[Feature] Add MTWI Data Converter (#867)

tools/data/textdet/mtwi_converter.py

@@ -0,0 +1,201 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import math
+import os
+import os.path as osp
+
+import cv2
+import mmcv
+from PIL import Image
+
+from mmocr.utils import convert_annotations
+
+
+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 = mmcv.track_parallel_progress(
+            load_img_info, files, nproc=nproc)
+    else:
+        images = mmcv.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, 'r') 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 mmcv.Timer(
+            print_tmpl='It takes {}s to convert MTWI Training annotation'):
+        convert_annotations(trn_infos,
+                            osp.join(root_path, 'instances_training.json'))
+
+    # Val set
+    if len(val_files) > 0:
+        val_infos = collect_annotations(val_files, nproc=args.nproc)
+        with mmcv.Timer(
+                print_tmpl='It takes {}s to convert MTWI Val annotation'):
+            convert_annotations(val_infos,
+                                osp.join(root_path, 'instances_val.json'))
+
+
+if __name__ == '__main__':
+    main()

tools/data/textrecog/mtwi_converter.py

@@ -0,0 +1,266 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import json
+import math
+import os
+import os.path as osp
+
+import cv2
+import mmcv
+from PIL import Image
+
+from mmocr.datasets.pipelines.crop import crop_img
+from mmocr.utils.fileio import list_to_file
+
+
+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 = mmcv.track_parallel_progress(
+            load_img_info, files, nproc=nproc)
+    else:
+        images = mmcv.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, 'r') as f:
+        lines = f.readlines()
+    for line in lines:
+        points = line.split(',')[0:8]
+        word = line.split(',')[8].rstrip('\n')
+        if word == '###':
+            continue
+        bbox = [math.floor(float(pt)) for pt in points]
+        anno = dict(bbox=bbox, word=word)
+        anno_info.append(anno)
+
+    img_info.update(anno_info=anno_info)
+
+    return img_info
+
+
+def generate_ann(root_path, split, image_infos, preserve_vertical, format):
+    """Generate cropped annotations and label txt file.
+
+    Args:
+        root_path (str): The root path of the dataset
+        split (str): The split of dataset. Namely: training or test
+        image_infos (list[dict]): A list of dicts of the img and
+            annotation information
+        preserve_vertical (bool): Whether to preserve vertical texts
+        format (str): Annotation format, whether be txt or jsonl
+    """
+    print('Cropping images...')
+    dst_image_root = osp.join(root_path, 'crops', split)
+    ignore_image_root = osp.join(root_path, 'ignores', split)
+    if split == 'training':
+        dst_label_file = osp.join(root_path, f'train_label.{format}')
+    elif split == 'val':
+        dst_label_file = osp.join(root_path, f'val_label.{format}')
+    mmcv.mkdir_or_exist(dst_image_root)
+    mmcv.mkdir_or_exist(ignore_image_root)
+
+    lines = []
+    for image_info in image_infos:
+        index = 1
+        src_img_path = osp.join(root_path, 'imgs', image_info['file_name'])
+        image = mmcv.imread(src_img_path)
+        src_img_root = image_info['file_name'].split('.')[0]
+
+        for anno in image_info['anno_info']:
+            word = anno['word']
+            dst_img = crop_img(image, anno['bbox'], 0, 0)
+            h, w, _ = dst_img.shape
+
+            dst_img_name = f'{src_img_root}_{index}.png'
+            index += 1
+            # Skip invalid annotations
+            if min(dst_img.shape) == 0:
+                continue
+            # Skip vertical texts
+            if not preserve_vertical and h / w > 2 and split == 'training':
+                dst_img_path = osp.join(ignore_image_root, dst_img_name)
+            else:
+                dst_img_path = osp.join(dst_image_root, dst_img_name)
+            mmcv.imwrite(dst_img, dst_img_path)
+
+            if format == 'txt':
+                lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} '
+                             f'{word}')
+            elif format == 'jsonl':
+                lines.append(
+                    json.dumps(
+                        {
+                            'filename':
+                            f'{osp.basename(dst_image_root)}/{dst_img_name}',
+                            'text': word
+                        },
+                        ensure_ascii=False))
+            else:
+                raise NotImplementedError
+
+    list_to_file(dst_label_file, lines)
+
+
+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(
+        '--preserve-vertical',
+        help='Preserve samples containing vertical texts',
+        action='store_true')
+    parser.add_argument(
+        '--format',
+        default='jsonl',
+        help='Use jsonl or string to format annotations',
+        choices=['jsonl', 'txt'])
+    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 mmcv.Timer(
+            print_tmpl='It takes {}s to convert MTWI Training annotation'):
+        generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
+                     args.format)
+
+    # Val set
+    if len(val_files) > 0:
+        val_infos = collect_annotations(val_files, nproc=args.nproc)
+        with mmcv.Timer(
+                print_tmpl='It takes {}s to convert MTWI Val annotation'):
+            generate_ann(root_path, 'val', val_infos, args.preserve_vertical,
+                         args.format)
+
+
+if __name__ == '__main__':
+    main()