import cv2 import numpy as np import torch from PIL import Image NODE_NAME = 'TTPlanet_Tile_Preprocessor' # 图像转换函数 def pil2tensor(image: Image) -> torch.Tensor: return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) def tensor2pil(t_image: torch.Tensor) -> Image: return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) class TTPlanet_Tile_Preprocessor: def __init__(self): pass @classmethod def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), # 输入的tensor图像 "scale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 8.0, "step": 0.1}), # 缩放因子 }, "optional": {} } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image_output",) FUNCTION = 'process_image' CATEGORY = 'TTP_TILE' def process_image(self, image, scale_factor): ret_images = [] for i in image: # Convert tensor to PIL for processing _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') # Convert PIL to OpenCV format img_np = np.array(_canvas)[:, :, ::-1] # 获取原始尺寸 height, width = img_np.shape[:2] # 计算新尺寸 new_width = int(width / scale_factor) new_height = int(height / scale_factor) # 1. 使用cv2.INTER_AREA方法缩小图像 resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) # 2. 使用linear方法放大回原尺寸 resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_CUBIC) # Convert OpenCV back to PIL and then to tensor pil_img = Image.fromarray(resized_img[:, :, ::-1]) tensor_img = pil2tensor(pil_img) ret_images.append(tensor_img) return (torch.cat(ret_images, dim=0),) NODE_CLASS_MAPPINGS = { "Image Processing: TTPlanet_Tile_Preprocessor": TTPlanet_Tile_Preprocessor } NODE_DISPLAY_NAME_MAPPINGS = { "Image Processing: TTPlanet_Tile_Preprocessor": "TTPlanet Tile Preprocessor" }