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Dataset Migration
Based on the new design of BaseDataset in MMEngine, we have refactored the base OCR dataset class OCRDataset in MMOCR 1.0. The following document describes the differences between the old and new dataset formats in MMOCR, and how to migrate from the deprecated version to the latest. For users who do not want to migrate datasets at this time, we also provide a temporary solution in Section Compatibility.
The Key Information Extraction task still uses the original WildReceipt dataset annotation format.
Review of Old Dataset Formats
MMOCR version 0.x implements a number of dataset classes, such as IcdarDataset, TextDetDataset for text detection tasks, and OCRDataset, OCRSegDataset for text recognition tasks. At the same time, the annotations may vary in different formats, such as .txt, .json, .jsonl. Users have to manually configure the Loader and the Parser while customizing the datasets.
Text Detection
For the text detection task, IcdarDataset uses a COCO-like annotation format.
{
"images": [
{
"id": 1,
"width": 800,
"height": 600,
"file_name": "test.jpg"
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [0,0,10,10],
"segmentation": [
[0,0,10,0,10,10,0,10]
],
"area": 100,
"iscrowd": 0
}
]
}
The TextDetDataset uses the JSON Line storage format, converting COCO-like labels to strings and saves them in .txt or .jsonl format files.
{"file_name": "test/img_2.jpg", "height": 720, "width": 1280, "annotations": [{"iscrowd": 0, "category_id": 1, "bbox": [602.0, 173.0, 33.0, 24.0], "segmentation": [[602, 173, 635, 175, 634, 197, 602, 196]]}, {"iscrowd": 0, "category_id": 1, "bbox": [734.0, 310.0, 58.0, 54.0], "segmentation": [[734, 310, 792, 320, 792, 364, 738, 361]]}]}
{"file_name": "test/img_5.jpg", "height": 720, "width": 1280, "annotations": [{"iscrowd": 1, "category_id": 1, "bbox": [405.0, 409.0, 32.0, 52.0], "segmentation": [[408, 409, 437, 436, 434, 461, 405, 433]]}, {"iscrowd": 1, "category_id": 1, "bbox": [435.0, 434.0, 8.0, 33.0], "segmentation": [[437, 434, 443, 440, 441, 467, 435, 462]]}]}
Text Recognition
For text recognition tasks, there are two annotation formats in MMOCR version 0.x. The simple .txt annotations separate image name and word annotation by a blank space, which cannot handle the case when spaces are included in a text instance.
img1.jpg OpenMMLab
img2.jpg MMOCR
The JSON Line format uses a dictionary-like structure to represent the annotations, where the keys filename and text store the image name and word label, respectively.
{"filename": "img1.jpg", "text": "OpenMMLab"}
{"filename": "img2.jpg", "text": "MMOCR"}
New Dataset Format
To solve the dataset issues, MMOCR 1.x adopts a unified dataset design introduced in MMEngine. Each annotation file is a .json file that stores a dict, containing both metainfo and data_list, where the former includes basic information about the dataset and the latter consists of the label item of each target instance.
{
"metainfo":
{
"classes": ("cat", "dog"),
// ...
},
"data_list":
[
{
"img_path": "xxx/xxx_0.jpg",
"img_label": 0,
// ...
},
// ...
]
}
Based on the above structure, we introduced TextDetDataset, TextRecogDataset for MMOCR-specific tasks.
Text Detection
Introduction of the New Format
The TextDetDataset holds the information required by the text detection task, such as bounding boxes and labels. We refer users to tests/data/det_toy_dataset/instances_test.json which is an example annotation for TextDetDataset.
{
"metainfo":
{
"dataset_type": "TextDetDataset",
"task_name": "textdet",
"category": [{"id": 0, "name": "text"}]
},
"data_list":
[
{
"img_path": "test_img.jpg",
"height": 640,
"width": 640,
"instances":
[
{
"polygon": [0, 0, 0, 10, 10, 20, 20, 0],
"bbox": [0, 0, 10, 20],
"bbox_label": 0,
"ignore": False
}，
// ...
