LayerMask: MaskMotionBlur

Documentation

• Class name: LayerMask: MaskMotionBlur
• Category: 😺dzNodes/LayerMask
• Output node: False

The MaskMotionBlur node applies a motion blur effect to a mask, optionally inverting the mask before processing. It allows for the customization of the blur intensity and direction, enabling dynamic visual effects on mask elements.

Input types

Required

• mask
  • The mask to which the motion blur effect will be applied. It serves as the primary input for the transformation.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• invert_mask
  • A boolean flag that determines whether the input mask should be inverted before applying the motion blur effect. Inversion can highlight or alter areas of interest.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• blur
  • Specifies the intensity of the motion blur effect. Higher values result in a more pronounced blur, simulating faster motion.
  • Comfy dtype: INT
  • Python dtype: int
• angle
  • Defines the angle of the motion blur effect, allowing for directional blurring that simulates motion in a specific direction.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

Output types

• mask
  • Comfy dtype: MASK
  • The resulting mask after applying the motion blur effect, potentially inverted based on the input flag.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class MaskMotionBlur:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "mask": ("MASK",),
                "invert_mask": ("BOOLEAN", {"default": True}),  # 反转mask
                "blur": ("INT", {"default": 20, "min": 1, "max": 9999, "step": 1}),
                "angle": ("FLOAT", {"default": 0, "min": -360, "max": 360, "step": 0.1}),
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("MASK",)
    RETURN_NAMES = ("mask",)
    FUNCTION = 'mask_motion_blur'
    CATEGORY = '😺dzNodes/LayerMask'

    def mask_motion_blur(self, mask, invert_mask, blur, angle,):

        l_masks = []
        ret_masks = []

        if mask.dim() == 2:
            mask = torch.unsqueeze(mask, 0)

        for m in mask:
            if invert_mask:
                m = 1 - m
            l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))

        for i in range(len(l_masks)):
            _mask = l_masks[i]
            _blurimage = motion_blur(_mask, angle, blur)
            ret_masks.append(image2mask(_blurimage))

        log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
        return (torch.cat(ret_masks, dim=0),)