File size: 4,370 Bytes
2c58401 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import argparse
import json
import os
import cv2
import numpy as np
from tqdm import tqdm
from refer import REFER
parser = argparse.ArgumentParser(description='Data preparation')
parser.add_argument('--data_root', type=str)
parser.add_argument('--output_dir', type=str)
parser.add_argument('--dataset',
type=str,
choices=['refcoco', 'refcoco+', 'refcocog', 'refclef'],
default='refcoco')
parser.add_argument('--split', type=str, default='umd')
parser.add_argument('--generate_mask', action='store_true')
args = parser.parse_args()
img_path = os.path.join(args.data_root, 'images', 'train2014')
h, w = (416, 416)
refer = REFER(args.data_root, args.dataset, args.split)
print('dataset [%s_%s] contains: ' % (args.dataset, args.split))
ref_ids = refer.getRefIds()
image_ids = refer.getImgIds()
print('%s expressions for %s refs in %s images.' %
(len(refer.Sents), len(ref_ids), len(image_ids)))
print('\nAmong them:')
if args.dataset == 'refclef':
if args.split == 'unc':
splits = ['train', 'val', 'testA', 'testB', 'testC']
else:
splits = ['train', 'val', 'test']
elif args.dataset == 'refcoco':
splits = ['train', 'val', 'testA', 'testB']
elif args.dataset == 'refcoco+':
splits = ['train', 'val', 'testA', 'testB']
elif args.dataset == 'refcocog':
splits = ['train', 'val',
'test'] # we don't have test split for refcocog right now.
for split in splits:
ref_ids = refer.getRefIds(split=split)
print('%s refs are in split [%s].' % (len(ref_ids), split))
def cat_process(cat):
if cat >= 1 and cat <= 11:
cat = cat - 1
elif cat >= 13 and cat <= 25:
cat = cat - 2
elif cat >= 27 and cat <= 28:
cat = cat - 3
elif cat >= 31 and cat <= 44:
cat = cat - 5
elif cat >= 46 and cat <= 65:
cat = cat - 6
elif cat == 67:
cat = cat - 7
elif cat == 70:
cat = cat - 9
elif cat >= 72 and cat <= 82:
cat = cat - 10
elif cat >= 84 and cat <= 90:
cat = cat - 11
return cat
def bbox_process(bbox):
x_min = int(bbox[0])
y_min = int(bbox[1])
x_max = x_min + int(bbox[2])
y_max = y_min + int(bbox[3])
return list(map(int, [x_min, y_min, x_max, y_max]))
def prepare_dataset(dataset, splits, output_dir, generate_mask=False):
ann_path = os.path.join(output_dir, 'anns', dataset)
mask_path = os.path.join(output_dir, 'masks', dataset)
if not os.path.exists(ann_path):
os.makedirs(ann_path)
if not os.path.exists(mask_path):
os.makedirs(mask_path)
for split in splits:
dataset_array = []
ref_ids = refer.getRefIds(split=split)
print('Processing split:{} - Len: {}'.format(split, len(ref_ids)))
for i in tqdm(ref_ids):
ref_dict = {}
refs = refer.Refs[i]
bboxs = refer.getRefBox(i)
sentences = refs['sentences']
image_urls = refer.loadImgs(image_ids=refs['image_id'])[0]
cat = cat_process(refs['category_id'])
image_urls = image_urls['file_name']
if dataset == 'refclef' and image_urls in [
'19579.jpg', '17975.jpg', '19575.jpg'
]:
continue
box_info = bbox_process(bboxs)
ref_dict['bbox'] = box_info
ref_dict['cat'] = cat
ref_dict['segment_id'] = i
ref_dict['img_name'] = image_urls
if generate_mask:
cv2.imwrite(os.path.join(mask_path,
str(i) + '.png'),
refer.getMask(refs)['mask'] * 255)
sent_dict = []
for i, sent in enumerate(sentences):
sent_dict.append({
'idx': i,
'sent_id': sent['sent_id'],
'sent': sent['sent'].strip()
})
ref_dict['sentences'] = sent_dict
ref_dict['sentences_num'] = len(sent_dict)
dataset_array.append(ref_dict)
print('Dumping json file...')
with open(os.path.join(output_dir, 'anns', dataset, split + '.json'),
'w') as f:
json.dump(dataset_array, f)
prepare_dataset(args.dataset, splits, args.output_dir, args.generate_mask)
|