Source code for kornia.color.yuv

from typing import Tuple

import torch
import torch.nn as nn


[docs]def rgb_to_yuv(image: torch.Tensor) -> torch.Tensor: r"""Convert an RGB image to YUV. .. image:: _static/img/rgb_to_yuv.png The image data is assumed to be in the range of (0, 1). Args: image: RGB Image to be converted to YUV with shape :math:`(*, 3, H, W)`. Returns: YUV version of the image with shape :math:`(*, 3, H, W)`. Example: >>> input = torch.rand(2, 3, 4, 5) >>> output = rgb_to_yuv(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}") r: torch.Tensor = image[..., 0, :, :] g: torch.Tensor = image[..., 1, :, :] b: torch.Tensor = image[..., 2, :, :] y: torch.Tensor = 0.299 * r + 0.587 * g + 0.114 * b u: torch.Tensor = -0.147 * r - 0.289 * g + 0.436 * b v: torch.Tensor = 0.615 * r - 0.515 * g - 0.100 * b out: torch.Tensor = torch.stack([y, u, v], -3) return out
[docs]def rgb_to_yuv420(image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r"""Convert an RGB image to YUV 420 (subsampled). The image data is assumed to be in the range of (0, 1). Input need to be padded to be evenly divisible by 2 horizontal and vertical. This function will output chroma siting (0.5,0.5) Args: image: RGB Image to be converted to YUV with shape :math:`(*, 3, H, W)`. Returns: A Tensor containing the Y plane with shape :math:`(*, 1, H, W)` A Tensor containing the UV planes with shape :math:`(*, 2, H/2, W/2)` Example: >>> input = torch.rand(2, 3, 4, 6) >>> output = rgb_to_yuv420(input) # (2x1x4x6, 2x2x2x3) """ 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}") if len(image.shape) < 2 or image.shape[-2] % 2 == 1 or image.shape[-1] % 2 == 1: raise ValueError(f"Input H&W must be evenly disible by 2. Got {image.shape}") yuvimage = rgb_to_yuv(image) return (yuvimage[..., :1, :, :], torch.nn.functional.avg_pool2d(yuvimage[..., 1:3, :, :], (2, 2)))
[docs]def rgb_to_yuv422(image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r"""Convert an RGB image to YUV 422 (subsampled). The image data is assumed to be in the range of (0, 1). Input need to be padded to be evenly divisible by 2 vertical. This function will output chroma siting (0.5) Args: image: RGB Image to be converted to YUV with shape :math:`(*, 3, H, W)`. Returns: A Tensor containing the Y plane with shape :math:`(*, 1, H, W)` A Tensor containing the UV planes with shape :math:`(*, 2, H, W/2)` Example: >>> input = torch.rand(2, 3, 4, 6) >>> output = rgb_to_yuv420(input) # (2x1x4x6, 2x1x4x3) """ 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}") if len(image.shape) < 2 or image.shape[-2] % 2 == 1 or image.shape[-1] % 2 == 1: raise ValueError(f"Input H&W must be evenly disible by 2. Got {image.shape}") yuvimage = rgb_to_yuv(image) return (yuvimage[..., :1, :, :], torch.nn.functional.avg_pool2d(yuvimage[..., 1:3, :, :], (1, 2)))
[docs]def yuv_to_rgb(image: torch.Tensor) -> torch.Tensor: r"""Convert an YUV image to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Args: image: YUV Image to be converted to RGB with shape :math:`(*, 3, H, W)`. Returns: RGB version of the image with shape :math:`(*, 3, H, W)`. Example: >>> input = torch.rand(2, 3, 4, 5) >>> output = yuv_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}") y: torch.Tensor = image[..., 0, :, :] u: torch.Tensor = image[..., 1, :, :] v: torch.Tensor = image[..., 2, :, :] r: torch.Tensor = y + 1.14 * v # coefficient for g is 0 g: torch.Tensor = y + -0.396 * u - 0.581 * v b: torch.Tensor = y + 2.029 * u # coefficient for b is 0 out: torch.Tensor = torch.stack([r, g, b], -3) return out
