def test_center_crop_array(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
array = feature_extractor.to_numpy_array(image)
# Test various crop sizes: bigger on all dimensions, on one of the dimensions only and on both dimensions.
crop_sizes = [8, (8, 64), 20, (32, 64)]
for size in crop_sizes:
cropped_array = feature_extractor.center_crop(array, size)
self.assertTrue(isinstance(cropped_array, np.ndarray))
expected_size = (size, size) if isinstance(size, int) else size
self.assertEqual(cropped_array.shape[-2:], expected_size)
# Check result is consistent with PIL.Image.crop
cropped_image = feature_extractor.center_crop(image, size)
self.assertTrue(
np.array_equal(
cropped_array,
feature_extractor.to_numpy_array(cropped_image)))
def test_center_crop_image(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
# Test various crop sizes: bigger on all dimensions, on one of the dimensions only and on both dimensions.
crop_sizes = [8, (8, 64), 20, (32, 64)]
for size in crop_sizes:
cropped_image = feature_extractor.center_crop(image, size)
self.assertTrue(isinstance(cropped_image, PIL.Image.Image))
# PIL Image.size is transposed compared to NumPy or PyTorch (width first instead of height first).
expected_size = (size,
size) if isinstance(size, int) else (size[1],
size[0])
self.assertEqual(cropped_image.size, expected_size)