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| #!/usr/bin/env python3 | |
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import math | |
| import os | |
| import os.path as osp | |
| from functools import partial | |
| import mmcv | |
| import numpy as np | |
| from mmocr.utils.fileio import list_to_file | |
| from PIL import Image | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and validation set of TextOCR ' 'by cropping box image.' | |
| ) | |
| parser.add_argument('root_path', help='Root dir path of TextOCR') | |
| parser.add_argument('n_proc', default=1, type=int, help='Number of processes to run') | |
| parser.add_argument('--rectify_pose', action='store_true', help='Fix pose of rotated text to make them horizontal') | |
| args = parser.parse_args() | |
| return args | |
| def rectify_image_pose(image, top_left, points): | |
| # Points-based heuristics for determining text orientation w.r.t. bounding box | |
| points = np.asarray(points).reshape(-1, 2) | |
| dist = ((points - np.asarray(top_left)) ** 2).sum(axis=1) | |
| left_midpoint = (points[0] + points[-1]) / 2 | |
| right_corner_points = ((points - left_midpoint) ** 2).sum(axis=1).argsort()[-2:] | |
| right_midpoint = points[right_corner_points].sum(axis=0) / 2 | |
| d_x, d_y = abs(right_midpoint - left_midpoint) | |
| if dist[0] + dist[-1] <= dist[right_corner_points].sum(): | |
| if d_x >= d_y: | |
| rot = 0 | |
| else: | |
| rot = 90 | |
| else: | |
| if d_x >= d_y: | |
| rot = 180 | |
| else: | |
| rot = -90 | |
| if rot: | |
| image = image.rotate(rot, expand=True) | |
| return image | |
| def process_img(args, src_image_root, dst_image_root): | |
| # Dirty hack for multiprocessing | |
| img_idx, img_info, anns, rectify_pose = args | |
| src_img = Image.open(osp.join(src_image_root, img_info['file_name'])) | |
| labels = [] | |
| for ann_idx, ann in enumerate(anns): | |
| text_label = ann['utf8_string'] | |
| # Ignore illegible or non-English words | |
| if text_label == '.': | |
| 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)) | |
| if rectify_pose: | |
| dst_img = rectify_image_pose(dst_img, (x, y), ann['points']) | |
| 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_textocr( | |
| root_path, dst_image_path, dst_label_filename, annotation_filename, img_start_idx=0, nproc=1, rectify_pose=False | |
| ): | |
| 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 = [] | |
| for img_idx, img_info in enumerate(annotation['imgs'].values()): | |
| ann_ids = annotation['imgToAnns'][img_info['id']] | |
| anns = [annotation['anns'][ann_id] for ann_id in ann_ids] | |
| tasks.append((img_idx + img_start_idx, img_info, anns, rectify_pose)) | |
| 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['imgs']) | |
| def main(): | |
| args = parse_args() | |
| root_path = args.root_path | |
| print('Processing training set...') | |
| num_train_imgs = convert_textocr( | |
| root_path=root_path, | |
| dst_image_path='image', | |
| dst_label_filename='train_label.txt', | |
| annotation_filename='TextOCR_0.1_train.json', | |
| nproc=args.n_proc, | |
| rectify_pose=args.rectify_pose, | |
| ) | |
| print('Processing validation set...') | |
| convert_textocr( | |
| root_path=root_path, | |
| dst_image_path='image', | |
| dst_label_filename='val_label.txt', | |
| annotation_filename='TextOCR_0.1_val.json', | |
| img_start_idx=num_train_imgs, | |
| nproc=args.n_proc, | |
| rectify_pose=args.rectify_pose, | |
| ) | |
| print('Finish') | |
| if __name__ == '__main__': | |
| main() | |