[docs]def yuv420_to_rgb(imagey: torch.Tensor, imageuv: torch.Tensor) -> torch.Tensor: r"""Convert an YUV420 image to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Input need to be padded to be evenly divisible by 2 horizontal and vertical. This function assumed chroma siting is (0.5, 0.5) Args: imagey: Y (luma) Image plane to be converted to RGB with shape :math:`(*, 1, H, W)`. imageuv: UV (chroma) Image planes to be converted to RGB with shape :math:`(*, 2, H/2, W/2)`. Returns: RGB version of the image with shape :math:`(*, 3, H, W)`. Example: >>> inputy = torch.rand(2, 1, 4, 6) >>> inputuv = torch.rand(2, 2, 2, 3) >>> output = yuv420_to_rgb(inputy, inputuv) # 2x3x4x6 """ if not isinstance(imagey, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(imagey)}") if not isinstance(imageuv, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(imageuv)}") if len(imagey.shape) < 3 or imagey.shape[-3] != 1: raise ValueError(f"Input imagey size must have a shape of (*, 1, H, W). Got {imagey.shape}") if len(imageuv.shape) < 3 or imageuv.shape[-3] != 2: raise ValueError(f"Input imageuv size must have a shape of (*, 2, H/2, W/2). Got {imageuv.shape}") if len(imagey.shape) < 2 or imagey.shape[-2] % 2 == 1 or imagey.shape[-1] % 2 == 1: raise ValueError(f"Input H&W must be evenly disible by 2. Got {imagey.shape}") if (len(imageuv.shape) < 2 or len(imagey.shape) < 2 or imagey.shape[-2] / imageuv.shape[-2] != 2 or imagey.shape[-1] / imageuv.shape[-1] != 2): raise ValueError(f"Input imageuv H&W must be half the size of the luma plane. " f"Got {imagey.shape} and {imageuv.shape}") # first upsample yuv444image = torch.cat([imagey, imageuv.repeat_interleave(2, dim=-1).repeat_interleave(2, dim=-2)], dim=-3) # then convert the yuv444 tensor return yuv_to_rgb(yuv444image)
[docs]def yuv422_to_rgb(imagey: torch.Tensor, imageuv: torch.Tensor) -> torch.Tensor: r"""Convert an YUV422 image to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Input need to be padded to be evenly divisible by 2 vertical. This function assumed chroma siting is (0.5) Args: imagey: Y (luma) Image plane to be converted to RGB with shape :math:`(*, 1, H, W)`. imageuv: UV (luma) Image planes to be converted to RGB with shape :math:`(*, 2, H, W/2)`. Returns: RGB version of the image with shape :math:`(*, 3, H, W)`. Example: >>> inputy = torch.rand(2, 1, 4, 6) >>> inputuv = torch.rand(2, 2, 2, 3) >>> output = yuv420_to_rgb(inputy, inputuv) # 2x3x4x5 """ if not isinstance(imagey, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(imagey)}") if not isinstance(imageuv, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(imageuv)}") if len(imagey.shape) < 3 or imagey.shape[-3] != 1: raise ValueError(f"Input imagey size must have a shape of (*, 1, H, W). Got {imagey.shape}") if len(imageuv.shape) < 3 or imageuv.shape[-3] != 2: raise ValueError(f"Input imageuv size must have a shape of (*, 2, H, W/2). Got {imageuv.shape}") if len(imagey.shape) < 2 or imagey.shape[-2] % 2 == 1 or imagey.shape[-1] % 2 == 1: raise ValueError(f"Input H&W must be evenly disible by 2. Got {imagey.shape}") if len(imageuv.shape) < 2 or len(imagey.shape) < 2 or imagey.shape[-1] / imageuv.shape[-1] != 2: raise ValueError(f"Input imageuv W must be half the size of the luma plane. " f"Got {imagey.shape} and {imageuv.shape}") # first upsample yuv444image = torch.cat([imagey, imageuv.repeat_interleave(2, dim=-1)], dim=-3) # then convert the yuv444 tensor return yuv_to_rgb(yuv444image)
