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from .imagefunc import *
NODE_NAME = 'CropByMask'
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_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"
} |