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Jasonkks · Mar 16, 2022 · 7740c11 · 7740c11
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diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,2 @@
+.DS_Store
+*pyc
diff --git a/Dataset.py b/Dataset.py
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+from torch.utils.data import Dataset
+from data_classes import PointCloud, Box
+from pyquaternion import Quaternion
+import numpy as np
+import pandas as pd
+import os
+import torch
+from tqdm import tqdm
+import kitty_utils as utils
+from kitty_utils import getModel
+from searchspace import KalmanFiltering
+import logging
+from functools import partial
+import copy
+
+class kittiDataset():
+    def __init__(self, path):
+        self.KITTI_Folder = path
+        self.KITTI_velo = os.path.join(self.KITTI_Folder, "velodyne")
+        self.KITTI_label = os.path.join(self.KITTI_Folder, "label_02")
+
+    def getSceneID(self, split):
+        if "TRAIN" in split.upper():  # Training SET
+            if "TINY" in split.upper():
+                sceneID = [0]
+            else:
+                sceneID = list(range(0, 17))
+        elif "VALID" in split.upper():  # Validation Set
+            if "TINY" in split.upper():
+                sceneID = [18]
+            else:
+                sceneID = list(range(17, 19))
+        elif "TEST" in split.upper():  # Testing Set
+            if "TINY" in split.upper():
+                sceneID = [19]
+            else:
+                sceneID = list(range(19, 21))
+
+        else:  # Full Dataset
+            sceneID = list(range(21))
+        return sceneID
+
+    def getBBandPC(self, anno):
+        calib_path = os.path.join(self.KITTI_Folder, 'calib',
+                                  anno['scene'] + ".txt")
+        calib = self.read_calib_file(calib_path)
+        transf_mat = np.vstack((calib["Tr_velo_cam"], np.array([0, 0, 0, 1])))
+        PC, box = self.getPCandBBfromPandas(anno, transf_mat)
+        return PC, box
+
+    def getListOfAnno(self, sceneID, category_name="Car"):
+        list_of_scene = [
+            path for path in os.listdir(self.KITTI_velo)
+            if os.path.isdir(os.path.join(self.KITTI_velo, path)) and
+            int(path) in sceneID
+        ]
+        list_of_tracklet_anno = []
+        for scene in list_of_scene:
+
+            label_file = os.path.join(self.KITTI_label, scene + ".txt")
+            df = pd.read_csv(
+                label_file,
+                sep=' ',
+                names=[
+                    "frame", "track_id", "type", "truncated", "occluded",
+                    "alpha", "bbox_left", "bbox_top", "bbox_right",
+                    "bbox_bottom", "height", "width", "length", "x", "y", "z",
+                    "rotation_y"
+                ])
+            df = df[df["type"] == category_name]
+            df.insert(loc=0, column="scene", value=scene)
+            for track_id in df.track_id.unique():
+                df_tracklet = df[df["track_id"] == track_id]
+                df_tracklet = df_tracklet.reset_index(drop=True)
+                tracklet_anno = [anno for index, anno in df_tracklet.iterrows()]
+                list_of_tracklet_anno.append(tracklet_anno)
+
+        return list_of_tracklet_anno
+
+    def getPCandBBfromPandas(self, box, calib):
+        center = [box["x"], box["y"] - box["height"] / 2, box["z"]]
+        size = [box["width"], box["length"], box["height"]]
+        orientation = Quaternion(
+            axis=[0, 1, 0], radians=box["rotation_y"]) * Quaternion(
+                axis=[1, 0, 0], radians=np.pi / 2)
+        BB = Box(center, size, orientation)
+
+        try:
+            # VELODYNE PointCloud
+            velodyne_path = os.path.join(self.KITTI_velo, box["scene"],
+                                         '{:06}.bin'.format(box["frame"]))
+            PC = PointCloud(
+                np.fromfile(velodyne_path, dtype=np.float32).reshape(-1, 4).T)
+            PC.transform(calib)
+        except :
+            # in case the Point cloud is missing
+            # (0001/[000177-000180].bin)
+            PC = PointCloud(np.array([[0, 0, 0]]).T)
+
+        return PC, BB
+
+    def read_calib_file(self, filepath):
+        """Read in a calibration file and parse into a dictionary."""
