132 lines
6.5 KiB
Python
132 lines
6.5 KiB
Python
import os
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import numpy as np
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import json
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import cv2
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# Use the same script for MOT16
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DATA_PATH = 'datasets/MOT20'
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OUT_PATH = os.path.join(DATA_PATH, 'annotations')
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SPLITS = ['train_half', 'val_half', 'train', 'test'] # --> split training data to train_half and val_half.
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HALF_VIDEO = True
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CREATE_SPLITTED_ANN = True
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CREATE_SPLITTED_DET = True
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if __name__ == '__main__':
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if not os.path.exists(OUT_PATH):
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os.makedirs(OUT_PATH)
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for split in SPLITS:
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if split == "test":
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data_path = os.path.join(DATA_PATH, 'test')
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else:
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data_path = os.path.join(DATA_PATH, 'train')
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out_path = os.path.join(OUT_PATH, '{}.json'.format(split))
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out = {'images': [], 'annotations': [], 'videos': [],
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'categories': [{'id': 1, 'name': 'pedestrian'}]}
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seqs = os.listdir(data_path)
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image_cnt = 0
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ann_cnt = 0
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video_cnt = 0
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tid_curr = 0
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tid_last = -1
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for seq in sorted(seqs):
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if '.DS_Store' in seq:
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continue
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video_cnt += 1 # video sequence number.
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out['videos'].append({'id': video_cnt, 'file_name': seq})
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seq_path = os.path.join(data_path, seq)
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img_path = os.path.join(seq_path, 'img1')
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ann_path = os.path.join(seq_path, 'gt/gt.txt')
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images = os.listdir(img_path)
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num_images = len([image for image in images if 'jpg' in image]) # half and half
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if HALF_VIDEO and ('half' in split):
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image_range = [0, num_images // 2] if 'train' in split else \
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[num_images // 2 + 1, num_images - 1]
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else:
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image_range = [0, num_images - 1]
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for i in range(num_images):
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if i < image_range[0] or i > image_range[1]:
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continue
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img = cv2.imread(os.path.join(data_path, '{}/img1/{:06d}.jpg'.format(seq, i + 1)))
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height, width = img.shape[:2]
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image_info = {'file_name': '{}/img1/{:06d}.jpg'.format(seq, i + 1), # image name.
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'id': image_cnt + i + 1, # image number in the entire training set.
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'frame_id': i + 1 - image_range[0], # image number in the video sequence, starting from 1.
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'prev_image_id': image_cnt + i if i > 0 else -1, # image number in the entire training set.
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'next_image_id': image_cnt + i + 2 if i < num_images - 1 else -1,
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'video_id': video_cnt,
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'height': height, 'width': width}
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out['images'].append(image_info)
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print('{}: {} images'.format(seq, num_images))
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if split != 'test':
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det_path = os.path.join(seq_path, 'det/det.txt')
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anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=',')
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dets = np.loadtxt(det_path, dtype=np.float32, delimiter=',')
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if CREATE_SPLITTED_ANN and ('half' in split):
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anns_out = np.array([anns[i] for i in range(anns.shape[0])
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if int(anns[i][0]) - 1 >= image_range[0] and
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int(anns[i][0]) - 1 <= image_range[1]], np.float32)
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anns_out[:, 0] -= image_range[0]
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gt_out = os.path.join(seq_path, 'gt/gt_{}.txt'.format(split))
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fout = open(gt_out, 'w')
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for o in anns_out:
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fout.write('{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:.6f}\n'.format(
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int(o[0]), int(o[1]), int(o[2]), int(o[3]), int(o[4]), int(o[5]),
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int(o[6]), int(o[7]), o[8]))
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fout.close()
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if CREATE_SPLITTED_DET and ('half' in split):
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dets_out = np.array([dets[i] for i in range(dets.shape[0])
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if int(dets[i][0]) - 1 >= image_range[0] and
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int(dets[i][0]) - 1 <= image_range[1]], np.float32)
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dets_out[:, 0] -= image_range[0]
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det_out = os.path.join(seq_path, 'det/det_{}.txt'.format(split))
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dout = open(det_out, 'w')
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for o in dets_out:
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dout.write('{:d},{:d},{:.1f},{:.1f},{:.1f},{:.1f},{:.6f}\n'.format(
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int(o[0]), int(o[1]), float(o[2]), float(o[3]), float(o[4]), float(o[5]),
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float(o[6])))
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dout.close()
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print('{} ann images'.format(int(anns[:, 0].max())))
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for i in range(anns.shape[0]):
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frame_id = int(anns[i][0])
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if frame_id - 1 < image_range[0] or frame_id - 1 > image_range[1]:
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continue
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track_id = int(anns[i][1])
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cat_id = int(anns[i][7])
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ann_cnt += 1
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if not ('15' in DATA_PATH):
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#if not (float(anns[i][8]) >= 0.25): # visibility.
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#continue
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if not (int(anns[i][6]) == 1): # whether ignore.
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continue
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if int(anns[i][7]) in [3, 4, 5, 6, 9, 10, 11]: # Non-person
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continue
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if int(anns[i][7]) in [2, 7, 8, 12]: # Ignored person
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#category_id = -1
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continue
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else:
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category_id = 1 # pedestrian(non-static)
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if not track_id == tid_last:
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tid_curr += 1
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tid_last = track_id
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else:
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category_id = 1
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ann = {'id': ann_cnt,
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'category_id': category_id,
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'image_id': image_cnt + frame_id,
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'track_id': tid_curr,
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'bbox': anns[i][2:6].tolist(),
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'conf': float(anns[i][6]),
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'iscrowd': 0,
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'area': float(anns[i][4] * anns[i][5])}
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out['annotations'].append(ann)
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image_cnt += num_images
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print(tid_curr, tid_last)
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print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))
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json.dump(out, open(out_path, 'w')) |