85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
import torch
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import torchvision.transforms.functional as TF
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import cv2
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import numpy as np
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class ImageAddMask:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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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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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "imageAddMask"
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CATEGORY = "lam"
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def imageAddMask(self, image, mask):
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image = tensor2rgba(image)
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mask_np=mask.numpy()
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mask_shape=mask_np.shape
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img_shape=image.size()
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if img_shape[1] != mask_shape[1] or img_shape[2] != mask_shape[2]:
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maskList=[]
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for i in range(mask.shape[0]):
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maskList.append(cv2.resize(mask_np[i],(img_shape[2],img_shape[1])))
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mask_np=np.array(maskList)
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mask=torch.from_numpy(mask_np)
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mask = 1.0 - mask
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image[:,:,:,-1] = mask
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return (image,)
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def tensor2rgba(t: torch.Tensor) -> torch.Tensor:
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size = t.size()
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if (len(size) < 4):
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return t.unsqueeze(3).repeat(1, 1, 1, 4)
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elif size[3] == 1:
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return t.repeat(1, 1, 1, 4)
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elif size[3] == 3:
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alpha_tensor = torch.ones((size[0], size[1], size[2], 1))
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return torch.cat((t, alpha_tensor), dim=3)
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else:
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return t
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def tensor2rgb(t: torch.Tensor) -> torch.Tensor:
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size = t.size()
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if (len(size) < 4):
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return t.unsqueeze(3).repeat(1, 1, 1, 3)
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if size[3] == 1:
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return t.repeat(1, 1, 1, 3)
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elif size[3] == 4:
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return t[:, :, :, :3]
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else:
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return t
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def tensor2mask(t: torch.Tensor) -> torch.Tensor:
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size = t.size()
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if (len(size) < 4):
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return t
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if size[3] == 1:
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return t[:,:,:,0]
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elif size[3] == 4:
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# Not sure what the right thing to do here is. Going to try to be a little smart and use alpha unless all alpha is 1 in case we'll fallback to RGB behavior
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if torch.min(t[:, :, :, 3]).item() != 1.:
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return t[:,:,:,3]
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return TF.rgb_to_grayscale(tensor2rgb(t).permute(0,3,1,2), num_output_channels=1)[:,0,:,:]
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NODE_CLASS_MAPPINGS = {
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"ImageAddMask": ImageAddMask
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ImageAddMask": "图片加遮罩"
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}
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