Spaces:
Sleeping
Sleeping
File size: 6,153 Bytes
14c9181 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import cv2
import mmcv
import mmengine
from PIL import Image
from mmocr.utils import dump_ocr_data
def collect_files(img_dir, gt_dir, ratio):
"""Collect all images and their corresponding groundtruth files.
Args:
img_dir (str): The image directory
gt_dir (str): The groundtruth directory
ratio (float): Split ratio for val set
Returns:
files (list): The list of tuples (img_file, groundtruth_file)
"""
assert isinstance(img_dir, str)
assert img_dir
assert isinstance(gt_dir, str)
assert gt_dir
assert isinstance(ratio, float)
assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0'
ann_list, imgs_list = [], []
for ann_file in os.listdir(gt_dir):
img_file = osp.join(img_dir, ann_file.replace('txt', 'jpg'))
# This dataset contains some images obtained from .gif,
# which cannot be loaded by mmcv.imread(), convert them
# to RGB mode.
try:
if mmcv.imread(img_file) is None:
print(f'Convert {img_file} to RGB mode.')
img = Image.open(img_file)
img = img.convert('RGB')
img.save(img_file)
except cv2.error:
print(f'Skip broken img {img_file}')
continue
ann_list.append(osp.join(gt_dir, ann_file))
imgs_list.append(img_file)
all_files = list(zip(imgs_list, ann_list))
assert len(all_files), f'No images found in {img_dir}'
print(f'Loaded {len(all_files)} images from {img_dir}')
trn_files, val_files = [], []
if ratio > 0:
for i, file in enumerate(all_files):
if i % math.floor(1 / ratio):
trn_files.append(file)
else:
val_files.append(file)
else:
trn_files, val_files = all_files, []
print(f'training #{len(trn_files)}, val #{len(val_files)}')
return trn_files, val_files
def collect_annotations(files, nproc=1):
"""Collect the annotation information.
Args:
files (list): The list of tuples (image_file, groundtruth_file)
nproc (int): The number of process to collect annotations
Returns:
images (list): The list of image information dicts
"""
assert isinstance(files, list)
assert isinstance(nproc, int)
if nproc > 1:
images = mmengine.track_parallel_progress(
load_img_info, files, nproc=nproc)
else:
images = mmengine.track_progress(load_img_info, files)
return images
def load_img_info(files):
"""Load the information of one image.
Args:
files (tuple): The tuple of (img_file, groundtruth_file)
Returns:
img_info (dict): The dict of the img and annotation information
"""
assert isinstance(files, tuple)
img_file, gt_file = files
assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
'.')[0]
# read imgs while ignoring orientations
img = mmcv.imread(img_file)
img_info = dict(
file_name=osp.join(osp.basename(img_file)),
height=img.shape[0],
width=img.shape[1],
segm_file=osp.join(osp.basename(gt_file)))
if osp.splitext(gt_file)[1] == '.txt':
img_info = load_txt_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_txt_info(gt_file, img_info):
"""Collect the annotation information.
The annotation format is as the following:
x1,y1,x2,y2,x3,y3,x4,y4,text
45.45,226.83,11.87,181.79,183.84,13.1,233.79,49.95,时尚袋袋
345.98,311.18,345.98,347.21,462.26,347.21,462.26,311.18,73774
462.26,292.34,461.44,299.71,502.39,299.71,502.39,292.34,73/74/737
Args:
gt_file (str): The path to ground-truth
img_info (dict): The dict of the img and annotation information
Returns:
img_info (dict): The dict of the img and annotation information
"""
anno_info = []
with open(gt_file) as f:
lines = f.readlines()
for line in lines:
points = line.split(',')[0:8]
word = line.split(',')[8].rstrip('\n')
segmentation = [math.floor(float(pt)) for pt in points]
x = max(0, min(segmentation[0::2]))
y = max(0, min(segmentation[1::2]))
w = abs(max(segmentation[0::2]) - x)
h = abs(max(segmentation[1::2]) - y)
bbox = [x, y, w, h]
anno = dict(
iscrowd=1 if word == '###' else 0,
category_id=1,
bbox=bbox,
area=w * h,
segmentation=[segmentation])
anno_info.append(anno)
img_info.update(anno_info=anno_info)
return img_info
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and val set of MTWI.')
parser.add_argument('root_path', help='Root dir path of MTWI')
parser.add_argument(
'--val-ratio', help='Split ratio for val set', default=0.0, type=float)
parser.add_argument(
'--nproc', default=1, type=int, help='Number of process')
args = parser.parse_args()
return args
def main():
args = parse_args()
root_path = args.root_path
ratio = args.val_ratio
trn_files, val_files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Training annotation'):
dump_ocr_data(trn_infos, osp.join(root_path,
'instances_training.json'),
'textdet')
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmengine.Timer(
print_tmpl='It takes {}s to convert MTWI Val annotation'):
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')
if __name__ == '__main__':
main()
|