added totaltext recog converter (#273)

* added totaltext recog converter * modified datasets.md and totaltext_converter.py * added Note to datasets.md * deleted comments
2021-06-11 11:09:35 +08:00 · 2021-06-11 11:09:35 +08:00 · d7fa9544e6
parent 9fe1ad5410
commit d7fa9544e6
2 changed files with 320 additions and 2 deletions
--- a/docs/datasets.md
+++ b/docs/datasets.md
@ -115,7 +115,7 @@ The structure of the text detection dataset directory is organized as follows.
  mv Polygon/Test ../annotations/test

  ```
-  - Step2: Generate `instances_training.json` and `instances_test.json` with following command:
+  - Step2: Generate `instances_training.json` and `instances_test.json` with the following command:
  ```bash
  python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
  ```
@ -171,6 +171,11 @@ The structure of the text detection dataset directory is organized as follows.
 │   │   ├── label.txt
 │   │   ├── label.lmdb
 │   │   ├── SynthText_Add
+│   ├── Totaltext
+│   │   ├── imgs
+│   │   ├── annotations
+│   │   ├── train_label.txt
+│   │   ├── test_label.txt
 ```

 |  Dataset   |                                        images                                         |                                                                                                                                            annotation file                                                                                                                                             |                                             annotation file                                             |
@ -187,6 +192,7 @@ The structure of the text detection dataset directory is organized as follows.
 |  Syn90k  |               [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/)                |                                                       [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt)                                                       |                                                    -                                                    |       |
 | SynthText  |           [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/)              | [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt) \| [instances_train.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt) |                                                    -                                                    |       |
 |  SynthAdd  |  [SynthText_Add.zip](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg)  (code:627x)   |                                                                                                           [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt)                                                                                                            |                                                    -                                                    |       |
+|  Totaltext  |  [homepage](https://github.com/cs-chan/Total-Text-Dataset)   |                                                                                                           -                                                                                                           |                                                    -                                                    |       |

 - For `icdar_2013`:
  - Step1: Download `Challenge2_Test_Task3_Images.zip` and `Challenge2_Training_Task3_Images_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads)
@ -269,7 +275,7 @@ The structure of the text detection dataset directory is organized as follows.

  ln -s /path/to/SynthAdd SynthAdd
  ```
-**Note:**
+  **Note:**
 To convert label file with `txt` format to `lmdb` format,
 ```bash
 python tools/data/utils/txt2lmdb.py -i <txt_label_path> -o <lmdb_label_path>
@ -279,6 +285,32 @@ For example,
 python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb
 ```

+- For `Totaltext`:
+  - Step1: Download `totaltext.zip` from [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) and `groundtruth_text.zip` from [github Groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) (We recommend downloading the text groundtruth with .mat format since our totaltext_converter.py supports groundtruth with .mat format only).
+  ```bash
+  mkdir totaltext && cd totaltext
+  mkdir imgs && mkdir annotations
+
+  # For images
+  # in ./totaltext
+  unzip totaltext.zip
+  mv Images/Train imgs/training
+  mv Images/Test imgs/test
+
+  # For annotations
+  unzip groundtruth_text.zip
+  cd Groundtruth
+  mv Polygon/Train ../annotations/training
+  mv Polygon/Test ../annotations/test
+
+  ```
+  - Step2: Generate cropped images, `train_label.txt` and `test_label.txt` with the following command (the cropped images will be saved to `data/totaltext/dst_imgs/`.):
+  ```bash
+  python tools/data/textrecog/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
+  ```
+
+
+
 ## Key Information Extraction

 The structure of the key information extraction dataset directory is organized as follows.
--- a/tools/data/textrecog/totaltext_converter.py
+++ b/tools/data/textrecog/totaltext_converter.py
@ -0,0 +1,286 @@
+import argparse
+import glob
+import os
+import os.path as osp
+from functools import partial
+
+import mmcv
+import numpy as np
+import scipy.io as scio
+from shapely.geometry import Polygon
+
+from mmocr.datasets.pipelines.crop import crop_img
+from mmocr.utils import drop_orientation, is_not_png
+from mmocr.utils.fileio import list_to_file
+
+
+def collect_files(img_dir, gt_dir, split):
+    """Collect all images and their corresponding groundtruth files.
