButton').addEventListener('click', function() { + video.play(); +// document.getElementById('startMessage').style.display = 'none'; +}); + +video.addEventListener('loadedmetadata', () => { +// resizeCanvas(); + video.addEventListener('play', processVideoFrames); +// resizeCanvas(); + console.log("loadedmetadata", video.videoWidth, video.videoHeight) + canvas_video.width = video.videoWidth; + canvas_video.height = video.videoHeight; +}); + + +async function loadRefData() { + const response = await fetch('assets/ref_data.txt'); + if (!response.ok) { + throw new Error('Network response was not ok ' + response.statusText); + } + const fileContent = await response.text(); + var refData = fileContent.split('\n').map(line => parseFloat(line.trim())); + const expectedSize = 18 * 14 * 20; + refData = refData.slice(0, expectedSize); + const refDataSize = refData.length; + if (refDataSize !== expectedSize) { + throw new Error(`Invalid refData size: expected ${expectedSize}, got ${refData.length}`); + } + console.log(refData.length, refData) + // Convert refData to Float32Array + const floatArray = new Float32Array(refData); + const floatArrayBytes = floatArray.byteLength; + + var refDataPtr = Module._malloc(floatArrayBytes); + Module.HEAPF32.set(floatArray, refDataPtr / 4); + Module._setRefData(refDataPtr, floatArrayBytes); +// Module._free(refDataPtr); +} + + // 加载JSON数据 +async function loadJsonData() { + try { + const response = await fetch('assets/json_data.json'); + if (!response.ok) { + throw new Error('Network response was not ok ' + response.statusText); + } + dataSets = await response.json(); + console.log('Data loaded successfully:', dataSets.length, 'sets.'); + } catch (error) { + console.error('Error loading the file:', error); + } +} + + +// 加载OBJ文件 +async function loadObjFile(url) { + const response = await fetch(url); + if (!response.ok) { + throw new Error('Network response was not ok ' + response.statusText); + } + const text = await response.text(); + const { vertices, faces } = parseObjFile(text); + return { vertices, faces }; +} + + // 解析OBJ文件 +function parseObjFile(text) { + const vertices = []; + const vt = []; + const faces = []; + const lines = text.split('\n'); + + lines.forEach(line => { +// console.log(line) + const parts = line.trim().split(/\s+/); + if (parts[0] === 'v') { + vertices.push(parseFloat(parts[1]), parseFloat(parts[2]), parseFloat(parts[3]), + parseFloat(parts[4]), parseFloat(parts[5])); +// vertices.push(parseFloat(parts[1]), parseFloat(parts[2]), parseFloat(parts[3])); + } else if (parts[0] === 'f') { + const face = parts.slice(1).map(part => { + const indices = part.split('/').map(index => parseInt(index, 10) - 1); + return indices[0]; + }); + faces.push(...face); + } + }); + + return { vertices, faces }; +} + + + +async function init_gl() { + loadJsonData(); + loadRefData(); + objData = await loadObjFile('assets/face3D.obj'); + + { + // WebGL Shaders + const vertexShaderSource = `#version 300 es + layout(location = 0) in vec3 a_position; + layout(location = 1) in vec2 a_texture; + uniform float bsVec[12]; + uniform mat4 gProjection; + uniform mat4 gWorld0; + uniform sampler2D texture_bs; + uniform vec2 vertBuffer[209]; + out vec2 v_texture; + out vec2 v_bias; + + vec4 calculateMorphPosition(vec3 position, vec2 textureCoord) { + vec4 tmp_Position2 = vec4(position, 1.0); + if (textureCoord.x < 3.0 && textureCoord.x >= 0.0) { + vec3 morphSum = vec3(0.0); + for (int i = 0; i < 6; i++) { + ivec2 coord = ivec2(int(textureCoord.y), i); + vec3 morph = texelFetch(texture_bs, coord, 0).xyz * 2.0 - 1.0; + morphSum += bsVec[i] * morph; + } + tmp_Position2.xyz += morphSum; + } + else if (textureCoord.x == 4.0) + { + // lower teeth + vec3 morphSum = vec3(0.0, (bsVec[0] + bsVec[1])/ 2.7 + 6.0, 0.0); + tmp_Position2.xyz += morphSum; + } + return tmp_Position2; +} + + void main() { + mat4 gWorld = gWorld0; + + vec4 tmp_Position2 = calculateMorphPosition(a_position, a_texture); + vec4 tmp_Position = gWorld * tmp_Position2; + + v_bias = vec2(0.0, 0.0); + if (a_texture.x == -1.0f) + { + v_bias = vec2(0.0, 0.0); + } + else if (a_texture.y < 209.0f) + { + vec4 vert_new = gProjection * vec4(tmp_Position.x, tmp_Position.y, tmp_Position.z, 1.0); + v_bias = vert_new.xy - (vertBuffer[int(a_texture.y)].xy/128.0 * 2.0 - 1.0); + } + + if (a_texture.x >= 3.0f) + { + gl_Position = gProjection * vec4(tmp_Position.x, tmp_Position.y, 500.0, 1.0); + } + else + { + gl_Position = gProjection * vec4(tmp_Position.x, tmp_Position.y, tmp_Position.z, 1.0); + } + + v_texture = a_texture; + } + + `; + + const