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Datasets:
MCG-NJU
/
SportsMOT

Tasks:
Object Detection
Other
Modalities:
Text
Video
Formats:
text
Languages:
English
Size:
< 1K
ArXiv:
Tags:
video
multi-object tracking
Libraries:
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License:
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  1. scripts/mot_to_coco.py +160 -0
  2. scripts/sportsmot_to_trackeval.py +50 -0
  3. splits_txt/basketball.txt +80 -0
  4. splits_txt/football.txt +80 -0
  5. splits_txt/test.txt +150 -0
  6. splits_txt/train.txt +45 -0
  7. splits_txt/val.txt +45 -0
  8. splits_txt/volleyball.txt +80 -0
scripts/mot_to_coco.py ADDED
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1
+ """
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+ https://github.com/xingyizhou/CenterTrack
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+ Modified by Xiaoyu Zhao
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+
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+ https://github.com/xingyizhou/CenterTrack/blob/master/src/tools/convert_mot_to_coco.py
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+
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+ There are extra many convert_X_to_coco.py
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+
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+ https://cocodataset.org/#format-data
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+ """
11
+ import os
12
+ import numpy as np
13
+ import json
14
+ import cv2
15
+ from tqdm import tqdm
16
+
17
+ DATA_PATH = "PATH/TO/sportsmot"
18
+ OUT_PATH = os.path.join(DATA_PATH, "annotations")
19
+ os.makedirs(OUT_PATH)
20
+ SPLITS = ["train", "val", "test"]
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+ HALF_VIDEO = False
22
+ CREATE_SPLITTED_ANN = True
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+ USE_DET = False
24
+ CREATE_SPLITTED_DET = False
25
+
26
+ for split in SPLITS:
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+ data_path = os.path.join(DATA_PATH, split)
28
+ out_path = os.path.join(OUT_PATH, "{}.json".format(split))
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+ out = {
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+ "images": [],
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+ "annotations": [],
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+ "videos": [],
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+ "categories": [{
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+ "id": 1,
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+ "name": "pedestrian"
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+ }]
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+ }
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+ video_list = 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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+ for seq in tqdm(sorted(video_list)):
43
+ 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")
50
+ images = os.listdir(img_path)
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+ num_images = len([image for image in images
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+ if "jpg" in image]) # half and half
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+
54
+ if HALF_VIDEO and ("half" in split):
55
+ 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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+
60
+ for i in range(num_images):
61
+ if i < image_range[0] or i > image_range[1]:
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+ continue
63
+ img = cv2.imread(
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+ os.path.join(data_path,
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+ "{}/img1/{:06d}.jpg".format(seq, i + 1)))
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+ height, width = img.shape[:2]
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+ image_info = {
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+ "file_name": "{}/img1/{:06d}.jpg".format(seq,
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+ i + 1), # image name.
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+ "id":
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+ image_cnt + i + 1, # image number in the entire training set.
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+ "frame_id": i + 1 - image_range[
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+ 0], # image number in the video sequence, starting from 1.
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+ "prev_image_id": image_cnt +
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+ i if i > 0 else -1, # image number in the entire training set.
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+ "next_image_id":
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+ 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,
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+ "width": width
81
+ }
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+ out["images"].append(image_info)
83
+ print("{}: {} images".format(seq, num_images))
84
+ if split != "test":
85
+ det_path = os.path.join(seq_path, "det/det.txt")
86
+ anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=",")
87
+ if USE_DET:
88
+ dets = np.loadtxt(det_path, dtype=np.float32, delimiter=",")
89
+ if CREATE_SPLITTED_ANN and ("half" in split):
90
+ anns_out = np.array([
91
+ anns[i] for i in range(anns.shape[0])
92
+ if int(anns[i][0]) - 1 >= image_range[0]
93
+ and int(anns[i][0]) - 1 <= image_range[1]
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+ ], np.float32)
95
+ anns_out[:, 0] -= image_range[0]
96
+ gt_out = os.path.join(seq_path, "gt/gt_{}.txt".format(split))
97
+ fout = open(gt_out, "w")
98
+ for o in anns_out:
99
+ fout.write(
100
+ "{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:.6f}\n".
101
+ format(int(o[0]), int(o[1]), int(o[2]), int(o[3]),
102
+ int(o[4]), int(o[5]), int(o[6]), int(o[7]),
103
+ o[8]))
104
+ fout.close()
105
+ if CREATE_SPLITTED_DET and ("half" in split) and USE_DET:
106
+ dets_out = np.array([
107
+ dets[i] for i in range(dets.shape[0])
108
+ if int(dets[i][0]) - 1 >= image_range[0]
109
+ and int(dets[i][0]) - 1 <= image_range[1]
110
+ ], np.float32)
111
+ dets_out[:, 0] -= image_range[0]
112
+ det_out = os.path.join(seq_path,
113
+ "det/det_{}.txt".format(split))
114
+ dout = open(det_out, "w")
115
+ for o in dets_out:
116
+ dout.write(
117
+ "{:d},{:d},{:.1f},{:.1f},{:.1f},{:.1f},{:.6f}\n".