+
+    Args:
+        img_dir(str): The image directory
+        gt_dir(str): The groundtruth directory
+        split(str): The split of dataset. Namely: training or test
+
+    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
+
+    # note that we handle png and jpg only. Pls convert others such as gif to
+    # jpg or png offline
+    suffixes = ['.png', '.jpg', '.jpeg']
+    # suffixes = ['.png']
+
+    imgs_list = []
+    for suffix in suffixes:
+        imgs_list.extend(glob.glob(osp.join(img_dir, '*' + suffix)))
+
+    imgs_list = [
+        drop_orientation(f) if is_not_png(f) else f for f in imgs_list
+    ]
+
+    files = []
+    if split == 'training':
+        for img_file in imgs_list:
+            gt_file = osp.join(
+                gt_dir,
+                'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
+            files.append((img_file, gt_file))
+        assert len(files), f'No images found in {img_dir}'
+        print(f'Loaded {len(files)} images from {img_dir}')
+    elif split == 'test':
+        for img_file in imgs_list:
+            gt_file = osp.join(
+                gt_dir,
+                'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
+            files.append((img_file, gt_file))
+        assert len(files), f'No images found in {img_dir}'
+        print(f'Loaded {len(files)} images from {img_dir}')
+
+    return files
+
+
+def collect_annotations(files, split, nproc=1):
+    """Collect the annotation information.
+
+    Args:
+        files(list): The list of tuples (image_file, groundtruth_file)
+        split(str): The split of dataset. Namely: training or test
+        nproc(int): The number of process to collect annotations
+
+    Returns:
+        images(list): The list of image information dicts
+    """
+    assert isinstance(files, list)
+    assert isinstance(split, str)
+    assert isinstance(nproc, int)
+
+    load_img_info_with_split = partial(load_img_info, split=split)
+    if nproc > 1:
+        images = mmcv.track_parallel_progress(
+            load_img_info_with_split, files, nproc=nproc)
+    else:
+        images = mmcv.track_progress(load_img_info_with_split, files)
+
+    return images
+
+
+def get_contours(gt_path, split):
+    """Get the contours and words for each ground_truth file.
+
+    Args:
+        gt_path(str): The relative path of the ground_truth mat file
+        split(str): The split of dataset: training or test
+
+    Returns:
+        contours(list[lists]): A list of lists of contours
+        for the text instances
+        words(list[list]): A list of lists of words (string)
+        for the text instances
+    """
+    assert isinstance(gt_path, str)
+    assert isinstance(split, str)
+
+    contours = []
+    words = []
+    data = scio.loadmat(gt_path)
+    if split == 'training':
+        data_polygt = data['polygt']
+    elif split == 'test':
+        data_polygt = data['polygt']
+
+    for lines in data_polygt:
+        X = np.array(lines[1])
+        Y = np.array(lines[3])
+
+        point_num = len(X[0])
+        word = lines[4]
+        if len(word) == 0:
+            word = '???'
+        else:
+            word = word[0]
+
+        if word == '#':
+            word = '###'
+            continue
+
+        words.append(word)
+
+        arr = np.concatenate([X, Y]).T
+        contour = []
+        for i in range(point_num):
+            contour.append(arr[i][0])
+            contour.append(arr[i][1])
+        contours.append(np.asarray(contour))
+
+    return contours, words
+
+
+def load_mat_info(img_info, gt_file, split):
+    """Load the information of one ground truth in .mat format.
