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Running
on
Zero
| from .imagefunc import * | |
| NODE_NAME = 'CropByMask' | |
| class CropByMask: | |
| def __init__(self): | |
| pass | |
| 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_up_to_multiple(width, 8) | |
| height = num_round_up_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),) | |
| NODE_CLASS_MAPPINGS = { | |
| "LayerUtility: CropByMask": CropByMask | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "LayerUtility: CropByMask": "LayerUtility: CropByMask" | |
| } |