LayerColor: YUV

Documentation

• Class name: LayerColor: YUV
• Category: 😺dzNodes/LayerColor
• Output node: False

The node 'LayerColor: YUV' is designed for color correction in the YUV color space. It adjusts the Y (luminance), U (chrominance), and V (chrominance) components of an image to achieve desired color effects or corrections.

Input types

Required

• image
  • The 'image' parameter represents the input image(s) to be processed. It is crucial for the color correction operation, serving as the base upon which YUV adjustments are applied.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• Y
  • The 'Y' parameter adjusts the luminance component of the image. Its modification affects the brightness and contrast of the image.
  • Comfy dtype: INT
  • Python dtype: int
• U
  • The 'U' parameter modifies the chrominance component related to blue color projection. Altering this value impacts the blue-yellow balance of the image.
  • Comfy dtype: INT
  • Python dtype: int
• V
  • The 'V' parameter adjusts the chrominance component associated with red color projection, influencing the red-green balance of the image.
  • Comfy dtype: INT
  • Python dtype: int

Optional

Output types

• image
  • Comfy dtype: IMAGE
  • The output 'image' is the result of applying YUV color corrections to the input image(s), reflecting the adjustments made to luminance and chrominance components.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ColorCorrectYUV:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),  #
                "Y": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
                "U": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
                "V": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'color_correct_YUV'
    CATEGORY = '😺dzNodes/LayerColor'

    def color_correct_YUV(self, image, Y, U, V):

        ret_images = []

        for i in image:
            i = torch.unsqueeze(i, 0)
            __image = tensor2pil(i)
            _y, _u, _v = tensor2pil(i).convert('YCbCr').split()
            if Y != 0 :
                _y = image_gray_offset(_y, Y)
            if U != 0 :
                _u = image_gray_offset(_u, U)
            if V != 0 :
                _v = image_gray_offset(_v, V)
            ret_image = image_channel_merge((_y, _u, _v), 'YCbCr')

            if __image.mode == 'RGBA':
                ret_image = RGB2RGBA(ret_image, __image.split()[-1])

            ret_images.append(pil2tensor(ret_image))

        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0),)