Gradient Filter Mask Regions

Documentation

• Class name: SaltMaskGradientRegion
• Category: SALT/Masking/Filter
• Output node: False

This node applies a gradient filter to mask regions, enhancing edges and transitions within the mask to highlight areas of change. It's designed to process mask inputs and apply morphological gradient operations to emphasize the contours and transitions in the mask regions.

Input types

Required

• masks
  • The masks input represents the mask regions to which the gradient filter will be applied. This input is crucial for defining the areas of interest where edge enhancement and transition highlighting are desired.
  • Comfy dtype: MASK
  • Python dtype: List[torch.Tensor]

Optional

• kernel_size
  • The kernel_size parameter specifies the size of the kernel used in the morphological gradient operation. It affects the extent of edge enhancement and transition highlighting in the mask regions.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• MASKS
  • Comfy dtype: MASK
  • The output masks are the result of applying the gradient filter to the input mask regions, featuring enhanced edges and transitions.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class SaltMaskGradientRegion:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "masks": ("MASK",),
            },
            "optional": {
                "kernel_size": ("INT", {"default": 3, "min": 1, "max": 31, "step": 2}),
            }
        }

    CATEGORY = f"{NAME}/Masking/Filter"

    RETURN_TYPES = ("MASK",)
    RETURN_NAMES = ("MASKS",)

    FUNCTION = "gradient_region"

    def gradient_region(self, masks, kernel_size=3):
        if not isinstance(kernel_size, list):
            kernel_size = [kernel_size]
        regions = []
        for i, mask in enumerate(masks):
            pil_image = mask2pil(mask.unsqueeze(0))
            image_array = np.array(pil_image.convert('L'))

            current_kernel_size = kernel_size[i if i < len(kernel_size) else -1]
            kernel = np.ones((current_kernel_size, current_kernel_size), np.uint8)

            gradient = cv2.morphologyEx(image_array, cv2.MORPH_GRADIENT, kernel)
            gradient_pil = Image.fromarray(gradient)

            region_tensor = pil2mask(gradient_pil)
            regions.append(region_tensor)

        regions_tensor = torch.cat(regions, dim=0)
        return (regions_tensor,)