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"""
https://github.com/xingyizhou/CenterTrack
Modified by Xiaoyu Zhao
https://github.com/xingyizhou/CenterTrack/blob/master/src/tools/convert_mot_to_coco.py
There are extra many convert_X_to_coco.py
https://cocodataset.org/#format-data
"""
import os
import numpy as np
import json
import cv2
from tqdm import tqdm
DATA_PATH = "PATH/TO/sportsmot"
OUT_PATH = os.path.join(DATA_PATH, "annotations")
os.makedirs(OUT_PATH)
SPLITS = ["train", "val", "test"]
HALF_VIDEO = False
CREATE_SPLITTED_ANN = True
USE_DET = False
CREATE_SPLITTED_DET = False
for split in SPLITS:
data_path = os.path.join(DATA_PATH, split)
out_path = os.path.join(OUT_PATH, "{}.json".format(split))
out = {
"images": [],
"annotations": [],
"videos": [],
"categories": [{
"id": 1,
"name": "pedestrian"
}]
}
video_list = os.listdir(data_path)
image_cnt = 0
ann_cnt = 0
video_cnt = 0
for seq in tqdm(sorted(video_list)):
if ".DS_Store" in seq:
continue
video_cnt += 1 # video sequence number.
out["videos"].append({"id": video_cnt, "file_name": seq})
seq_path = os.path.join(data_path, seq)
img_path = os.path.join(seq_path, "img1")
ann_path = os.path.join(seq_path, "gt/gt.txt")
images = os.listdir(img_path)
num_images = len([image for image in images
if "jpg" in image]) # half and half
if HALF_VIDEO and ("half" in split):
image_range = [0, num_images // 2] if "train" in split else \
[num_images // 2 + 1, num_images - 1]
else:
image_range = [0, num_images - 1]
for i in range(num_images):
if i < image_range[0] or i > image_range[1]:
continue
img = cv2.imread(
os.path.join(data_path,
"{}/img1/{:06d}.jpg".format(seq, i + 1)))
height, width = img.shape[:2]
image_info = {
"file_name": "{}/img1/{:06d}.jpg".format(seq,
i + 1), # image name.
"id":
image_cnt + i + 1, # image number in the entire training set.
"frame_id": i + 1 - image_range[
0], # image number in the video sequence, starting from 1.
"prev_image_id": image_cnt +
i if i > 0 else -1, # image number in the entire training set.
"next_image_id":
image_cnt + i + 2 if i < num_images - 1 else -1,
"video_id": video_cnt,
"height": height,
"width": width
}
out["images"].append(image_info)
print("{}: {} images".format(seq, num_images))
if split != "test":
det_path = os.path.join(seq_path, "det/det.txt")
anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=",")
if USE_DET:
dets = np.loadtxt(det_path, dtype=np.float32, delimiter=",")
if CREATE_SPLITTED_ANN and ("half" in split):
anns_out = np.array([
anns[i] for i in range(anns.shape[0])
if int(anns[i][0]) - 1 >= image_range[0]
and int(anns[i][0]) - 1 <= image_range[1]
], np.float32)
anns_out[:, 0] -= image_range[0]
gt_out = os.path.join(seq_path, "gt/gt_{}.txt".format(split))
fout = open(gt_out, "w")
for o in anns_out:
fout.write(
"{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:.6f}\n".
format(int(o[0]), int(o[1]), int(o[2]), int(o[3]),
int(o[4]), int(o[5]), int(o[6]), int(o[7]),
o[8]))
fout.close()
if CREATE_SPLITTED_DET and ("half" in split) and USE_DET:
dets_out = np.array([
dets[i] for i in range(dets.shape[0])
if int(dets[i][0]) - 1 >= image_range[0]
and int(dets[i][0]) - 1 <= image_range[1]
], np.float32)
dets_out[:, 0] -= image_range[0]
det_out = os.path.join(seq_path,
"det/det_{}.txt".format(split))
dout = open(det_out, "w")
for o in dets_out:
dout.write(
"{:d},{:d},{:.1f},{:.1f},{:.1f},{:.1f},{:.6f}\n".
format(int(o[0]), int(o[1]), float(o[2]), float(o[3]),
float(o[4]), float(o[5]), float(o[6])))
dout.close()
print("{} ann images".format(int(anns[:, 0].max())))
for i in range(anns.shape[0]):
frame_id = int(anns[i][0])
if frame_id - 1 < image_range[0] or frame_id - 1 > image_range[
1]:
continue
track_id = int(anns[i][1])
cat_id = int(anns[i][7])
ann_cnt += 1
if not ("15" in DATA_PATH):
if not (float(anns[i][8]) >= 0.25): # visibility.
continue
if not (int(anns[i][6]) == 1): # whether ignore.
continue
if int(anns[i][7]) in [3, 4, 5, 6, 9, 10,
11]: # Non-person
continue
if int(anns[i][7]) in [2, 7, 8, 12]: # Ignored person
category_id = -1
else:
category_id = 1 # pedestrian(non-static)
else:
category_id = 1
ann = {
"id": ann_cnt,
"category_id": category_id,
"image_id": image_cnt + frame_id,
"track_id": track_id,
"bbox": anns[i][2:6].tolist(),
"conf": float(anns[i][6]),
"iscrowd": 0,
"area": float(anns[i][4] * anns[i][5])
}
out["annotations"].append(ann)
image_cnt += num_images
print("loaded {} for {} images and {} samples".format(
split, len(out["images"]), len(out["annotations"])))
with open(out_path, "w") as f:
json.dump(out, f, indent=2) |