mirror of https://github.com/alibaba/EasyCV.git
support train motchallenge and crowdhuman (#265)
Support motchallenge and crowdhuman training. Support multi-dataset training.pull/271/head^2
tuofeilun
GitHub
parent
acd0619ef9
commit
ac9f62d6dc
|
|
@ -0,0 +1,42 @@
|
|||
_base_ = './fcos_r50_torch_1x_coco.py'
|
||||
|
||||
data_root0 = 'data/tracking/crowdhuman/'
|
||||
data_root1 = 'data/tracking/MOT20/'
|
||||
CLASSES = ('pedestrian', )
|
||||
train_dataset = dict(
|
||||
data_source=dict(
|
||||
ann_file=[
|
||||
data_root1 + 'annotations/train_cocoformat.json', data_root0 +
|
||||
'/annotations/crowdhuman_train.json', data_root0 +
|
||||
'/annotations/crowdhuman_val.json'
|
||||
],
|
||||
img_prefix=[
|
||||
data_root1 + 'train', data_root0 + 'train', data_root0 + 'val'
|
||||
],
|
||||
classes=CLASSES))
|
||||
|
||||
val_dataset = dict(
|
||||
data_source=dict(
|
||||
ann_file=data_root0 + '/annotations/crowdhuman_val.json',
|
||||
img_prefix=data_root0 + 'val',
|
||||
classes=CLASSES))
|
||||
|
||||
data = dict(
|
||||
imgs_per_gpu=2, workers_per_gpu=2, train=train_dataset, val=val_dataset)
|
||||
|
||||
model = dict(head=dict(num_classes=1))
|
||||
|
||||
optimizer = dict(lr=0.001)
|
||||
|
||||
eval_pipelines = [
|
||||
dict(
|
||||
mode='test',
|
||||
evaluators=[
|
||||
dict(type='CocoDetectionEvaluator', classes=CLASSES),
|
||||
],
|
||||
)
|
||||
]
|
||||
|
||||
checkpoint_config = dict(interval=1)
|
||||
|
||||
load_from = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/fcos/fcos_epoch_12.pth'
|
||||
|
|
@ -8,22 +8,22 @@ from .registry import DALIDATASETS, DATASETS, DATASOURCES, SAMPLERS
|
|||
|
||||
|
||||
def _concat_dataset(cfg, default_args=None):
|
||||
ann_files = cfg['ann_file']
|
||||
img_prefixes = cfg.get('img_prefix', None)
|
||||
seg_prefixes = cfg.get('seg_prefix', None)
|
||||
proposal_files = cfg.get('proposal_file', None)
|
||||
ann_files = cfg['data_source']['ann_file']
|
||||
img_prefixes = cfg['data_source'].get('img_prefix', None)
|
||||
seg_prefixes = cfg['data_source'].get('seg_prefix', None)
|
||||
proposal_files = cfg['data_source'].get('proposal_file', None)
|
||||
|
||||
datasets = []
|
||||
num_dset = len(ann_files)
|
||||
for i in range(num_dset):
|
||||
data_cfg = copy.deepcopy(cfg)
|
||||
data_cfg['ann_file'] = ann_files[i]
|
||||
data_cfg['data_source']['ann_file'] = ann_files[i]
|
||||
if isinstance(img_prefixes, (list, tuple)):
|
||||
data_cfg['img_prefix'] = img_prefixes[i]
|
||||
data_cfg['data_source']['img_prefix'] = img_prefixes[i]
|
||||
if isinstance(seg_prefixes, (list, tuple)):
|
||||
data_cfg['seg_prefix'] = seg_prefixes[i]
|
||||
data_cfg['data_source']['seg_prefix'] = seg_prefixes[i]
|
||||
if isinstance(proposal_files, (list, tuple)):
|
||||
data_cfg['proposal_file'] = proposal_files[i]
|
||||
data_cfg['data_source']['proposal_file'] = proposal_files[i]
|
||||
datasets.append(build_dataset(data_cfg, default_args))
|
||||
|
||||
return ConcatDataset(datasets)
|
||||
|
|
@ -35,6 +35,8 @@ def build_dataset(cfg, default_args=None):
|
|||
elif cfg['type'] == 'RepeatDataset':
|
||||
dataset = RepeatDataset(
|
||||
build_dataset(cfg['dataset'], default_args), cfg['times'])
