Pixelwise(SEGS & MASKS ForEach)

Documentation

• Class name: ImpactSegsAndMaskForEach
• Category: ImpactPack/Operation
• Output node: False

This node applies a bitwise AND operation between each segmentation (seg) and its corresponding mask, effectively filtering the segments based on the mask. It's designed for batch processing, where each segment in a collection is paired with a mask to produce a modified set of segments that only include the areas covered by the masks.

Input types

Required

• segs
  • The collection of segments to be processed. Each segment represents a portion of an image that has been identified for further operations, such as filtering or modification.
  • Comfy dtype: SEGS
  • Python dtype: Tuple[torch.Tensor, List[SEG]]
• masks
  • A collection of masks to be applied to the corresponding segments. Each mask defines the areas within a segment to retain, effectively filtering out the rest.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• segs
  • Comfy dtype: SEGS
  • The modified collection of segments after applying the bitwise AND operation with the corresponding masks. This output retains only the areas of each segment that overlap with the mask.
  • Python dtype: Tuple[torch.Tensor, List[SEG]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class SegsBitwiseAndMaskForEach:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                        "segs": ("SEGS",),
                        "masks": ("MASK",),
                    }
                }

    RETURN_TYPES = ("SEGS",)
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Operation"

    def doit(self, segs, masks):
        return (core.apply_mask_to_each_seg(segs, masks), )