116 lines
4.9 KiB
Python
116 lines
4.9 KiB
Python
import torch
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from .imagefunc import log, tensor2pil, pil2tensor, mask2image, image2mask, gaussian_blur, min_bounding_rect, max_inscribed_rect, mask_area
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from .imagefunc import num_round_up_to_multiple, draw_rect
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class CropByMaskV3:
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def __init__(self):
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self.NODE_NAME = 'CropByMask V3'
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@classmethod
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def INPUT_TYPES(self):
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detect_mode = ['mask_area', 'min_bounding_rect', 'max_inscribed_rect']
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multiple_list = ['8', '16', '32', '64', '128', '256', '512', 'None']
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return {
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"required": {
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"image": ("IMAGE", ), #
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"mask": ("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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"round_to_multiple": (multiple_list,),
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},
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"optional": {
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"crop_box": ("BOX",),
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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_v3'
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CATEGORY = '😺dzNodes/LayerUtility'
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def crop_by_mask_v3(self, image, mask, invert_mask, detect,
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top_reserve, bottom_reserve,
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left_reserve, right_reserve, round_to_multiple,
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crop_box=None
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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.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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# 如果有多张mask输入，使用第一张
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if mask.shape[0] > 1:
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log(f"Warning: Multiple mask inputs, using the first.", message_type='warning')
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mask = torch.unsqueeze(mask[0], 0)
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if invert_mask:
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mask = 1 - mask
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l_masks.append(tensor2pil(torch.unsqueeze(mask, 0)).convert('L'))
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_mask = mask2image(mask)
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preview_image = tensor2pil(mask).convert('RGBA')
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if crop_box is None:
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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, w, h) = min_bounding_rect(bluredmask)
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elif detect == "max_inscribed_rect":
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(x, y, w, h) = max_inscribed_rect(bluredmask)
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else:
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(x, y, w, h) = mask_area(_mask)
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canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGBA').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 + w + right_reserve if x + w + right_reserve < canvas_width else canvas_width
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y2 = y + h + bottom_reserve if y + h + bottom_reserve < canvas_height else canvas_height
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if round_to_multiple != 'None':
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multiple = int(round_to_multiple)
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width = num_round_up_to_multiple(x2 - x1, multiple)
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height = num_round_up_to_multiple(y2 - y1, multiple)
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x1 = x1 - (width - (x2 - x1)) // 2
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y1 = y1 - (height - (y2 - y1)) // 2
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x2 = x1 + width
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y2 = y1 + height
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log(f"{self.NODE_NAME}: Box detected. x={x1},y={y1},width={width},height={height}")
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crop_box = (x1, y1, x2, y2)
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preview_image = draw_rect(preview_image, x, y, w, h, line_color="#F00000",
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line_width=(w + h) // 100)
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preview_image = draw_rect(preview_image, crop_box[0], crop_box[1],
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crop_box[2] - crop_box[0], crop_box[3] - crop_box[1],
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line_color="#00F000",
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line_width=(crop_box[2] - crop_box[0] + crop_box[3] - crop_box[1]) // 200)
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for i in range(len(l_images)):
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_canvas = tensor2pil(l_images[i]).convert('RGBA')
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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"{self.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 V3": CropByMaskV3
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: CropByMask V3": "LayerUtility: CropByMask V3"
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} |