|
||||
elif isinstance(cfg['data_source'].get('ann_file'), (list, tuple)):
|
||||
dataset = _concat_dataset(cfg, default_args)
|
||||
else:
|
||||
dataset = build_from_cfg(cfg, DATASETS, default_args)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,102 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import os.path as osp
|
||||
from collections import defaultdict
|
||||
|
||||
import mmcv
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
# python tools/convert_datasets/crowdhuman2coco.py -i /apsarapangu/disk4/easycv_nfs/data/tracking/crowdhuman -o /apsarapangu/disk4/easycv_nfs/data/tracking/crowdhuman/annotations
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='CrowdHuman to COCO Video format')
|
||||
parser.add_argument(
|
||||
'-i',
|
||||
'--input',
|
||||
help='root directory of CrowdHuman annotations',
|
||||
)
|
||||
parser.add_argument(
|
||||
'-o',
|
||||
'--output',
|
||||
help='directory to save coco formatted label file',
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def load_odgt(filename):
|
||||
with open(filename, 'r') as f:
|
||||
lines = f.readlines()
|
||||
data_infos = [json.loads(line.strip('\n')) for line in lines]
|
||||
return data_infos
|
||||
|
||||
|
||||
def convert_crowdhuman(ann_dir, save_dir, mode='train'):
|
||||
"""Convert CrowdHuman dataset in COCO style.
|
||||
|
||||
Args:
|
||||
ann_dir (str): The path of CrowdHuman dataset.
|
||||
save_dir (str): The path to save annotation files.
|
||||
mode (str): Convert train dataset or validation dataset. Options are
|
||||
'train', 'val'. Default: 'train'.
|
||||
"""
|
||||
assert mode in ['train', 'val']
|
||||
|
||||
records = dict(img_id=1, ann_id=1)
|
||||
outputs = defaultdict(list)
|
||||
outputs['categories'] = [dict(id=1, name='pedestrian')]
|
||||
|
||||
data_infos = load_odgt(osp.join(ann_dir, f'annotation_{mode}.odgt'))
|
||||
for data_info in tqdm(data_infos):
|
||||
img_name = osp.join('Images', f"{data_info['ID']}.jpg")
|
||||
img = Image.open(osp.join(ann_dir, mode, img_name))
|
||||
width, height = img.size[:2]
|
||||
image = dict(
|
||||
file_name=img_name,
|
||||
height=height,
|
||||
width=width,
|
||||
id=records['img_id'])
|
||||
outputs['images'].append(image)
|
||||
|
||||
if mode != 'test':
|
||||
for ann_info in data_info['gtboxes']:
|
||||
bbox = ann_info['fbox']
|
||||
if 'extra' in ann_info and 'ignore' in ann_info[
|
||||
'extra'] and ann_info['extra']['ignore'] == 1:
|
||||
iscrowd = True
|
||||
else:
|
||||
iscrowd = False
|
||||
ann = dict(
|
||||
id=records['ann_id'],
|
||||
image_id=records['img_id'],
|
||||
category_id=outputs['categories'][0]['id'],
|
||||
vis_bbox=ann_info['vbox'],
|
||||
bbox=bbox,
|
||||
area=bbox[2] * bbox[3],
|
||||
iscrowd=iscrowd)
|
||||
outputs['annotations'].append(ann)
|
||||
records['ann_id'] += 1
|
||||
records['img_id'] += 1
|
||||
|
||||
if not osp.isdir(save_dir):
|
||||
os.makedirs(save_dir)
|
||||
mmcv.dump(outputs, osp.join(save_dir, f'crowdhuman_{mode}.json'))
|
||||
print(f'-----CrowdHuman {mode} set------')
|
||||
print(f'total {records["img_id"] - 1} images')
|
||||
if mode != 'test':
|
||||
print(f'{records["ann_id"] - 1} pedestrians are annotated.')
