CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/ComfyUI_essentials/docs/MaskFromRGBCMYBW+.md

tags

tags
Mask MaskGeneration

🔧 Mask From RGB/CMY/BW

Documentation

• Class name: MaskFromRGBCMYBW+
• Category: essentials
• Output node: False

This node is designed to generate masks based on specific RGB, CMY, and BW (Black and White) values within images. It abstracts the complexity of color space conversions and thresholding operations to provide a straightforward way for users to create masks that highlight or isolate particular color ranges or intensity levels in images.

Input types

Required

• image
  • The 'image' parameter is the input image from which the mask will be generated. It plays a crucial role in determining the areas of interest based on the specified color or intensity values.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• threshold_r
  • Specifies the threshold for the red component used to filter the image and generate the mask. This parameter helps in isolating areas based on the red intensity.
  • Comfy dtype: FLOAT
  • Python dtype: int
• threshold_g
  • Specifies the threshold for the green component used to filter the image and generate the mask. This parameter helps in isolating areas based on the green intensity.
  • Comfy dtype: FLOAT
  • Python dtype: int
• threshold_b
  • Specifies the threshold for the blue component used to filter the image and generate the mask. This parameter helps in isolating areas based on the blue intensity.
  • Comfy dtype: FLOAT
  • Python dtype: int
• remove_isolated_pixels
  • Determines whether to remove isolated pixels from the generated masks, enhancing the mask's clarity and relevance to the targeted colors or intensity levels.
  • Comfy dtype: INT
  • Python dtype: int
• fill_holes
  • Indicates whether to fill holes in the generated masks, ensuring a more continuous and useful mask for further image processing or analysis.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• red
  • Comfy dtype: MASK
  • The output mask isolating the red components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• green
  • Comfy dtype: MASK
  • The output mask isolating the green components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• blue
  • Comfy dtype: MASK
  • The output mask isolating the blue components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• cyan
  • Comfy dtype: MASK
  • The output mask isolating the cyan components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• magenta
  • Comfy dtype: MASK
  • The output mask isolating the magenta components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• yellow
  • Comfy dtype: MASK
  • The output mask isolating the yellow components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• black
  • Comfy dtype: MASK
  • The output mask isolating the black components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor
• white
  • Comfy dtype: MASK
  • The output mask isolating the white components based on the specified threshold and processing parameters.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class MaskFromRGBCMYBW:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE", ),
                "threshold_r": ("FLOAT", { "default": 0.15, "min": 0.0, "max": 1, "step": 0.01, }),
                "threshold_g": ("FLOAT", { "default": 0.15, "min": 0.0, "max": 1, "step": 0.01, }),
                "threshold_b": ("FLOAT", { "default": 0.15, "min": 0.0, "max": 1, "step": 0.01, }),
                "remove_isolated_pixels": ("INT", { "default": 0, "min": 0, "max": 32, "step": 1, }),
                "fill_holes": ("BOOLEAN", { "default": False }),
            }
        }

    RETURN_TYPES = ("MASK","MASK","MASK","MASK","MASK","MASK","MASK","MASK",)
    RETURN_NAMES = ("red","green","blue","cyan","magenta","yellow","black","white",)
    FUNCTION = "execute"
    CATEGORY = "essentials"

    def execute(self, image, threshold_r, threshold_g, threshold_b, remove_isolated_pixels, fill_holes):
        red = ((image[..., 0] >= 1-threshold_r) & (image[..., 1] < threshold_g) & (image[..., 2] < threshold_b)).float()
        green = ((image[..., 0] < threshold_r) & (image[..., 1] >= 1-threshold_g) & (image[..., 2] < threshold_b)).float()
        blue = ((image[..., 0] < threshold_r) & (image[..., 1] < threshold_g) & (image[..., 2] >= 1-threshold_b)).float()

        cyan = ((image[..., 0] < threshold_r) & (image[..., 1] >= 1-threshold_g) & (image[..., 2] >= 1-threshold_b)).float()
        magenta = ((image[..., 0] >= 1-threshold_r) & (image[..., 1] < threshold_g) & (image[..., 2] > 1-threshold_b)).float()
        yellow = ((image[..., 0] >= 1-threshold_r) & (image[..., 1] >= 1-threshold_g) & (image[..., 2] < threshold_b)).float()

        black = ((image[..., 0] <= threshold_r) & (image[..., 1] <= threshold_g) & (image[..., 2] <= threshold_b)).float()
        white = ((image[..., 0] >= 1-threshold_r) & (image[..., 1] >= 1-threshold_g) & (image[..., 2] >= 1-threshold_b)).float()

        if remove_isolated_pixels > 0 or fill_holes:
            colors = [red, green, blue, cyan, magenta, yellow, black, white]
            color_names = ['red', 'green', 'blue', 'cyan', 'magenta', 'yellow', 'black', 'white']
            processed_colors = {}

            for color_name, color in zip(color_names, colors):
                color = color.cpu().numpy()
                masks = []

                for i in range(image.shape[0]):
                    mask = color[i]
                    if remove_isolated_pixels > 0:
                        mask = scipy.ndimage.binary_opening(mask, structure=np.ones((remove_isolated_pixels, remove_isolated_pixels)))
                    if fill_holes:
                        mask = scipy.ndimage.binary_fill_holes(mask)
                    mask = torch.from_numpy(mask)
                    masks.append(mask)

                processed_colors[color_name] = torch.stack(masks, dim=0).float()

            red = processed_colors['red']
            green = processed_colors['green']
            blue = processed_colors['blue']
            cyan = processed_colors['cyan']
            magenta = processed_colors['magenta']
            yellow = processed_colors['yellow']
            black = processed_colors['black']
            white = processed_colors['white']

            del colors, processed_colors
        
        return (red, green, blue, cyan, magenta, yellow, black, white,)