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Image transforms add center crop (#19718)

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* 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>
This commit is contained in:
amyeroberts
2022-10-19 16:15:01 +01:00
committed by GitHub
parent 44a40c1466
commit 5041bc3511
3 changed files with 120 additions and 0 deletions
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24
tests/test_image_transforms.py
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@@ -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))