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| # coding=utf-8 | |
| # Copyright 2022 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import unittest | |
| import numpy as np | |
| from parameterized import parameterized | |
| from transformers.testing_utils import require_flax, require_tf, require_torch, require_vision | |
| from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available | |
| if is_torch_available(): | |
| import torch | |
| if is_tf_available(): | |
| import tensorflow as tf | |
| if is_flax_available(): | |
| import jax | |
| if is_vision_available(): | |
| import PIL.Image | |
| from transformers.image_transforms import ( | |
| center_crop, | |
| center_to_corners_format, | |
| convert_to_rgb, | |
| corners_to_center_format, | |
| flip_channel_order, | |
| get_resize_output_image_size, | |
| id_to_rgb, | |
| normalize, | |
| pad, | |
| resize, | |
| rgb_to_id, | |
| to_channel_dimension_format, | |
| to_pil_image, | |
| ) | |
| def get_random_image(height, width, num_channels=3, channels_first=True): | |
| shape = (num_channels, height, width) if channels_first else (height, width, num_channels) | |
| random_array = np.random.randint(0, 256, shape, dtype=np.uint8) | |
| return random_array | |
| class ImageTransformsTester(unittest.TestCase): | |
| def test_to_pil_image(self, name, image_shape, dtype): | |
| image = np.random.randint(0, 256, image_shape).astype(dtype) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| # make sure image is correctly rescaled | |
| self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0) | |
| def test_to_pil_image_from_float(self, name, image_shape, dtype): | |
| image = np.random.rand(*image_shape).astype(dtype) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| # make sure image is correctly rescaled | |
| self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0) | |
| # Make sure that an exception is raised if image is not in [0, 1] | |
| image = np.random.randn(*image_shape).astype(dtype) | |
| with self.assertRaises(ValueError): | |
| to_pil_image(image) | |
| def test_to_pil_image_from_mask(self): | |
| # Make sure binary mask remains a binary mask | |
| image = np.random.randint(0, 2, (3, 4, 5)).astype(np.uint8) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| np_img = np.asarray(pil_image) | |
| self.assertTrue(np_img.min() == 0) | |
| self.assertTrue(np_img.max() == 1) | |
| image = np.random.randint(0, 2, (3, 4, 5)).astype(np.float32) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| np_img = np.asarray(pil_image) | |
| self.assertTrue(np_img.min() == 0) | |
| self.assertTrue(np_img.max() == 1) | |
| def test_to_pil_image_from_tensorflow(self): | |
| # channels_first | |
| image = tf.random.uniform((3, 4, 5)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| # channels_last | |
| image = tf.random.uniform((4, 5, 3)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| def test_to_pil_image_from_torch(self): | |
| # channels first | |
| image = torch.rand((3, 4, 5)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| # channels last | |
| image = torch.rand((4, 5, 3)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| def test_to_pil_image_from_jax(self): | |
| key = jax.random.PRNGKey(0) | |
| # channel first | |
| image = jax.random.uniform(key, (3, 4, 5)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| # channel last | |
| image = jax.random.uniform(key, (4, 5, 3)) | |
| pil_image = to_pil_image(image) | |
| self.assertIsInstance(pil_image, PIL.Image.Image) | |
| self.assertEqual(pil_image.size, (5, 4)) | |
| def test_to_channel_dimension_format(self): | |
| # Test that function doesn't reorder if channel dim matches the input. | |
| image = np.random.rand(3, 4, 5) | |
| image = to_channel_dimension_format(image, "channels_first") | |
