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# FallingThingsStereo

class torchvision.datasets.FallingThingsStereo(root: str, variant: str = 'single', transforms: Optional[Callable] = None)

  FallingThings dataset.

  The dataset is expected to have the following structure:

      root
          FallingThings
              single
                  dir1
                      scene1
                          _object_settings.json
                          _camera_settings.json
                          image1.left.depth.png
                          image1.right.depth.png
                          image1.left.jpg
                          image1.right.jpg
                          image2.left.depth.png
                          image2.right.depth.png
                          image2.left.jpg
                          image2.right
                          ...
                      scene2
                  ...
              mixed
                  scene1
                      _object_settings.json
                      _camera_settings.json
                      image1.left.depth.png
                      image1.right.depth.png
                      image1.left.jpg
                      image1.right.jpg
                      image2.left.depth.png
                      image2.right.depth.png
                      image2.left.jpg
                      image2.right
                      ...
                  scene2
                  ...

  Parameters:

    - root (string) – Root directory where FallingThings is located.

    - variant (string) – Which variant to use. Either “single”, “mixed”, or “both”.

    - transforms (callable, optional) – A function/transform that takes in a sample and returns a transformed version.

  Special-members:

  __getitem__(index: int) → Tuple[Image, Image, Optional[ndarray], ndarray]

    Return example at given index.

    Parameters:

      index (int) – The index of the example to retrieve

    Returns:

      A 3-tuple with `(img_left, img_right, disparity)`. The disparity is a numpy array of shape (1, H, W) and the images are PIL images. If a `valid_mask` is generated within the `transforms` parameter, a 4-tuple with `(img_left, img_right, disparity, valid_mask)` is returned.

    Return type:

      tuple

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