mmagic.visualization.concat_visualizer
¶
Module Contents¶
Classes¶
Visualize multiple images by concatenation. |
- class mmagic.visualization.concat_visualizer.ConcatImageVisualizer(fn_key: str, img_keys: Sequence[str], pixel_range={}, bgr2rgb=False, name: str = 'visualizer', *args, **kwargs)[source]¶
Bases:
mmengine.visualization.Visualizer
Visualize multiple images by concatenation.
This visualizer will horizontally concatenate images belongs to different keys and vertically concatenate images belongs to different frames to visualize.
- Image to be visualized can be:
torch.Tensor or np.array
Image sequences of shape (T, C, H, W)
Multi-channel image of shape (1/3, H, W)
Single-channel image of shape (C, H, W)
- Parameters
fn_key (str) – key used to determine file name for saving image. Usually it is the path of some input image. If the value is dir/basename.ext, the name used for saving will be basename.
img_keys (str) – keys, values of which are images to visualize.
pixel_range (dict) – min and max pixel value used to denormalize images, note that only float array or tensor will be denormalized, uint8 arrays are assumed to be unnormalized.
bgr2rgb (bool) – whether to convert the image from BGR to RGB.
name (str) – name of visualizer. Default: ‘visualizer’.
**kwargs (*args and) – Other arguments are passed to Visualizer. # noqa
- add_datasample(data_sample: mmagic.structures.DataSample, step=0) None [source]¶
Concatenate image and draw.
- Parameters
input (torch.Tensor) – Single input tensor from data_batch.
data_sample (DataSample) – Single data_sample from data_batch.
output (DataSample) – Single prediction output by model.
step (int) – Global step value to record. Default: 0.