]
}
]
}
The bounding box format is as follows: [min_x, min_y, max_x, max_y]
Migration Script
We provide a migration script to help users migrate old annotation files to the new format.
python tools/dataset_converters/textdet/data_migrator.py ${IN_PATH} ${OUT_PATH}
| ARGS | Type | Description |
|---|---|---|
| in_path | str | （Required）Path to the old annotation file. |
| out_path | str | （Required）Path to the new annotation file. |
| --task | 'auto', 'textdet', 'textspotter' | Specifies the compatible task for the output dataset annotation. If 'textdet' is specified, the text field in coco format will not be dumped. The default is 'auto', which automatically determines the output format based on the the old annotation files. |
Text Recognition
Introduction of the New Format
The TextRecogDataset holds the information required by the text detection task, such as text and image path. We refer users to tests/data/rec_toy_dataset/labels.json which is an example annotation for TextRecogDataset.
{
"metainfo":
{
"dataset_type": "TextRecogDataset",
"task_name": "textrecog",
},
"data_list":
[
{
"img_path": "test_img.jpg",
"instances":
[
{
"text": "GRAND"
}
]
}
]
}
Migration Script
We provide a migration script to help users migrate old annotation files to the new format.
python tools/dataset_converters/textrecog/data_migrator.py ${IN_PATH} ${OUT_PATH} --format ${txt, jsonl, lmdb}
| ARGS | Type | Description |
|---|---|---|
| in_path | str | （Required）Path to the old annotation file. |
| out_path | str | （Required）Path to the new annotation file. |
| --format | 'txt', 'jsonl', 'lmdb' | Specify the format of the old dataset annotation. |
Compatibility
In consideration of the cost to users for data migration, we have temporarily made MMOCR version 1.x compatible with the old MMOCR 0.x format.
The code and components used for compatibility with the old data format may be completely removed in a future release. Therefore, we strongly recommend that users migrate their datasets to the new data format.
Specifically, we provide three dataset classes IcdarDataset, RecogTextDataset, RecogLMDBDataset to support the old formats.
IcdarDataset supports COCO-like format annotations for text detection. You just need to add a new dataset config to
configs/textdet/_base_/datasetsand specify its dataset type asIcdarDataset.data_root = 'data/det/icdar2015' train_anno_path = 'instances_training.json' train_dataset = dict( type='IcdarDataset', data_root=data_root, ann_file=train_anno_path, data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None)RecogTextDataset supports
.txtand.jsonlformat annotations for text recognition. You just need to add a new dataset config toconfigs/textrecog/_base_/datasetsand specify its dataset type asRecogTextDataset. For example, the following example shows how to configure and load the 0.x format labelsold_label.txtandold_label.jsonlfrom the toy dataset.data_root = 'tests/data/rec_toy_dataset/' # loading 0.x txt format annos txt_dataset = dict( type='RecogTextDataset', data_root=data_root, ann_file='old_label.txt', data_prefix=dict(img_path='imgs'), parser_cfg=dict( type='LineStrParser', keys=['filename', 'text'], keys_idx=[0, 1]), pipeline=[]) # loading 0.x json line format annos jsonl_dataset = dict( type='RecogTextDataset', data_root=data_root, ann_file='old_label.jsonl', data_prefix=dict(img_path='imgs'), parser_cfg=dict( type='LineJsonParser', keys=['filename', 'text'], pipeline=[]))RecogLMDBDataset supports LMDB format dataset (img+labels) for text recognition. You just need to add a new dataset config to
configs/textrecog/_base_/datasetsand specify its dataset type asRecogLMDBDataset. For example, the following example shows how to configure and load the both labels and imagesimgs.lmdbfrom the toy dataset.
- set the dataset type to
RecogLMDBDataset
# Specify the dataset type as RecogLMDBDataset
data_root = 'tests/data/rec_toy_dataset/'
lmdb_dataset = dict(
type='RecogLMDBDataset',
data_root=data_root,
ann_file='imgs.lmdb',
pipeline=None)
- replace the
LoadImageFromFilewithLoadImageFromNDArrayin the data pipelines intrain_pipelineandtest_pipeline., for example：
train_pipeline = [dict(type='LoadImageFromNDArray')]