| self.assertEqual(image.shape, (3, 4, 5)) | |
| image = np.random.rand(4, 5, 3) | |
| image = to_channel_dimension_format(image, "channels_last") | |
| self.assertEqual(image.shape, (4, 5, 3)) | |
| # Test that function reorders if channel dim doesn't match the input. | |
| image = np.random.rand(3, 4, 5) | |
| image = to_channel_dimension_format(image, "channels_last") | |
| self.assertEqual(image.shape, (4, 5, 3)) | |
| image = np.random.rand(4, 5, 3) | |
| image = to_channel_dimension_format(image, "channels_first") | |
| self.assertEqual(image.shape, (3, 4, 5)) | |
| # Can pass in input_data_format and works if data format is ambiguous or unknown. | |
| image = np.random.rand(4, 5, 6) | |
| image = to_channel_dimension_format(image, "channels_first", input_channel_dim="channels_last") | |
| self.assertEqual(image.shape, (6, 4, 5)) | |
| def test_get_resize_output_image_size(self): | |
| image = np.random.randint(0, 256, (3, 224, 224)) | |
| # Test the output size defaults to (x, x) if an int is given. | |
| self.assertEqual(get_resize_output_image_size(image, 10), (10, 10)) | |
| self.assertEqual(get_resize_output_image_size(image, [10]), (10, 10)) | |
| self.assertEqual(get_resize_output_image_size(image, (10,)), (10, 10)) | |
| # Test the output size is the same as the input if a two element tuple/list is given. | |
| self.assertEqual(get_resize_output_image_size(image, (10, 20)), (10, 20)) | |
| self.assertEqual(get_resize_output_image_size(image, [10, 20]), (10, 20)) | |
| self.assertEqual(get_resize_output_image_size(image, (10, 20), default_to_square=True), (10, 20)) | |
| # To match pytorch behaviour, max_size is only relevant if size is an int | |
| self.assertEqual(get_resize_output_image_size(image, (10, 20), max_size=5), (10, 20)) | |
| # Test output size = (int(size * height / width), size) if size is an int and height > width | |
| image = np.random.randint(0, 256, (3, 50, 40)) | |
| self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (25, 20)) | |
| # Test output size = (size, int(size * width / height)) if size is an int and width <= height | |
| image = np.random.randint(0, 256, (3, 40, 50)) | |
| self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (20, 25)) | |
| # Test size is resized if longer size > max_size | |
| image = np.random.randint(0, 256, (3, 50, 40)) | |
| self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False, max_size=22), (22, 17)) | |
| # Test output size = (int(size * height / width), size) if size is an int and height > width and | |
| # input has 4 channels | |
| image = np.random.randint(0, 256, (4, 50, 40)) | |
| self.assertEqual( | |
| get_resize_output_image_size(image, 20, default_to_square=False, input_data_format="channels_first"), | |
| (25, 20), | |
| ) | |
| # Test correct channel dimension is returned if output size if height == 3 | |
| # Defaults to input format - channels first | |
| image = np.random.randint(0, 256, (3, 18, 97)) | |
| resized_image = resize(image, (3, 20)) | |
| self.assertEqual(resized_image.shape, (3, 3, 20)) | |
| # Defaults to input format - channels last | |
| image = np.random.randint(0, 256, (18, 97, 3)) | |
| resized_image = resize(image, (3, 20)) | |
| self.assertEqual(resized_image.shape, (3, 20, 3)) | |
| image = np.random.randint(0, 256, (3, 18, 97)) | |
| resized_image = resize(image, (3, 20), data_format="channels_last") | |
| self.assertEqual(resized_image.shape, (3, 20, 3)) | |
| image = np.random.randint(0, 256, (18, 97, 3)) | |
| resized_image = resize(image, (3, 20), data_format="channels_first") | |
| self.assertEqual(resized_image.shape, (3, 3, 20)) | |
| def test_resize(self): | |
| image = np.random.randint(0, 256, (3, 224, 224)) | |
| # Check the channel order is the same by default | |
| resized_image = resize(image, (30, 40)) | |
