186 lines
8.5 KiB
Python
186 lines
8.5 KiB
Python
import torch
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from PIL import Image
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import math
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask, num_round_up_to_multiple, fit_resize_image, is_valid_mask
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class ImageScaleByAspectRatioV2:
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def __init__(self):
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self.NODE_NAME = 'ImageScaleByAspectRatio V2'
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@classmethod
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def INPUT_TYPES(self):
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ratio_list = ['original', 'custom', '1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16']
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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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multiple_list = ['8', '16', '32', '64', '128', '256', '512', 'None']
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scale_to_list = ['None', 'longest', 'shortest', 'width', 'height', 'total_pixel(kilo pixel)']
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return {
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"required": {
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"aspect_ratio": (ratio_list,),
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"proportional_width": ("INT", {"default": 1, "min": 1, "max": 1e8, "step": 1}),
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"proportional_height": ("INT", {"default": 1, "min": 1, "max": 1e8, "step": 1}),
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"fit": (fit_mode,),
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"method": (method_mode,),
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"round_to_multiple": (multiple_list,),
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"scale_to_side": (scale_to_list,), # 是否按长边缩放
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"scale_to_length": ("INT", {"default": 1024, "min": 4, "max": 1e8, "step": 1}),
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"background_color": ("STRING", {"default": "#000000"}), # 背景颜色
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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", "INT", "INT",)
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RETURN_NAMES = ("image", "mask", "original_size", "width", "height",)
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FUNCTION = 'image_scale_by_aspect_ratio'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_scale_by_aspect_ratio(self, aspect_ratio, proportional_width, proportional_height,
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fit, method, round_to_multiple, scale_to_side, scale_to_length,
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background_color,
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image=None, mask = None,
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):
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orig_images = []
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orig_masks = []
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orig_width = 0
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orig_height = 0
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target_width = 0
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target_height = 0
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ratio = 1.0
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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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orig_images.append(i)
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orig_width, orig_height = tensor2pil(orig_images[0]).size
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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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if not is_valid_mask(m) and m.shape==torch.Size([1,64,64]):
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log(f"Warning: {self.NODE_NAME} input mask is empty, ignore it.", message_type='warning')
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else:
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orig_masks.append(m)
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if len(orig_masks) > 0:
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_width, _height = tensor2pil(orig_masks[0]).size
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if (orig_width > 0 and orig_width != _width) or (orig_height > 0 and orig_height != _height):
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log(f"Error: {self.NODE_NAME} execute failed, because the mask is does'nt match image.", message_type='error')
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return (None, None, None, 0, 0,)
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elif orig_width + orig_height == 0:
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orig_width = _width
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orig_height = _height
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if orig_width + orig_height == 0:
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log(f"Error: {self.NODE_NAME} execute failed, because the image or mask at least one must be input.", message_type='error')
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return (None, None, None, 0, 0,)
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if aspect_ratio == 'original':
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ratio = orig_width / orig_height
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elif aspect_ratio == 'custom':
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ratio = proportional_width / proportional_height
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else:
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s = aspect_ratio.split(":")
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ratio = int(s[0]) / int(s[1])
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# calculate target width and height
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if ratio > 1:
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if scale_to_side == 'longest':
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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elif scale_to_side == 'shortest':
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target_height = scale_to_length
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target_width = int(target_height * ratio)
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elif scale_to_side == 'width':
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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elif scale_to_side == 'height':
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target_height = scale_to_length
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target_width = int(target_height * ratio)
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elif scale_to_side == 'total_pixel(kilo pixel)':
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target_width = math.sqrt(ratio * scale_to_length * 1000)
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target_height = target_width / ratio
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target_width = int(target_width)
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target_height = int(target_height)
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else:
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target_width = orig_width
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target_height = int(target_width / ratio)
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else:
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if scale_to_side == 'longest':
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target_height = scale_to_length
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target_width = int(target_height * ratio)
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elif scale_to_side == 'shortest':
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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elif scale_to_side == 'width':
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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elif scale_to_side == 'height':
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target_height = scale_to_length
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target_width = int(target_height * ratio)
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elif scale_to_side == 'total_pixel(kilo pixel)':
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target_width = math.sqrt(ratio * scale_to_length * 1000)
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target_height = target_width / ratio
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target_width = int(target_width)
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target_height = int(target_height)
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else:
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target_height = orig_height
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target_width = int(target_height * ratio)
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if round_to_multiple != 'None':
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multiple = int(round_to_multiple)
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target_width = num_round_up_to_multiple(target_width, multiple)
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target_height = num_round_up_to_multiple(target_height, multiple)
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_mask = Image.new('L', size=(target_width, target_height), color='black')
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_image = Image.new('RGB', size=(target_width, target_height), color='black')
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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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if len(orig_images) > 0:
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for i in orig_images:
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_image = tensor2pil(i).convert('RGB')
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_image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler, background_color)
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ret_images.append(pil2tensor(_image))
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if len(orig_masks) > 0:
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for m in orig_masks:
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_mask = tensor2pil(m).convert('L')
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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"{self.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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elif len(ret_images) > 0 and len(ret_masks) == 0:
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log(f"{self.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], target_width, target_height,)
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elif len(ret_images) == 0 and len(ret_masks) > 0:
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log(f"{self.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], target_width, target_height,)
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else:
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log(f"Error: {self.NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
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return (None, None, None, 0, 0,)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageScaleByAspectRatio V2": ImageScaleByAspectRatioV2
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageScaleByAspectRatio V2": "LayerUtility: ImageScaleByAspectRatio V2"
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} |