Add RandomCropFromBorders and support for bbox in ElasticTransform …

…from #476 (#1240)

README.md

@@ -193,6 +193,7 @@ Spatial-level transforms will simultaneously change both an input image as well
 | [PiecewiseAffine](https://albumentations.ai/docs/api_reference/augmentations/geometric/transforms/#albumentations.augmentations.geometric.transforms.PiecewiseAffine)           | ✓     | ✓     | ✓      | ✓         |
 | [PixelDropout](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.PixelDropout)                                     | ✓     | ✓     | ✓      | ✓         |
 | [RandomCrop](https://albumentations.ai/docs/api_reference/augmentations/crops/transforms/#albumentations.augmentations.crops.transforms.RandomCrop)                             | ✓     | ✓     | ✓      | ✓         |
+| [RandomCropFromBorders](https://albumentations.ai/docs/api_reference/augmentations/crops/transforms/#albumentations.augmentations.crops.transforms.RandomCropFromBorders)       | ✓     | ✓     | ✓      | ✓         |
 | [RandomCropNearBBox](https://albumentations.ai/docs/api_reference/augmentations/crops/transforms/#albumentations.augmentations.crops.transforms.RandomCropNearBBox)             | ✓     | ✓     | ✓      | ✓         |
 | [RandomGridShuffle](https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.RandomGridShuffle)                           | ✓     | ✓     |        | ✓         |
 | [RandomResizedCrop](https://albumentations.ai/docs/api_reference/augmentations/crops/transforms/#albumentations.augmentations.crops.transforms.RandomResizedCrop)               | ✓     | ✓     | ✓      | ✓         |

albumentations/augmentations/crops/transforms.py

@@ -21,6 +21,7 @@
     "RandomCropNearBBox",
     "RandomSizedBBoxSafeCrop",
     "CropAndPad",
+    "RandomCropFromBorders",
 ]
 
 
@@ -852,3 +853,68 @@ def get_transform_init_args_names(self) -> Tuple[str, ...]:
             "sample_independently",
             "interpolation",
         )
+
+
+class RandomCropFromBorders(DualTransform):
+    """Crop bbox from image randomly cut parts from borders without resize at the end
+
+    Args:
+        crop_left (float): single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
+        from left side in range [0, crop_left * width)
+        crop_right (float): single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
+        from right side in range [(1 - crop_right) * width, width)
+        crop_top (float): singlefloat value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
+        from top side in range [0, crop_top * height)
+        crop_bottom (float): single float value in (0.0, 1.0) range. Default 0.1. Image will be randomly cut
+        from bottom side in range [(1 - crop_bottom) * height, height)
+        p (float): probability of applying the transform. Default: 1.
+
+    Targets:
+        image, mask, bboxes, keypoints
+
+    Image types:
+        uint8, float32
+    """
+
+    def __init__(
+        self,
+        crop_left=0.1,
+        crop_right=0.1,
+        crop_top=0.1,
+        crop_bottom=0.1,
+        always_apply=False,
+        p=1.0,
+    ):
+        super(RandomCropFromBorders, self).__init__(always_apply, p)
+        self.crop_left = crop_left
+        self.crop_right = crop_right
+        self.crop_top = crop_top
+        self.crop_bottom = crop_bottom
+
+    def get_params_dependent_on_targets(self, params):
+        img = params["image"]
+        x_min = random.randint(0, int(self.crop_left * img.shape[1]))
+        x_max = random.randint(max(x_min + 1, int((1 - self.crop_right) * img.shape[1])), img.shape[1])
+        y_min = random.randint(0, int(self.crop_top * img.shape[0]))
+        y_max = random.randint(max(y_min + 1, int((1 - self.crop_bottom) * img.shape[0])), img.shape[0])
+        return {"x_min": x_min, "x_max": x_max, "y_min": y_min, "y_max": y_max}
+
+    def apply(self, img, x_min=0, x_max=0, y_min=0, y_max=0, **params):
+        return F.clamping_crop(img, x_min, y_min, x_max, y_max)
+
+    def apply_to_mask(self, mask, x_min=0, x_max=0, y_min=0, y_max=0, **params):
+        return F.clamping_crop(mask, x_min, y_min, x_max, y_max)
+
+    def apply_to_bbox(self, bbox, x_min=0, x_max=0, y_min=0, y_max=0, **params):
+        rows, cols = params["rows"], params["cols"]
+        return F.bbox_crop(bbox, x_min, y_min, x_max, y_max, rows, cols)
+
+    def apply_to_keypoint(self, keypoint, x_min=0, x_max=0, y_min=0, y_max=0, **params):
+        return F.crop_keypoint_by_coords(keypoint, crop_coords=(x_min, y_min, x_max, y_max))
+
+    @property
+    def targets_as_params(self):
+        return ["image"]
+
+    def get_transform_init_args_names(self):
+        return "crop_left", "crop_right", "crop_top", "crop_bottom"

tests/test_augmentations.py

@@ -267,6 +267,7 @@ def test_augmentations_wont_change_float_input(augmentation_cls, params, float_i
             A.ToSepia,
             A.PixelDistributionAdaptation,
             A.UnsharpMask,
+            A.RandomCropFromBorders,
         },
     ),
 )
@@ -325,6 +326,7 @@ def test_augmentations_wont_change_shape_grayscale(augmentation_cls, params, ima
             A.SmallestMaxSize,
             A.PadIfNeeded,
             A.RandomScale,
+            A.RandomCropFromBorders,
         },
     ),
 )

tests/test_serialization.py

@@ -319,6 +319,7 @@ def test_augmentations_serialization(augmentation_cls, params, p, seed, image, m
     [A.AdvancedBlur, dict(blur_limit=(3, 5), rotate_limit=(60, 90))],
     [A.PixelDropout, {"dropout_prob": 0.1, "per_channel": True, "drop_value": None}],
     [A.PixelDropout, {"dropout_prob": 0.1, "per_channel": False, "drop_value": None, "mask_drop_value": 15}],
+    [A.RandomCropFromBorders, dict(crop_left=0.2, crop_right=0.3, crop_top=0.05, crop_bottom=0.5)],
 ]
 
 AUGMENTATION_CLS_EXCEPT = {