Blur Mask (Fast)

Documentation

• Class name: BlurMaskFast
• Category: mask/filters
• Output node: False

The BlurMaskFast node is designed to apply a Gaussian blur to masks, allowing for the softening of edges and the creation of a smoother mask appearance. This operation is particularly useful in image processing tasks where the harshness of mask boundaries needs to be reduced.

Input types

Required

• masks
  • Specifies the masks to be blurred. This input is crucial for defining the areas within the image where the blur effect will be applied.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• radius_x
  • Determines the horizontal radius of the Gaussian blur. A larger radius results in a more pronounced blur effect horizontally.
  • Comfy dtype: INT
  • Python dtype: int
• radius_y
  • Determines the vertical radius of the Gaussian blur. A larger radius results in a more pronounced blur effect vertically.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• mask
  • Comfy dtype: MASK
  • The output is a mask that has been smoothed by the Gaussian blur process, with softer edges compared to the original.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class BlurMaskFast:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "masks": ("MASK",),
                "radius_x": ("INT", {
                    "default": 1,
                    "min": 0,
                    "max": 1023,
                    "step": 1
                }),
                "radius_y": ("INT", {
                    "default": 1,
                    "min": 0,
                    "max": 1023,
                    "step": 1
                }),
            },
        }

    RETURN_TYPES = ("MASK",)
    FUNCTION = "blur_mask"

    CATEGORY = "mask/filters"

    def blur_mask(self, masks, radius_x, radius_y):

        if radius_x + radius_y == 0:
            return (masks,)

        dx = radius_x * 2 + 1
        dy = radius_y * 2 + 1

        dup = copy.deepcopy(masks.cpu().numpy())

        for index, mask in enumerate(dup):
            dup[index] = cv2.GaussianBlur(mask, (dx, dy), 0)

        return (torch.from_numpy(dup),)