fragmentShaderSource = `#version 300 es + precision mediump float; + in mediump vec2 v_texture; + in mediump vec2 v_bias; + out vec4 out_color; +void main() +{ + if (v_texture.x == 2.0f) + { + out_color = vec4(1.0, 0.0, 0.0, 1.0); + } + else if (v_texture.x > 2.0f && v_texture.x < 2.1f) + { + out_color = vec4(0.5f, 0.0, 0.0, 1.0); + } + else if (v_texture.x == 3.0f) + { + out_color = vec4(0.0, 1.0, 0.0, 1.0); + } + else if (v_texture.x == 4.0f) + { + out_color = vec4(0.0, 0.0, 1.0, 1.0); + } + else if (v_texture.x > 3.0f && v_texture.x < 4.0f) + { + out_color = vec4(0.0, 0.0, 0.0, 1.0); + } + else + { + vec2 wrap = (v_bias.xy + 1.0)/2.0; + out_color = vec4(wrap.xy, 0.5, 1.0); + } +} + `; + + // Compile shaders and link program + const vertexShader = gl.createShader(gl.VERTEX_SHADER); + gl.shaderSource(vertexShader, vertexShaderSource); + gl.compileShader(vertexShader); + + const fragmentShader = gl.createShader(gl.FRAGMENT_SHADER); + gl.shaderSource(fragmentShader, fragmentShaderSource); + gl.compileShader(fragmentShader); + + program = gl.createProgram(); + gl.attachShader(program, vertexShader); + gl.attachShader(program, fragmentShader); + gl.linkProgram(program); + gl.useProgram(program); + + // Set up vertex data + const positionLocation = gl.getAttribLocation(program, "a_position"); + const positionBuffer = gl.createBuffer(); + + gl.bindBuffer(gl.ARRAY_BUFFER, positionBuffer); + gl.bufferData(gl.ARRAY_BUFFER, new Float32Array(objData.vertices), gl.STATIC_DRAW); + // 启用顶点属性数组 + gl.enableVertexAttribArray(0); + gl.vertexAttribPointer(0, 3, gl.FLOAT, false, 20, 0); + + // 启用纹理坐标属性数组 + gl.enableVertexAttribArray(1); + gl.vertexAttribPointer(1, 2, gl.FLOAT, false, 20, 12); +// gl.enableVertexAttribArray(positionLocation); +// gl.vertexAttribPointer(positionLocation, 3, gl.FLOAT, false, 0, 0); + + + indexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, indexBuffer); + gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, new Uint16Array(objData.faces), gl.STATIC_DRAW); + + + // 假设已经有了WebGL上下文gl + +var image = new Image(); +image.onload = function() { + gl.bindTexture(gl.TEXTURE_2D, texture_bs); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.bindTexture(gl.TEXTURE_2D, null); +}; +image.src = 'assets/bs_texture_halfFace.png'; + + } +} +//init(); + + + + function render(mat_world, subPoints, bsArray) { +// console.log("render") + gl.useProgram(program); + const worldMatUniformLocation = gl.getUniformLocation(program, "gWorld0"); + gl.uniformMatrix4fv(worldMatUniformLocation, false, mat_world); + + gl.uniform2fv(gl.getUniformLocation(program, "vertBuffer"), subPoints); + gl.uniform1fv(gl.getUniformLocation(program, "bsVec"), bsArray); + + // 获取着色器程序中uniform变量的位置 + const projectionUniformLocation = gl.getUniformLocation(program, "gProjection"); + + // 使用 gl-matrix 创建正交投影矩阵 + const orthoMatrix = mat4.create(); + + mat4.ortho(orthoMatrix, 0, 128, 0, 128, 1000, -1000); + // console.log(orthoMatrix) + // 将正交投影矩阵传递给着色器 + gl.uniformMatrix4fv(projectionUniformLocation, false, orthoMatrix); + + gl.enable(gl.DEPTH_TEST); + // 启用混合 + gl.enable(gl.BLEND); + gl.blendFunc(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA); + // 启用面剔除 + gl.enable(gl.CULL_FACE); + gl.cullFace(gl.BACK); // 剔除背面 + gl.frontFace(gl.CW); // 通常顶点顺序是顺时针 + // 设置清屏颜色 + gl.clearColor(0.5, 0.5, 0.5, 0); + // 清屏 + gl.clear(gl.COLOR_BUFFER_BIT | gl.DEPTH_BUFFER_BIT); + + gl.activeTexture(gl.TEXTURE0); + gl.bindTexture(gl.TEXTURE_2D, texture_bs); + gl.uniform1i(gl.getUniformLocation(program, 'texture_bs'), 0); + + gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, indexBuffer); + + // Render to canvas + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + + const width = gl.drawingBufferWidth; + const height = gl.drawingBufferHeight; + gl.drawElements(gl.TRIANGLES, objData.faces.length, gl.UNSIGNED_SHORT, 0); + + // 读取像素数据 + gl.readPixels(0, 0, width, height, gl.RGBA, gl.UNSIGNED_BYTE, pixels_fbo); + } + +function resizeCanvas() { + const screenWidth = window.innerWidth; + const screenHeight = window.innerHeight; + + const videoWidth = video.videoWidth; + const videoHeight = video.videoHeight; + const videoAspectRatio = videoWidth / videoHeight; + + let canvasWidth = screenWidth; + let canvasHeight = screenWidth / videoAspectRatio; + + if (canvasHeight > screenHeight) { + canvasHeight = screenHeight; + canvasWidth = screenHeight * videoAspectRatio; + } + + canvas_video.width = canvasWidth; + canvas_video.height = canvasHeight; + + // Center the canvas + canvas_video.style.position = 