96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
import argparse
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import os
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import pickle as pkl
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import numpy as np
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import random
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from PIL import Image
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import concurrent.futures
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import json
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import mmcv
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def parse_args():
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parser = argparse.ArgumentParser(description='Generate MMDetection Annotations for Crowdhuman-like dataset')
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parser.add_argument('--dataset', help='dataset name', type=str)
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parser.add_argument('--dataset-split', help='dataset split, e.g. train, val', type=str)
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args = parser.parse_args()
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return args.dataset, args.dataset_split
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def load_func(fpath):
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assert os.path.exists(fpath)
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with open(fpath, 'r') as fid:
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lines = fid.readlines()
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records = [json.loads(line.strip('\n')) for line in lines]
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return records
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def decode_annotations(records, dataset_path):
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rec_ids = list(range(len(records)))
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img_list = []
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ann_list = []
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ann_id = 1
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for idx, rec_id in enumerate(rec_ids):
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img_id = records[rec_id]['ID']
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img_url = dataset_path + 'Images/' + img_id + '.jpg'
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assert os.path.exists(img_url)
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im = Image.open(img_url)
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im_w, im_h = im.width, im.height
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gt_box = records[rec_id]['gtboxes']
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gt_box_len = len(gt_box)
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img_dict = dict(
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file_name=img_id + '.jpg',
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height=im_h,
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width=im_w,
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id=idx
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)
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img_list.append(img_dict)
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for ii in range(gt_box_len):
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each_data = gt_box[ii]
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x, y, w, h = each_data['fbox']
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if w <= 0 or h <= 0:
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continue
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# x1 = x; y1 = y; x2 = x + w; y2 = y + h
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valid_bbox = [x, y, w, h]
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if each_data['tag'] == 'person':
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tag = 1
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else:
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tag = -2
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if 'extra' in each_data:
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if 'ignore' in each_data['extra']:
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if each_data['extra']['ignore'] != 0:
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tag = -2
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ann_dict = dict(
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area=w * h,
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iscrowd=1 if tag == -2 else 0,
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image_id=idx,
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bbox=[x, y, w, h],
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category_id=1,
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id=ann_id,
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# ignore=1 if tag == -2 else 1,
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)
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ann_id += 1
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ann_list.append(ann_dict)
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cate_list = [{'supercategory': 'none', 'id': 1, 'name': 'person'}]
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json_dict = dict(
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images=img_list,
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annotations=ann_list,
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categories=cate_list
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)
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return json_dict
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if __name__ == "__main__":
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dataset_name, dataset_type = parse_args()
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dataset_path = 'data/%s/' % dataset_name
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ch_file_path = dataset_path + 'annotations/annotation_%s.odgt' % dataset_type
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json_file_path = dataset_path + 'annotations/annotation_%s.json' % dataset_type
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records = load_func(ch_file_path)
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print("Loading Annotations Done")
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json_dict = decode_annotations(records, dataset_path)
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print("Parsing Bbox Number: %d" % len(json_dict['annotations']))
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mmcv.dump(json_dict, json_file_path)
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