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118 lines
4.5 KiB
118 lines
4.5 KiB
"""
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We load Linjie's features from: datasets/npy_per_img_id.visual_grounding_coco_gt
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Each feature is named as: visual_grounding_coco_000000581857.npz
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containing {norm_bb, features, conf, soft_labels}
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The order of extracted bbox and features should align with ann_ids for each
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img_id.
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We save this order for the use of REFER dataloader.
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"""
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import time
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import pickle
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import numpy as np
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from pprint import pprint
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from tqdm import tqdm
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import json
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import os.path as osp
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import argparse
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def recover_ann_ids(denorm_bb, raw_bb, raw_ann_ids):
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"""
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Inputs:
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- denorm_bb : [xywh], extracted from BUTD detectors.
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- raw_bb : [xywh]
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- raw_ann_ids
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Return:
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- ordered_ann_ids: ordered by denorm_bb
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"""
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assert denorm_bb.shape[0] == raw_bb.shape[0]
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num_bb = denorm_bb.shape[0]
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ordered_ann_ids = []
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for i in range(num_bb):
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ref_bb = denorm_bb[i]
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min_err, ix = 1e5, None
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for j in range(num_bb):
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if np.sum(np.abs(ref_bb - raw_bb[j])) < min_err:
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min_err, ix = np.sum(np.abs(ref_bb-raw_bb[j])), j
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ordered_ann_ids.append(raw_ann_ids[ix])
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return ordered_ann_ids
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def main(args):
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# Load all instances from refcoco, refcoco+ and refcocog
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tic = time.time()
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iid_to_ann_ids = {}
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warning_img_ids = set()
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for dataset in ['refcoco', 'refcoco+', 'refcocog']:
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print('Checking %s...' % dataset)
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instances = json.load(open(osp.join(args.refer_dir, dataset,
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'instances.json')))
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Anns, Imgs, iid_to_raw_ann_ids = {}, {}, {}
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for ann in instances['annotations']:
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Anns[ann['id']] = ann
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iid_to_raw_ann_ids[ann['image_id']] = iid_to_raw_ann_ids.get(
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ann['image_id'], []) + [ann['id']]
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for img in instances['images']:
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Imgs[img['id']] = img
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# Make iid_to_ann_ids for this dataset
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img_ids = list(Imgs.keys())
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for img_id in tqdm(img_ids):
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if img_id in iid_to_ann_ids:
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continue
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raw_ann_ids = iid_to_raw_ann_ids[img_id]
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# raw_gd_bb
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raw_gd_bb = np.array([Anns[ann_id]['bbox']
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for ann_id in raw_ann_ids]) # (n, 4) xywh
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# denorm_bb
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im_width = Imgs[img_id]['width']
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im_height = Imgs[img_id]['height']
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img_feat = np.load(osp.join(args.feats_dir,
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f'visual_grounding_coco_gt_{int(img_id):012}.npz'))
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norm_bb = img_feat['norm_bb']
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x1, x2 = norm_bb[:, 0] * im_width, norm_bb[:, 2] * im_width
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y1, y2 = norm_bb[:, 1] * im_height, norm_bb[:, 3] * im_height
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w, h = norm_bb[:, 4] * im_width, norm_bb[:, 5] * im_height
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denorm_bb = np.stack([x1, y1, w, h], axis=1) # (n,4)
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# re-order ann_ids
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ordered_ann_ids = recover_ann_ids(denorm_bb, raw_gd_bb, raw_ann_ids)
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# check difference
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ordered_gd_bb = np.array([Anns[ann_id]['bbox']
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for ann_id in ordered_ann_ids]) # (n, 4)
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for i in range(denorm_bb.shape[0]):
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assert np.sum(np.abs(denorm_bb[i]-ordered_gd_bb[i])) < 0.01, \
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'%s, %s' %(denorm_bb[i], ordered_gd_bb[i])
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# check ann_ids set
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if set(ordered_ann_ids) != set(raw_ann_ids):
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print('Please check img_id[%s]'%img_id)
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warning_img_ids.add(img_id)
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# check length of ann_ids
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assert len(ordered_ann_ids) == len(raw_ann_ids)
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# add to iid_to_ann_ids
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iid_to_ann_ids[img_id] = ordered_ann_ids
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print('%s images contain dupicated bounding boxes.' % len(warning_img_ids))
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pprint(list(warning_img_ids))
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# save
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output_file = osp.join(args.output_dir, 'iid_to_ann_ids.json')
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with open(output_file, 'w') as f:
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json.dump({'iid_to_ann_ids': iid_to_ann_ids}, f)
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print('%s iid_to_ann_ids saved in %s.' % (len(iid_to_ann_ids), output_file))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--refer_dir',
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default='datasets/refer',
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help='folder saving all downloaded refer datasets')
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parser.add_argument('--feats_dir',
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default='datasets/npy_per_img_id/visual_grounding_coco_gt',
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help='folder saving butd features.')
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parser.add_argument('--output_dir',
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default='index',
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help='output folder saving img_id --> [ann_id]')
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args = parser.parse_args()
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main(args)
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