.. _`Transform Image Dataset (Experimental)`:

.. _`com.sympathyfordata.advancedmachinelearning.transformimagedataset`:

Transform Image Dataset (Experimental)
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.. image:: image_ds_transform.svg
   :width: 48


Transforms images within an image dataset




Documentation
:::::::::::::

Algorithms
==========

    **Center Crop**
        Crops the given image at the center. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.


        :Width: Desired output width of the crop

        :Height: Desired output height of the crop




    **Grayscale**
        Convert image to grayscale. If the image is torch Tensor, it is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions


        :Number of output channels: (1 or 3) number of channels desired for output image




    **Normalize**
        Normalize a tensor image with mean and standard deviation. This transform does not support PIL Image. Given mean: (mean[1],...,mean[n]) and std: (std[1],..,std[n]) for n channels, this transform will normalize each channel of the input torch.*Tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]


        :Standard deviation: Sequence of standard deviations for each channel.

        :Mean: Sequence of means for each channel.




    **Pad**
        Pad the given image on all sides with the given “pad” value. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means at most 2 leading dimensions for mode reflect and symmetric, at most 3 leading dimensions for mode edge, and an arbitrary number of leading dimensions for mode constant


        :Fill: Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or str or tuple value is supported for PIL Image.

        :Padding mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. - constant: pads with a constant value, this value is specified with fill - edge: pads with the last value at the edge of the image. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2 - reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2] - symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]

        :Padding size: Padding on each border. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.




    **Resize**
        Resize the input image to the given size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions


        :Width: Desired output width.

        :Interpolation: Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Default is InterpolationMode.BILINEAR. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.BILINEAR and InterpolationMode.BICUBIC are supported. For backward compatibility integer values (e.g. PIL.Image.NEAREST) are still acceptable.

        :Height: Desired output height.




    **To PIL Image**
        Convert a tensor or an ndarray to PIL Image. This transform does not support torchscript.





    **To Tensor**
        Convert a PIL Image or numpy.ndarray to tensor. This transform does not support torchscript.


Definition
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Input ports
===========

    **dataset**  dataset
        Dataset


Output ports
============

    **dataset**  dataset
        Dataset




Configuration
=============

    **Fill** (Fill)
        (no description)
    **Height** (Height)
        (no description)
    **Interpolation** (Interpolation)
        (no description)
    **Mean** (Mean)
        (no description)
    **Number of output channels** (Number of output channels)
        (no description)
    **Padding mode** (Padding mode)
        (no description)
    **Padding size** (Padding size)
        (no description)
    **Standard deviation** (Standard deviation)
        (no description)
    **Width** (Width)
        (no description)
    **Algorithm** (algorithm)
        (no description)









Implementation
==============

.. automodule:: node_transformdataset
    :noindex:

.. class:: TransformImageDataset
    :noindex: