def compute_isometric(self, image: tools.ImageInputType):
image = tools.to_image_array(image)
if hasattr(self, '_compute_isometric'):
# pylint: disable=no-member
hashes = self._compute_isometric(image) # type: ignore
elif hasattr(self, '_compute_isometric_from_hash'):
# pylint: disable=no-member
hashes = self._compute_isometric_from_hash( # type: ignore
self._compute(image))
else:
transforms = tools.get_isometric_transforms(image)
for name, transform in transforms.items():
transforms[name] = self._compute(transform)
hashes = transforms
return {
transform_name: self.vector_to_string(vector)
for transform_name, vector in hashes.items()
}
def compute(self,
image: tools.ImageInputType,
hash_format='base64') -> typing.Union[str, np.ndarray]:
"""Compute a hash from an image.
Args:
image: An image represented as a filepath, a PIL image object,
or as an np.ndarray object. If it is an np.ndarray object,
it must be in RGB color order (note the OpenCV default is
BGR).
hash_format: One 'base64', 'hex', or 'vector'
"""
vector = self._compute(tools.to_image_array(image))
return self.vector_to_string(
vector,
hash_format=hash_format) if not self.returns_multiple else [
self.vector_to_string(v, hash_format=hash_format)
for v in vector
]
def compute_with_quality(self,
image: tools.ImageInputType,
hash_format='base64') -> typing.Tuple[str, int]:
"""Compute hash and hash quality from image.
Args:
image: An image represented as a filepath, a PIL image object,
or as an np.ndarray object. If it is an np.ndarray object,
it must be in RGB color order (note the OpenCV default is
BGR).
hash_format: One 'base64' or 'hex'
Returns:
A tuple of (hash, quality)
"""
vector, quality = self._compute_with_quality(
tools.to_image_array(image))
if hash_format == 'vector':
return vector, quality
return (self.vector_to_string(vector, hash_format=hash_format),
quality) if not self.returns_multiple else (
[self.vector_to_string(v) for v in vector], quality)