[Feature] Add SROIE Converter (#810)

* add SROIE converter * add sroie converter * fix docstring indentation * fix lint * remove val split; add test split * delete google drive timestamp Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * remove timestamp * update docs; support jsonl; fix crop * move tree structure * move tree structure * move directory tree * fix indentation Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
2025-06-03 21:54:47 +08:00 · 2022-03-30 13:14:23 +08:00 · 2022-03-30 13:14:23 +08:00 · bdd32c8052
commit bdd32c8052
parent 958e4a3e87
4 changed files with 464 additions and 0 deletions
--- a/docs/en/datasets/det.md
+++ b/docs/en/datasets/det.md
@ -59,6 +59,7 @@ The structure of the text detection dataset directory is organized as follows.
 |     Totaltext     |                                                                                                           [homepage](https://github.com/cs-chan/Total-Text-Dataset)                                                                                                            |                                                                                                              -                                                                                                               |                                              -                                               |                                               -                                                |
 | CurvedSynText150k | [homepage](https://github.com/aim-uofa/AdelaiDet/blob/master/datasets/README.md) \| [Part1](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) \| [Part2](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) |                                                          [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json)                                                          |                                              -                                               |                                               -                                                |
 |       FUNSD       |                                                                                                              [homepage](https://guillaumejaume.github.io/FUNSD/)                                                                                                               |                                                                                                              -                                                                                                               |                                              -                                               |                                               -                                                |
+|       SROIE       |                                                                                                                   [homepage](https://rrc.cvc.uab.es/?ch=13)                                                                                                                    |                                                                                                              -                                                                                                               |                                              -                                               |                                               -                                                |
 | Lecture Video DB  |                                                                                               [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb)                                                                                               |                                                                                                              -                                                                                                               |                                              -                                               |                                               -                                                |


@ -219,6 +220,45 @@ rm dataset.zip && rm -rf dataset
 python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
 ```

+### SROIE
+
+- Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` from [homepage](https://rrc.cvc.uab.es/?ch=13&com=downloads) to `sroie/`
+
+- Step2:
+
+  ```bash
+  mkdir sroie && cd sroie
+  mkdir imgs && mkdir annotations && mkdir imgs/training
+
+  # Warnninig: The zip files downloaded from Google Drive and BaiduYun Cloud may
+  # be different, the user should revise the following commands to the correct
+  # file name if encounter with errors while extracting and move the files.
+  unzip -q 0325updated.task1train\(626p\).zip && unzip -q task1\&2_test\(361p\).zip && unzip -q text.task1\&2-test（361p\).zip
+
+  # For images
+  mv 0325updated.task1train\(626p\)/*.jpg imgs/training && mv fulltext_test\(361p\) imgs/test
+
+  # For annotations
+  mv 0325updated.task1train\(626p\) annotations/training && mv text.task1\&2-testги361p\)/ annotations/test
+
+  rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（361p\).zip
+  ```
+
+- Step3: Generate `instances_training.json` and `instances_test.json` with the following command:
+
+  ```bash
+  python tools/data/textdet/sroie_converter.py PATH/TO/sroie --nproc 4
+  ```
+
+- After running the above codes, the directory structure should be as follows:
+
+  ```text
+  ├── sroie
+  │   ├── annotations
+  │   ├── imgs
+  │   ├── instances_test.json
+  │   └── instances_training.json
+  ```
 ### Lecture Video DB

 - Step1: Download [IIIT-CVid.zip](http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip) to `lv/`.
