Add Sintel Stereo dataset (#6348)

* added SceneFLow variant datasets

* Changed split name to variant name

* removed trailing commented code line

* Added Sintel Stereo dataset

* small refactor in tests

* Fixed doc formatting.

* candidate fix for FileNotFound on windows test

* Adressing comments

* Added Sintel Stereo dataset

* small refactor in tests

* Fixed doc formatting.

* candidate fix for FileNotFound on windows test

* Adressing comments

* rebased on main

* lint fix

docs/source/datasets.rst

@@ -112,6 +112,8 @@ Stereo Matching
     Kitti2012Stereo
     Kitti2015Stereo
     SceneFlowStereo
+    SintelStereo
+
 
 Image pairs
 ~~~~~~~~~~~

test/test_datasets.py

@@ -2916,5 +2916,81 @@ class SceneFlowStereoTestCase(datasets_utils.ImageDatasetTestCase):
                 pass
 
 
+class SintelStereoTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.SintelStereo
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(pass_name=("final", "clean", "both"))
+    FEATURE_TYPES = (PIL.Image.Image, PIL.Image.Image, (np.ndarray, type(None)), (np.ndarray, type(None)))
+
+    def inject_fake_data(self, tmpdir, config):
+        sintel_dir = pathlib.Path(tmpdir) / "Sintel"
+        os.makedirs(sintel_dir, exist_ok=True)
+
+        split_dir = pathlib.Path(sintel_dir) / "training"
+        os.makedirs(split_dir, exist_ok=True)
+
+        # a single setting, since there are no splits
+        num_examples = {"final": 2, "clean": 3}
+        pass_names = {
+            "final": ["final"],
+            "clean": ["clean"],
+            "both": ["final", "clean"],
+        }.get(config["pass_name"], [])
+
+        for p in pass_names:
+            for view in [f"{p}_left", f"{p}_right"]:
+                root = split_dir / view
+                os.makedirs(root, exist_ok=True)
+
+                datasets_utils.create_image_folder(
+                    root=root,
+                    name="scene1",
+                    file_name_fn=lambda i: f"{i:06d}.png",
+                    num_examples=num_examples[p],
+                    size=(3, 100, 200),
+                )
+
+        datasets_utils.create_image_folder(
+            root=split_dir / "occlusions",
+            name="scene1",
+            file_name_fn=lambda i: f"{i:06d}.png",
+            num_examples=max(num_examples.values()),
+            size=(1, 100, 200),
+        )
+
+        datasets_utils.create_image_folder(
+            root=split_dir / "outofframe",
+            name="scene1",
+            file_name_fn=lambda i: f"{i:06d}.png",
+            num_examples=max(num_examples.values()),
+            size=(1, 100, 200),
+        )
+
+        datasets_utils.create_image_folder(
+            root=split_dir / "disparities",
+            name="scene1",
+            file_name_fn=lambda i: f"{i:06d}.png",
+            num_examples=max(num_examples.values()),
+            size=(3, 100, 200),
+        )
+
+        if config["pass_name"] == "both":
+            num_examples = sum(num_examples.values())
+        else:
+            num_examples = num_examples.get(config["pass_name"], 0)
+
+        return num_examples
+
+    def test_splits(self):
+        for pass_name in ["final", "clean", "both"]:
+            with self.create_dataset(pass_name=pass_name) as (dataset, _):
+                for left, right, disparity, valid_mask in dataset:
+                    datasets_utils.shape_test_for_stereo(left, right, disparity, valid_mask)
+
+    def test_bad_input(self):
+        with pytest.raises(ValueError, match="Unknown value 'bad' for argument pass_name"):
+            with self.create_dataset(pass_name="bad"):
+                pass
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

 from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
-from ._stereo_matching import CarlaStereo, Kitti2012Stereo, Kitti2015Stereo, SceneFlowStereo
+from ._stereo_matching import CarlaStereo, Kitti2012Stereo, Kitti2015Stereo, SceneFlowStereo, SintelStereo
 from .caltech import Caltech101, Caltech256
 from .celeba import CelebA
 from .cifar import CIFAR10, CIFAR100
@@ -110,4 +110,5 @@ __all__ = (
     "Kitti2015Stereo",
     "CarlaStereo",
     "SceneFlowStereo",
+    "SintelStereo",
 )

torchvision/datasets/_stereo_matching.py

 import functools
+import os
 from abc import ABC, abstractmethod
 from glob import glob
 from pathlib import Path
@@ -465,3 +466,125 @@ class SceneFlowStereo(StereoMatchingDataset):
             a 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned.
         """
         return super().__getitem__(index)
+
+
+class SintelStereo(StereoMatchingDataset):
+    """Sintel `Stereo Dataset <http://sintel.is.tue.mpg.de/stereo>`_.
+
+    The dataset is expected to have the following structure: ::
+
+        root