|
||||
print('-----------------------')
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
convert_crowdhuman(args.input, args.output, mode='train')
|
||||
convert_crowdhuman(args.input, args.output, mode='val')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -0,0 +1,230 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
# This script converts MOT labels into COCO style.
|
||||
# Official website of the MOT dataset: https://motchallenge.net/
|
||||
#
|
||||
# Label format of MOT dataset:
|
||||
# GTs:
|
||||
# <frame_id> # starts from 1 but COCO style starts from 0,
|
||||
# <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 are not included into the json file.
|
||||
# IGNORES classes are included with `ignore=True`.
|
||||
|
||||
# python tools/convert_datasets/mot2coco.py -i /apsara/yunji.cjy/tracking/MOT20 -o /apsara/yunji.cjy/tracking/MOT20/annotations --split-train --convert-det
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import os.path as osp
|
||||
from collections import defaultdict
|
||||
|
||||
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 label and detections to COCO-VID format.')
|
||||
parser.add_argument('-i', '--input', help='path of MOT data')
|
||||
parser.add_argument(
|
||||
'-o', '--output', help='path to save coco formatted label file')
|
||||
parser.add_argument(
|
||||
'--convert-det',
|
||||
action='store_true',
|
||||
help='convert official detection results.')
|
||||
parser.add_argument(
|
||||
'--split-train',
|
||||
action='store_true',
|
||||
help='split the train set into half-train and half-validate.')
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def parse_gts(gts, is_mot15):
|
||||
outputs = defaultdict(list)
|
||||
for gt in gts:
|
||||
gt = gt.strip().split(',')
|
||||
frame_id, ins_id = map(int, gt[:2])
|
||||
bbox = list(map(float, gt[2:6]))
|
||||
if is_mot15:
|
||||
conf = 1.
|
||||
class_id = 1
|
||||
visibility = 1.
|
||||
else:
|
||||
conf = float(gt[6])
|
||||
class_id = int(gt[7])
|
||||
visibility = float(gt[8])
|
||||
if class_id in USELESS:
|
||||
continue
|
||||
elif class_id in IGNORES:
|
||||
continue
|
||||
anns = dict(
|
||||
category_id=1,
|
||||
bbox=bbox,
|
||||
area=bbox[2] * bbox[3],
|
||||
iscrowd=False,
|
||||
visibility=visibility,
|
||||
mot_instance_id=ins_id,
|
||||
mot_conf=conf,
|
||||
mot_class_id=class_id)
|
||||
outputs[frame_id].append(anns)
|
||||
return outputs
|
||||
|
||||
|
||||
def parse_dets(dets):
|
||||
outputs = defaultdict(list)
|
||||
for det in dets:
|
||||
det = det.strip().split(',')
|
||||
frame_id, ins_id = map(int, det[:2])
|
||||
assert ins_id == -1
|
||||
bbox = list(map(float, det[2:7]))
|
||||
# [x1, y1, x2, y2] to be consistent with mmdet
|
||||
bbox = [
|
||||
bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3], bbox[4]
|
||||
]
|
||||
outputs[frame_id].append(bbox)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
if not osp.isdir(args.output):
|
||||
os.makedirs(args.output)
|
||||
|
||||
sets = ['train', 'test']
|
||||
if args.split_train:
|
||||
sets += ['half-train', 'half-val']
|
||||
vid_id, img_id, ann_id = 1, 1, 1
|
||||
|
||||
for subset in sets:
|
||||
ins_id = 0
|
||||
print(f'Converting {subset} set to COCO format')
|
||||
if 'half' in subset:
|
||||
in_folder = osp.join(args.input, 'train')
|
||||
else:
|
||||
in_folder = osp.join(args.input, subset)
|
||||
out_file = osp.join(args.output, f'{subset}_cocoformat.json')
