Mask Crop Dominant Region

Documentation

• Class name: Mask Crop Dominant Region
• Category: WAS Suite/Image/Masking
• Output node: False

This node is designed to identify and extract the dominant region from a given set of masks, applying a specified padding around the cropped area. It leverages image processing techniques to focus on the most significant part of the image, enhancing the relevance of the extracted region.

Input types

Required

• masks
  • The input masks from which the dominant region is to be cropped. It plays a crucial role in determining the area of interest within the image.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• padding
  • Specifies the padding to be applied around the cropped dominant region, allowing for adjustable margins around the extracted area.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• MASKS
  • Comfy dtype: MASK
  • The output consists of the cropped dominant regions from the input masks, potentially with applied padding.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class WAS_Mask_Crop_Dominant_Region:

    def __init__(self):
        self.WT = WAS_Tools_Class()

    @classmethod
    def INPUT_TYPES(cls):
        return {
                    "required": {
                        "masks": ("MASK",),
                        "padding": ("INT", {"default": 24, "min": 0, "max": 4096, "step": 1}),
                    }
                }

    CATEGORY = "WAS Suite/Image/Masking"

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

    FUNCTION = "crop_dominant_region"

    def crop_dominant_region(self, masks, padding=24):
        if masks.ndim > 3:
            regions = []
            for mask in masks:
                mask_pil = Image.fromarray(np.clip(255. * mask.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
                region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding)
                region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
                regions.append(region_tensor)
            regions_tensor = torch.cat(regions, dim=0)
            return (regions_tensor,)
        else:
            mask_pil = Image.fromarray(np.clip(255. * masks.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
            region_mask = self.WT.Masking.crop_dominant_region(mask_pil, padding)
            region_tensor = pil2mask(region_mask).unsqueeze(0).unsqueeze(1)
            return (region_tensor,)