def vips_image_to_numpy(img: Image) -> np.ndarray:
"""
https://libvips.github.io/pyvips/intro.html#numpy-and-pil
"""
np_3d = np.ndarray(buffer=img.write_to_memory(),
dtype=FORMAT_TO_DTYPE[img.format],
shape=[img.height, img.width, img.bands])
return np_3d
def vips_to_numpy(vips_image: VIPSImage) -> np.ndarray:
"""
Convert a VIPS image to a Numpy array.
Parameters
----------
vips_image : VIPSImage
VIPS image to convert
Returns
-------
image
Array representation of VIPS image.
Shape is always (height, width, bands).
"""
return np.ndarray(
buffer=vips_image.write_to_memory(),
dtype=vips_format_to_dtype[vips_image.format],
shape=[vips_image.height, vips_image.width, vips_image.bands])
def vips_image_to_tensor(img: pyvips.Image):
if img.format == 'uchar':
tensor = torch.ByteTensor(
torch.ByteStorage.from_buffer(img.write_to_memory()))
tensor = tensor.view(img.height, img.width, img.bands)
else:
np_img = vips_image_to_numpy(img)
if np_img.ndim == 2:
np_img = np_img[:, :, None]
tensor = torch.from_numpy(np_img)
tensor = tensor.permute((2, 0, 1)).contiguous()
if isinstance(tensor, torch.ByteTensor):
return tensor.float().div(255)
else:
return tensor
def to_numpy(vips_image: pyvips.Image) -> np.ndarray:
return np.ndarray(
buffer=vips_image.write_to_memory(),
dtype=FORMAT_MAP[vips_image.format],
shape=[vips_image.height, vips_image.width, vips_image.bands])
