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TorchShow API References

torchshow.show

torchshow.show(x, 
               mode='auto',
               auto_permute=True,
               display=True, 
               nrows=None, 
               ncols=None, 
               channel_mode='auto', 
               show_axis=False, 
               tight_layout=True, 
               suptitle=None, 
               axes_title=None, 
               figsize=None, 
               dpi=None,
               cmap='gray')

Parameters:

  • x: *tensor-like (support both torch.Tensor, np.ndarray and PIL Image) or List of tensor-like. * The tensor data that we want to visualize. Filename and list of filenames are also supported, for example: ts.show("my_image.jpg").

  • mode: str. The visualize mode. The default value is "auto" where TorchShow will automatically infer the mode. Available options are: "image", "flow", "grayscale", "categorical_mask".

  • auto_permute: bool. If enable, TorchShow will automatically convert CHW to HWC format.

  • display: bool. If set to false, TorchShow will not display the data but return the list of processed data. Use it when you want to visualize them using other libraries such as OpenCV.

  • nrows: Int. The number of rows to plot in a grid layout. If not specified it will be automatically inferred by TorchShow.

  • ncols: Int. The number of columns to plot in a grid layout. If not specified it will be automatically inferred by TorchShow.

  • channel_mode: Str. The channel mode of your input data. Available options are "auto", "channel_last" and "channel_fist". The default value is "auto" and it will be automatically inferred by TorchShow.

  • show_axis: Bool. Whether to show the axis in the plot.

  • tight_layout: Bool. Routines to adjust subplot params so that subplots are nicely fit in the figure. Corresponding to fig.tight_layout() in matplotlib.

  • suptitle: Str. Add a centered suptitle to the figure.

  • axes_title: Str. Add titles to each of the axes. It can be used with predefined placeholders. Available placeholders are: {img_id}, {img_id_from_1}, {row}, {column}.

    Below is an example that shows the image id on top of each image:

    batch = torch.rand(8, 3, 100, 100)
ts.show(batch, axes_title="Image ID: {img_id_from_1}")

  • figsize: 2-tuple of floats. Figure dimension (width, height) in inches.

  • dpi: float. Dots per inch.

  • cmap: str. Specifying the color map for grayscale image.

torchshow.save

torchshow.save(x,
               path=None,
               **kwargs)

Parameters:

  • x: tensor-like (support both torch.Tensor and np.ndarray) or List of tensor-like. The tensor data that we want to visualize.
  • path: str. The path to save the figure.
  • kwargs: You can pass in any other parameters available in torchshow.show().

torchshow.overlay

torchshow.overlay(x,
                  alpha=None,
                  extent=None,
                  save_as=None,
                  **kwargs)

A function use to overlay multiple visualization.

Parameters

  • x: list of tensor-like. A list of tensor data that we want to overlay their visualization. Filenames are also supported.
  • alpha: list of (number or array-like). (Optional) The list of alpha values for blending, each alpha value is between 0 (transparent) and 1 (opaque). If alpha is an array-like, the alpha blending values are applied pixel by pixel, and alpha must have the same shape as X.
  • extent: tuple. (Optional) Format: (x_min, x_max, y_min, y_max). The extent defines the size of the rendering area which will be used to render all plots. If unspecified TorchShow will use the extent of the first visualization.
  • save_as: srt. (Optional) A filepath to save the plot. If specified TorchShow will save the result to this file.
  • kwargs: You can pass in any other parameters available in torchshow.show().

Examples:

ts.overlay([tensor1, tensor2, tensor3], alpha=[0.5, 0.5])
ts.overlay(["example_rgb.jpg", "example_category_mask.png"], alpha=[1, 0.5])

torchshow.show_video

torchshow.show_video(x,
                     display=True,
                     show_axis=False,
                     tight_layout=False,
                     suptitle=None,
                     figsize=None,
                     dpi=None)

  • x: tensor-like (Support both torch.Tensor and np.ndarray) or List of tensor-like. The tensor data that we want to visualize.

  • display: bool. If set to false, TorchShow will not display the data but return the list of processed data. Use it when you want to visualize them using other libraries such as OpenCV.

  • show_axis: Bool. Whether to show the axis in the plot.

  • tight_layout: Bool. Routines to adjust subplot params so that subplots are nicely fit in the figure. Corresponding to fig.tight_layout() in matplotlib.

  • suptitle: Str. Add a centered suptitle to the figure.

  • figsize: 2-tuple of floats. Figure dimension (width, height) in inches.

  • dpi: float. Dots per inch.

torchshow.set_color_mode

torchshow.set_color_mode(mode)
  • mode: str. "rgb" or "bgr". Set channel mode of the color image. The default config is "rgb".

torchshow.set_image_mean

torchshow.set_image_mean(mean)
  • mean: list of number: Set the channel-wise mean when unnormalize the image. The default mean is [0., 0., 0.].

torchshow.set_image_std

torchshow.set_image_std(std)
  • std: list of number: Set the channel-wise std when unnormalize the image. The default std is [1., 1., 1.].

torchshow.show_rich_info

torchshow.show_rich_info(flag)
  • flag: bool: Whether to show rich info in the interactive window.