[docs]class RgbToYuv(nn.Module): r"""Convert an image from RGB to YUV. The image data is assumed to be in the range of (0, 1). Returns: YUV version of the image. Shape: - image: :math:`(*, 3, H, W)` - output: :math:`(*, 3, H, W)` Examples: >>> input = torch.rand(2, 3, 4, 5) >>> yuv = RgbToYuv() >>> output = yuv(input) # 2x3x4x5 Reference:: [1] https://es.wikipedia.org/wiki/YUV#RGB_a_Y'UV """ def forward(self, input: torch.Tensor) -> torch.Tensor: return rgb_to_yuv(input)
[docs]class RgbToYuv420(nn.Module): r"""Convert an image from RGB to YUV420. The image data is assumed to be in the range of (0, 1). Width and Height evenly divisible by 2. Returns: YUV420 version of the image. Shape: - image: :math:`(*, 3, H, W)` - output: :math:`(*, 1, H, W)` and :math:`(*, 2, H/2, W/2)` Examples: >>> yuvinput = torch.rand(2, 3, 4, 6) >>> yuv = RgbToYuv420() >>> output = yuv(yuvinput) # # (2x1x4x6, 2x1x2x3) Reference:: [1] https://es.wikipedia.org/wiki/YUV#RGB_a_Y'UV """ def forward(self, yuvinput: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # skipcq: PYL-R0201 return rgb_to_yuv420(yuvinput)
[docs]class RgbToYuv422(nn.Module): r"""Convert an image from RGB to YUV422. The image data is assumed to be in the range of (0, 1). Width evenly disvisible by 2. Returns: YUV422 version of the image. Shape: - image: :math:`(*, 3, H, W)` - output: :math:`(*, 1, H, W)` and :math:`(*, 2, H, W/2)` Examples: >>> yuvinput = torch.rand(2, 3, 4, 6) >>> yuv = RgbToYuv422() >>> output = yuv(yuvinput) # # (2x1x4x6, 2x2x4x3) Reference:: [1] https://es.wikipedia.org/wiki/YUV#RGB_a_Y'UV """ def forward(self, yuvinput: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # skipcq: PYL-R0201 return rgb_to_yuv422(yuvinput)
[docs]class YuvToRgb(nn.Module): r"""Convert an image from YUV to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. 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 = YuvToRgb() >>> output = rgb(input) # 2x3x4x5 """ def forward(self, input: torch.Tensor) -> torch.Tensor: return yuv_to_rgb(input)
[docs]class Yuv420ToRgb(nn.Module): r"""Convert an image from YUV to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Width and Height evenly divisible by 2. Returns: RGB version of the image. Shape: - imagey: :math:`(*, 1, H, W)` - imageuv: :math:`(*, 2, H/2, W/2)` - output: :math:`(*, 3, H, W)` Examples: >>> inputy = torch.rand(2, 1, 4, 6) >>> inputuv = torch.rand(2, 2, 2, 3) >>> rgb = Yuv420ToRgb() >>> output = rgb(inputy, inputuv) # 2x3x4x6 """ def forward(self, inputy: torch.Tensor, inputuv: torch.Tensor) -> torch.Tensor: # skipcq: PYL-R0201 return yuv420_to_rgb(inputy, inputuv)
[docs]class Yuv422ToRgb(nn.Module): r"""Convert an image from YUV to RGB. The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Width evenly divisible by 2. Returns: RGB version of the image. Shape: - imagey: :math:`(*, 1, H, W)` - imageuv: :math:`(*, 2, H, W/2)` - output: :math:`(*, 3, H, W)` Examples: >>> inputy = torch.rand(2, 1, 4, 6) >>> inputuv = torch.rand(2, 2, 4, 3) >>> rgb = Yuv422ToRgb() >>> output = rgb(inputy, inputuv) # 2x3x4x6 """ def forward(self, inputy: torch.Tensor, inputuv: torch.Tensor) -> torch.Tensor: # skipcq: PYL-R0201 return yuv422_to_rgb(inputy, inputuv)