bytetrack / tools /convert_ethz_to_coco.py
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import os
import numpy as np
import json
from PIL import Image
DATA_PATH = 'datasets/ETHZ/'
DATA_FILE_PATH = 'datasets/data_path/eth.train'
OUT_PATH = DATA_PATH + 'annotations/'
def load_paths(data_path):
with open(data_path, 'r') as file:
img_files = file.readlines()
img_files = [x.replace('\n', '') for x in img_files]
img_files = list(filter(lambda x: len(x) > 0, img_files))
label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt') for x in img_files]
return img_files, label_files
if __name__ == '__main__':
if not os.path.exists(OUT_PATH):
os.mkdir(OUT_PATH)
out_path = OUT_PATH + 'train.json'
out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}
img_paths, label_paths = load_paths(DATA_FILE_PATH)
image_cnt = 0
ann_cnt = 0
video_cnt = 0
for img_path, label_path in zip(img_paths, label_paths):
image_cnt += 1
im = Image.open(img_path)
image_info = {'file_name': img_path,
'id': image_cnt,
'height': im.size[1],
'width': im.size[0]}
out['images'].append(image_info)
# Load labels
if os.path.isfile(label_path):
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
# Normalized xywh to pixel xyxy format
labels = labels0.copy()
labels[:, 2] = image_info['width'] * (labels0[:, 2] - labels0[:, 4] / 2)
labels[:, 3] = image_info['height'] * (labels0[:, 3] - labels0[:, 5] / 2)
labels[:, 4] = image_info['width'] * labels0[:, 4]
labels[:, 5] = image_info['height'] * labels0[:, 5]
else:
labels = np.array([])
for i in range(len(labels)):
ann_cnt += 1
fbox = labels[i, 2:6].tolist()
ann = {'id': ann_cnt,
'category_id': 1,
'image_id': image_cnt,
'track_id': -1,
'bbox': fbox,
'area': fbox[2] * fbox[3],
'iscrowd': 0}
out['annotations'].append(ann)
print('loaded train for {} images and {} samples'.format(len(out['images']), len(out['annotations'])))
json.dump(out, open(out_path, 'w'))