mmagic.visualization.visualizer
¶
Module Contents¶
Classes¶
MMagic Visualizer. |
Attributes¶
- class mmagic.visualization.visualizer.Visualizer(name='visualizer', vis_backends: Optional[List[Dict]] = None, save_dir: Optional[str] = None)[源代码]¶
Bases:
mmengine.visualization.Visualizer
MMagic Visualizer.
- 参数
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.
Examples:
>>> # Draw image >>> vis = Visualizer() >>> 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)
- static _post_process_image(image: torch.Tensor) torch.Tensor [源代码]¶
Post process images.
- 参数
image (Tensor) – Image to post process. The value range of image should be in [0, 255] and the channel order should be BGR.
- 返回
Image in RGB color order.
- 返回类型
Tensor
- static _get_n_row_and_padding(samples: Tuple[dict, torch.Tensor], n_row: Optional[int] = None) Tuple[int, Optional[torch.Tensor]] [源代码]¶
Get number of sample in each row and tensor for padding the empty position.
- 参数
samples (Tuple[dict, Tensor]) – Samples to visualize.
n_row (int, optional) – Number of images displayed in each row of. If not passed, n_row will be set as
int(sqrt(batch_size))
.
- 返回
- Number of sample in each row and tensor
for padding the empty position.
- 返回类型
Tuple[int, Optional[int]]
- _vis_gif_sample(gen_samples: mmagic.utils.typing.SampleList, target_keys: Union[str, List[str], None], n_row: int) numpy.ndarray [源代码]¶
Visualize gif samples.
- 参数
gen_samples (SampleList) – List of data samples to visualize
target_keys (Union[str, List[str], None]) – Keys of the visualization target in data samples.
n_rows (int, optional) – Number of images in one row.
- 返回
The visualization results.
- 返回类型
np.ndarray
- _vis_image_sample(gen_samples: mmagic.utils.typing.SampleList, target_keys: Union[str, List[str], None], n_row: int) numpy.ndarray [源代码]¶
Visualize image samples.
- 参数
gen_samples (SampleList) – List of data samples to visualize
target_keys (Union[str, List[str], None]) – Keys of the visualization target in data samples.
color_order (str) – The color order of the passed images.
target_mean (Sequence[Union[float, int]]) – The target mean of the visualization results.
target_std (Sequence[Union[float, int]]) – The target std of the visualization results.
n_rows (int, optional) – Number of images in one row.
- 返回
The visualization results.
- 返回类型
np.ndarray
- _get_vis_data_by_key(sample: mmagic.structures.DataSample, key: str) torch.Tensor [源代码]¶
Get tensor in
DataSample
by the given key.- 参数
sample (DataSample) – Input data sample.
key (str) – Name of the target tensor.
- 返回
Tensor from the data sample.
- 返回类型
Tensor
- add_datasample(name: str, *, gen_samples: Sequence[mmagic.structures.DataSample], target_keys: Optional[Tuple[str, List[str]]] = None, vis_mode: Optional[str] = None, n_row: Optional[int] = None, show: bool = False, wait_time: int = 0, step: int = 0, **kwargs) None [源代码]¶
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.- 参数
name (str) – The image identifier.
gen_samples (List[DataSample]) – 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 None.
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 results. 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 [源代码]¶
Record the image. Support input kwargs.
- 参数
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.