'absolute'; + canvas_video.style.left = (screenWidth - canvasWidth) / 2 + 'px'; + canvas_video.style.top = (screenHeight - canvasHeight) / 2 + 'px'; + } + + + +async function processVideoFrames() { + let lastDataSetIndex = -1; // 初始化为一个不可能的索引值 + let isProcessing = false; // 标志位 + + let lastVideoTime = 0; // 初始化为一个不可能的索引值 + + const frameCallback = async (currentTime) => { + if (!video.paused && !video.ended && !isProcessing) { + isProcessing = true; + + try { + // 计算当前数据集索引 + const currentDataSetIndex = Math.floor(video.currentTime * 25); + +// console.log("currentDataSetIndex", currentDataSetIndex, video.currentTime, video.currentTime - lastVideoTime) + lastVideoTime = video.currentTime + if (lastDataSetIndex !== currentDataSetIndex && currentDataSetIndex < dataSets.length - 1) { + lastDataSetIndex = currentDataSetIndex; +// console.log("currentDataSetIndex", currentDataSetIndex, lastDataSetIndex, video.currentTime, video.currentTime - lastVideoTime) + // 清除画布并绘制当前视频帧到canvas + ctx_video.clearRect(0, 0, canvas_video.width, canvas_video.height); + ctx_video.drawImage(video, 0, 0, canvas_video.width, canvas_video.height); + // 处理当前数据集 + if (currentDataSetIndex < dataSets.length - 1) { + + // 创建一个ArrayBuffer来存储浮点数 + const floatArraySize = 12; + const floatArrayBytes = floatArraySize * 4; // 每个浮点数占用4个字节 + + var bsPtr = Module._malloc(floatArrayBytes); + Module._updateBlendShape(bsPtr, floatArrayBytes); + // 从Wasm内存中取出数据 + var bsArray = new Float32Array(Module.HEAPU8.buffer, bsPtr, floatArraySize); +// console.log("bs", bsArray); + + + + const dataSet = dataSets[currentDataSetIndex]; + const rect = dataSet.rect; + + const currentTimeStamp = 0.04 * currentDataSetIndex; + const nextTimeStamp = 0.04 * (currentDataSetIndex + 1); + const currentpoints = dataSets[currentDataSetIndex].points; + const nextpoints = dataSets[currentDataSetIndex + 1].points; + +// console.log("rect", rect) + + // 线性插值计算 + const t = (video.currentTime - currentTimeStamp) / (nextTimeStamp - currentTimeStamp); + let points = currentpoints.map((xi, index) => (1-t) * xi + t * nextpoints[index]); + // 创建一个新的 mat4 对象 + let matrix = mat4.create(); + + mat4.set(matrix, + points[0], points[1], points[2], points[3], + points[4], points[5], points[6], points[7], + points[8], points[9], points[10], points[11], + points[12], points[13], points[14], points[15] + ); +// console.log(matrix); + const subPoints = points.slice(16); + render(matrix, subPoints, bsArray); + + // 创建临时画布用于裁剪、缩放和绘点 + const tempCanvas = document.createElement('canvas'); + const tempCtx = tempCanvas.getContext('2d'); + + // 获取rect区域图像数据并绘制到临时画布 + tempCanvas.width = rect[2] - rect[0]; + tempCanvas.height = rect[3] - rect[1]; + tempCtx.drawImage( + video, + rect[0], + rect[1], + rect[2] - rect[0], + rect[3] - rect[1], + 0, + 0, + tempCanvas.width, + tempCanvas.height + ); + + // 缩放到128x128 + resizedCanvas.width = 128; + resizedCanvas.height = 128; + resizedCtx.drawImage(tempCanvas, 0, 0, 128, 128); + + // 获取128x128图像数据并处理 + const imageData = resizedCtx.getImageData(0, 0, 128, 128); + + + var data = imageData.data; + var imageDataPtr = Module._malloc(data.length); + Module.HEAPU8.set(data, imageDataPtr); + + + var imageDataGlPtr = Module._malloc(pixels_fbo.length); + Module.HEAPU8.set(pixels_fbo, imageDataGlPtr); + const processedData = Module._processImage(imageDataPtr, 128, 128, imageDataGlPtr, 128, 128); + var result = Module.HEAPU8.subarray(imageDataPtr, imageDataPtr + data.length); + imageData.data.set(result); + + // 更新 canvas 上的图像显示 + resizedCtx.putImageData(imageData, 0, 0); + // 恢复图像到原始尺寸 + tempCtx.clearRect(0, 0, tempCanvas.width, tempCanvas.height); + tempCtx.drawImage(resizedCanvas, 0, 0, tempCanvas.width, tempCanvas.height); + + // 将临时画布的内容放回原画布 + ctx_video.drawImage(tempCanvas, rect[0], rect[1]); + + // 释放分配的内存 + Module._free(imageDataPtr); + imageDataPtr = null; + Module._free(imageDataGlPtr); + + Module._free(bsPtr); + } + } + + isProcessing = false; // 处理完成后将标志位置为false + } catch (error) { + console.error('Error processing frame:', error); + isProcessing = false; // 即使出错也要将标志位置为false + } + + requestAnimationFrame(frameCallback); + } + }; + + requestAnimationFrame(frameCallback); + } + +document.getElementById('uploadButton').addEventListener('click', function() { + document.getElementById('fileInput').click(); +}); + +document.getElementById('fileInput').addEventListener('change', function(event) { + const file = event.target.files[0]; + if (file) { + readWavFile(file); + } +}); + +function readWavFile(file) { + const reader = new FileReader(); + + reader.onload = function(event) { + const arrayBuffer = event.target.result; + const dataView = new DataView(arrayBuffer); + + // Check if the file is a valid WAV file + if (dataView.getUint32(0, true) !