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# Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import os.path as osp
from argparse import ArgumentParser
from functools import partial
import mmcv
from PIL import Image
from mmocr.utils.fileio import list_to_file
def parse_args():
parser = ArgumentParser(description='Generate training and validation set '
'of OpenVINO annotations for Open '
'Images by cropping box image.')
parser.add_argument(
'root_path', help='Root dir containing images and annotations')
parser.add_argument(
'n_proc', default=1, type=int, help='Number of processes to run')
args = parser.parse_args()
return args
def process_img(args, src_image_root, dst_image_root):
# Dirty hack for multi-processing
img_idx, img_info, anns = args
src_img = Image.open(osp.join(src_image_root, img_info['file_name']))
labels = []
for ann_idx, ann in enumerate(anns):
attrs = ann['attributes']
text_label = attrs['transcription']
# Ignore illegible or non-English words
if not attrs['legible'] or attrs['language'] != 'english':
continue
x, y, w, h = ann['bbox']
x, y = max(0, math.floor(x)), max(0, math.floor(y))
w, h = math.ceil(w), math.ceil(h)
dst_img = src_img.crop((x, y, x + w, y + h))
dst_img_name = f'img_{img_idx}_{ann_idx}.jpg'
dst_img_path = osp.join(dst_image_root, dst_img_name)
# Preserve JPEG quality
dst_img.save(dst_img_path, qtables=src_img.quantization)
labels.append(f'{osp.basename(dst_image_root)}/{dst_img_name}'
f' {text_label}')
src_img.close()
return labels
def convert_openimages(root_path,
dst_image_path,
dst_label_filename,
annotation_filename,
img_start_idx=0,
nproc=1):
annotation_path = osp.join(root_path, annotation_filename)
if not osp.exists(annotation_path):
raise Exception(
f'{annotation_path} not exists, please check and try again.')
src_image_root = root_path
# outputs
dst_label_file = osp.join(root_path, dst_label_filename)
dst_image_root = osp.join(root_path, dst_image_path)
os.makedirs(dst_image_root, exist_ok=True)
annotation = mmcv.load(annotation_path)
process_img_with_path = partial(
process_img,
src_image_root=src_image_root,
dst_image_root=dst_image_root)
tasks = []
anns = {}
for ann in annotation['annotations']:
anns.setdefault(ann['image_id'], []).append(ann)
for img_idx, img_info in enumerate(annotation['images']):
tasks.append((img_idx + img_start_idx, img_info, anns[img_info['id']]))
labels_list = mmcv.track_parallel_progress(
process_img_with_path, tasks, keep_order=True, nproc=nproc)
final_labels = []
for label_list in labels_list:
final_labels += label_list
list_to_file(dst_label_file, final_labels)
return len(annotation['images'])
def main():
args = parse_args()
root_path = args.root_path
print('Processing training set...')
num_train_imgs = 0
for s in '125f':
num_train_imgs = convert_openimages(
root_path=root_path,
dst_image_path=f'image_{s}',
dst_label_filename=f'train_{s}_label.txt',
annotation_filename=f'text_spotting_openimages_v5_train_{s}.json',
img_start_idx=num_train_imgs,
nproc=args.n_proc)
print('Processing validation set...')
convert_openimages(
root_path=root_path,
dst_image_path='image_val',
dst_label_filename='val_label.txt',
annotation_filename='text_spotting_openimages_v5_validation.json',
img_start_idx=num_train_imgs,
nproc=args.n_proc)
print('Finish')
if __name__ == '__main__':
main()
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