Python代码实现Snake算法及C++版本

Python代码实现pythonimport cv2import numpy as np

def snake(img, x, y, alpha=0.001, beta=0.4, gamma=100, sigma=20, iterations=500): ''' Snake算法实现图像分割

参数    ------    img : ndarray        输入图像

x,y : ndarry        初始轮廓的X坐标和Y坐标    alpha,beta: number        内部能量参数    gamma : number        控制外部能量的参数    sigma : number        标准差    iterations : number        迭代次数    '''    # 计算矩阵    N = np.size(x)    a = gamma * (2 * alpha + 6 * beta) + 1    b = gamma * (-alpha - 4 * beta)    c = gamma * beta    p = np.zeros((N, N), dtype=np.float)    p[0] = np.c_[a, b, c, np.zeros((1, N - 5)), c, b]    for i in range(N):        p[i] = np.roll(p[0], i)    p = np.linalg.inv(p)    # 对图像进行滤波    smoothed = cv2.GaussianBlur((img - img.min()) / (img.max() - img.min()), (89, 89), sigma)    giy, gix = np.gradient(smoothed)    gmi = (gix**2 + giy**2)**0.5    gmi = (gmi - gmi.min()) / (gmi.max() - gmi.min())    Iy, Ix = np.gradient(gmi)

# 防止曲率演化到图像外部    def fmax(x, y):        x[x < 0] = 0        y[y < 0] = 0        x[x > img.shape[1] - 1] = img.shape[1] - 1        y[y > img.shape[0] - 1] = img.shape[0] - 1        return y.round().astype(int), x.round().astype(int)

for i in range(iterations):        fex = Ix[fmax(x, y)]        fey = Iy[fmax(x, y)]        x = np.dot(p, x + gamma * fex)        y = np.dot(p, y + gamma * fey)    return x, y

C++代码实现cpp#include #include #include #include <opencv2/opencv.hpp>

cv::Mat snake(const cv::Mat& img, std::vector& x, std::vector& y, double alpha = 0.001, double beta = 0.4, double gamma = 100, double sigma = 20, int iterations = 500) { // 计算矩阵 int N = x.size(); double a = gamma * (2 * alpha + 6 * beta) + 1; double b = gamma * (-alpha - 4 * beta); double c = gamma * beta; cv::Mat p = cv::Mat::zeros(N, N, CV_64F); p.at(0, 0) = a; p.at(0, 1) = b; p.at(0, 2) = c; p.at(0, N - 3) = c; p.at(0, N - 2) = b; p.at(0, N - 1) = 0; for (int i = 1; i < N; i++) { p.row(i) = p.row(i - 1).clone(); cv::Mat temp = p.row(i).clone(); cv::rotate(temp, temp, cv::ROTATE_LEFT); p.row(i) = temp; } p = p.inv();

// 对图像进行滤波    cv::Mat smoothed;    cv::GaussianBlur((img - img.min()) / (img.max() - img.min()), smoothed, cv::Size(89, 89), sigma);    cv::Mat giy, gix;    cv::Sobel(smoothed, gix, CV_64F, 1, 0);    cv::Sobel(smoothed, giy, CV_64F, 0, 1);    cv::Mat gmi;    cv::magnitude(gix, giy, gmi);    gmi = (gmi - gmi.min()) / (gmi.max() - gmi.min());    cv::Mat Iy, Ix;    cv::Sobel(gmi, Ix, CV_64F, 1, 0);    cv::Sobel(gmi, Iy, CV_64F, 0, 1);

// 防止曲率发散到图像外部    auto fmax = [](double& x, double& y, int rows, int cols) {        if (x < 0) x = 0;        if (y < 0) y = 0;        if (x > cols - 1) x = cols - 1;        if (y > rows - 1) y = rows - 1;    };    int rows = img.rows;    int cols = img.cols;    for (int i = 0; i < iterations; i++) {        for (int j = 0; j < N; j++) {            int r = std::round(y[j]);            int c = std::round(x[j]);            fmax(x[j], y[j], rows, cols);            double fex = Ix.at<double>(r, c);            double fey = Iy.at<double>(r, c);            cv::Mat tempX = p * (x + gamma * fex);            cv::Mat tempY = p * (y + gamma * fey);            x = tempX;            y = tempY;        }    }

return img;}

int main() { // 读取图像 cv::Mat img = cv::imread('input_image.jpg', cv::IMREAD_GRAYSCALE);

// 设置初始轮廓    std::vector<double> x;    std::vector<double> y;

// 调用Snake轮廓提取函数    cv::Mat result = snake(img, x, y);

return 0;}

注意:

以上代码仅提供算法的基本结构，具体实现需要根据您的具体需求进行调整。* 您需要将图像文件路径 'input_image.jpg' 替换为您自己的图像文件路径。

希望以上示例能为您提供帮助！如有任何问题，请随时提问。