== 0x46464952 || dataView.getUint32(8, true) !== 0x45564157) { + alert('Not a valid WAV file'); + return; + } + + // Get the PCM data chunk + const chunkSize = dataView.getUint32(4, true); + const format = dataView.getUint32(12, true); + const subChunk1Size = dataView.getUint32(16, true); + const audioFormat = dataView.getUint16(20, true); + const numChannels = dataView.getUint16(22, true); + const sampleRate = dataView.getUint32(24, true); + const byteRate = dataView.getUint32(28, true); + const blockAlign = dataView.getUint16(32, true); + const bitsPerSample = dataView.getUint16(34, true); + + // Find the data chunk + let dataOffset = 36; + while (dataOffset < arrayBuffer.byteLength) { + const chunkId = dataView.getUint32(dataOffset, true); + const chunkSize = dataView.getUint32(dataOffset + 4, true); + if (chunkId === 0x61746164) { // "data" chunk + const data = new Uint16Array(arrayBuffer, dataOffset + 8, chunkSize / 2); + console.log('PCM Data:', data); + // Convert PCM data to Uint8Array + const view = new Uint8Array(arrayBuffer); + + // Allocate memory in WebAssembly heap + const arrayBufferPtr = Module._malloc(arrayBuffer.byteLength); + + // Copy data to WebAssembly heap + Module.HEAPU8.set(view, arrayBufferPtr); + + // Call WebAssembly module's C function + console.log("buffer.byteLength", arrayBuffer.byteLength); + Module._setAudioBuffer(arrayBufferPtr, arrayBuffer.byteLength); + + // Free the allocated memory + Module._free(arrayBufferPtr); + + // Set the audio source and play it when the video starts + + audioSource.src = URL.createObjectURL(new Blob([arrayBuffer], { type: 'audio/wav' })); + audio.load(); + audio.play(); + + break; + } + dataOffset += 8 + chunkSize; + } + }; + + reader.readAsArrayBuffer(file); +} \ No newline at end of file diff --git a/web_demo/static/js/MiniMateLoader.js b/web_demo/static/js/MiniMateLoader.js new file mode 100644 index 0000000..8a79d45 --- /dev/null +++ b/web_demo/static/js/MiniMateLoader.js @@ -0,0 +1,232 @@ +document.addEventListener('DOMContentLoaded', function () { + init(); +}); + +async function init() +{ + const spinner = document.querySelector('#loadingSpinner'); + const screen = document.querySelector('#screen'); + const showUi = () => { + spinner.style.display = 'none'; + screen.style.display = 'block'; + } + const instance = await qtLoad({ + qt: { + onLoaded: () => showUi(), + entryFunction: window.createQtAppInstance, + containerElements: [screen], + } + }); + await init_gl(); +} + +async function qtLoad(config) +{ + const throwIfEnvUsedButNotExported = (instance, config) => + { + const environment = config.environment; + if (!environment || Object.keys(environment).length === 0) + return; + const isEnvExported = typeof instance.ENV === 'object'; + if (!isEnvExported) + throw new Error('ENV must be exported if environment variables are passed'); + }; + + const throwIfFsUsedButNotExported = (instance, config) => + { + const environment = config.environment; + if (!environment || Object.keys(environment).length === 0) + return; + const isFsExported = typeof instance.FS === 'object'; + if (!isFsExported) + throw new Error('FS must be exported if preload is used'); + }; + + if (typeof config !== 'object') + throw new Error('config is required, expected an object'); + if (typeof config.qt !== 'object') + throw new Error('config.qt is required, expected an object'); + if (typeof config.qt.entryFunction !== 'function') + config.qt.entryFunction = window.createQtAppInstance; + + config.qt.qtdir ??= 'qt'; + config.qt.preload ??= []; + + config.qtContainerElements = config.qt.containerElements; + delete config.qt.containerElements; + config.qtFontDpi = config.qt.fontDpi; + delete config.qt.fontDpi; + + // Used for rejecting a failed load's promise where emscripten itself does not allow it, + // like in instantiateWasm below. This allows us to throw in case of a load error instead of + // hanging on a promise to entry function, which emscripten unfortunately does. + let circuitBreakerReject; + const circuitBreaker = new Promise((_, reject) => { circuitBreakerReject = reject; }); + + // If module async getter is present, use it so that module reuse is possible. + if (config.qt.module) { + config.instantiateWasm = async (imports, successCallback) => + { + try { + const module = await config.qt.module; + successCallback( + await