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to_pil - don't rescale if int and in range 0-255 (#22158)

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* Don't rescale if in and in range 0-255

* Raise value error if int values too large

* Update tests/test_image_transforms.py

* Update tests/test_image_transforms.py
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amyeroberts 2023-03-14 15:43:44 +00:00 committed by GitHub
parent 3b22bfbc6a
commit c6318c3788
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2 changed files with 34 additions and 5 deletions
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16
src/transformers/image_transforms.py
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@ -156,12 +156,20 @@ def to_pil_image(
# If there is a single channel, we squeeze it, as otherwise PIL can't handle it. # If there is a single channel, we squeeze it, as otherwise PIL can't handle it.
image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image
# PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed. # PIL.Image can only store uint8 values so we rescale the image to be between 0 and 255 if needed.
if do_rescale is None: if do_rescale is None:
if np.all(0 <= image) and np.all(image <= 1): if image.dtype == np.uint8:
do_rescale = True
elif np.allclose(image, image.astype(int)):
do_rescale = False do_rescale = False
elif np.allclose(image, image.astype(int)):
if np.all(0 <= image) and np.all(image <= 255):
do_rescale = False
else:
raise ValueError(
"The image to be converted to a PIL image contains values outside the range [0, 255], "
f"got [{image.min()}, {image.max()}] which cannot be converted to uint8."
)
elif np.all(0 <= image) and np.all(image <= 1):
do_rescale = True
else: else:
raise ValueError( raise ValueError(
"The image to be converted to a PIL image contains values outside the range [0, 1], " "The image to be converted to a PIL image contains values outside the range [0, 1], "

23
tests/test_image_transforms.py
View File

@ -101,6 +101,27 @@ class ImageTransformsTester(unittest.TestCase):
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
to_pil_image(image) to_pil_image(image)
@require_vision
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)
@require_tf @require_tf
def test_to_pil_image_from_tensorflow(self): def test_to_pil_image_from_tensorflow(self):
# channels_first # channels_first
@ -222,7 +243,7 @@ class ImageTransformsTester(unittest.TestCase):
self.assertIsInstance(resized_image, np.ndarray) self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (30, 40, 3)) self.assertEqual(resized_image.shape, (30, 40, 3))
# Check PIL.Image.Image is return if return_numpy=False # Check PIL.Image.Image is returned if return_numpy=False
resized_image = resize(image, (30, 40), return_numpy=False) resized_image = resize(image, (30, 40), return_numpy=False)
self.assertIsInstance(resized_image, PIL.Image.Image) self.assertIsInstance(resized_image, PIL.Image.Image)
# PIL size is in (width, height) order # PIL size is in (width, height) order