118
+ format(int(o[0]), int(o[1]), float(o[2]), float(o[3]),
119
+ float(o[4]), float(o[5]), float(o[6])))
120
+ dout.close()
121
+
122
+ print("{} ann images".format(int(anns[:, 0].max())))
123
+ for i in range(anns.shape[0]):
124
+ frame_id = int(anns[i][0])
125
+ if frame_id - 1 < image_range[0] or frame_id - 1 > image_range[
126
+ 1]:
127
+ continue
128
+ track_id = int(anns[i][1])
129
+ cat_id = int(anns[i][7])
130
+ ann_cnt += 1
131
+ if not ("15" in DATA_PATH):
132
+ if not (float(anns[i][8]) >= 0.25): # visibility.
133
+ continue
134
+ if not (int(anns[i][6]) == 1): # whether ignore.
135
+ continue
136
+ if int(anns[i][7]) in [3, 4, 5, 6, 9, 10,
137
+ 11]: # Non-person
138
+ continue
139
+ if int(anns[i][7]) in [2, 7, 8, 12]: # Ignored person
140
+ category_id = -1
141
+ else:
142
+ category_id = 1 # pedestrian(non-static)
143
+ else:
144
+ category_id = 1
145
+ ann = {
146
+ "id": ann_cnt,
147
+ "category_id": category_id,
148
+ "image_id": image_cnt + frame_id,
149
+ "track_id": track_id,
150
+ "bbox": anns[i][2:6].tolist(),
151
+ "conf": float(anns[i][6]),
152
+ "iscrowd": 0,
153
+ "area": float(anns[i][4] * anns[i][5])
154
+ }
155
+ out["annotations"].append(ann)
156
+ image_cnt += num_images
157
+ print("loaded {} for {} images and {} samples".format(
158
+ split, len(out["images"]), len(out["annotations"])))
159
+ with open(out_path, "w") as f:
160
+ json.dump(out, f, indent=2)
scripts/sportsmot_to_trackeval.py ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import shutil
3
+
4
+ TRACKEVAL_DATA_ROOT = "ROOTDIR/trackeval"
5
+ SPORTSMOT_DATA_ROOT = "ROOTDIR/sportsmot"
6
+ NO_SUBDIR = True
7
+
8
+ for split in ["train", "val", "test"]:
9
+ outputs_dir = f"ROOTDIR/results/{split}"
10
+ mybenchmark_name = "sportsmot"
11
+ split_txt = f"ROOTDIR/splits_txt/{split}.txt"
12
+ tracker_name = "tracker_to_eval"
13
+
14
+ # ========default hierarchy========
15
+ # folder hierarchy according to
16
+ # https://github.com/JonathonLuiten/TrackEval/blob/master/docs/MOTChallenge-Official/Readme.md#evaluating-on-your-own-data
17
+ eval_gt_dir = os.path.join(
18
+ TRACKEVAL_DATA_ROOT, "gt" if NO_SUBDIR else "data/gt/mot_challenge",
19
+ mybenchmark_name + "-" + split)
20
+ os.makedirs(eval_gt_dir, exist_ok=True)
21
+ eval_seqmap_dir = os.path.join(
22
+ TRACKEVAL_DATA_ROOT, "gt" if NO_SUBDIR else "data/gt/mot_challenge",
23
+ "seqmaps")
24
+ os.makedirs(eval_seqmap_dir, exist_ok=True)
25
+ eval_trackers_dir = os.path.join(
26
+ TRACKEVAL_DATA_ROOT,
27
+ "trackers" if NO_SUBDIR else "data/trackers/mot_challenge",
28
+ mybenchmark_name + "-" + split, tracker_name, "data")
29
+ os.makedirs(eval_trackers_dir, exist_ok=True)
30
+
31
+ shutil.copy(
32
+ split_txt,
33
+ os.path.join(eval_seqmap_dir, f"{mybenchmark_name}-{split}.txt"))
34
+ with open(split_txt, "r", encoding="utf-8") as f:
35
+ for line in f.readlines():
36
+ # copy gt/ and seqinfo.ini
37
+ video_name = line.strip()
38
+ source_dir = os.path.join(SPORTSMOT_DATA_ROOT, split, video_name)
39
+ target_dir = os.path.join(eval_gt_dir, video_name)
40
+
41
+ source_gt_dir = os.path.join(source_dir, "gt")
42
+ target_gt_dir = os.path.join(target_dir, "gt")
43
+ shutil.copytree(source_gt_dir, target_gt_dir)
44
+ source_seqinfo = os.path.join(source_dir, "seqinfo.ini")
45
+ shutil.copy(source_seqinfo, target_dir)
46
+
47
+ # copy tracker output
48
+ source_track = os.path.join(outputs_dir, f"{video_name}.txt")
49
+ assert os.path.isfile(source_track)
50
+ shutil.copy(source_track, eval_trackers_dir)
splits_txt/basketball.txt ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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splits_txt/football.txt ADDED
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splits_txt/test.txt ADDED
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splits_txt/train.txt ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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44
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splits_txt/val.txt ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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splits_txt/volleyball.txt ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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