+            Sintel
+                training
+                    final_left
+                        scene1
+                            img1.png
+                            img2.png
+                            ...
+                        ...
+                    final_right
+                        scene2
+                            img1.png
+                            img2.png
+                            ...
+                        ...
+                    disparities
+                        scene1
+                            img1.png
+                            img2.png
+                            ...
+                        ...
+                    occlusions
+                        scene1
+                            img1.png
+                            img2.png
+                            ...
+                        ...
+                    outofframe
+                        scene1
+                            img1.png
+                            img2.png
+                            ...
+                        ...
+
+    Args:
+        root (string): Root directory where Sintel Stereo is located.
+        pass_name (string): The name of the pass to use, either "final", "clean" or "both".
+        transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
+    """
+
+    _has_built_in_disparity_mask = True
+
+    def __init__(self, root: str, pass_name: str = "final", transforms: Optional[Callable] = None):
+        super().__init__(root, transforms)
+
+        verify_str_arg(pass_name, "pass_name", valid_values=("final", "clean", "both"))
+
+        root = Path(root) / "Sintel"
+        pass_names = {
+            "final": ["final"],
+            "clean": ["clean"],
+            "both": ["final", "clean"],
+        }[pass_name]
+
+        for p in pass_names:
+            left_img_pattern = str(root / "training" / f"{p}_left" / "*" / "*.png")
+            right_img_pattern = str(root / "training" / f"{p}_right" / "*" / "*.png")
+            self._images += self._scan_pairs(left_img_pattern, right_img_pattern)
+
+            disparity_pattern = str(root / "training" / "disparities" / "*" / "*.png")
+            self._disparities += self._scan_pairs(disparity_pattern, None)
+
+    def _get_occlussion_mask_paths(self, file_path: str) -> Tuple[str, str]:
+        # helper function to get the occlusion mask paths
+        # a path will look like  .../.../.../training/disparities/scene1/img1.png
+        # we want to get something like .../.../.../training/occlusions/scene1/img1.png
+        fpath = Path(file_path)
+        basename = fpath.name
+        scenedir = fpath.parent
+        # the parent of the scenedir is actually the disparity dir
+        sampledir = scenedir.parent.parent
+
+        occlusion_path = str(sampledir / "occlusions" / scenedir.name / basename)
+        outofframe_path = str(sampledir / "outofframe" / scenedir.name / basename)
+
+        if not os.path.exists(occlusion_path):
+            raise FileNotFoundError(f"Occlusion mask {occlusion_path} does not exist")
+
+        if not os.path.exists(outofframe_path):
+            raise FileNotFoundError(f"Out of frame mask {outofframe_path} does not exist")
+
+        return occlusion_path, outofframe_path
+
+    def _read_disparity(self, file_path: str) -> Tuple:
+        if file_path is None:
+            return None, None
+
+        # disparity decoding as per Sintel instructions in the README provided with the dataset
+        disparity_map = np.asarray(Image.open(file_path), dtype=np.float32)
+        r, g, b = np.split(disparity_map, 3, axis=-1)
+        disparity_map = r * 4 + g / (2**6) + b / (2**14)
+        # reshape into (C, H, W) format
+        disparity_map = np.transpose(disparity_map, (2, 0, 1))
+        # find the appropiate file paths
+        occlued_mask_path, out_of_frame_mask_path = self._get_occlussion_mask_paths(file_path)
+        # occlusion masks
+        valid_mask = np.asarray(Image.open(occlued_mask_path)) == 0
+        # out of frame masks
+        off_mask = np.asarray(Image.open(out_of_frame_mask_path)) == 0
+        # combine the masks together
+        valid_mask = np.logical_and(off_mask, valid_mask)
+        return disparity_map, valid_mask
+
+    def __getitem__(self, index: int) -> Tuple:
+        """Return example at given index.
+
+        Args:
+            index(int): The index of the example to retrieve
+
+        Returns:
+            tuple: A 4-tuple with ``(img_left, img_right, disparity, valid_mask)`` is returned.
+            The disparity is a numpy array of shape (1, H, W) and the images are PIL images whilst
+            the valid_mask is a numpy array of shape (H, W).
+        """
+        return super().__getitem__(index)