--- a/docs/en/datasets/recog.md
+++ b/docs/en/datasets/recog.md
@ -103,6 +103,7 @@
 |       Totaltext       |                       [homepage](https://github.com/cs-chan/Total-Text-Dataset)                       |                                                                                                                                                                                                           -                                                                                                                                                                                                           |                                                             -                                                             |   |
 |       OpenVINO        |                  [Open Images](https://github.com/cvdfoundation/open-images-dataset)                  |                                                                                                                                               [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text)                                                                                                                                               | [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) |   |
 |         FUNSD         |                          [homepage](https://guillaumejaume.github.io/FUNSD/)                          |                                                                                                                                                                                                           -                                                                                                                                                                                                           |                                                             -                                                             |   |
+|         SROIE         |                               [homepage](https://rrc.cvc.uab.es/?ch=13)                               |                                                                                                                                                                                                           -                                                                                                                                                                                                           |                                                             -                                                             | - |
 |   Lecture Video DB    |          [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb)           |                                                                                                                                                                                                           -                                                                                                                                                                                                           |                                                             -                                                             | - |


@ -327,6 +328,44 @@ rm dataset.zip && rm -rf dataset
 python tools/data/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
 ```

+### SROIE
+
+- Step1: Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` from [homepage](https://rrc.cvc.uab.es/?ch=13&com=downloads) to `sroie/`
+
+- Step2:
+
+  ```bash
+  mkdir sroie && cd sroie
+  mkdir imgs && mkdir annotations && mkdir imgs/training
+
+  # Warnninig: The zip files downloaded from Google Drive and BaiduYun Cloud may
+  # be different, the user should revise the following commands to the correct
+  # file name if encounter with errors while extracting and move the files.
+  unzip -q 0325updated.task1train\(626p\).zip && unzip -q task1\&2_test\(361p\).zip && unzip -q text.task1\&2-test（361p\).zip
+
+  # For images
+  mv 0325updated.task1train\(626p\)/*.jpg imgs/training && mv fulltext_test\(361p\) imgs/test
+
+  # For annotations
+  mv 0325updated.task1train\(626p\) annotations/training && mv text.task1\&2-testги361p\)/ annotations/test
+
+  rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（361p\).zip
+  ```
+
+- Step3: Generate `train_label.jsonl` and `test_label.jsonl` and crop images using 4 processes with the following command:
+
+  ```bash
+  python tools/data/textrecog/sroie_converter.py PATH/TO/sroie --nproc 4
+  ```
+
+- After running the above codes, the directory structure should be as follows:
+
+  ```text
+  ├── sroie
+  │   ├── crops
+  │   ├── train_label.jsonl
+  │   ├── test_label.jsonl
+  ```
 ### Lecture Video DB

 **The LV dataset has already provided cropped images and the corresponding annotations**
--- a/tools/data/textdet/sroie_converter.py
+++ b/tools/data/textdet/sroie_converter.py
@ -0,0 +1,166 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+from mmocr.utils import convert_annotations
+
+
+def collect_files(img_dir, gt_dir):
+    """Collect all images and their corresponding groundtruth files.
+
+    Args:
+        img_dir (str): The image directory
+        gt_dir (str): The groundtruth directory
+
+    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
+
+    ann_list, imgs_list = [], []
+    for gt_file in os.listdir(gt_dir):
+        # Filtering repeated and missing images
+        if '(' in gt_file or gt_file == 'X51006619570.txt':
+            continue
+        ann_list.append(osp.join(gt_dir, gt_file))
+        imgs_list.append(osp.join(img_dir, gt_file.replace('.txt', '.jpg')))
+
+    files = list(zip(sorted(imgs_list), sorted(ann_list)))
+    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, 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, 'unchanged')
+
+    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.
+
+    Args:
+        gt_file (list): The list of tuples (image_file, groundtruth_file)
+        img_info (int): The dict of the img and annotation information
+
+    Returns:
+        img_info (list): The dict of the img and annotation information
+    """
+
+    with open(gt_file, 'r', encoding='unicode_escape') as f:
+        anno_info = []
+        for ann in f.readlines():
+
+            # annotation format [x1, y1, x2, y2, x3, y3, x4, y4, transcript]
+            try:
+                ann_box = np.array(ann.split(',')[0:8]).astype(int).tolist()
+            except ValueError:
+                # skip invalid annotation line
+                continue
+            x = max(0, min(ann_box[0::2]))
+            y = max(0, min(ann_box[1::2]))
+            w, h = max(ann_box[0::2]) - x, max(ann_box[1::2]) - y
+            bbox = [x, y, w, h]
+            segmentation = ann_box
+
+            anno = dict(
+                iscrowd=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 test set of SROIE')
+    parser.add_argument('root_path', help='Root dir path of SROIE')
+    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
+
+    for split in ['training', 'test']:
+        print(f'Processing {split} set...')
