直方图均衡化、边缘增强的代码

以下是使用Python和OpenCV库实现直方图均衡化和边缘增强的代码示例：

直方图均衡化：

import cv2

def histogram_equalization(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Apply histogram equalization
    equalized = cv2.equalizeHist(gray)
    
    return equalized

边缘增强：

import cv2
import numpy as np

def edge_enhancement(image):
    # Convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(gray, (3, 3), 0)
    
    # Apply Sobel operator to detect edges
    sobelx = cv2.Sobel(blurred, cv2.CV_64F, 1, 0, ksize=3)
    sobely = cv2.Sobel(blurred, cv2.CV_64F, 0, 1, ksize=3)
    
    # Calculate gradient magnitude
    gradient_magnitude = np.sqrt(sobelx**2 + sobely**2)
    
    # Normalize gradient magnitude to 0-255 range
    normalized_gradient = cv2.normalize(gradient_magnitude, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
    
    return normalized_gradient

你可以将以上代码复制到你的Python环境中，并传入你的图像进行测试。记得在运行代码之前，确保你已经安装了OpenCV库。