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Move Clip image utils to image_utils.py (#17628)

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* move clip image utils to image_utils.py

* dont default to square images

* fix typo, revert change to test file

* edit convert_rgb comments
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Alara Dirik 2022-06-10 09:12:17 +03:00 committed by GitHub
parent af4a1ecad0
commit 6e93d94792
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2 changed files with 15 additions and 68 deletions
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14
src/transformers/image_utils.py
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@ -116,6 +116,20 @@ class ImageFeatureExtractionMixin:
return PIL.Image.fromarray(image)
return image
def convert_rgb(self, image):
"""
Converts `PIL.Image.Image` to RGB format.
Args:
image (`PIL.Image.Image`):
The image to convert.
"""
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
return image
return image.convert("RGB")
def to_numpy_array(self, image, rescale=None, channel_first=True):
"""
Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first

69
src/transformers/models/clip/feature_extraction_clip.py
View File

@ -149,7 +149,7 @@ class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
if self.do_convert_rgb:
images = [self.convert_rgb(image) for image in images]
if self.do_resize and self.size is not None and self.resample is not None:
images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images]
images = [self.resize(image=image, size=self.size, resample=self.resample, default_to_square=False) for image in images]
if self.do_center_crop and self.crop_size is not None:
images = [self.center_crop(image, self.crop_size) for image in images]
if self.do_normalize:
@ -160,70 +160,3 @@ class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
return encoded_inputs
def convert_rgb(self, image):
"""
Converts `image` to RGB format. Note that this will trigger a conversion of `image` to a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to convert.
"""
self._ensure_format_supported(image)
if not isinstance(image, Image.Image):
return image
return image.convert("RGB")
def center_crop(self, image, size):
"""
Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
size is given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to which crop the image.
"""
self._ensure_format_supported(image)
if not isinstance(size, tuple):
size = (size, size)
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
image_width, image_height = image.size
crop_height, crop_width = size
crop_top = int((image_height - crop_height + 1) * 0.5)
crop_left = int((image_width - crop_width + 1) * 0.5)
return image.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))
def resize(self, image, size, resample=Image.BICUBIC):
"""
Resizes `image`. Note that this will trigger a conversion of `image` to a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to use for resizing the image. If `int` it will be resized to match the shorter side
resample (`int`, *optional*, defaults to `PIL.Image.BILINEAR`):
The filter to user for resampling.
"""
self._ensure_format_supported(image)
if not isinstance(image, Image.Image):
image = self.to_pil_image(image)
if isinstance(size, tuple):
new_w, new_h = size
else:
width, height = image.size
short, long = (width, height) if width <= height else (height, width)
if short == size:
return image
new_short, new_long = size, int(size * long / short)
new_w, new_h = (new_short, new_long) if width <= height else (new_long, new_short)
return image.resize((new_w, new_h), resample)