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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
import os
import numpy as np
def parse_args():
parser = argparse.ArgumentParser(
description='Convert and merge hand pose dataset to COCO style')
parser.add_argument(
'--data_root',
type=str,
default='./data/',
help='the root to all involved datasets')
parser.add_argument(
'--out_anno_prefix',
type=str,
default='hand_det',
help='the prefix of output annotation files')
args = parser.parse_args()
return args
def get_data_root(path):
path = path.split('/')
index = path.index('annotations') - 1
root = path[index]
if root == 'halpe':
root = 'halpe/hico_20160224_det/images/train2015/'
return root
def parse_coco_style(file_path, anno_idx=0):
with open(file_path) as f:
contents = json.load(f)
data_root = get_data_root(file_path) + '/'
images = contents['images']
annos = contents['annotations']
images_out, annos_out = [], []
for img, anno in zip(images, annos):
assert img['id'] == anno['image_id']
img_out = dict(
file_name=data_root + img['file_name'],
height=img['height'],
width=img['width'],
id=anno_idx)
anno_out = dict(
area=anno['area'],
iscrowd=anno['iscrowd'],
image_id=anno_idx,
bbox=anno['bbox'],
category_id=0,
id=anno_idx)
anno_idx += 1
images_out.append(img_out)
annos_out.append(anno_out)
return images_out, annos_out, anno_idx
def parse_halpe(file_path, anno_idx):
def get_bbox(keypoints):
"""Get bbox from keypoints."""
if len(keypoints) == 0:
return [0, 0, 0, 0]
x1, y1, _ = np.amin(keypoints, axis=0)
x2, y2, _ = np.amax(keypoints, axis=0)
w, h = x2 - x1, y2 - y1
return [x1, y1, w, h]
with open(file_path) as f:
contents = json.load(f)
data_root = get_data_root(file_path) + '/'
images = contents['images']
annos = contents['annotations']
images_out, annos_out = [], []
for img, anno in zip(images, annos):
assert img['id'] == anno['image_id']
keypoints = np.array(anno['keypoints']).reshape(-1, 3)
lefthand_kpts = keypoints[-42:-21, :]
righthand_kpts = keypoints[-21:, :]
left_mask = lefthand_kpts[:, 2] > 0
right_mask = righthand_kpts[:, 2] > 0
lefthand_box = get_bbox(lefthand_kpts[left_mask])
righthand_box = get_bbox(righthand_kpts[right_mask])
if max(lefthand_box) > 0:
img_out = dict(
file_name=data_root + img['file_name'],
height=img['height'],
width=img['width'],
id=anno_idx)
anno_out = dict(
area=lefthand_box[2] * lefthand_box[3],
iscrowd=anno['iscrowd'],
image_id=anno_idx,
bbox=lefthand_box,
category_id=0,
id=anno_idx)
anno_idx += 1
images_out.append(img_out)
annos_out.append(anno_out)
if max(righthand_box) > 0:
img_out = dict(
file_name=data_root + img['file_name'],
height=img['height'],
width=img['width'],
id=anno_idx)
anno_out = dict(
area=righthand_box[2] * righthand_box[3],
iscrowd=anno['iscrowd'],
image_id=anno_idx,
bbox=righthand_box,
category_id=0,
id=anno_idx)
anno_idx += 1
images_out.append(img_out)
annos_out.append(anno_out)
return images_out, annos_out, anno_idx
train_files = [
'freihand/annotations/freihand_train.json',
'halpe/annotations/halpe_train_v1.json',
'onehand10k/annotations/onehand10k_train.json',
'/rhd/annotations/rhd_train.json'
]
val_files = ['onehand10k/annotations/onehand10k_test.json']
def convert2dict(data_root, anno_files):
anno_files = [data_root + _ for _ in anno_files]
images, annos, anno_idx = [], [], 0
for anno_file in anno_files:
if 'freihand' in anno_file or 'onehand10k' in anno_file \
or 'rhd' in anno_file:
images_out, annos_out, anno_idx = parse_coco_style(
anno_file, anno_idx)
images += images_out
annos += annos_out
elif 'halpe' in anno_file:
images_out, annos_out, anno_idx = parse_halpe(anno_file, anno_idx)
images += images_out
annos += annos_out
else:
print(f'{anno_file} not supported')
result = dict(
images=images,
annotations=annos,
categories=[{
'id': 0,
'name': 'hand'
}])
return result
if __name__ == '__main__':
args = parse_args()
data_root = args.data_root + '/'
prefix = args.out_anno_prefix
os.makedirs('hand_det', exist_ok=True)
result = convert2dict(data_root, train_files)
with open(f'hand_det/{prefix}_train.json', 'w') as f:
json.dump(result, f)
result = convert2dict(data_root, val_files)
with open(f'hand_det/{prefix}_val.json', 'w') as f:
json.dump(result, f)
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