159 lines
5.3 KiB
Markdown
159 lines
5.3 KiB
Markdown
---
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title: 'How to add a new annotation format support'
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linkTitle: 'New annotation format support'
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weight: 10
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description: 'Instructions on adding support for new annotation formats. This section on [GitHub](https://github.com/cvat-ai/cvat/tree/develop/cvat/apps/dataset_manager/formats).'
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---
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1. Add a python script to `dataset_manager/formats`
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2. Add an import statement to [registry.py](https://github.com/cvat-ai/cvat/tree/develop/cvat/apps/dataset_manager/formats/registry.py).
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3. Implement some importers and exporters as the format requires.
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Each format is supported by an importer and exporter.
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It can be a function or a class decorated with
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`importer` or `exporter` from [registry.py](https://github.com/cvat-ai/cvat/tree/develop/cvat/apps/dataset_manager/formats/registry.py).
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Examples:
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```python
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@importer(name="MyFormat", version="1.0", ext="ZIP")
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def my_importer(file_object, task_data, **options):
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...
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@importer(name="MyFormat", version="2.0", ext="XML")
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class my_importer(file_object, task_data, **options):
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def __call__(self, file_object, task_data, **options):
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...
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@exporter(name="MyFormat", version="1.0", ext="ZIP"):
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def my_exporter(file_object, task_data, **options):
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...
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```
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Each decorator defines format parameters such as:
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- _name_
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- _version_
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- _file extension_. For the `importer` it can be a comma-separated list.
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These parameters are combined to produce a visible name. It can be
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set explicitly by the `display_name` argument.
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Importer arguments:
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- _file_object_ - a file with annotations or dataset
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- _task_data_ - an instance of `TaskData` class.
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Exporter arguments:
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- _file_object_ - a file for annotations or dataset
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- _task_data_ - an instance of `TaskData` class.
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- _options_ - format-specific options. `save_images` is the option to
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distinguish if dataset or just annotations are requested.
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[`TaskData`](https://github.com/cvat-ai/cvat/blob/develop/cvat/apps/dataset_manager/bindings.py) provides
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many task properties and interfaces to add and read task annotations.
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Public members:
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- **TaskData. Attribute** - class, `namedtuple('Attribute', 'name, value')`
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- **TaskData. LabeledShape** - class, `namedtuple('LabeledShape', 'type, frame, label, points, occluded, attributes, group, z_order')`
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- **TrackedShape** - `namedtuple('TrackedShape', 'type, points, occluded, frame, attributes, outside, keyframe, z_order')`
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- **Track** - class, `namedtuple('Track', 'label, group, shapes')`
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- **Tag** - class, `namedtuple('Tag', 'frame, label, attributes, group')`
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- **Frame** - class, `namedtuple('Frame', 'frame, name, width, height, labeled_shapes, tags')`
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- **TaskData. shapes** - property, an iterator over `LabeledShape` objects
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- **TaskData. tracks** - property, an iterator over `Track` objects
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- **TaskData. tags** - property, an iterator over `Tag` objects
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- **TaskData. meta** - property, a dictionary with task information
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- **TaskData. group_by_frame()** - method, returns
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an iterator over `Frame` objects, which groups annotation objects by frame.
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Note that `TrackedShape` s will be represented as `LabeledShape` s.
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- **TaskData. add_tag(tag)** - method,
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tag should be an instance of the `Tag` class
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- **TaskData. add_shape(shape)** - method,
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shape should be an instance of the `Shape` class
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- **TaskData. add_track(track)** - method,
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track should be an instance of the `Track` class
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Sample exporter code:
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```python
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...
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# dump meta info if necessary
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...
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# iterate over all frames
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for frame_annotation in task_data.group_by_frame():
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# get frame info
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image_name = frame_annotation.name
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image_width = frame_annotation.width
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image_height = frame_annotation.height
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# iterate over all shapes on the frame
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for shape in frame_annotation.labeled_shapes:
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label = shape.label
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xtl = shape.points[0]
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ytl = shape.points[1]
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xbr = shape.points[2]
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ybr = shape.points[3]
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# iterate over shape attributes
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for attr in shape.attributes:
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attr_name = attr.name
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attr_value = attr.value
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...
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# dump annotation code
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file_object.write(...)
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...
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```
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Sample importer code:
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```python
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...
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#read file_object
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...
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for parsed_shape in parsed_shapes:
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shape = task_data.LabeledShape(
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type="rectangle",
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points=[0, 0, 100, 100],
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occluded=False,
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attributes=[],
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label="car",
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outside=False,
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frame=99,
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)
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task_data.add_shape(shape)
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```
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## Format specifications
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- {{< ilink "/docs/manual/advanced/formats/format-cvat" "CVAT" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-datumaro" "Datumaro" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-labelme" "LabelMe" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-mot" "MOT" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-mots" "MOTS" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-coco" "COCO" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-voc" "PASCAL VOC and mask" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-yolo" "YOLO" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-imagenet" "ImageNet" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-camvid" "CamVid" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-widerface" "WIDER Face" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-vggface2" "VGGFace2" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-market1501" "Market-1501" >}}
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- {{< ilink "/docs/manual/advanced/formats/format-icdar" "ICDAR13/15" >}}
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