| self.assertIsInstance(resized_image, np.ndarray) | |
| self.assertEqual(resized_image.shape, (3, 30, 40)) | |
| # Check channel order is changed if specified | |
| resized_image = resize(image, (30, 40), data_format="channels_last") | |
| self.assertIsInstance(resized_image, np.ndarray) | |
| self.assertEqual(resized_image.shape, (30, 40, 3)) | |
| # Check PIL.Image.Image is returned if return_numpy=False | |
| resized_image = resize(image, (30, 40), return_numpy=False) | |
| self.assertIsInstance(resized_image, PIL.Image.Image) | |
| # PIL size is in (width, height) order | |
| self.assertEqual(resized_image.size, (40, 30)) | |
| # Check an image with float values between 0-1 is returned with values in this range | |
| image = np.random.rand(3, 224, 224) | |
| resized_image = resize(image, (30, 40)) | |
| self.assertIsInstance(resized_image, np.ndarray) | |
| self.assertEqual(resized_image.shape, (3, 30, 40)) | |
| self.assertTrue(np.all(resized_image >= 0)) | |
| self.assertTrue(np.all(resized_image <= 1)) | |
| # Check that an image with 4 channels is resized correctly | |
| image = np.random.randint(0, 256, (4, 224, 224)) | |
| resized_image = resize(image, (30, 40), input_data_format="channels_first") | |
| self.assertIsInstance(resized_image, np.ndarray) | |
| self.assertEqual(resized_image.shape, (4, 30, 40)) | |
| def test_normalize(self): | |
| image = np.random.randint(0, 256, (224, 224, 3)) / 255 | |
| # Test that exception is raised if inputs are incorrect | |
| # Not a numpy array image | |
| with self.assertRaises(ValueError): | |
| normalize(5, 5, 5) | |
| # Number of mean values != number of channels | |
| with self.assertRaises(ValueError): | |
| normalize(image, mean=(0.5, 0.6), std=1) | |
| # Number of std values != number of channels | |
| with self.assertRaises(ValueError): | |
| normalize(image, mean=1, std=(0.5, 0.6)) | |
| # Test result is correct - output data format is channels_first and normalization | |
| # correctly computed | |
| mean = (0.5, 0.6, 0.7) | |
| std = (0.1, 0.2, 0.3) | |
| expected_image = ((image - mean) / std).transpose((2, 0, 1)) | |
| normalized_image = normalize(image, mean=mean, std=std, data_format="channels_first") | |
| self.assertIsInstance(normalized_image, np.ndarray) | |
| self.assertEqual(normalized_image.shape, (3, 224, 224)) | |
| self.assertTrue(np.allclose(normalized_image, expected_image)) | |
| # Test image with 4 channels is normalized correctly | |
| image = np.random.randint(0, 256, (224, 224, 4)) / 255 | |
| mean = (0.5, 0.6, 0.7, 0.8) | |
| std = (0.1, 0.2, 0.3, 0.4) | |
| expected_image = (image - mean) / std | |
| self.assertTrue( | |
| np.allclose(normalize(image, mean=mean, std=std, input_data_format="channels_last"), 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)) | |
| # Test image with 4 channels is cropped correctly | |
| image = np.random.randint(0, 256, (224, 224, 4)) | |
| expected_image = image[52:172, 82:142, :] | |
| self.assertTrue(np.allclose(center_crop(image, (120, 60), input_data_format="channels_last"), expected_image)) | |
| def test_center_to_corners_format(self): | |
| bbox_center = np.array([[10, 20, 4, 8], [15, 16, 3, 4]]) | |
| expected = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]]) | |
| self.assertTrue(np.allclose(center_to_corners_format(bbox_center), expected)) | |
| # Check that the function and inverse function are inverse of each other | |
| self.assertTrue(np.allclose(corners_to_center_format(center_to_corners_format(bbox_center)), bbox_center)) | |
| def test_corners_to_center_format(self): | |
| bbox_corners = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]]) | |
| expected = np.array([[10, 20, 4, 8], [15, 16, 3, 4]]) | |
| self.assertTrue(np.allclose(corners_to_center_format(bbox_corners), expected)) | |
| # Check that the function and inverse function are inverse of each other | |
| self.assertTrue(np.allclose(center_to_corners_format(corners_to_center_format(bbox_corners)), bbox_corners)) | |
| def test_rgb_to_id(self): | |
| # test list input | |
| rgb = [125, 4, 255] | |
| self.assertEqual(rgb_to_id(rgb), 16712829) | |
| # test numpy array input | |
| color = np.array( | |
| [ | |
| [ | |
| [213, 54, 165], | |
| [88, 207, 39], | |
| [156, 108, 128], | |
| ], | |
| [ | |
| [183, 194, 46], | |
| [137, 58, 88], | |
| [114, 131, 233], | |
| ], | |
| ] | |
| ) | |
| expected = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]]) | |
| self.assertTrue(np.allclose(rgb_to_id(color), expected)) | |
| def test_id_to_rgb(self): | |
| # test int input | |
| self.assertEqual(id_to_rgb(16712829), [125, 4, 255]) | |
| # test array input | |
| id_array = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]]) | |
| color = np.array( | |
| [ | |
| [ | |
| [213, 54, 165], | |
| [88, 207, 39], | |
| [156, 108, 128], | |
| ], | |
| [ | |
| [183, 194, 46], | |
| [137, 58, 88], | |
| [114, 131, 233], | |
| ], | |
| ] | |
| ) | |
| self.assertTrue(np.allclose(id_to_rgb(id_array), color)) | |
| def test_pad(self): | |
| # fmt: off | |
| image = np.array([[ | |
| [0, 1], | |
| [2, 3], | |
| ]]) | |
| # fmt: on | |
| # Test that exception is raised if unknown padding mode is specified | |
| with self.assertRaises(ValueError): | |
| pad(image, 10, mode="unknown") | |
| # Test that exception is raised if invalid padding is specified | |
| with self.assertRaises(ValueError): | |
| # Cannot pad on channel dimension | |
| pad(image, (5, 10, 10)) | |
| # Test image is padded equally on all sides is padding is an int | |
| # fmt: off | |
| expected_image = np.array([ | |
| [[0, 0, 0, 0], | |
| [0, 0, 1, 0], | |
| [0, 2, 3, 0], | |
| [0, 0, 0, 0]], | |
| ]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, 1))) | |
| # Test the left and right of each axis is padded (pad_left, pad_right) | |
| # fmt: off | |
| expected_image = np.array( | |
| [[0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0], | |
| [0, 0, 0, 1, 0], | |
| [0, 0, 2, 3, 0], | |
| [0, 0, 0, 0, 0]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, (2, 1)))) | |
| # Test only one axis is padded (pad_left, pad_right) | |
| # fmt: off | |
| expected_image = np.array([[ | |
| [9, 9], | |
| [9, 9], | |
| [0, 1], | |
| [2, 3], | |
| [9, 9] | |
| ]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, ((2, 1), (0, 0)), constant_values=9))) | |
| # Test padding with a constant value | |
| # fmt: off | |
| expected_image = np.array([[ | |
| [8, 8, 0, 1, 9], | |
| [8, 8, 2, 3, 9], | |
| [8, 8, 7, 7, 9], | |
| [8, 8, 7, 7, 9] | |
| ]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), constant_values=((6, 7), (8, 9))))) | |
| # fmt: off | |
| image = np.array([[ | |
| [0, 1, 2], | |
| [3, 4, 5], | |
| [6, 7, 8], | |
| ]]) | |
| # fmt: on | |
| # Test padding with PaddingMode.REFLECT | |
| # fmt: off | |
| expected_image = np.array([[ | |
| [2, 1, 0, 1, 2, 1], | |
| [5, 4, 3, 4, 5, 4], | |
| [8, 7, 6, 7, 8, 7], | |
| [5, 4, 3, 4, 5, 4], | |
| [2, 1, 0, 1, 2, 1], | |
| ]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect"))) | |
| # Test padding with PaddingMode.REPLICATE | |
| # fmt: off | |
| expected_image = np.array([[ | |
| [0, 0, 0, 1, 2, 2], | |
| [3, 3, 3, 4, 5, 5], | |
| [6, 6, 6, 7, 8, 8], | |
| [6, 6, 6, 7, 8, 8], | |
| [6, 6, 6, 7, 8, 8], | |
| ]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="replicate"))) | |
| # Test padding with PaddingMode.SYMMETRIC | |
| # fmt: off | |
| expected_image = np.array([[ | |
| [1, 0, 0, 1, 2, 2], | |
| [4, 3, 3, 4, 5, 5], | |
| [7, 6, 6, 7, 8, 8], | |
| [7, 6, 6, 7, 8, 8], | |
| [4, 3, 3, 4, 5, 5], | |
| ]]) | |
| # fmt: on | |