+        data = {}
+        with open(filepath, 'r') as f:
+            for line in f.readlines():
+                values = line.split()
+                # The only non-float values in these files are dates, which
+                # we don't care about anyway
+                try:
+                    data[values[0]] = np.array(
+                        [float(x) for x in values[1:]]).reshape(3, 4)
+                except ValueError:
+                    pass
+        return data
+
+
+class SiameseDataset(Dataset):
+
+    def __init__(self,
+                 input_size,
+                 path,
+                 split,
+                 category_name="Car",
+                 regress="GAUSSIAN",
+                 offset_BB=0,
+                 scale_BB=1.0):
+        self.dataset = kittiDataset(path=path)
+
+        self.input_size = input_size
+        self.split = split
+        self.sceneID = self.dataset.getSceneID(split=split)
+        self.getBBandPC = self.dataset.getBBandPC
+
+        self.category_name = category_name
+        self.regress = regress
+
+        self.list_of_tracklet_anno = self.dataset.getListOfAnno(
+            self.sceneID, category_name)
+
+        self.list_of_anno = [
+            anno for tracklet_anno in self.list_of_tracklet_anno
+            for anno in tracklet_anno
+        ]
+
+    def isTiny(self):
+        return ("TINY" in self.split.upper())
+
+    def __getitem__(self, index):
+        return self.getitem(index)
+
+
+class SiameseTrain(SiameseDataset):
+    def __init__(self,
+                 input_size,
+                 path,
+                 split="",
+                 category_name="Car",
+                 regress="GAUSSIAN",
+                 sigma_Gaussian=1,
+                 offset_BB=0,
+                 scale_BB=1.0):
+        super(SiameseTrain,self).__init__(
+            input_size=input_size,
+            path=path,
+            split=split,
+            category_name=category_name,
+            regress=regress,
+            offset_BB=offset_BB,
+            scale_BB=scale_BB)
+
+        self.sigma_Gaussian = sigma_Gaussian
+        self.offset_BB = offset_BB
+        self.scale_BB = scale_BB
+
+        self.num_candidates_perframe = 4
+        logging.info("preloading PC...")
+        self.list_of_PCs = [None] * len(self.list_of_anno)
+        self.list_of_BBs = [None] * len(self.list_of_anno)
+        for index in tqdm(range(len(self.list_of_anno))):
+            anno = self.list_of_anno[index]
+            PC, box = self.getBBandPC(anno)
+            new_PC = utils.cropPC(PC, box, offset=10)
+
+            self.list_of_PCs[index] = new_PC
+            self.list_of_BBs[index] = box
+        logging.info("PC preloaded!")
+
+        logging.info("preloading Model..")
+        self.model_PC = [None] * len(self.list_of_tracklet_anno)
+        for i in tqdm(range(len(self.list_of_tracklet_anno))):
+            list_of_anno = self.list_of_tracklet_anno[i]
+            PCs = []
+            BBs = []
+            cnt = 0
+            for anno in list_of_anno:
+                this_PC, this_BB = self.getBBandPC(anno)
+                PCs.append(this_PC)
+                BBs.append(this_BB)
+                anno["model_idx"] = i
+                anno["relative_idx"] = cnt
+                cnt += 1
+
+            self.model_PC[i] = getModel(
+                PCs, BBs, offset=self.offset_BB, scale=self.scale_BB)
+        logging.info("Model preloaded!")
+
+    def __getitem__(self, index):
+        return self.getitem(index)
+
+    def getPCandBBfromIndex(self, anno_idx):
+        this_PC = self.list_of_PCs[anno_idx]
+        this_BB = self.list_of_BBs[anno_idx]
+        return this_PC, this_BB
+
+    def getitem(self, index):
+        anno_idx = self.getAnnotationIndex(index)
+        sample_idx = self.getSearchSpaceIndex(index)
+
+        def random_box(box, center_offset, w_ratio, h_ratio, flag):
+            if not flag:
+                return box
+            box = copy.deepcopy(box)
+            box.center[0] += center_offset[0] * box.wlh[1]
+            box.center[1] += center_offset[1] * box.wlh[0]
+            box.wlh[0] *= w_ratio
+            box.wlh[1] *= h_ratio
+            return box
+
+        random_box_func = partial(random_box, **dict(
+            center_offset=[np.random.uniform(-0.4, 0.4),
+                           np.random.uniform(-0.4, 0.4)],
+            w_ratio=np.random.uniform(0.3, 1.0),
+            h_ratio=np.random.uniform(0.3, 1.0),
+            flag=np.random.uniform() < 0.0 # prob
+        ))
+
+        if sample_idx == 0:
+            sample_offsets = np.zeros(4)
+        else:
+            gaussian = KalmanFiltering(bnd=[1, 1, 1, 1])
+            sample_offsets = gaussian.sample(1)[0]
