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# Middlebury2014Stereo

class torchvision.datasets.Middlebury2014Stereo(root: str, split: str = 'train', calibration: Optional[str] = 'perfect', use_ambient_views: bool = False, transforms: Optional[Callable] = None, download: bool = False)

  Publicly available scenes from the Middlebury dataset 2014 version <https://vision.middlebury.edu/stereo/data/scenes2014/>.

  The dataset mostly follows the original format, without containing the ambient subdirectories. :

      root
          Middlebury2014
              train
                  scene1-{perfect,imperfect}
                      calib.txt
                      im{0,1}.png
                      im1E.png
                      im1L.png
                      disp{0,1}.pfm
                      disp{0,1}-n.png
                      disp{0,1}-sd.pfm
                      disp{0,1}y.pfm
                  scene2-{perfect,imperfect}
                      calib.txt
                      im{0,1}.png
                      im1E.png
                      im1L.png
                      disp{0,1}.pfm
                      disp{0,1}-n.png
                      disp{0,1}-sd.pfm
                      disp{0,1}y.pfm
                  ...
              additional
                  scene1-{perfect,imperfect}
                      calib.txt
                      im{0,1}.png
                      im1E.png
                      im1L.png
                      disp{0,1}.pfm
                      disp{0,1}-n.png
                      disp{0,1}-sd.pfm
                      disp{0,1}y.pfm
                  ...
              test
                  scene1
                      calib.txt
                      im{0,1}.png
                  scene2
                      calib.txt
                      im{0,1}.png
                  ...

  Parameters:

    - root (string) – Root directory of the Middleburry 2014 Dataset.

    - split (string, optional) – The dataset split of scenes, either “train” (default), “test”, or “additional”

    - use_ambient_views (boolean, optional) – Whether to use different expose or lightning views when possible. The dataset samples with equal probability between `[im1.png, im1E.png, im1L.png]`.

    - calibration (string, optional) – Whether or not to use the calibrated (default) or uncalibrated scenes.

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

    - download (boolean, optional) – Whether or not to download the dataset in the `root` directory.

  Special-members:

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

    Return example at given index.

    Parameters:

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

    Returns:

      A 4-tuple with `(img_left, img_right, disparity, valid_mask)`. The disparity is a numpy array of shape (1, H, W) and the images are PIL images. `valid_mask` is implicitly `None` for split=test.

    Return type:

      tuple

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