+        with mmcv.Timer(print_tmpl='It takes {}s to convert SROIE annotation'):
+            files = collect_files(
+                osp.join(root_path, 'imgs', split),
+                osp.join(root_path, 'annotations', split))
+            image_infos = collect_annotations(files, nproc=args.nproc)
+            convert_annotations(
+                image_infos, osp.join(root_path,
+                                      'instances_' + split + '.json'))
+
+
+if __name__ == '__main__':
+    main()
--- a/tools/data/textrecog/sroie_converter.py
+++ b/tools/data/textrecog/sroie_converter.py
@ -0,0 +1,219 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import json
+import os
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+from mmocr.datasets.pipelines.crop import crop_img
+from mmocr.utils.fileio import list_to_file
+
+
+def collect_files(img_dir, gt_dir):
+    """Collect all images and their corresponding groundtruth files.
+
+    Args:
+        img_dir (str): The image directory
+        gt_dir (str): The groundtruth directory
+
+    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
+
+    ann_list, imgs_list = [], []
+    for gt_file in os.listdir(gt_dir):
+        # Filtering repeated and missing images
+        if '(' in gt_file or gt_file == 'X51006619570.txt':
+            continue
+        ann_list.append(osp.join(gt_dir, gt_file))
+        imgs_list.append(osp.join(img_dir, gt_file.replace('.txt', '.jpg')))
+
+    files = list(zip(sorted(imgs_list), sorted(ann_list)))
+    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, 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, 'unchanged')
+
+    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.
+
+    Annotation Format
+    x1, y1, x2, y2, x3, y3, x4, y4, transcript
+
+    Args:
+        gt_file (list): The list of tuples (image_file, groundtruth_file)
+        img_info (int): The dict of the img and annotation information
+
+    Returns:
+        img_info (list): The dict of the img and annotation information
+    """
+
+    with open(gt_file, 'r', encoding='unicode_escape') as f:
+        anno_info = []
+        for ann in f.readlines():
+            # skip invalid annotation line
+            try:
+                bbox = np.array(ann.split(',')[0:8]).astype(int).tolist()
+            except ValueError:
+
+                continue
+            word = ann.split(',')[-1].replace('\n', '').strip()
+
+            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, 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
+        format (str): Annotation format, should be either 'jsonl' or 'txt'
+    """
+
+    dst_image_root = osp.join(root_path, 'crops', split)
+    if split == 'training':
+        dst_label_file = osp.join(root_path, f'train_label.{format}')
+    elif split == 'test':
+        dst_label_file = osp.join(root_path, f'test_label.{format}')
+    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', split,
+                                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)
+
+            # Skip invalid annotations
+            if min(dst_img.shape) == 0 or len(word) == 0:
+                continue
+
+            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)
+
+            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
+                    }))
+            else:
+                raise NotImplementedError
+
+    list_to_file(dst_label_file, lines)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate training and test set of SROIE')
+    parser.add_argument('root_path', help='Root dir path of SROIE')
+    parser.add_argument(
+        '--nproc', default=1, type=int, help='Number of process')
+    parser.add_argument(
+        '--format',
+        default='jsonl',
+        help='Use jsonl or string to format annotations',
+        choices=['jsonl', 'txt'])
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    root_path = args.root_path
+
+    for split in ['training', 'test']:
+        print(f'Processing {split} set...')
+        with mmcv.Timer(print_tmpl='It takes {}s to convert SROIE annotation'):
+            files = collect_files(
+                osp.join(root_path, 'imgs', split),
+                osp.join(root_path, 'annotations', split))
+            image_infos = collect_annotations(files, nproc=args.nproc)
+            generate_ann(root_path, split, image_infos, args.format)
+
+
+if __name__ == '__main__':
+    main()