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demo_mini.py
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demo_mini.py
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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 <video_path> <audio_path> <output_video_name>")
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()