WebAssembly.instantiate(module, imports), module); + } catch (e) { + circuitBreakerReject(e); + } + } + } + + const qtPreRun = (instance) => { + // Copy qt.environment to instance.ENV + throwIfEnvUsedButNotExported(instance, config); + for (const [name, value] of Object.entries(config.qt.environment ?? {})) + instance.ENV[name] = value; + + // Copy self.preloadData to MEMFS + const makeDirs = (FS, filePath) => { + const parts = filePath.split("/"); + let path = "/"; + for (let i = 0; i < parts.length - 1; ++i) { + const part = parts[i]; + if (part == "") + continue; + path += part + "/"; + try { + FS.mkdir(path); + } catch (error) { + const EEXIST = 20; + if (error.errno != EEXIST) + throw error; + } + } + } + throwIfFsUsedButNotExported(instance, config); + for ({destination, data} of self.preloadData) { + makeDirs(instance.FS, destination); + instance.FS.writeFile(destination, new Uint8Array(data)); + } + } + + if (!config.preRun) + config.preRun = []; + config.preRun.push(qtPreRun); + + config.onRuntimeInitialized = () => config.qt.onLoaded?.(); + + const originalLocateFile = config.locateFile; + config.locateFile = filename => + { + const originalLocatedFilename = originalLocateFile ? originalLocateFile(filename) : filename; + if (originalLocatedFilename.startsWith('libQt6')) + return `${config.qt.qtdir}/lib/${originalLocatedFilename}`; + return originalLocatedFilename; + } + + const originalOnExit = config.onExit; + config.onExit = code => { + originalOnExit?.(); + config.qt.onExit?.({ + code, + crashed: false + }); + } + + const originalOnAbort = config.onAbort; + config.onAbort = text => + { + originalOnAbort?.(); + + aborted = true; + config.qt.onExit?.({ + text, + crashed: true + }); + }; + + const fetchPreloadFiles = async () => { + const fetchJson = async path => (await fetch(path)).json(); + const fetchArrayBuffer = async path => (await fetch(path)).arrayBuffer(); + const loadFiles = async (paths) => { + const source = paths['source'].replace('$QTDIR', config.qt.qtdir); + return { + destination: paths['destination'], + data: await fetchArrayBuffer(source) + }; + } + const fileList = (await Promise.all(config.qt.preload.map(fetchJson))).flat(); + self.preloadData = (await Promise.all(fileList.map(loadFiles))).flat(); + } + + await fetchPreloadFiles(); + + // Call app/emscripten module entry function. It may either come from the emscripten + // runtime script or be customized as needed. + let instance; + try { + instance = await Promise.race( + [circuitBreaker, config.qt.entryFunction(config)]); + } catch (e) { + config.qt.onExit?.({ + text: e.message, + crashed: true + }); + throw e; + } + + return instance; +} + +// Compatibility API. This API is deprecated, +// and will be removed in a future version of Qt. +function QtLoader(qtConfig) { + + const warning = 'Warning: The QtLoader API is deprecated and will be removed in ' + + 'a future version of Qt. Please port to the new qtLoad() API.'; + console.warn(warning); + + let emscriptenConfig = qtConfig.moduleConfig || {} + qtConfig.moduleConfig = undefined; + const showLoader = qtConfig.showLoader; + qtConfig.showLoader = undefined; + const showError = qtConfig.showError; + qtConfig.showError = undefined; + const showExit = qtConfig.showExit; + qtConfig.showExit = undefined; + const showCanvas = qtConfig.showCanvas; + qtConfig.showCanvas = undefined; + if (qtConfig.canvasElements) { + qtConfig.containerElements = qtConfig.canvasElements + qtConfig.canvasElements = undefined; + } else { + qtConfig.containerElements = qtConfig.containerElements; + qtConfig.containerElements = undefined; + } + emscriptenConfig.qt = qtConfig; + + let qtloader = { + exitCode: undefined, + exitText: "", + loadEmscriptenModule: _name => { + try { + qtLoad(emscriptenConfig); + } catch (e) { + showError?.(e.message); + } + } + } + + qtConfig.onLoaded = () => { + showCanvas?.(); + } + + qtConfig.onExit = exit => { + qtloader.exitCode = exit.code + qtloader.exitText = exit.text; + showExit?.(); + } + + showLoader?.("Loading"); + + return qtloader; +}; diff --git a/web_demo/static/js/gl-matrix-min.js b/web_demo/static/js/gl-matrix-min.js new file mode 100644 index 0000000..7571ca0 --- /dev/null +++ b/web_demo/static/js/gl-matrix-min.js @@ -0,0 +1,28 @@ +/*! +@fileoverview gl-matrix - High performance matrix and vector operations +@author Brandon Jones +@author Colin MacKenzie IV +@version 3.4.1 + +Copyright (c) 2015-2021, Brandon Jones, Colin MacKenzie IV. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. + +*/ +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((t="undefined"!