CavinHuang-comfyui-nodes-docs/docs/LayerUtility%3A CropByMask.md

Documentation

• Class name: CropByMask
• Category: 😺dzNodes/LayerMask
• Output node: False
• Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle

将图片按照mask范围裁切，可设置四周边框保留大小。这个节点与RestoreCropBox和ImageScaleRestore配合使用，可以对图片的局部进行裁切，放大修改后贴回原处。

Input types

Required

• image

  • 输入图片。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• mask_for_crop

  • image的遮罩，将自动按照遮罩范围进行裁切。
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

• invert_mask

  • 是否反转mask。
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

• detect

  • 探测方法，min_bounding_rect是大块形状最小外接矩形, max_inscribed_rect是大块形状最大内接矩形, mask_area是遮罩像素有效区域。
  • Comfy dtype: STRING
  • Python dtype: str

• top_reserve

  • 裁切顶端保留大小。
  • Comfy dtype: INT
  • Python dtype: int

• bottom_reserve

  • 裁切底端保留大小。
  • Comfy dtype: INT
  • Python dtype: int

• left_reserve

  • 裁切左侧保留大小。
  • Comfy dtype: INT
  • Python dtype: int

• right_reserve

  • 裁切右侧保留大小。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• croped_image

  • 裁切后的图片。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• croped_mask

  • 裁切后的mask。
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

• crop_box

  • 裁切box数据，在RestoreCropBox节点恢复时使用。
  • Comfy dtype: BOX
  • Python dtype: list

• box_preview

  • 裁切位置预览图，红色是探测到的范围，绿色是加上保留边框后裁切的范围。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU

Source code

class CropByMask:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):
        detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
        return {
            "required": {
                "image": ("IMAGE", ),  #
                "mask_for_crop": ("MASK",),
                "invert_mask": ("BOOLEAN", {"default": False}),  # 反转mask#
                "detect": (detect_mode,),
                "top_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
                "bottom_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
                "left_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
                "right_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE", "MASK", "BOX", "IMAGE",)
    RETURN_NAMES = ("croped_image", "croped_mask", "crop_box", "box_preview")
    FUNCTION = 'crop_by_mask'
    CATEGORY = '😺dzNodes/LayerUtility'

    def crop_by_mask(self, image, mask_for_crop, invert_mask, detect,
                  top_reserve, bottom_reserve, left_reserve, right_reserve
                  ):

        ret_images = []
        ret_masks = []
        l_images = []
        l_masks = []


        for l in image:
            l_images.append(torch.unsqueeze(l, 0))
        if mask_for_crop.dim() == 2:
            mask_for_crop = torch.unsqueeze(mask_for_crop, 0)
        # 如果有多张mask输入，使用第一张
        if mask_for_crop.shape[0] > 1:
            log(f"Warning: Multiple mask inputs, using the first.", message_type='warning')
            mask_for_crop = torch.unsqueeze(mask_for_crop[0], 0)
        if invert_mask:
            mask_for_crop = 1 - mask_for_crop
        l_masks.append(tensor2pil(torch.unsqueeze(mask_for_crop, 0)).convert('L'))

        _mask = mask2image(mask_for_crop)
        bluredmask = gaussian_blur(_mask, 20).convert('L')
        x = 0
        y = 0
        width = 0
        height = 0
        if detect == "min_bounding_rect":
            (x, y, width, height) = min_bounding_rect(bluredmask)
        elif detect == "max_inscribed_rect":
            (x, y, width, height) = max_inscribed_rect(bluredmask)
        else:
            (x, y, width, height) = mask_area(_mask)

        width = num_round_to_multiple(width, 8)
        height = num_round_to_multiple(height, 8)
        log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
        canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
        x1 = x - left_reserve if x - left_reserve > 0 else 0
        y1 = y - top_reserve if y - top_reserve > 0 else 0
        x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
        y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
        preview_image = tensor2pil(mask_for_crop).convert('RGB')
        preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000", line_width=(width+height)//100)
        preview_image = draw_rect(preview_image, x1, y1, x2 - x1, y2 - y1,
                                  line_color="#00F000", line_width=(width+height)//200)
        crop_box = (x1, y1, x2, y2)
        for i in range(len(l_images)):
            _canvas = tensor2pil(l_images[i]).convert('RGB')
            _mask = l_masks[0]
            ret_images.append(pil2tensor(_canvas.crop(crop_box)))
            ret_masks.append(image2mask(_mask.crop(crop_box)))

        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), list(crop_box), pil2tensor(preview_image),)