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#include "opencv2/opencv.hpp"
#include <iostream>
#include <vector>
#include <random>
std::tuple<int, int> GetSample(const int& index_size,
const std::vector<std::tuple<int, int>>& sampled_indexes)
{
assert(index_size > 2);
static std::random_device rd;
static std::mt19937_64 gen(rd());
static std::uniform_int_distribution<int> dist(0, index_size - 1);
while (true)
{
int index1 = dist(gen);
int index2 = dist(gen);
if (index1 == index2) { continue; }
int min_index = std::min(index1, index2);
int max_index = std::max(index1, index2);
bool has_sampled = false;
for (auto& [i, j] : sampled_indexes)
{
if (min_index == i && j == max_index)
{
has_sampled = true;
break;
}
}
if (has_sampled) { continue; }
else { return { min_index, max_index }; }
}
}
void Show(const std::vector<cv::Point2f>& all_pts,
const std::vector<cv::Point2f>& inlier_pts,
const std::vector<cv::Point2f>& sample_pts,
const int windows_size,
const double a,
const double b,
const double c)
{
cv::Mat image = cv::Mat(windows_size, windows_size, CV_8UC3, cv::Scalar::all(255));
for (auto& p : all_pts)
{
if (p.x >= 0 && p.x < windows_size && p.y >= 0 && p.y < windows_size)
{
cv::circle(image, p, 5, { 0, 0, 255 }, -1);
}
}
cv::Point2f p1, p2;
if (a * b > 0)
{
p1.x = windows_size - 1;
p1.y = -(a * p1.x + c) / b;
p2.x = 0;
p2.y = -(a * p2.x + c) / b;
}else
{
p1.y = 0;
p1.x = -(b * p1.y + c) / a;
p2.y = windows_size - 1;
p2.x = -(b * p2.y + c) / a;
}
cv::line(image, p1, p2, { 0, 0, 0 }, 2, cv::LINE_AA);
for (auto& p : inlier_pts)
{
if (p.x >= 0 && p.x < windows_size && p.y >= 0 && p.y < windows_size)
{
cv::circle(image, p, 5, { 255, 0, 0 },-1);
}
}
for (auto& p : sample_pts)
{
if (p.x >= 0 && p.x < windows_size && p.y >= 0 && p.y < windows_size)
{
cv::circle(image, p, 5, { 0, 255, 0 }, -1);
}
}
cv::imshow("ransac-line-fit", image);
cv::waitKey(500);
}
std::tuple<double, double, double> LineFit(const std::vector<cv::Point2f>& pts)
{
cv::Vec4f line;
cv::fitLine(pts, line, cv::DIST_L2, 0, 1e-2, 1e-2);
double a = line[1];
double b = -line[0];
double c = line[0] * line[3] - line[1] * line[2];
return { a, b, c };
}
void RansacLinefit(const std::vector<cv::Point2f>& pts,
const float inlier_threshold,
std::vector<int>& inlier_indexes)
{
if (pts.size() <= 3) { return; }
if (inlier_threshold < 1e-6) { return; }
//1. 参数检查，初始化
int iterate_nums = 500; //迭代次数
float sample_points_min_distance = 5.0f; //两个采样点之间最小的距离
std::vector<std::tuple<int, int>> sampled_indexes;//已经采样的坐标点
sampled_indexes.reserve(iterate_nums);
std::vector<int> is_inlier(pts.size(), 0);
std::vector<int> is_inlier_tmp(pts.size(), 0);
int max_inlier_num = 0;
int sample_count = 0;
//2. 循环迭代
while (sample_count < iterate_nums)
{
//3. 随机抽取两点(采样）
auto [p1, p2] = GetSample(pts.size(), sampled_indexes);
if (std::abs(pts[p1].x - pts[p2].x) < sample_points_min_distance
&& std::abs(pts[p1].y - pts[p2].y) < sample_points_min_distance)
{
continue;
}
else
{
sampled_indexes.push_back({ p1, p2 });
}
//4. 直线拟合
auto [a, b, c] = LineFit({ pts[p1], pts[p2] });
//5. 基于拟合的直线区分内外点
int inlier_num = 0;
std::vector<cv::Point2f> inliers;
for (int i = 0; i < pts.size(); i++)
{
auto& p = pts[i];
is_inlier_tmp[i] = 0;
if (std::abs(a * p.x + b * p.y + c) < inlier_threshold)
{
is_inlier_tmp[i] = 1;
inlier_num++;
inliers.push_back(pts[i]);
}
}
Show(pts, inliers, { { pts[p1], pts[p2] } }, 500, a, b, c);
if (inlier_num > max_inlier_num)
{
max_inlier_num = inlier_num;
is_inlier = is_inlier_tmp;
}
//6. 更新迭代的最佳次数
if (inlier_num == 0)
{
iterate_nums = 500;
}
else
{
double epsilon = 1.0 - double(inlier_num) / (double)pts.size(); //野值点比例
double p = 0.99; //所有样本中存在1个好样本的概率
double s = 2.0;
iterate_nums = int(std::log(1.0 - p) / std::log(1.0 - std::pow((1.0 - epsilon), s)));
}
sample_count++;
}
//7. 基于最优的结果所对应的内点做最终拟合
std::vector<cv::Point2f> inliers;
inliers.reserve(max_inlier_num);
for (int i = 0; i < is_inlier.size(); i++)
{
if (1 == is_inlier[i])
{
inliers.push_back(pts[i]);
}
}
auto [a, b, c] = LineFit(inliers);
std::cout << " a = " << a << " b = " << b << " c = " << c << std::endl;
Show(pts, inliers, {}, 500, a, b, c);
cv::waitKey(0);
}
int main()
{
double a = 1.0, b = 2.0, c = -800.0;
std::random_device rd;
std::mt19937_64 gen(rd());
std::uniform_int_distribution<int> dist(0, 499);
std::normal_distribution<double> disty(0, 50);
std::normal_distribution<double> distx(0, 10);
std::vector<cv::Point2f> pts;
for (int i = 1; i < 50; i ++)
{
float x = dist(gen);
float y = -(a * x + c) / b;
if (i % 2 != 0)
{
x += distx(gen);
y += disty(gen);
}
pts.emplace_back(x, y);
}
std::vector<int> inliers_indexes;
RansacLinefit(pts, 10, inliers_indexes);
return 0;
}
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