# LICENSE HEADER MANAGED BY add-license-header # # Copyright 2018 Kornia Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """The RGB to Luv color transformations were translated from scikit image's rgb2luv and luv2rgb. https://github.com/scikit-image/scikit-image/blob/a48bf6774718c64dade4548153ae16065b595ca9/skimage/color/colorconv.py """ from __future__ import annotations from typing import ClassVar import torch from kornia.core import ImageModule as Module from .rgb import linear_rgb_to_rgb, rgb_to_linear_rgb from .xyz import rgb_to_xyz, xyz_to_rgb def rgb_to_luv(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor: r"""Convert a RGB image to Luv. .. image:: _static/img/rgb_to_luv.png The image data is assumed to be in the range of :math:`[0, 1]`. Luv color is computed using the D65 illuminant and Observer 2. Args: image: RGB Image to be converted to Luv with shape :math:`(*, 3, H, W)`. eps: for numerically stability when dividing. Returns: Luv version of the image with shape :math:`(*, 3, H, W)`. Example: >>> input = torch.rand(2, 3, 4, 5) >>> output = rgb_to_luv(input) # 2x3x4x5 """ if not isinstance(image, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") if len(image.shape) < 3 or image.shape[-3] != 3: raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") # Convert from sRGB to Linear RGB lin_rgb = rgb_to_linear_rgb(image) xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb) x: torch.Tensor = xyz_im[..., 0, :, :] y: torch.Tensor = xyz_im[..., 1, :, :] z: torch.Tensor = xyz_im[..., 2, :, :] threshold = 0.008856 L: torch.Tensor = torch.where( y > threshold, 116.0 * torch.pow(y.clamp(min=threshold), 1.0 / 3.0) - 16.0, 903.3 * y, ) # Compute reference white point xyz_ref_white: tuple[float, float, float] = (0.95047, 1.0, 1.08883) u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2]) v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2]) u_p: torch.Tensor = (4 * x) / (x + 15 * y + 3 * z + eps) v_p: torch.Tensor = (9 * y) / (x + 15 * y + 3 * z + eps) u: torch.Tensor = 13 * L * (u_p - u_w) v: torch.Tensor = 13 * L * (v_p - v_w) out = torch.stack([L, u, v], dim=-3) return out def luv_to_rgb(image: torch.Tensor, eps: float = 1e-12) -> torch.Tensor: r"""Convert a Luv image to RGB. Args: image: Luv image to be converted to RGB with shape :math:`(*, 3, H, W)`. eps: for numerically stability when dividing. Returns: Luv version of the image with shape :math:`(*, 3, H, W)`. Example: >>> input = torch.rand(2, 3, 4, 5) >>> output = luv_to_rgb(input) # 2x3x4x5 """ if not isinstance(image, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") if len(image.shape) < 3 or image.shape[-3] != 3: raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") L: torch.Tensor = image[..., 0, :, :] u: torch.Tensor = image[..., 1, :, :] v: torch.Tensor = image[..., 2, :, :] # Convert from Luv to XYZ y: torch.Tensor = torch.where(L > 7.999625, torch.pow((L + 16) / 116, 3.0), L / 903.3) # Compute white point xyz_ref_white: tuple[float, float, float] = (0.95047, 1.0, 1.08883) u_w: float = (4 * xyz_ref_white[0]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2]) v_w: float = (9 * xyz_ref_white[1]) / (xyz_ref_white[0] + 15 * xyz_ref_white[1] + 3 * xyz_ref_white[2]) a: torch.Tensor = u_w + u / (13 * L + eps) d: torch.Tensor = v_w + v / (13 * L + eps) c: torch.Tensor = 3 * y * (5 * d - 3) z: torch.Tensor = ((a - 4) * c - 15 * a * d * y) / (12 * d + eps) x: torch.Tensor = -(c / (d + eps) + 3.0 * z) xyz_im: torch.Tensor = torch.stack([x, y, z], -3) rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im) # Convert from RGB Linear to sRGB rgb_im = linear_rgb_to_rgb(rgbs_im) return rgb_im class RgbToLuv(Module): r"""Convert an image from RGB to Luv. The image data is assumed to be in the range of :math:`[0, 1]`. Luv color is computed using the D65 illuminant and Observer 2. Returns: Luv version of the image. Shape: - image: :math:`(*, 3, H, W)` - output: :math:`(*, 3, H, W)` Examples: >>> input = torch.rand(2, 3, 4, 5) >>> luv = RgbToLuv() >>> output = luv(input) # 2x3x4x5 Reference: [1] https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html [2] https://www.easyrgb.com/en/math.php [3] http://www.poynton.com/ColorFAQ.html """ ONNX_DEFAULT_INPUTSHAPE: ClassVar[list[int]] = [-1, 3, -1, -1] ONNX_DEFAULT_OUTPUTSHAPE: ClassVar[list[int]] = [-1, 3, -1, -1] def forward(self, image: torch.Tensor) -> torch.Tensor: return rgb_to_luv(image) class LuvToRgb(Module): r"""Convert an image from Luv to RGB. Returns: RGB version of the image. Shape: - image: :math:`(*, 3, H, W)` - output: :math:`(*, 3, H, W)` Examples: >>> input = torch.rand(2, 3, 4, 5) >>> rgb = LuvToRgb() >>> output = rgb(input) # 2x3x4x5 References: [1] https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html [2] https://www.easyrgb.com/en/math.php [3] http://www.poynton.com/ColorFAQ.html """ ONNX_DEFAULT_INPUTSHAPE: ClassVar[list[int]] = [-1, 3, -1, -1] ONNX_DEFAULT_OUTPUTSHAPE: ClassVar[list[int]] = [-1, 3, -1, -1] def forward(self, image: torch.Tensor) -> torch.Tensor: return luv_to_rgb(image)