LayerFilter: ColorMap

Documentation

• Class name: LayerFilter: ColorMap
• Category: 😺dzNodes/LayerFilter
• Output node: False

The ColorMap node applies a specified color map to an image, optionally adjusting its opacity, to transform the visual appearance of the image. This node supports a variety of color maps, allowing for a wide range of aesthetic effects.

Input types

Required

• image
  • The input image to which the color map will be applied. This is the primary data the node operates on, determining the visual output.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• color_map
  • Specifies the color map to apply to the input image. The choice of color map affects the aesthetic and thematic presentation of the image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• opacity
  • Determines the opacity level of the color map applied to the image, allowing for fine-tuning of the visual effect.
  • Comfy dtype: INT
  • Python dtype: int

Optional

Output types

• image
  • Comfy dtype: IMAGE
  • The output image after the color map and opacity adjustments have been applied, showcasing the transformed visual appearance.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ColorMap:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),
                "color_map": (colormap_list,),
                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),  # 透明度
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'color_map'
    CATEGORY = '😺dzNodes/LayerFilter'

    def color_map(self, image, color_map, opacity
                  ):

        ret_images = []

        for i in image:
            i = torch.unsqueeze(i, 0)
            _canvas = tensor2pil(i)
            _image = image_to_colormap(_canvas, colormap_list.index(color_map))
            ret_image = chop_image(_canvas, _image, 'normal', opacity)

            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),)