import cv2 import numpy as np from PIL import Image import torch 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)) def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0): if ksize % 2 == 0: ksize += 1 # ksize must be odd blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX) return blurred_image def apply_guided_filter(image_np, radius, eps): # Convert image to float32 for the guided filter image_np_float = np.float32(image_np) / 255.0 # Apply the guided filter filtered_image = cv2.ximgproc.guidedFilter(image_np_float, image_np_float, radius, eps) # Scale back to uint8 filtered_image = np.clip(filtered_image * 255, 0, 255).astype(np.uint8) return filtered_image class TTPlanet_Tile_Preprocessor_GF: def __init__(self, blur_strength=3.0, radius=7, eps=0.01): self.blur_strength = blur_strength self.radius = radius self.eps = eps @classmethod def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), "scale_factor": ("FLOAT", {"default": 1.00, "min": 1.00, "max": 8.00, "step": 0.05}), "blur_strength": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 10.0, "step": 0.1}), "radius": ("INT", {"default": 7, "min": 1, "max": 20, "step": 1}), "eps": ("FLOAT", {"default": 0.01, "min": 0.001, "max": 0.1, "step": 0.001}), }, "optional": {} } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image_output",) FUNCTION = 'process_image' CATEGORY = 'TTP_TILE' def process_image(self, image, scale_factor, blur_strength, radius, eps): ret_images = [] for i in image: # Convert tensor to PIL for processing _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR # Apply Gaussian blur img_np = apply_gaussian_blur(img_np, ksize=int(blur_strength), sigmaX=blur_strength / 2) # Apply Guided Filter img_np = apply_guided_filter(img_np, radius, eps) # Resize image height, width = img_np.shape[:2] new_width = int(width / scale_factor) new_height = int(height / scale_factor) resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) 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]) # BGR to RGB tensor_img = pil2tensor(pil_img) ret_images.append(tensor_img) return (torch.cat(ret_images, dim=0),) class TTPlanet_Tile_Preprocessor_Simple: def __init__(self, blur_strength=3.0): self.blur_strength = blur_strength @classmethod def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), "scale_factor": ("FLOAT", {"default": 2.00, "min": 1.00, "max": 8.00, "step": 0.05}), "blur_strength": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 20.0, "step": 0.1}), }, "optional": {} } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image_output",) FUNCTION = 'process_image' CATEGORY = 'TTP_TILE' def process_image(self, image, scale_factor, blur_strength): ret_images = [] for i in image: # Convert tensor to PIL for processing _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') # Convert PIL image to OpenCV format img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR # Resize image first if you want blur to apply after resizing height, width = img_np.shape[:2] new_width = int(width / scale_factor) new_height = int(height / scale_factor) resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LANCZOS4) # Apply Gaussian blur after resizing img_np = apply_gaussian_blur(resized_img, ksize=int(blur_strength), sigmaX=blur_strength / 2) # Convert OpenCV back to PIL and then to tensor _canvas = Image.fromarray(img_np[:, :, ::-1]) # BGR to RGB tensor_img = pil2tensor(_canvas) ret_images.append(tensor_img) return (torch.cat(ret_images, dim=0),) class TTPlanet_Tile_Preprocessor_cufoff: def __init__(self, blur_strength=3.0, cutoff_frequency=30, filter_strength=1.0): self.blur_strength = blur_strength self.cutoff_frequency = cutoff_frequency self.filter_strength = filter_strength @classmethod def INPUT_TYPES(cls): return { "required": { "image": ("IMAGE",), "scale_factor": ("FLOAT", {"default": 1.00, "min": 1.00, "max": 8.00, "step": 0.05}), "blur_strength": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 10.0, "step": 0.1}), "cutoff_frequency": ("INT", {"default": 100, "min": 0, "max": 256, "step": 1}), "filter_strength": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}), }, "optional": {} } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image_output",) FUNCTION = 'process_image' CATEGORY = 'TTP_TILE' def process_image(self, image, scale_factor, blur_strength, cutoff_frequency, filter_strength): ret_images = [] for i in image: # Convert tensor to PIL for processing _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR # Apply low pass filter with new strength parameter img_np = apply_low_pass_filter(img_np, cutoff_frequency, filter_strength) # Resize image height, width = img_np.shape[:2] new_width = int(width / scale_factor) new_height = int(height / scale_factor) resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LANCZOS4) # Apply Gaussian blur img_np = apply_gaussian_blur(img_np, ksize=int(blur_strength), sigmaX=blur_strength / 2) # Convert OpenCV back to PIL and then to tensor pil_img = Image.fromarray(resized_img[:, :, ::-1]) # BGR to RGB tensor_img = pil2tensor(pil_img) ret_images.append(tensor_img) return (torch.cat(ret_images, dim=0),) NODE_CLASS_MAPPINGS = { "TTPlanet_Tile_Preprocessor_GF": TTPlanet_Tile_Preprocessor_GF, "TTPlanet_Tile_Preprocessor_Simple": TTPlanet_Tile_Preprocessor_Simple, "TTPlanet_Tile_Preprocessor_cufoff": TTPlanet_Tile_Preprocessor_cufoff } NODE_DISPLAY_NAME_MAPPINGS = { "TTPlanet_Tile_Preprocessor_GF": "🪐TTPlanet Tile Preprocessor GF", "TTPlanet_Tile_Preprocessor_Simple": "🪐TTPlanet Tile Preprocessor Simple", "TTPlanet_Tile_Preprocessor_cufoff": "🪐TTPlanet Tile Preprocessor cufoff" }