Image transforms add center crop (#19718)

* Add center crop to transforms library

* Return PIL images if PIL image input by default

* Fixup and add docstring

* Trigger CI

* Update src/transformers/image_transforms.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/image_transforms.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* PR comments - move comments; unindent

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/en/internal/image_processing_utils.mdx

@@ -19,6 +19,8 @@ Most of those are only useful if you are studying the code of the image processo
 
 ## Image Transformations
 
+[[autodoc]] image_transforms.center_crop
+
 [[autodoc]] image_transforms.normalize
 
 [[autodoc]] image_transforms.rescale

src/transformers/image_transforms.py

@@ -317,3 +317,97 @@ def normalize(
 
     image = to_channel_dimension_format(image, data_format) if data_format is not None else image
     return image
+
+
+def center_crop(
+    image: np.ndarray,
+    size: Tuple[int, int],
+    data_format: Optional[Union[str, ChannelDimension]] = None,
+    return_numpy: Optional[bool] = None,
+) -> np.ndarray:
+    """
+    Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to
+    the size given, it will be padded (so the returned result will always be of size `size`).
+
+    Args:
+        image (`np.ndarray`):
+            The image to crop.
+        size (`Tuple[int, int]`):
+            The target size for the cropped image.
+        data_format (`str` or `ChannelDimension`, *optional*):
+            The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            If unset, will use the inferred format of the input image.
+        return_numpy (`bool`, *optional*):
+            Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the
+            previous ImageFeatureExtractionMixin method.
+                - Unset: will return the same type as the input image.
+                - `True`: will return a numpy array.
+                - `False`: will return a `PIL.Image.Image` object.
+    Returns:
+        `np.ndarray`: The cropped image.
+    """
+    if isinstance(image, PIL.Image.Image):
+        warnings.warn(
+            "PIL.Image.Image inputs are deprecated and will be removed in v4.26.0. Please use numpy arrays instead.",
+            FutureWarning,
+        )
+        image = to_numpy_array(image)
+        return_numpy = False if return_numpy is None else return_numpy
+    else:
+        return_numpy = True if return_numpy is None else return_numpy
+
+    if not isinstance(image, np.ndarray):
+        raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}")
+
+    if not isinstance(size, Iterable) or len(size) != 2:
+        raise ValueError("size must have 2 elements representing the height and width of the output image")
+
+    input_data_format = infer_channel_dimension_format(image)
+    output_data_format = data_format if data_format is not None else input_data_format
+
+    # We perform the crop in (C, H, W) format and then convert to the output format
+    image = to_channel_dimension_format(image, ChannelDimension.FIRST)
+
+    orig_height, orig_width = get_image_size(image)
+    crop_height, crop_width = size
+
+    # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result.
+    top = (orig_height - crop_height) // 2
+    bottom = top + crop_height
+    # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result.
+    left = (orig_width - crop_width) // 2
+    right = left + crop_width
+
+    # Check if cropped area is within image boundaries
+    if top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width:
+        image = image[..., top:bottom, left:right]
+        image = to_channel_dimension_format(image, output_data_format)
+        return image
+
+    # Otherwise, we may need to pad if the image is too small. Oh joy...
+    new_height = max(crop_height, orig_height)
+    new_width = max(crop_width, orig_width)
+    new_shape = image.shape[:-2] + (new_height, new_width)
+    new_image = np.zeros_like(image, shape=new_shape)
+
+    # If the image is too small, pad it with zeros
+    top_pad = (new_height - orig_height) // 2
+    bottom_pad = top_pad + orig_height
+    left_pad = (new_width - orig_width) // 2
+    right_pad = left_pad + orig_width
+    new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image
+
+    top += top_pad
+    bottom += top_pad
+    left += left_pad
+    right += left_pad
+
+    new_image = new_image[..., max(0, top) : min(new_height, bottom), max(0, left) : min(new_width, right)]
+    new_image = to_channel_dimension_format(new_image, output_data_format)
+
+    if not return_numpy:
+        new_image = to_pil_image(new_image)
+
+    return new_image

tests/test_image_transforms.py

@@ -35,6 +35,7 @@ if is_vision_available():
     import PIL.Image
 
     from transformers.image_transforms import (
+        center_crop,
         get_resize_output_image_size,
         normalize,
         resize,
@@ -195,3 +196,26 @@ class ImageTransformsTester(unittest.TestCase):
         self.assertIsInstance(normalized_image, np.ndarray)
         self.assertEqual(normalized_image.shape, (3, 224, 224))
         self.assertTrue(np.allclose(normalized_image, expected_image))
+
+    def test_center_crop(self):
+        image = np.random.randint(0, 256, (3, 224, 224))
+
+        # Test that exception is raised if inputs are incorrect
+        with self.assertRaises(ValueError):
+            center_crop(image, 10)
+
+        # Test result is correct - output data format is channels_first and center crop
+        # correctly computed
+        expected_image = image[:, 52:172, 82:142].transpose(1, 2, 0)
+        cropped_image = center_crop(image, (120, 60), data_format="channels_last")
+        self.assertIsInstance(cropped_image, np.ndarray)
+        self.assertEqual(cropped_image.shape, (120, 60, 3))
+        self.assertTrue(np.allclose(cropped_image, expected_image))
+
+        # Test that image is padded with zeros if crop size is larger than image size
+        expected_image = np.zeros((300, 260, 3))
+        expected_image[38:262, 18:242, :] = image.transpose((1, 2, 0))
+        cropped_image = center_crop(image, (300, 260), data_format="channels_last")
+        self.assertIsInstance(cropped_image, np.ndarray)
+        self.assertEqual(cropped_image.shape, (300, 260, 3))
+        self.assertTrue(np.allclose(cropped_image, expected_image))