FindComplementaryColor

Documentation

• Class name: FindComplementaryColor
• Category: Bmad/CV/Color A.
• Output node: False

This node is designed to find a color complementary to the dominant color in an image, based on a given color dictionary and a specified power factor. It optionally allows for the application of a mask to focus the search on a specific area of the image.

Input types

Required

• image
  • The image in which to find the complementary color. It serves as the primary input for analysis.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• color_dict
  • A dictionary mapping color names to their RGB values, used to identify and compare colors within the image.
  • Comfy dtype: COLOR_DICT
  • Python dtype: Dict[str, Tuple[int, int, int]]
• power
  • A factor that influences the selection of the complementary color, adjusting the algorithm's sensitivity or bias towards certain colors.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• mask
  • An optional mask that can be applied to the image to limit the search for the complementary color to a specific area.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Output types

• color
  • Comfy dtype: COLOR
  • The RGB values of the complementary color found in the image.
  • Python dtype: List[int]
• string
  • Comfy dtype: STRING
  • The name of the complementary color found, as defined in the input color dictionary.
  • Python dtype: str

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class FindComplementaryColor:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "image": ("IMAGE",),
            "color_dict": ("COLOR_DICT",),
            "power": ("FLOAT", {"default": 0.5, "min": .01, "max": 10, "step": "0.01"}),
        },
            "optional":
                {
                    "mask": ("IMAGE",)
                }
        }

    RETURN_TYPES = ("COLOR", "STRING",)
    FUNCTION = "find_color"
    CATEGORY = "Bmad/CV/Color A."

    def find_color(self, image, color_dict, power, mask=None):
        image = tensor2opencv(image, 3)

        if mask is not None:
            mask = tensor2opencv(mask, 1)

            # this is a quality of life feature, so that it is easier to run the node and test stuff
            # the behavior (img resize w/ lin. interpolation) can be avoided by setting up the data prior to this node
            if image.shape[0:2] != mask.shape[0:2]:
                print("FindComplementaryColor node will resize image to fit mask.")
                image = cv.resize(image, (mask.shape[1], mask.shape[0]), interpolation=cv.INTER_LINEAR)

        color = find_complementary_color(image, color_dict, mask, power)
        return (list(color_dict[color]), color,)