Image Gradient Map

Documentation

• Class name: Image Gradient Map
• Category: WAS Suite/Image/Filter
• Output node: False

The node applies a gradient map to an image, optionally flipping the gradient map horizontally. It transforms the input image by mapping its grayscale values to colors defined in the gradient map, creating a visually appealing effect that retains the original image's structure while altering its color scheme.

Input types

Required

• image
  • The original image to which the gradient map will be applied. It serves as the base for the transformation, dictating the structure of the output image.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• gradient_image
  • The gradient map image that defines the color transformation. This image's colors are mapped to the grayscale values of the original image, determining the output image's color scheme.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• flip_left_right
  • A boolean flag indicating whether the gradient map should be flipped horizontally before being applied. This can alter the visual effect of the gradient mapping on the original image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting image after applying the gradient map to the original image. This output retains the structure of the original image but features the colors defined by the gradient map.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class WAS_Image_Gradient_Map:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "gradient_image": ("IMAGE",),
                "flip_left_right": (["false", "true"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "image_gradient_map"

    CATEGORY = "WAS Suite/Image/Filter"

    def image_gradient_map(self, image, gradient_image, flip_left_right='false'):

        # Convert images to PIL
        image = tensor2pil(image)
        gradient_image = tensor2pil(gradient_image)

        # WAS Filters
        WTools = WAS_Tools_Class()

        image = WTools.gradient_map(image, gradient_image, (True if flip_left_right == 'true' else False))

        return (pil2tensor(image), )