97 lines
4.1 KiB
Python
97 lines
4.1 KiB
Python
from .imagefunc import *
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NODE_NAME = 'CropByMask'
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class CropByMask:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
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return {
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"required": {
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"image": ("IMAGE", ), #
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"mask_for_crop": ("MASK",),
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"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask#
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"detect": (detect_mode,),
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"top_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
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"bottom_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
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"left_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
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"right_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "BOX", "IMAGE",)
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RETURN_NAMES = ("croped_image", "croped_mask", "crop_box", "box_preview")
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FUNCTION = 'crop_by_mask'
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CATEGORY = '😺dzNodes/LayerUtility'
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def crop_by_mask(self, image, mask_for_crop, invert_mask, detect,
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top_reserve, bottom_reserve, left_reserve, right_reserve
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):
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ret_images = []
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ret_masks = []
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l_images = []
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l_masks = []
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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if mask_for_crop.dim() == 2:
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mask_for_crop = torch.unsqueeze(mask_for_crop, 0)
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# 如果有多张mask输入，使用第一张
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if mask_for_crop.shape[0] > 1:
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log(f"Warning: Multiple mask inputs, using the first.", message_type='warning')
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mask_for_crop = torch.unsqueeze(mask_for_crop[0], 0)
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if invert_mask:
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mask_for_crop = 1 - mask_for_crop
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l_masks.append(tensor2pil(torch.unsqueeze(mask_for_crop, 0)).convert('L'))
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_mask = mask2image(mask_for_crop)
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bluredmask = gaussian_blur(_mask, 20).convert('L')
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x = 0
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y = 0
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width = 0
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height = 0
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if detect == "min_bounding_rect":
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(x, y, width, height) = min_bounding_rect(bluredmask)
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elif detect == "max_inscribed_rect":
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(x, y, width, height) = max_inscribed_rect(bluredmask)
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else:
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(x, y, width, height) = mask_area(_mask)
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width = num_round_up_to_multiple(width, 8)
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height = num_round_up_to_multiple(height, 8)
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log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
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canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
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x1 = x - left_reserve if x - left_reserve > 0 else 0
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y1 = y - top_reserve if y - top_reserve > 0 else 0
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x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
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y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
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preview_image = tensor2pil(mask_for_crop).convert('RGB')
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preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000", line_width=(width+height)//100)
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preview_image = draw_rect(preview_image, x1, y1, x2 - x1, y2 - y1,
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line_color="#00F000", line_width=(width+height)//200)
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crop_box = (x1, y1, x2, y2)
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for i in range(len(l_images)):
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_canvas = tensor2pil(l_images[i]).convert('RGB')
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_mask = l_masks[0]
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ret_images.append(pil2tensor(_canvas.crop(crop_box)))
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ret_masks.append(image2mask(_mask.crop(crop_box)))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), list(crop_box), pil2tensor(preview_image),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: CropByMask": CropByMask
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: CropByMask": "LayerUtility: CropByMask"
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} |