mirror of https://github.com/open-mmlab/mmocr.git
Feature/iss 262 (#266)
* fix issue#262 * fix #262: modified totaltext_converter and added totaltext for datasets.md * fix issue#262: modified datasets.md * fix issue#262: removed download json * Update totaltext_converter.py Co-authored-by: Hongbin Sun <hongbin306@gmail.com>pull/269/head
quincylin1
GitHub
parent
71208cb8e7
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271129f812
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@ -33,6 +33,10 @@ The structure of the text detection dataset directory is organized as follows.
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├── synthtext
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│ ├── imgs
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│ └── instances_training.lmdb
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├── totaltext
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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| Dataset | Images | | | Annotation Files | |
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@ -42,6 +46,7 @@ The structure of the text detection dataset directory is organized as follows.
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| ICDAR2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) | - | [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) |
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| ICDAR2017 | [homepage](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [renamed_imgs](https://download.openmmlab.com/mmocr/data/icdar2017/renamed_imgs.tar) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | |
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| Synthtext | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | | [instances_training.lmdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb) | - |
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| Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | | - | - | -
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- For `icdar2015`:
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- Step1: Download `ch4_training_images.zip`, `ch4_test_images.zip`, `ch4_training_localization_transcription_gt.zip`, `Challenge4_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads)
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@ -90,7 +95,29 @@ The structure of the text detection dataset directory is organized as follows.
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```bash
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python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
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```
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- For `Totaltext`:
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- 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).
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```bash
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mkdir totaltext && cd totaltext
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mkdir imgs && mkdir annotations
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# For images
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# in ./totaltext
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unzip totaltext.zip
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mv Images/Train imgs/training
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mv Images/Test imgs/test
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# For annotations
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unzip groundtruth_text.zip
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cd Groundtruth
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mv Polygon/Train ../annotations/training
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mv Polygon/Test ../annotations/test
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```
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- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
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```bash
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python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
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```
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## Text Recognition
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**The structure of the text recognition dataset directory is organized as follows.**
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@ -44,17 +44,17 @@ def collect_files(img_dir, gt_dir, split):
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files = []
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if split == 'training':
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for img_file in imgs_list:
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gt_file = gt_dir + '/gt_' + osp.splitext(
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osp.basename(img_file))[0] + '.mat'
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# gt_file = gt_dir + '/' + osp.splitext(
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# osp.basename(img_file))[0] + '.png'
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gt_file = osp.join(
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gt_dir,
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'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
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files.append((img_file, gt_file))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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elif split == 'test':
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for img_file in imgs_list:
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gt_file = gt_dir + '/poly_gt_' + osp.splitext(
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osp.basename(img_file))[0] + '.mat'
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gt_file = osp.join(
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gt_dir,
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'poly_gt_' + osp.splitext(osp.basename(img_file))[0] + '.mat')
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files.append((img_file, gt_file))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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@ -107,7 +107,7 @@ def get_contours(gt_path, split):
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words = []
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data = scio.loadmat(gt_path)
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if split == 'training':
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data_polygt = data['gt']
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data_polygt = data['polygt']
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elif split == 'test':
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data_polygt = data['polygt']
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