132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
import math
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from .imagefunc import *
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NODE_NAME = 'ImageScaleRestore V2'
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class ImageScaleRestoreV2:
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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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method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
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scale_by_list = ['by_scale', 'longest', 'shortest', 'width', 'height', 'total_pixel(kilo pixel)']
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return {
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"required": {
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"image": ("IMAGE", ), #
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"scale": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
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"method": (method_mode,),
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"scale_by": (scale_by_list,), # 是否按长边缩放
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"scale_by_length": ("INT", {"default": 1024, "min": 4, "max": 99999999, "step": 1}),
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},
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"optional": {
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"mask": ("MASK",), #
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"original_size": ("BOX",),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "BOX", "INT", "INT")
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RETURN_NAMES = ("image", "mask", "original_size", "width", "height",)
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FUNCTION = 'image_scale_restore'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_scale_restore(self, image, scale, method,
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scale_by, scale_by_length,
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mask = None, original_size = None
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):
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l_images = []
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l_masks = []
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ret_images = []
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ret_masks = []
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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m = tensor2pil(l)
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if m.mode == 'RGBA':
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l_masks.append(m.split()[-1])
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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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l_masks = []
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for m in mask:
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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(l_images), len(l_masks))
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orig_width, orig_height = tensor2pil(l_images[0]).size
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if original_size is not None:
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target_width = original_size[0]
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target_height = original_size[1]
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else:
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target_width = int(orig_width * scale)
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target_height = int(orig_height * scale)
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if scale_by == 'longest':
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if orig_width > orig_height:
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target_width = scale_by_length
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target_height = int(target_width * orig_height / orig_width)
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else:
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target_height = scale_by_length
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target_width = int(target_height * orig_width / orig_height)
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if scale_by == 'shortest':
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if orig_width < orig_height:
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target_width = scale_by_length
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target_height = int(target_width * orig_height / orig_width)
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else:
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target_height = scale_by_length
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target_width = int(target_height * orig_width / orig_height)
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if scale_by == 'width':
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target_width = scale_by_length
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target_height = int(target_width * orig_height / orig_width)
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if scale_by == 'height':
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target_height = scale_by_length
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target_width = int(target_height * orig_width / orig_height)
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if scale_by == 'total_pixel(kilo pixel)':
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r = orig_width / orig_height
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target_width = math.sqrt(r * scale_by_length * 1000)
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target_height = target_width / r
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target_width = int(target_width)
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target_height = int(target_height)
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if target_width < 4:
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target_width = 4
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if target_height < 4:
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target_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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for i in range(max_batch):
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_image = l_images[i] if i < len(l_images) else l_images[-1]
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_canvas = tensor2pil(_image).convert('RGB')
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ret_image = _canvas.resize((target_width, target_height), resize_sampler)
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ret_mask = Image.new('L', size=ret_image.size, color='white')
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if mask is not None:
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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ret_mask = _mask.resize((target_width, target_height), resize_sampler)
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(ret_mask))
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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], target_width, target_height,)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageScaleRestore V2": ImageScaleRestoreV2
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageScaleRestore V2": "LayerUtility: ImageScaleRestore V2"
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} |