class mmedit.visualization.GenVisualizer(name='visualizer', vis_backends: Optional[List[Dict]] = None, save_dir: Optional[str] = None)[source]

MMEditing Visualizer.

  • name (str) – Name of the instance. Defaults to ‘visualizer’.

  • vis_backends (list, optional) – Visual backend config list. Defaults to None.

  • save_dir (str, optional) – Save file dir for all storage backends. If it is None, the backend storage will not save any data.


>>> # Draw image
>>> vis = GenVisualizer()
>>> vis.add_datasample(
>>>     'random_noise',
>>>     gen_samples=torch.rand(2, 3, 10, 10),
>>>     gt_samples=dict(imgs=torch.randn(2, 3, 10, 10)),
>>>     gt_keys='imgs',
>>>     vis_mode='image',
>>>     n_rows=2,
>>>     step=10)
add_datasample(name: str, *, gen_samples: Sequence[mmedit.structures.edit_data_sample.EditDataSample], target_keys: Optional[Tuple[str, List[str]]] = None, vis_mode: Optional[str] = None, n_row: Optional[int] = 1, color_order: str = 'bgr', target_mean: Sequence[Union[float, int]] = 127.5, target_std: Sequence[Union[float, int]] = 127.5, show: bool = False, wait_time: int = 0, step: int = 0, **kwargs) None[source]

Draw datasample and save to all backends.

If GT and prediction are plotted at the same time, they are displayed in a stitched image where the left image is the ground truth and the right image is the prediction.

If show is True, all storage backends are ignored, and the images will be displayed in a local window.

  • name (str) – The image identifier.

  • gen_samples (List[EditDataSample]) – Data samples to visualize.

  • vis_mode (str, optional) – Visualization mode. If not passed, will visualize results as image. Defaults to None.

  • n_rows (int, optional) – Number of images in one row. Defaults to 1.

  • color_order (str) – The color order of the passed images. Defaults to ‘bgr’.

  • target_mean (Sequence[Union[float, int]]) – The target mean of the visualization results. Defaults to 127.5.

  • target_std (Sequence[Union[float, int]]) – The target std of the visualization resutts. Defaults to 127.5.

  • show (bool) – Whether to display the drawn image. Default to False.

  • wait_time (float) – The interval of show (s). Defaults to 0.

  • step (int) – Global step value to record. Defaults to 0.

add_image(name: str, image: numpy.ndarray, step: int = 0, **kwargs) None[source]

Record the image. Support input kwargs.

  • name (str) – The image identifier.

  • image (np.ndarray, optional) – The image to be saved. The format should be RGB. Default to None.

  • step (int) – Global step value to record. Default to 0.

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