+            sample_offsets[1] /= 2.0
+            sample_offsets[0] *= 2
+
+        this_anno = self.list_of_anno[anno_idx]
+        this_PC, this_BB = self.getPCandBBfromIndex(anno_idx)
+        # Random bbox
+        sample_BB = utils.getOffsetBB(this_BB, sample_offsets)
+
+        sample_BB = random_box_func(box=sample_BB)
+
+        sample_PC, sample_label, sample_reg = utils.cropAndCenterPC_label(
+            this_PC, sample_BB, this_BB, sample_offsets,
+            offset=self.offset_BB, scale=self.scale_BB)
+
+        if sample_PC.nbr_points() <= 10:
+            return self.getitem(np.random.randint(0, self.__len__()))
+
+        random_downsample = np.random.uniform() < 0.0
+        def _random_sample_pts(pc, num):
+            p = np.array(pc.points, dtype=np.float32)
+            if p.shape[1] < 10:
+                return pc
+            new_idx = np.random.randint(low=0, high=p.shape[1], size=num, dtype=np.int64)
+            p = p[:, new_idx]
+            pc.points = p
+            return pc
+        if random_downsample:
+            random_downsample_pc_func = partial(_random_sample_pts,
+                num=np.random.randint(min(128, sample_PC.points.shape[1] - 1),
+                                      sample_PC.points.shape[1]))
+            sample_PC = random_downsample_pc_func(sample_PC)
+        # sample_PC = utils.regularizePC(sample_PC, self.input_size)[0]
+        sample_PC, sample_label, sample_reg = utils.regularizePCwithlabel(
+            sample_PC, sample_label, sample_reg, self.input_size)
+
+        if this_anno["relative_idx"] == 0:
+            prev_idx = 0
+            fir_idx = 0
+        else:
+            prev_idx = anno_idx - 1
+            fir_idx = anno_idx - this_anno["relative_idx"]
+        gt_PC_pre, gt_BB_pre = self.getPCandBBfromIndex(prev_idx)
+        gt_PC_fir, gt_BB_fir = self.getPCandBBfromIndex(fir_idx)
+
+        gt_BB_pre = random_box_func(box=gt_BB_pre)
+        gt_BB_fir = random_box_func(box=gt_BB_fir)
+
+        if sample_idx == 0:
+            samplegt_offsets = np.zeros(4)
+        else:
+            samplegt_offsets = np.random.uniform(low=-0.3, high=0.3, size=4)
+            samplegt_offsets[0] *= 2
+
+        gt_BB_pre = utils.getOffsetBB(gt_BB_pre, samplegt_offsets)
+        gt_PC = getModel([gt_PC_pre], [gt_BB_pre], offset=self.offset_BB, scale=self.scale_BB)
+        if random_downsample:
+            gt_PC = random_downsample_pc_func(gt_PC)
+
+        if gt_PC.nbr_points() <= 20:
+            return self.getitem(np.random.randint(0, self.__len__()))
+        gt_PC = utils.regularizePC(gt_PC, self.input_size)
+
+        ret = {
+            'search' : sample_PC,
+            'template' : gt_PC,
+            'cls_label' : sample_label, # whether in box
+            'reg_label' : sample_reg # box
+        }
+        return ret # sample_PC, sample_label, sample_reg, gt_PC
+
+    def __len__(self):
+        nb_anno = len(self.list_of_anno)
+        return nb_anno * self.num_candidates_perframe
+
+    def getAnnotationIndex(self, index):
+        return int(index / (self.num_candidates_perframe))
+
+    def getSearchSpaceIndex(self, index):
+        return int(index % self.num_candidates_perframe)
+
+
+class SiameseTest(SiameseDataset):
+
+    def __init__(self,
+                 input_size,
+                 path,
+                 split="",
+                 category_name="Car",
+                 regress="GAUSSIAN",
+                 offset_BB=0,
+                 scale_BB=1.0):
+        super(SiameseTest,self).__init__(
+            input_size=input_size,
+            path=path,
+            split=split,
+            category_name=category_name,
+            regress=regress,
+            offset_BB=offset_BB,
+            scale_BB=scale_BB)
+        self.split = split
+        self.offset_BB = offset_BB
+        self.scale_BB = scale_BB
+
+    def getitem(self, index):
+        list_of_anno = self.list_of_tracklet_anno[index]
+        PCs = []
+        BBs = []
+        for anno in list_of_anno:
+            this_PC, this_BB = self.getBBandPC(anno)
+            PCs.append(this_PC)
+            BBs.append(this_BB)
+        return PCs, BBs, list_of_anno
+
+    def __len__(self):
+        return len(self.list_of_tracklet_anno)
+
+
+if __name__ == '__main__':
+
+    dataset_Training = SiameseTrain(
+           input_size=2048,
+           path='/data/qihaozhe/Kitty_data/training',
+           split='Tiny_Train',
+           category_name='Car',
+           offset_BB=0,
+           scale_BB=1.15)
+    aa = dataset_Training.getitem(201)
+    aa = dataset_Training.getitem(30)
+    aa = dataset_Training.getitem(100)
+    aa = dataset_Training.getitem(120)
+    aa = dataset_Training.getitem(200)
+    asdf = aa[2].numpy()