97 lines
3.7 KiB
Python
97 lines
3.7 KiB
Python
from .imagefunc import *
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NODE_NAME = 'ImageMaskScaleAs'
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any = AnyType("*")
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class ImageMaskScaleAs:
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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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fit_mode = ['letterbox', 'crop', 'fill']
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method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
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return {
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"required": {
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"scale_as": (any, {}),
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"fit": (fit_mode,),
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"method": (method_mode,),
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},
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"optional": {
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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", "MASK", "BOX",)
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RETURN_NAMES = ("image", "mask", "original_size")
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FUNCTION = 'image_mask_scale_as'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_mask_scale_as(self, scale_as, fit, method,
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image=None, mask = None,
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):
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if scale_as.shape[0] > 0:
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_asimage = tensor2pil(scale_as[0])
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else:
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_asimage = tensor2pil(scale_as)
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target_width, target_height = _asimage.size
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_mask = Image.new('L', size=_asimage.size, color='black')
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_image = Image.new('RGB', size=_asimage.size, color='black')
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orig_width = 4
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orig_height = 4
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resize_sampler = Image.LANCZOS
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if method == "bicubic":
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resize_sampler = Image.BICUBIC
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elif method == "hamming":
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resize_sampler = Image.HAMMING
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elif method == "bilinear":
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resize_sampler = Image.BILINEAR
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elif method == "box":
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resize_sampler = Image.BOX
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elif method == "nearest":
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resize_sampler = Image.NEAREST
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ret_images = []
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ret_masks = []
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if image is not None:
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for i in image:
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i = torch.unsqueeze(i, 0)
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_image = tensor2pil(i).convert('RGB')
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orig_width, orig_height = _image.size
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_image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler)
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ret_images.append(pil2tensor(_image))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for m in mask:
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m = torch.unsqueeze(m, 0)
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_mask = tensor2pil(m).convert('L')
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orig_width, orig_height = _mask.size
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_mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
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ret_masks.append(image2mask(_mask))
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if len(ret_images) > 0 and len(ret_masks) >0:
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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), [orig_width, orig_height],)
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elif len(ret_images) > 0 and len(ret_masks) == 0:
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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), None, [orig_width, orig_height],)
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elif len(ret_images) == 0 and len(ret_masks) > 0:
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log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
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return (None, torch.cat(ret_masks, dim=0), [orig_width, orig_height],)
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else:
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log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
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return (None, None, [orig_width, orig_height],)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageMaskScaleAs": ImageMaskScaleAs
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageMaskScaleAs": "LayerUtility: ImageMaskScaleAs"
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} |