随机采样一致性拟合

以下是使用C++代码实现基于RANSAC算法拟合圆的例子：

```cpp
#include <iostream>
#include <vector>
#include <cmath>
#include <random>

// Define a structure to represent a 2D point
struct Point {
double x;
double y;
};

// Calculate the distance between two points
double distance(const Point& p1, const Point& p2) {
double dx = p1.x - p2.x;
double dy = p1.y - p2.y;
return std::sqrt(dx * dx + dy * dy);
}

// RANSAC circle fitting algorithm
void ransacCircleFitting(const std::vector<Point>& data, double threshold, int iterations, int minInliers, double& centerX, double& centerY, double& radius) {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> dis(0, data.size() - 1);

centerX = 0.0;
centerY = 0.0;
radius = 0.0;

int maxInliers = 0;

for (int i = 0; i < iterations; i++) {
// Randomly select three points
std::vector<Point> sample;
sample.push_back(data[dis(gen)]);
sample.push_back(data[dis(gen)]);
sample.push_back(data[dis(gen)]);

// Calculate the center and radius of the circle passing through the three points
double x1 = sample[0].x;
double y1 = sample[0].y;
double x2 = sample[1].x;
double y2 = sample[1].y;
double x3 = sample[2].x;
double y3 = sample[2].y;

double D = 2.0 * (x1 * (y2 - y3) - y1 * (x2 - x3) + x2 * y3 - x3 * y2);
double centerXSample = ((x1 * x1 + y1 * y1) * (y2 - y3) + (x2 * x2 + y2 * y2) * (y3 - y1) + (x3 * x3 + y3 * y3) * (y1 - y2)) / D;
double centerYSample = ((x1 * x1 + y1 * y1) * (x3 - x2) + (x2 * x2 + y2 * y2) * (x1 - x3) + (x3 * x3 + y3 * y3) * (x2 - x1)) / D;
double radiusSample = std::sqrt((x1 - centerXSample) * (x1 - centerXSample) + (y1 - centerYSample) * (y1 - centerYSample));

// Count the number of inliers
int inliers = 0;
for (const auto& point : data) {
if (std::abs(distance(point, {centerXSample, centerYSample}) - radiusSample) < threshold) {
inliers++;
}
}

// Update the best circle parameters
if (inliers > maxInliers && inliers >= minInliers) {
maxInliers = inliers;
centerX = centerXSample;
centerY = centerYSample;
radius = radiusSample;
}
}
}

int main() {
// Generate some synthetic data points
std::vector<Point> data;
double centerX = 5.0;
double centerY = 5.0;
double radius = 2.0;
double noiseStdDev = 0.1;
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<> dis(0, noiseStdDev);

for (double angle = 0.0; angle <= 2.0 * M_PI; angle += 0.1) {
double x = centerX + radius * std::cos(angle) + dis(gen);
double y = centerY + radius * std::sin(angle) + dis(gen);
data.push_back({x, y});
}

// Apply RANSAC circle fitting algorithm
double threshold = 0.15; // Distance threshold for inliers
int iterations = 1000; // Number of RANSAC iterations
int minInliers = 10; // Minimum number of inliers required for a valid circle fit
double ransacCenterX, ransacCenterY, ransacRadius;
ransacCircleFitting(data, threshold, iterations, minInliers, ransacCenterX, ransacCenterY, ransacRadius);

std::cout << "True circle center: (" << centerX << ", " << centerY << ")" << std::endl;
std::cout << "True circle radius: " << radius << std::endl;
std::cout << "RANSAC circle center: (" << ransacCenterX << ", " << ransacCenterY << ")" << std::endl;
std::cout << "RANSAC circle radius: " << ransacRadius << std::endl;

return 0;
}
```

此代码示例生成一些合成数据点，然后使用RANSAC算法对这些数据点进行圆形拟合。最终输出真实圆形的中心坐标和半径，以及使用RANSAC算法拟合的圆形的中心坐标和半径。

请注意，此代码示例中提供的参数（阈值、迭代次数和最小内点数量等）仅作为示例。在实际使用时，您可能需要根据具体情况进行调整。同时，为了简洁起见，此示例代码没有进行输入验证和错误处理，您在实际应用中可能需要添加这些功能。