Add CropLike (#299)

* add ModifySize

* Rename

* Rename and fix

* tiny fix

Co-authored-by: liyinshuo <[email protected]>

mmedit/datasets/pipelines/__init__.py

@@ -5,7 +5,7 @@
                            RandomTransposeHW, Resize, TemporalReverse)
 from .compose import Compose
 from .crop import (Crop, CropAroundCenter, CropAroundFg, CropAroundUnknown,
-                   FixedCrop, ModCrop, PairedRandomCrop)
+                   CropLike, FixedCrop, ModCrop, PairedRandomCrop)
 from .down_sampling import RandomDownSampling
 from .formating import (Collect, FormatTrimap, GetMaskedImage, ImageToTensor,
                         ToTensor)
@@ -30,5 +30,6 @@
     'LoadPairedImageFromFile', 'GenerateSoftSeg', 'GenerateSeg', 'PerturbBg',
     'CropAroundFg', 'GetSpatialDiscountMask', 'RandomDownSampling',
     'GenerateTrimapWithDistTransform', 'TransformTrimap',
-    'GenerateCoordinateAndCell', 'GenerateSegmentIndices', 'MirrorSequence'
+    'GenerateCoordinateAndCell', 'GenerateSegmentIndices', 'MirrorSequence',
+    'CropLike'
 ]

mmedit/datasets/pipelines/crop.py

@@ -532,3 +532,50 @@ def __call__(self, results):
             raise ValueError(f'Wrong img ndim: {img.ndim}.')
         results['gt'] = img
         return results
+
+
+@PIPELINES.register_module()
+class CropLike:
+    """Crop/pad the image in the target_key according to the size of image
+        in the reference_key .
+
+    Args:
+        target_key (str): The key needs to be cropped.
+        reference_key (str | None): The reference key, need its size.
+            Default: None.
+    """
+
+    def __init__(self, target_key, reference_key=None):
+
+        assert reference_key and target_key
+        self.target_key = target_key
+        self.reference_key = reference_key
+
+    def __call__(self, results):
+        """Call function.
+
+        Args:
+            results (dict): A dict containing the necessary information and
+                data for augmentation.
+                Require self.target_key and self.reference_key.
+
+        Returns:
+            dict: A dict containing the processed data and information.
+                Modify self.target_key.
+        """
+        size = results[self.reference_key].shape
+        old_image = results[self.target_key]
+        old_size = old_image.shape
+        h, w = old_size[:2]
+        new_size = size[:2] + old_size[2:]
+        h_cover, w_cover = min(h, size[0]), min(w, size[1])
+
+        format_image = np.zeros(new_size, dtype=old_image.dtype)
+        format_image[:h_cover, :w_cover] = old_image[:h_cover, :w_cover]
+        results[self.target_key] = format_image
+
+        return results
+
+    def __repr__(self):
+        return (self.__class__.__name__ + f' target_key={self.target_key}, ' +
+                f'reference_key={self.reference_key}')

tests/test_crop.py

@@ -4,8 +4,8 @@
 import pytest
 
 from mmedit.datasets.pipelines import (Crop, CropAroundCenter, CropAroundFg,
-                                       CropAroundUnknown, FixedCrop, ModCrop,
-                                       PairedRandomCrop)
+                                       CropAroundUnknown, CropLike, FixedCrop,
+                                       ModCrop, PairedRandomCrop)
 
 
 class TestAugmentations:
@@ -405,3 +405,36 @@ def test_paired_random_crop(self):
             assert v.shape == (32, 32, 3)
         np.testing.assert_almost_equal(results['gt'][0], results['gt'][1])
         np.testing.assert_almost_equal(results['lq'][0], results['lq'][1])
+
+
+def test_crop_like():
+    img = np.uint8(np.random.randn(480, 640, 3) * 255)
+    img_ref = np.uint8(np.random.randn(512, 512, 3) * 255)
+
+    inputs = dict(gt=img, ref=img_ref)
+    crop_like = CropLike(target_key='gt', reference_key='ref')
+    results = crop_like(inputs)
+    assert set(list(results.keys())) == set(['gt', 'ref'])
+    assert repr(crop_like) == (
+        crop_like.__class__.__name__ +
+        f' target_key={crop_like.target_key}, ' +
+        f'reference_key={crop_like.reference_key}')
+    assert results['gt'].shape == (512, 512, 3)
+    sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512]))
+    assert sum_diff < 1e-6
+
+    inputs = dict(gt=img, ref=img_ref[:, :, 0])
+    crop_like = CropLike(target_key='gt', reference_key='ref')
+    results = crop_like(inputs)
+    assert set(list(results.keys())) == set(['gt', 'ref'])
+    assert results['gt'].shape == (512, 512, 3)
+    sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512]))
+    assert sum_diff < 1e-6
+
+    inputs = dict(gt=img[:, :, 0], ref=img_ref)
+    crop_like = CropLike(target_key='gt', reference_key='ref')
+    results = crop_like(inputs)
+    assert set(list(results.keys())) == set(['gt', 'ref'])
+    assert results['gt'].shape == (512, 512)
+    sum_diff = np.sum(abs(results['gt'][:480, :512] - img[:480, :512, 0]))
+    assert sum_diff < 1e-6