| self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="symmetric"))) | |
| # Test we can specify the output data format | |
| # Test padding with PaddingMode.REFLECT | |
| # fmt: off | |
| image = np.array([[ | |
| [0, 1], | |
| [2, 3], | |
| ]]) | |
| expected_image = np.array([ | |
| [[0], [1], [0], [1], [0]], | |
| [[2], [3], [2], [3], [2]], | |
| [[0], [1], [0], [1], [0]], | |
| [[2], [3], [2], [3], [2]] | |
| ]) | |
| # fmt: on | |
| self.assertTrue( | |
| np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect", data_format="channels_last")) | |
| ) | |
| # Test we can pad on an image with 2 channels | |
| # fmt: off | |
| image = np.array([ | |
| [[0, 1], [2, 3]], | |
| ]) | |
| expected_image = np.array([ | |
| [[0, 0], [0, 1], [2, 3]], | |
| [[0, 0], [0, 0], [0, 0]], | |
| ]) | |
| # fmt: on | |
| self.assertTrue( | |
| np.allclose( | |
| expected_image, pad(image, ((0, 1), (1, 0)), mode="constant", input_data_format="channels_last") | |
| ) | |
| ) | |
| def test_convert_to_rgb(self): | |
| # Test that an RGBA image is converted to RGB | |
| image = np.array([[[1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.uint8) | |
| pil_image = PIL.Image.fromarray(image) | |
| self.assertEqual(pil_image.mode, "RGBA") | |
| self.assertEqual(pil_image.size, (2, 1)) | |
| # For the moment, numpy images are returned as is | |
| rgb_image = convert_to_rgb(image) | |
| self.assertEqual(rgb_image.shape, (1, 2, 4)) | |
| self.assertTrue(np.allclose(rgb_image, image)) | |
| # And PIL images are converted | |
| rgb_image = convert_to_rgb(pil_image) | |
| self.assertEqual(rgb_image.mode, "RGB") | |
| self.assertEqual(rgb_image.size, (2, 1)) | |
| self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[1, 2, 3], [5, 6, 7]]], dtype=np.uint8))) | |
| # Test that a grayscale image is converted to RGB | |
| image = np.array([[0, 255]], dtype=np.uint8) | |
| pil_image = PIL.Image.fromarray(image) | |
| self.assertEqual(pil_image.mode, "L") | |
| self.assertEqual(pil_image.size, (2, 1)) | |
| rgb_image = convert_to_rgb(pil_image) | |
| self.assertEqual(rgb_image.mode, "RGB") | |
| self.assertEqual(rgb_image.size, (2, 1)) | |
| self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[0, 0, 0], [255, 255, 255]]], dtype=np.uint8))) | |
| def test_flip_channel_order(self): | |
| # fmt: off | |
| img_channels_first = np.array([ | |
| [[ 0, 1, 2, 3], | |
| [ 4, 5, 6, 7]], | |
| [[ 8, 9, 10, 11], | |
| [12, 13, 14, 15]], | |
| [[16, 17, 18, 19], | |
| [20, 21, 22, 23]], | |
| ]) | |
| # fmt: on | |
| img_channels_last = np.moveaxis(img_channels_first, 0, -1) | |
| # fmt: off | |
| flipped_img_channels_first = np.array([ | |
| [[16, 17, 18, 19], | |
| [20, 21, 22, 23]], | |
| [[ 8, 9, 10, 11], | |
| [12, 13, 14, 15]], | |
| [[ 0, 1, 2, 3], | |
| [ 4, 5, 6, 7]], | |
| ]) | |
| # fmt: on | |
| flipped_img_channels_last = np.moveaxis(flipped_img_channels_first, 0, -1) | |
| self.assertTrue(np.allclose(flip_channel_order(img_channels_first), flipped_img_channels_first)) | |
| self.assertTrue( | |
| np.allclose(flip_channel_order(img_channels_first, "channels_last"), flipped_img_channels_last) | |
| ) | |
| self.assertTrue(np.allclose(flip_channel_order(img_channels_last), flipped_img_channels_last)) | |
| self.assertTrue( | |
| np.allclose(flip_channel_order(img_channels_last, "channels_first"), flipped_img_channels_first) | |
| ) | |
| # Can flip when the image has 2 channels | |
| # fmt: off | |
| img_channels_first = np.array([ | |
| [[ 0, 1, 2, 3], | |
| [ 4, 5, 6, 7]], | |
| [[ 8, 9, 10, 11], | |
| [12, 13, 14, 15]], | |
| ]) | |
| # fmt: on | |
| flipped_img_channels_first = img_channels_first[::-1, :, :] | |
| self.assertTrue( | |
| np.allclose( | |
| flip_channel_order(img_channels_first, input_data_format="channels_first"), flipped_img_channels_first | |
| ) | |
| ) | |