66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import copy
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from pymatting import *
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from .imagefunc import *
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class PixelSpread:
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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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return {
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"required": {
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"image": ("IMAGE", ), #
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"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask
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"mask_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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FUNCTION = 'pixel_spread'
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CATEGORY = '😺dzNodes/LayerMask'
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OUTPUT_NODE = True
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def pixel_spread(self, image, invert_mask, mask_grow, mask=None):
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_image = tensor2pil(image)
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if _image.mode == 'RGBA':
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_mask = _image.split()[-1]
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if mask_grow != 0:
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_mask = expand_mask(image2mask(_mask), mask_grow, 0) # 扩张，模糊
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else:
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_mask = Image.new('L', _image.size, 'white')
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if mask is not None:
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if invert_mask:
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mask = 1 - mask
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if mask_grow != 0:
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_mask = expand_mask(mask, mask_grow, 0) # 扩张，模糊
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_mask = mask2image(mask).convert('L')
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image = pil2tensor(_image.convert('RGB'))
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_mask = _mask.convert('RGB')
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i_dup = copy.deepcopy(image.cpu().numpy().astype(np.float64))
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a_dup = copy.deepcopy(pil2tensor(_mask).cpu().numpy().astype(np.float64))
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fg = copy.deepcopy(image.cpu().numpy().astype(np.float64))
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for index, image in enumerate(i_dup):
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trimap = a_dup[index][:, :, 0] # convert to single channel
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trimap = fix_trimap(trimap, 0.01, 0.99)
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alpha = estimate_alpha_cf(image, trimap, laplacian_kwargs={"epsilon": 1e-6},
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cg_kwargs={"maxiter": 100})
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fg[index], _ = estimate_foreground_ml(image, np.array(alpha), return_background=True)
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return (torch.from_numpy(fg.astype(np.float32)), # fg
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)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: PixelSpread": PixelSpread
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: PixelSpread": "LayerMask: PixelSpread"
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} |