Kernel Photo Repair Crack Direct

# Detect cracks crack_detection = np.zeros(image.shape[:2]) for i in range(image.shape[0]): for j in range(image.shape[1]): patch = image[max(0, i-3):min(image.shape[0], i+4), max(0, j-3):min(image.shape[1], j+4)] crack_features = np.array([gaussian_kernel(np.array([i, j]), np.array([x, y]), sigma=1.0) for x, y in patch]) crack_detection[i, j] = np.mean(crack_features)

# Preprocess image image = np.float32(image) / 255.0

# Repair cracks kr = KernelRidge(kernel='rbf', alpha=0.1) valid_mask = np.logical_not(crack_mask) kr.fit(np.where(valid_mask, image, 0).reshape(-1, 1), np.where(valid_mask, image, 0).reshape(-1)) repaired_image = kr.predict(np.where(crack_mask, image, 0).reshape(-1, 1)).reshape(image.shape) kernel photo repair crack

The KPR feature aims to detect and repair cracks in images using advanced kernel-based algorithms. This feature can be integrated into image editing software, allowing users to effortlessly remove unwanted cracks from their photos.

Kernel Photo Repair (KPR) - Crack Detection and Repair # Detect cracks crack_detection = np

import numpy as np from sklearn.kernel_ridge import KernelRidge from sklearn.metrics import mean_squared_error

def kernel_photo_repair(image, crack_mask): # Define kernel functions def gaussian_kernel(x, y, sigma=1.0): return np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sigma ** 2)) j+4)] crack_features = np.array([gaussian_kernel(np.array([i

def laplacian_kernel(x, y, sigma=1.0): return -np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sigma ** 2))