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