=typeof globalThis?globalThis:t||self).glMatrix={})}(this,(function(t){"use strict";var n=1e-6,a="undefined"!=typeof Float32Array?Float32Array:Array,r=Math.random;var u=Math.PI/180;Math.hypot||(Math.hypot=function(){for(var t=0,n=arguments.length;n--;)t+=arguments[n]*arguments[n];return Math.sqrt(t)});var e=Object.freeze({__proto__:null,EPSILON:n,get ARRAY_TYPE(){return a},RANDOM:r,ANGLE_ORDER:"zyx",setMatrixArrayType:function(t){a=t},toRadian:function(t){return t*u},equals:function(t,a){return Math.abs(t-a)<=n*Math.max(1,Math.abs(t),Math.abs(a))}});function 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a=n[0],r=n[1],u=n[2],e=n[3],o=a+a,i=r+r,h=u+u,c=a*o,s=r*o,M=r*i,f=u*o,l=u*i,v=u*h,b=e*o,m=e*i,d=e*h;return t[0]=1-M-v,t[3]=s-d,t[6]=f+m,t[1]=s+d,t[4]=1-c-v,t[7]=l-b,t[2]=f-m,t[5]=l+b,t[8]=1-c-M,t},normalFromMat4:function(t,n){var a=n[0],r=n[1],u=n[2],e=n[3],o=n[4],i=n[5],h=n[6],c=n[7],s=n[8],M=n[9],f=n[10],l=n[11],v=n[12],b=n[13],m=n[14],d=n[15],p=a*i-r*o,x=a*h-u*o,y=a*c-e*o,q=r*h-u*i,g=r*c-e*i,_=u*c-e*h,A=s*b-M*v,w=s*m-f*v,z=s*d-l*v,R=M*m-f*b,O=M*d-l*b,j=f*d-l*m,E=p*j-x*O+y*R+q*z-g*w+_*A;return E?(E=1/E,t[0]=(i*j-h*O+c*R)*E,t[1]=(h*z-o*j-c*w)*E,t[2]=(o*O-i*z+c*A)*E,t[3]=(u*O-r*j-e*R)*E,t[4]=(a*j-u*z+e*w)*E,t[5]=(r*z-a*O-e*A)*E,t[6]=(b*_-m*g+d*q)*E,t[7]=(m*y-v*_-d*x)*E,t[8]=(v*g-b*y+d*p)*E,t):null},projection:function(t,n,a){return t[0]=2/n,t[1]=0,t[2]=0,t[3]=0,t[4]=-2/a,t[5]=0,t[6]=-1,t[7]=1,t[8]=1,t},str:function(t){return"mat3("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+")"},frob:function(t){return Math.hypot(t[0],t[1],t[2],t[3],t[4],t[5],t[6],t[7],t[8])},add:function(t,n,a){return t[0]=n[0]+a[0],t[1]=n[1]+a[1],t[2]=n[2]+a[2],t[3]=n[3]+a[3],t[4]=n[4]+a[4],t[5]=n[5]+a[5],t[6]=n[6]+a[6],t[7]=n[7]+a[7],t[8]=n[8]+a[8],t},subtract:p,multiplyScalar:function(t,n,a){return t[0]=n[0]*a,t[1]=n[1]*a,t[2]=n[2]*a,t[3]=n[3]*a,t[4]=n[4]*a,t[5]=n[5]*a,t[6]=n[6]*a,t[7]=n[7]*a,t[8]=n[8]*a,t},multiplyScalarAndAdd:function(t,n,a,r){return t[0]=n[0]+a[0]*r,t[1]=n[1]+a[1]*r,t[2]=n[2]+a[2]*r,t[3]=n[3]+a[3]*r,t[4]=n[4]+a[4]*r,t[5]=n[5]+a[5]*r,t[6]=n[6]+a[6]*r,t[7]=n[7]+a[7]*r,t[8]=n[8]+a[8]*r,t},exactEquals:function(t,n){return t[0]===n[0]&&t[1]===n[1]&&t[2]===n[2]&&t[3]===n[3]&&t[4]===n[4]&&t[5]===n[5]&&t[6]===n[6]&&t[7]===n[7]&&t[8]===n[8]},equals:function(t,a){var r=t[0],u=t[1],e=t[2],o=t[3],i=t[4],h=t[5],c=t[6],s=t[7],M=t[8],f=a[0],l=a[1],v=a[2],b=a[3],m=a[4],d=a[5],p=a[6],x=a[7],y=a[8];return Math.abs(r-f)<=n*Math.max(1,Math.abs(r),Math.abs(f))&&Math.abs(u-l)<=n*Math.max(1,Math.abs(u),Math.abs(l))&&Math.abs(e-v)<=n*Math.max(1,Math.abs(e),Math.abs(v))&&Math.abs(o-b)<=n*Math.max(1,Math.abs(o),Math.abs(b))&&Math.abs(i-m)<=n*Math.max(1,Math.abs(i),Math.abs(m))&&Math.abs(h-d)<=n*Math.max(1,Math.abs(h),Math.abs(d))&&Math.abs(c-p)<=n*Math.max(1,Math.abs(c),Math.abs(p))&&Math.abs(s-x)<=n*Math.max(1,Math.abs(s),Math.abs(x))&&Math.abs(M-y)<=n*Math.max(1,Math.abs(M),Math.abs(y))},mul:x,sub:y});function g(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}function _(t,n,a){var r=n[0],u=n[1],e=n[2],o=n[3],i=n[4],h=n[5],c=n[6],s=n[7],M=n[8],f=n[9],l=n[10],v=n[11],b=n[12],m=n[13],d=n[14],p=n[15],x=a[0],y=a[1],q=a[2],g=a[3];return t[0]=x*r+y*i+q*M+g*b,t[1]=x*u+y*h+q*f+g*m,t[2]=x*e+y*c+q*l+g*d,t[3]=x*o+y*s+q*v+g*p,x=a[4],y=a[5],q=a[6],g=a[7],t[4]=x*r+y*i+q*M+g*b,t[5]=x*u+y*h+q*f+g*m,t[6]=x*e+y*c+q*l+g*d,t[7]=x*o+y*s+q*v+g*p,x=a[8],y=a[9],q=a[10],g=a[11],t[8]=x*r+y*i+q*M+g*b,t[9]=x*u+y*h+q*f+g*m,t[10]=x*e+y*c+q*l+g*d,t[11]=x*o+y*s+q*v+g*p,x=a[12],y=a[13],q=a[14],g=a[15],t[12]=x*r+y*i+q*M+g*b,t[13]=x*u+y*h+q*f+g*m,t[14]=x*e+y*c+q*l+g*d,t[15]=x*o+y*s+q*v+g*p,t}function A(t,n,a){var r=n[0],u=n[1],e=n[2],o=n[3],i=r+r,h=u+u,c=e+e,s=r*i,M=r*h,f=r*c,l=u*h,v=u*c,b=e*c,m=o*i,d=o*h,p=o*c;return t[0]=1-(l+b),t[1]=M+p,t[2]=f-d,t[3]=0,t[4]=M-p,t[5]=1-(s+b),t[6]=v+m,t[7]=0,t[8]=f+d,t[9]=v-m,t[10]=1-(s+l),t[11]=0,t[12]=a[0],t[13]=a[1],t[14]=a[2],t[15]=1,t}function w(t,n){return t[0]=n[12],t[1]=n[13],t[2]=n[14],t}function z(t,n){var a=n[0],r=n[1],u=n[2],e=n[4],o=n[5],i=n[6],h=n[8],c=n[9],s=n[10];return t[0]=Math.hypot(a,r,u),t[1]=Math.hypot(e,o,i),t[2]=Math.hypot(h,c,s),t}function R(t,n){var r=new a(3);z(r,n);var u=1/r[0],e=1/r[1],o=1/r[2],i=n[0]*u,h=n[1]*e,c=n[2]*o,s=n[4]*u,M=n[5]*e,f=n[6]*o,l=n[8]*u,v=n[9]*e,b=n[10]*o,m=i+M+b,d=0;return m>0?(d=2*Math.sqrt(m+1),t[3]=.25*d,t[0]=(f-v)/d,t[1]=(l-c)/d,t[2]=(h-s)/d):i>M&&i>b?(d=2*Math.sqrt(1+i-M-b),t[3]=(f-v)/d,t[0]=.25*d,t[1]=(h+s)/d,t[2]=(l+c)/d):M>b?