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mot2reid.py
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# This script converts MOT dataset into ReID dataset.
# Offical website of the MOT dataset: https://motchallenge.net/
#
# Label format of MOT dataset:
# GTs:
# <frame_id> # starts from 1,
# <instance_id>, <x1>, <y1>, <w>, <h>,
# <conf> # conf is annotated as 0 if the object is ignored,
# <class_id>, <visibility>
#
# DETs and Results:
# <frame_id>, <instance_id>, <x1>, <y1>, <w>, <h>, <conf>,
# <x>, <y>, <z> # for 3D objects
#
# Classes in MOT:
# 1: 'pedestrian'
# 2: 'person on vehicle'
# 3: 'car'
# 4: 'bicycle'
# 5: 'motorbike'
# 6: 'non motorized vehicle'
# 7: 'static person'
# 8: 'distractor'
# 9: 'occluder'
# 10: 'occluder on the ground',
# 11: 'occluder full'
# 12: 'reflection'
#
# USELESS classes and IGNORES classes will not be selected
# into the dataset for reid model training.
import argparse
import os
import os.path as osp
import random
import mmcv
import numpy as np
from tqdm import tqdm
USELESS = [3, 4, 5, 6, 9, 10, 11]
IGNORES = [2, 7, 8, 12, 13]
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MOT dataset into ReID dataset.')
parser.add_argument('-i', '--input', help='path of MOT data')
parser.add_argument('-o', '--output', help='path to save ReID dataset')
parser.add_argument(
'--val-split',
type=float,
default=0.2,
help='proportion of the validation dataset to the whole ReID dataset')
parser.add_argument(
'--vis-threshold',
type=float,
default=0.3,
help='threshold of visibility for each person')
parser.add_argument(
'--min-per-person',
type=int,
default=8,
help='minimum number of images for each person')
parser.add_argument(
'--max-per-person',
type=int,
default=1000,
help='maxmum number of images for each person')
return parser.parse_args()
def main():
args = parse_args()
if not osp.exists(args.output):
os.makedirs(args.output)
elif os.listdir(args.output):
raise OSError(f'Directory must empty: \'{args.output}\'')
in_folder = osp.join(args.input, 'train')
video_names = os.listdir(in_folder)
if 'MOT17' in in_folder:
video_names = [
video_name for video_name in video_names if 'FRCNN' in video_name
]
for video_name in tqdm(video_names):
# load video infos
video_folder = osp.join(in_folder, video_name)
infos = mmcv.list_from_file(f'{video_folder}/seqinfo.ini')
# video-level infos
assert video_name == infos[1].strip().split('=')[1]
raw_img_folder = infos[2].strip().split('=')[1]
raw_img_names = os.listdir(f'{video_folder}/{raw_img_folder}')
raw_img_names = sorted(raw_img_names)
num_raw_imgs = int(infos[4].strip().split('=')[1])
assert num_raw_imgs == len(raw_img_names)
reid_train_folder = osp.join(args.output, 'imgs')
if not osp.exists(reid_train_folder):
os.makedirs(reid_train_folder)
gts = mmcv.list_from_file(f'{video_folder}/gt/gt.txt')
last_frame_id = -1
for gt in gts:
gt = gt.strip().split(',')
frame_id, ins_id = map(int, gt[:2])
ltwh = list(map(float, gt[2:6]))
class_id = int(gt[7])
visibility = float(gt[8])
if class_id in USELESS:
continue
elif class_id in IGNORES:
continue
elif visibility < args.vis_threshold:
continue
reid_img_folder = osp.join(reid_train_folder,
f'{video_name}_{ins_id:06d}')
if not osp.exists(reid_img_folder):
os.makedirs(reid_img_folder)
idx = len(os.listdir(reid_img_folder))
reid_img_name = f'{idx:06d}.jpg'
if frame_id != last_frame_id:
raw_img_name = raw_img_names[frame_id - 1]
raw_img = mmcv.imread(
f'{video_folder}/{raw_img_folder}/{raw_img_name}')
last_frame_id = frame_id
xyxy = np.asarray(
[ltwh[0], ltwh[1], ltwh[0] + ltwh[2], ltwh[1] + ltwh[3]])
reid_img = mmcv.imcrop(raw_img, xyxy)
mmcv.imwrite(reid_img, f'{reid_img_folder}/{reid_img_name}')
reid_meta_folder = osp.join(args.output, 'meta')
if not osp.exists(reid_meta_folder):
os.makedirs(reid_meta_folder)
reid_train_list = []
reid_val_list = []
reid_img_folder_names = os.listdir(reid_train_folder)
num_ids = len(reid_img_folder_names)
num_train_ids = int(num_ids * (1 - args.val_split))
train_label, val_label = 0, 0
random.seed(0)
for reid_img_folder_name in reid_img_folder_names[:num_train_ids]:
reid_img_names = os.listdir(
f'{reid_train_folder}/{reid_img_folder_name}')
# ignore ids whose number of image is less than min_per_person
if (len(reid_img_names) < args.min_per_person):
continue
# downsampling when there are too many images owned by one id
if (len(reid_img_names) > args.max_per_person):
reid_img_names = random.sample(reid_img_names, args.max_per_person)
# training set
for reid_img_name in reid_img_names:
reid_train_list.append(
f'{reid_img_folder_name}/{reid_img_name} {train_label}\n')
train_label += 1
reid_entire_dataset_list = reid_train_list.copy()
for reid_img_folder_name in reid_img_folder_names[num_train_ids:]:
reid_img_names = os.listdir(
f'{reid_train_folder}/{reid_img_folder_name}')
# ignore ids whose number of image is less than min_per_person
if (len(reid_img_names) < args.min_per_person):
continue
# downsampling when there are too many images owned by one id
if (len(reid_img_names) > args.max_per_person):
reid_img_names = random.sample(reid_img_names, args.max_per_person)
for reid_img_name in reid_img_names:
# validation set
reid_val_list.append(
f'{reid_img_folder_name}/{reid_img_name} {val_label}\n')
reid_entire_dataset_list.append(
f'{reid_img_folder_name}/{reid_img_name} '
f'{train_label + val_label}\n')
val_label += 1
with open(
osp.join(reid_meta_folder,
f'train_{int(100 * (1 - args.val_split))}.txt'),
'w') as f:
f.writelines(reid_train_list)
with open(
osp.join(reid_meta_folder, f'val_{int(100 * args.val_split)}.txt'),
'w') as f:
f.writelines(reid_val_list)
with open(osp.join(reid_meta_folder, 'train.txt'), 'w') as f:
f.writelines(reid_entire_dataset_list)
if __name__ == '__main__':
main()