|
||||
outputs = defaultdict(list)
|
||||
outputs['categories'] = [dict(id=1, name='pedestrian')]
|
||||
if args.convert_det:
|
||||
det_file = osp.join(args.output, f'{subset}_detections.pkl')
|
||||
detections = dict(det_bboxes=dict())
|
||||
video_names = os.listdir(in_folder)
|
||||
for video_name in tqdm(video_names):
|
||||
# basic params
|
||||
parse_gt = 'test' not in subset
|
||||
ins_maps = dict()
|
||||
# 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]
|
||||
img_folder = infos[2].strip().split('=')[1]
|
||||
img_names = os.listdir(f'{video_folder}/{img_folder}')
|
||||
img_names = sorted(img_names)
|
||||
fps = int(infos[3].strip().split('=')[1])
|
||||
num_imgs = int(infos[4].strip().split('=')[1])
|
||||
assert num_imgs == len(img_names)
|
||||
width = int(infos[5].strip().split('=')[1])
|
||||
height = int(infos[6].strip().split('=')[1])
|
||||
video = dict(
|
||||
id=vid_id,
|
||||
name=video_name,
|
||||
fps=fps,
|
||||
width=width,
|
||||
height=height)
|
||||
# parse annotations
|
||||
if parse_gt:
|
||||
gts = mmcv.list_from_file(f'{video_folder}/gt/gt.txt')
|
||||
if 'MOT15' in video_folder:
|
||||
img2gts = parse_gts(gts, True)
|
||||
else:
|
||||
img2gts = parse_gts(gts, False)
|
||||
if args.convert_det:
|
||||
dets = mmcv.list_from_file(f'{video_folder}/det/det.txt')
|
||||
img2dets = parse_dets(dets)
|
||||
# make half sets
|
||||
if 'half' in subset:
|
||||
split_frame = num_imgs // 2 + 1
|
||||
if 'train' in subset:
|
||||
img_names = img_names[:split_frame]
|
||||
elif 'val' in subset:
|
||||
img_names = img_names[split_frame:]
|
||||
else:
|
||||
raise ValueError(
|
||||
'subset must be named with `train` or `val`')
|
||||
mot_frame_ids = [str(int(_.split('.')[0])) for _ in img_names]
|
||||
with open(f'{video_folder}/gt/gt_{subset}.txt', 'wt') as f:
|
||||
for gt in gts:
|
||||
if gt.split(',')[0] in mot_frame_ids:
|
||||
f.writelines(f'{gt}\n')
|
||||
# image and box level infos
|
||||
for frame_id, name in enumerate(img_names):
|
||||
img_name = osp.join(video_name, img_folder, name)
|
||||
mot_frame_id = int(name.split('.')[0])
|
||||
image = dict(
|
||||
id=img_id,
|
||||
video_id=vid_id,
|
||||
file_name=img_name,
|
||||
height=height,
|
||||
width=width,
|
||||
frame_id=frame_id,
|
||||
mot_frame_id=mot_frame_id)
|
||||
if parse_gt:
|
||||
gts = img2gts[mot_frame_id]
|
||||
for gt in gts:
|
||||
gt.update(id=ann_id, image_id=img_id)
|
||||
mot_ins_id = gt['mot_instance_id']
|
||||
if mot_ins_id in ins_maps:
|
||||
gt['instance_id'] = ins_maps[mot_ins_id]
|
||||
else:
|
||||
gt['instance_id'] = ins_id
|
||||
ins_maps[mot_ins_id] = ins_id
|
||||
ins_id += 1
|
||||
outputs['annotations'].append(gt)
|
||||
ann_id += 1
|
||||
if args.convert_det:
|
||||
dets = np.array(img2dets[mot_frame_id])
|
||||
if dets.ndim == 1:
|
||||
assert len(dets) == 0
|
||||
dets = np.zeros((0, 5))
|
||||
detections['det_bboxes'][img_name] = [dets]
|
||||
outputs['images'].append(image)
|
||||
img_id += 1
|
||||
outputs['videos'].append(video)
|
||||
vid_id += 1
|
||||
outputs['num_instances'] = ins_id
|
||||
print(f'{subset} has {ins_id} instances.')
|
||||
mmcv.dump(outputs, out_file)
|
||||
if args.convert_det:
|
||||
mmcv.dump(detections, det_file)
|
||||
print(f'Done! Saved as {out_file} and {det_file}')
|
||||
else:
|
||||
print(f'Done! Saved as {out_file}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
Reference in New Issue