(d=2*Math.sqrt(1+M-i-b),t[3]=(l-c)/d,t[0]=(h+s)/d,t[1]=.25*d,t[2]=(f+v)/d):(d=2*Math.sqrt(1+b-i-M),t[3]=(h-s)/d,t[0]=(l+c)/d,t[1]=(f+v)/d,t[2]=.25*d),t}function O(t,n,a,r,u){var e=1/Math.tan(n/2);if(t[0]=e/a,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=-1,t[12]=0,t[13]=0,t[15]=0,null!=u&&u!==1/0){var o=1/(r-u);t[10]=(u+r)*o,t[14]=2*u*r*o}else t[10]=-1,t[14]=-2*r;return t}var j=O;function E(t,n,a,r,u,e,o){var i=1/(n-a),h=1/(r-u),c=1/(e-o);return t[0]=-2*i,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*h,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(n+a)*i,t[13]=(u+r)*h,t[14]=(o+e)*c,t[15]=1,t}var P=E;function T(t,n,a){return t[0]=n[0]-a[0],t[1]=n[1]-a[1],t[2]=n[2]-a[2],t[3]=n[3]-a[3],t[4]=n[4]-a[4],t[5]=n[5]-a[5],t[6]=n[6]-a[6],t[7]=n[7]-a[7],t[8]=n[8]-a[8],t[9]=n[9]-a[9],t[10]=n[10]-a[10],t[11]=n[11]-a[11],t[12]=n[12]-a[12],t[13]=n[13]-a[13],t[14]=n[14]-a[14],t[15]=n[15]-a[15],t}var S=_,D=T,F=Object.freeze({__proto__:null,create:function(){var t=new a(16);return a!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},clone:function(t){var n=new a(16);return n[0]=t[0],n[1]=t[1],n[2]=t[2],n[3]=t[3],n[4]=t[4],n[5]=t[5],n[6]=t[6],n[7]=t[7],n[8]=t[8],n[9]=t[9],n[10]=t[10],n[11]=t[11],n[12]=t[12],n[13]=t[13],n[14]=t[14],n[15]=t[15],n},copy:function(t,n){return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],t},fromValues:function(t,n,r,u,e,o,i,h,c,s,M,f,l,v,b,m){var d=new a(16);return d[0]=t,d[1]=n,d[2]=r,d[3]=u,d[4]=e,d[5]=o,d[6]=i,d[7]=h,d[8]=c,d[9]=s,d[10]=M,d[11]=f,d[12]=l,d[13]=v,d[14]=b,d[15]=m,d},set:function(t,n,a,r,u,e,o,i,h,c,s,M,f,l,v,b,m){return t[0]=n,t[1]=a,t[2]=r,t[3]=u,t[4]=e,t[5]=o,t[6]=i,t[7]=h,t[8]=c,t[9]=s,t[10]=M,t[11]=f,t[12]=l,t[13]=v,t[14]=b,t[15]=m,t},identity:g,transpose:function(t,n){if(t===n){var a=n[1],r=n[2],u=n[3],e=n[6],o=n[7],i=n[11];t[1]=n[4],t[2]=n[8],t[3]=n[12],t[4]=a,t[6]=n[9],t[7]=n[13],t[8]=r,t[9]=e,t[11]=n[14],t[12]=u,t[13]=o,t[14]=i}else t[0]=n[0],t[1]=n[4],t[2]=n[8],t[3]=n[12],t[4]=n[1],t[5]=n[5],t[6]=n[9],t[7]=n[13],t[8]=n[2],t[9]=n[6],t[10]=n[10],t[11]=n[14],t[12]=n[3],t[13]=n[7],t[14]=n[11],t[15]=n[15];return t},invert:function(t,n){var a=n[0],r=n[1],u=n[2],e=n[3],o=n[4],i=n[5],h=n[6],c=n[7],s=n[8],M=n[9],f=n[10],l=n[11],v=n[12],b=n[13],m=n[14],d=n[15],p=a*i-r*o,x=a*h-u*o,y=a*c-e*o,q=r*h-u*i,g=r*c-e*i,_=u*c-e*h,A=s*b-M*v,w=s*m-f*v,z=s*d-l*v,R=M*m-f*b,O=M*d-l*b,j=f*d-l*m,E=p*j-x*O+y*R+q*z-g*w+_*A;return E?(E=1/E,t[0]=(i*j-h*O+c*R)*E,t[1]=(u*O-r*j-e*R)*E,t[2]=(b*_-m*g+d*q)*E,t[3]=(f*g-M*_-l*q)*E,t[4]=(h*z-o*j-c*w)*E,t[5]=(a*j-u*z+e*w)*E,t[6]=(m*y-v*_-d*x)*E,t[7]=(s*_-f*y+l*x)*E,t[8]=(o*O-i*z+c*A)*E,t[9]=(r*z-a*O-e*A)*E,t[10]=(v*g-b*y+d*p)*E,t[11]=(M*y-s*g-l*p)*E,t[12]=(i*w-o*R-h*A)*E,t[13]=(a*R-r*w+u*A)*E,t[14]=(b*x-v*q-m*p)*E,t[15]=(s*q-M*x+f*p)*E,t):null},adjoint:function(t,n){var a=n[0],r=n[1],u=n[2],e=n[3],o=n[4],i=n[5],h=n[6],c=n[7],s=n[8],M=n[9],f=n[10],l=n[11],v=n[12],b=n[13],m=n[14],d=n[15],p=a*i-r*o,x=a*h-u*o,y=a*c-e*o,q=r*h-u*i,g=r*c-e*i,_=u*c-e*h,A=s*b-M*v,w=s*m-f*v,z=s*d-l*v,R=M*m-f*b,O=M*d-l*b,j=f*d-l*m;return t[0]=i*j-h*O+c*R,t[1]=u*O-r*j-e*R,t[2]=b*_-m*g+d*q,t[3]=f*g-M*_-l*q,t[4]=h*z-o*j-c*w,t[5]=a*j-u*z+e*w,t[6]=m*y-v*_-d*x,t[7]=s*_-f*y+l*x,t[8]=o*O-i*z+c*A,t[9]=r*z-a*O-e*A,t[10]=v*g-b*y+d*p,t[11]=M*y-s*g-l*p,t[12]=i*w-o*R-h*A,t[13]=a*R-r*w+u*A,t[14]=b*x-v*q-m*p,t[15]=s*q-M*x+f*p,t},determinant:function(t){var n=t[0],a=t[1],r=t[2],u=t[3],e=t[4],o=t[5],i=t[6],h=t[7],c=t[8],s=t[9],M=t[10],f=t[11],l=t[12],v=t[13],b=t[14],m=n*o-a*e,d=n*i-r*e,p=a*i-r*o,x=c*v-s*l,y=c*b-M*l,q=s*b-M*v;return h*(n*q-a*y+r*x)-u*(e*q-o*y+i*x)+t[15]*(c*p-s*d+M*m)-f*(l*p-v*d+b*m)},multiply:_,translate:function(t,n,a){var r,u,e,o,i,h,c,s,M,f,l,v,b=a[0],m=a[1],d=a[2];return n===t?(t[12]=n[0]*b+n[4]*m+n[8]*d+n[12],t[13]=n[1]*b+n[5]*m+n[9]*d+n[13],t[14]=n[2]*b+n[6]*m+n[10]*d+n[14],t[15]=n[3]*b+n[7]*m+n[11]*d+n[15]):(r=n[0],u=n[1],e=n[2],o=n[3],i=n[4],h=n[5],c=n[6],s=n[7],M=n[8],f=n[9],l=n[10],v=n[11],t[0]=r,t[1]=u,t[2]=e,t[3]=o,t[4]=i,t[5]=h,t[6]=c,t[7]=s,t[8]=M,t[9]=f,t[10]=l,t[11]=v,t[12]=r*b+i*m+M*d+n[12],t[13]=u*b+h*m+f*d+n[13],t[14]=e*b+c*m+l*d+n[14],t[15]=o*b+s*m+v*d+n[15]),t},scale:function(t,n,a){var r=a[0],u=a[1],e=a[2];return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=n[3]*r,t[4]=n[4]*u,t[5]=n[5]*u,t[6]=n[6]*u,t[7]=n[7]*u,t[8]=n[8]*e,t[9]=n[9]*e,t[10]=n[10]*e,t[11]=n[11]*e,t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],t},rotate:function(t,a,r,u){var e,o,i,h,c,s,M,f,l,v,b,m,d,p,x,y,q,g,_,A,w,z,R,O,j=u[0],E=u[1],P=u[2],T=Math.hypot(j,E,P);return T0?