+
+    Args:
+        img_info(dict): The dict of only the image information
+        gt_file(str): The relative path of the ground_truth mat
+        file for one image
+        split(str): The split of dataset: training or test
+
+    Returns:
+        img_info(dict): The dict of the img and annotation information
+    """
+    assert isinstance(img_info, dict)
+    assert isinstance(gt_file, str)
+    assert isinstance(split, str)
+
+    contours, words = get_contours(gt_file, split)
+    anno_info = []
+    for contour, word in zip(contours, words):
+        if contour.shape[0] == 2:
+            continue
+        coordinates = np.array(contour).reshape(-1, 2)
+        polygon = Polygon(coordinates)
+
+        # convert to COCO style XYWH format
+        min_x, min_y, max_x, max_y = polygon.bounds
+        bbox = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]
+        anno = dict(word=word, bbox=bbox)
+        anno_info.append(anno)
+
+    img_info.update(anno_info=anno_info)
+    return img_info
+
+
+def generate_ann(root_path, split, image_infos):
+
+    dst_image_root = osp.join(root_path, 'dst_imgs', split)
+    if split == 'training':
+        dst_label_file = osp.join(root_path, 'train_label.txt')
+    elif split == 'test':
+        dst_label_file = osp.join(root_path, 'test_label.txt')
+    os.makedirs(dst_image_root, exist_ok=True)
+
+    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 = osp.splitext(image_info['file_name'])[0].split('/')[1]
+
+        for anno in image_info['anno_info']:
+            word = anno['word']
+            dst_img = crop_img(image, anno['bbox'])
+            dst_img_name = f'{src_img_root}_{index}.png'
+            index += 1
+            dst_img_path = osp.join(dst_image_root, dst_img_name)
+            mmcv.imwrite(dst_img, dst_img_path)
+            lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} '
+                         f'{word}')
+    list_to_file(dst_label_file, lines)
+
+
+def load_img_info(files, split):
+    """Load the information of one image.
+
+    Args:
+        files(tuple): The tuple of (img_file, groundtruth_file)
+        split(str): The split of dataset: training or test
+
+    Returns:
+        img_info(dict): The dict of the img and annotation information
+    """
+    assert isinstance(files, tuple)
+    assert isinstance(split, str)
+
+    img_file, gt_file = files
+    # read imgs with ignoring orientations
+    img = mmcv.imread(img_file, 'unchanged')
+    # read imgs with orientations as dataloader does when training and testing
+    img_color = mmcv.imread(img_file, 'color')
+    # make sure imgs have no orientation info, or annotation gt is wrong.
+    assert img.shape[0:2] == img_color.shape[0:2]
+
+    split_name = osp.basename(osp.dirname(img_file))
+    img_info = dict(
+        # remove img_prefix for filename
+        file_name=osp.join(split_name, osp.basename(img_file)),
+        height=img.shape[0],
+        width=img.shape[1],
+        # anno_info=anno_info,
+        segm_file=osp.join(split_name, osp.basename(gt_file)))
+
+    if split == 'training':
+        img_info = load_mat_info(img_info, gt_file, split)
+    elif split == 'test':
+        img_info = load_mat_info(img_info, gt_file, split)
+    else:
+        raise NotImplementedError
+
+    return img_info
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert totaltext annotations to COCO format')
+    parser.add_argument('root_path', help='totaltext root path')
+    parser.add_argument('-o', '--out-dir', help='output path')
+    parser.add_argument(
+        '--split-list',
+        nargs='+',
+        help='a list of splits. e.g., "--split_list training test"')
+
+    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
+    out_dir = args.out_dir if args.out_dir else root_path
+    mmcv.mkdir_or_exist(out_dir)
+
+    img_dir = osp.join(root_path, 'imgs')
+    gt_dir = osp.join(root_path, 'annotations')
+
+    set_name = {}
+    for split in args.split_list:
+        set_name.update({split: 'instances_' + split + '.json'})
+        assert osp.exists(osp.join(img_dir, split))
+
+    for split, json_name in set_name.items():
+        print(f'Converting {split} into {json_name}')
+        with mmcv.Timer(
+                print_tmpl='It takes {}s to convert totaltext annotation'):
+            files = collect_files(
+                osp.join(img_dir, split), osp.join(gt_dir, split), split)
+            image_infos = collect_annotations(files, split, nproc=args.nproc)
+            generate_ann(root_path, split, image_infos)
+
+
+if __name__ == '__main__':
+    main()