(r[0]=2*(h*i+M*u+c*o-s*e)/f,r[1]=2*(c*i+M*e+s*u-h*o)/f,r[2]=2*(s*i+M*o+h*e-c*u)/f):(r[0]=2*(h*i+M*u+c*o-s*e),r[1]=2*(c*i+M*e+s*u-h*o),r[2]=2*(s*i+M*o+h*e-c*u)),A(t,n,r),t},getTranslation:w,getScaling:z,getRotation:R,decompose:function(t,n,a,r){n[0]=r[12],n[1]=r[13],n[2]=r[14];var u=r[0],e=r[1],o=r[2],i=r[4],h=r[5],c=r[6],s=r[8],M=r[9],f=r[10];a[0]=Math.hypot(u,e,o),a[1]=Math.hypot(i,h,c),a[2]=Math.hypot(s,M,f);var l=1/a[0],v=1/a[1],b=1/a[2],m=u*l,d=e*v,p=o*b,x=i*l,y=h*v,q=c*b,g=s*l,_=M*v,A=f*b,w=m+y+A,z=0;return w>0?(z=2*Math.sqrt(w+1),t[3]=.25*z,t[0]=(q-_)/z,t[1]=(g-p)/z,t[2]=(d-x)/z):m>y&&m>A?(z=2*Math.sqrt(1+m-y-A),t[3]=(q-_)/z,t[0]=.25*z,t[1]=(d+x)/z,t[2]=(g+p)/z):y>A?(z=2*Math.sqrt(1+y-m-A),t[3]=(g-p)/z,t[0]=(d+x)/z,t[1]=.25*z,t[2]=(q+_)/z):(z=2*Math.sqrt(1+A-m-y),t[3]=(d-x)/z,t[0]=(g+p)/z,t[1]=(q+_)/z,t[2]=.25*z),t},fromRotationTranslationScale:function(t,n,a,r){var u=n[0],e=n[1],o=n[2],i=n[3],h=u+u,c=e+e,s=o+o,M=u*h,f=u*c,l=u*s,v=e*c,b=e*s,m=o*s,d=i*h,p=i*c,x=i*s,y=r[0],q=r[1],g=r[2];return t[0]=(1-(v+m))*y,t[1]=(f+x)*y,t[2]=(l-p)*y,t[3]=0,t[4]=(f-x)*q,t[5]=(1-(M+m))*q,t[6]=(b+d)*q,t[7]=0,t[8]=(l+p)*g,t[9]=(b-d)*g,t[10]=(1-(M+v))*g,t[11]=0,t[12]=a[0],t[13]=a[1],t[14]=a[2],t[15]=1,t},fromRotationTranslationScaleOrigin:function(t,n,a,r,u){var e=n[0],o=n[1],i=n[2],h=n[3],c=e+e,s=o+o,M=i+i,f=e*c,l=e*s,v=e*M,b=o*s,m=o*M,d=i*M,p=h*c,x=h*s,y=h*M,q=r[0],g=r[1],_=r[2],A=u[0],w=u[1],z=u[2],R=(1-(b+d))*q,O=(l+y)*q,j=(v-x)*q,E=(l-y)*g,P=(1-(f+d))*g,T=(m+p)*g,S=(v+x)*_,D=(m-p)*_,F=(1-(f+b))*_;return t[0]=R,t[1]=O,t[2]=j,t[3]=0,t[4]=E,t[5]=P,t[6]=T,t[7]=0,t[8]=S,t[9]=D,t[10]=F,t[11]=0,t[12]=a[0]+A-(R*A+E*w+S*z),t[13]=a[1]+w-(O*A+P*w+D*z),t[14]=a[2]+z-(j*A+T*w+F*z),t[15]=1,t},fromQuat:function(t,n){var a=n[0],r=n[1],u=n[2],e=n[3],o=a+a,i=r+r,h=u+u,c=a*o,s=r*o,M=r*i,f=u*o,l=u*i,v=u*h,b=e*o,m=e*i,d=e*h;return t[0]=1-M-v,t[1]=s+d,t[2]=f-m,t[3]=0,t[4]=s-d,t[5]=1-c-v,t[6]=l+b,t[7]=0,t[8]=f+m,t[9]=l-b,t[10]=1-c-M,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},frustum:function(t,n,a,r,u,e,o){var i=1/(a-n),h=1/(u-r),c=1/(e-o);return t[0]=2*e*i,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*e*h,t[6]=0,t[7]=0,t[8]=(a+n)*i,t[9]=(u+r)*h,t[10]=(o+e)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*e*2*c,t[15]=0,t},perspectiveNO:O,perspective:j,perspectiveZO:function(t,n,a,r,u){var e=1/Math.tan(n/2);if(t[0]=e/a,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=-1,t[12]=0,t[13]=0,t[15]=0,null!=u&&u!==1/0){var o=1/(r-u);t[10]=u*o,t[14]=u*r*o}else t[10]=-1,t[14]=-r;return 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t[0]=n[0]*a,t[1]=n[1]*a,t[2]=n[2]*a,t[3]=n[3]*a,t[4]=n[4]*a,t[5]=n[5]*a,t[6]=n[6]*a,t[7]=n[7]*a,t[8]=n[8]*a,t[9]=n[9]*a,t[10]=n[10]*a,t[11]=n[11]*a,t[12]=n[12]*a,t[13]=n[13]*a,t[14]=n[14]*a,t[15]=n[15]*a,t},multiplyScalarAndAdd:function(t,n,a,r){return t[0]=n[0]+a[0]*r,t[1]=n[1]+a[1]*r,t[2]=n[2]+a[2]*r,t[3]=n[3]+a[3]*r,t[4]=n[4]+a[4]*r,t[5]=n[5]+a[5]*r,t[6]=n[6]+a[6]*r,t[7]=n[7]+a[7]*r,t[8]=n[8]+a[8]*r,t[9]=n[9]+a[9]*r,t[10]=n[10]+a[10]*r,t[11]=n[11]+a[11]*r,t[12]=n[12]+a[12]*r,t[13]=n[13]+a[13]*r,t[14]=n[14]+a[14]*r,t[15]=n[15]+a[15]*r,t},exactEquals:function(t,n){return t[0]===n[0]&&t[1]===n[1]&&t[2]===n[2]&&t[3]===n[3]&&t[4]===n[4]&&t[5]===n[5]&&t[6]===n[6]&&t[7]===n[7]&&t[8]===n[8]&&t[9]===n[9]&&t[10]===n[10]&&t[11]===n[11]&&t[12]===n[12]&&t[13]===n[13]&&t[14]===n[14]&&t[15]===n[15]},equals:function(t,a){var r=t[0],u=t[1],e=t[2],o=t[3],i=t[4],h=t[5],c=t[6],s=t[7],M=t[8],f=t[9],l=t[10],v=t[11],b=t[12],m=t[13],d=t[14],p=t[15],x=a[0],y=a[1],q=a[2],g=a[3],_=a[4],A=a[5],w=a[6],z=a[7],R=a[8],O=a[9],j=a[10],E=a[11],P=a[12],T=a[13],S=a[14],D=a[15];return 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t[0]=n[4],t[1]=n[5],t[2]=n[6],t[3]=n[7],t},setReal:mn,setDual:function(t,n){return t[4]=n[0],t[5]=n[1],t[6]=n[2],t[7]=n[3],t},getTranslation:function(t,n){var a=n[4],r=n[5],u=n[6],e=n[7],o=-n[0],i=-n[1],h=-n[2],c=n[3];return t[0]=2*(a*c+e*o+r*h-u*i),t[1]=2*(r*c+e*i+u*o-a*h),t[2]=2*(u*c+e*h+a*i-r*o),t},translate:function(t,n,a){var r=n[0],u=n[1],e=n[2],o=n[3],i=.5*a[0],h=.5*a[1],c=.5*a[2],s=n[4],M=n[5],f=n[6],l=n[7];return t[0]=r,t[1]=u,t[2]=e,t[3]=o,t[4]=o*i+u*c-e*h+s,t[5]=o*h+e*i-r*c+M,t[6]=o*c+r*h-u*i+f,t[7]=-r*i-u*h-e*c+l,t},rotateX:function(t,n,a){var r=-n[0],u=-n[1],e=-n[2],o=n[3],i=n[4],h=n[5],c=n[6],s=n[7],M=i*o+s*r+h*e-c*u,f=h*o+s*u+c*r-i*e,l=c*o+s*e+i*u-h*r,v=s*o-i*r-h*u-c*e;return Dt(t,n,a),r=t[0],u=t[1],e=t[2],o=t[3],t[4]=M*o+v*r+f*e-l*u,t[5]=f*o+v*u+l*r-M*e,t[6]=l*o+v*e+M*u-f*r,t[7]=v*o-M*r-f*u-l*e,t},rotateY:function(t,n,a){var r=-n[0],u=-n[1],e=-n[2],o=n[3],i=n[4],h=n[5],c=n[6],s=n[7],M=i*o+s*r+h*e-c*u,f=h*o+s*u+c*r-i*e,l=c*o+s*e+i*u-h*r,v=s*o-i*r-h*u-c*e;return 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