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draughtsdetector.cpp
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draughtsdetector.cpp
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#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <map>
#include <numeric>
using namespace cv;
using namespace std;
void add_line(Vec2f l, Mat cdst) {
float rho = l[0], theta = l[1];
Point pt1, pt2;
double a = cos(theta), b = sin(theta);
double x0 = a * rho, y0 = b * rho;
pt1.x = cvRound(x0 + 1000 * (-b));
pt1.y = cvRound(y0 + 1000 * (a));
pt2.x = cvRound(x0 - 1000 * (-b));
pt2.y = cvRound(y0 - 1000 * (a));
line(cdst, pt1, pt2, Scalar(0, 0, 255), 1, LINE_AA);
}
bool compare_lines_r(Vec2f l1, Vec2f l2) {
return (l1[0] < l2[0]);
}
bool compare_lines_theta(Vec2f l1, Vec2f l2) {
return (l1[1] < l2[1]);
}
vector<vector<Vec2f>> theta_hist(vector<Vec2f> lines, int res) {
vector<vector<Vec2f>> hist = vector<vector<Vec2f>>(res);
float step = CV_PI / (float)res;
for (size_t ii = 0; ii < lines.size(); ii++)
{
float theta = lines[ii][1];
int index = floor(theta / step);
hist[index].push_back(lines[ii]);
}
return hist;
}
float sum_theta(float sum, Vec2f l1) {
return sum + l1[1];
}
Vec2f sum_line(Vec2f sum, Vec2f l1) {
return sum + l1;
}
vector<vector<Vec2f>> main_directions(vector<Vec2f> lines) {
int res = 16;
vector<vector<Vec2f>> hist = theta_hist(lines, res);
vector<vector<Vec2f>> main_directions;
for (auto batch : hist) {
if (batch.size() > 0.7 * lines.size() / res) {
main_directions.push_back(batch);
}
}
return main_directions;
}
array<vector<Vec2f>, 2> cluster_directions(vector<vector<Vec2f>> lines_by_dir) {
int n_it = 5;
vector<float> average_thetas;
for (auto batch : lines_by_dir) {
average_thetas.push_back(accumulate(batch.begin(), batch.end(), 0.0, sum_theta) / batch.size());
}
float average_dir1 = 0, average_dir2 = CV_PI / 2;
float next_average_dir1 = 0, next_average_dir2 = 0;
int count_dir1 = 0, count_dir2 = 0;
for (int ii = 0; ii < n_it; ii++) {
next_average_dir1 = 0; next_average_dir2 = 0;
count_dir1 = 0; count_dir2 = 0;
for (float theta : average_thetas) {
if (theta > CV_PI / 2) theta = CV_PI - theta;
if (abs(theta - average_dir1) < abs(theta - average_dir2)) {
count_dir1++;
next_average_dir1 += theta;
}
else {
count_dir2++;
next_average_dir2 += theta;
}
}
next_average_dir1 /= count_dir1;
next_average_dir2 /= count_dir2;
average_dir1 = next_average_dir1;
average_dir2 = next_average_dir2;
}
array<vector<Vec2f>, 2> clusters;
for (int ii = 0; ii < average_thetas.size(); ii++) {
float theta = (average_thetas[ii] > CV_PI / 2) ? CV_PI - average_thetas[ii] : average_thetas[ii];
if (abs(theta - average_dir1) < abs(theta - average_dir2)) {
clusters[0].insert(clusters[0].end(), lines_by_dir[ii].begin(), lines_by_dir[ii].end());
}
else {
clusters[1].insert(clusters[1].end(), lines_by_dir[ii].begin(), lines_by_dir[ii].end());
}
}
return clusters;
}
void print_lines(vector<Vec2f> lines) {
cout << ">>> printing lines ..." << endl;
for (size_t ii = 0; ii < lines.size(); ii++) {
std::cout << lines[ii] << std::endl;
};
cout << endl;
}
void print_hist(vector<vector<Vec2f>> hist) {
for (size_t ii = 0; ii < hist.size(); ii++) {
cout << " ----------------------- " << endl;
for (size_t jj = 0; jj < hist[ii].size(); jj++) {
std::cout << hist[ii][jj] << std::endl;
};
};
}
vector<Vec2f> get_lines(const Mat& dst) {
vector<Vec2f> lines;
int thres = 80; //number of intersections in the houghline curve
int minLength = 5; //min length for segments to be detected
int maxGapSegments = 0.3 * dst.rows; //max lenght to join segments
HoughLines(dst, lines, 1, CV_PI / 360, thres, minLength, maxGapSegments);
return lines;
}
void draw_lines(vector<Vec2f> lines, const Mat& cdst) {
for (size_t ii = 0; ii < lines.size(); ii++) {
add_line(lines[ii], cdst);
};
imshow("detected lines", cdst);
}
void draw_batches(vector<vector<Vec2f>> batches, const Mat& cdst) {
for (auto batch : batches) {
for (auto line : batch) {
add_line(line, cdst);
};
};
imshow("detected lines", cdst);
}
vector<vector<Vec2f>> r_hist(vector<Vec2f> lines, int res, float max_r) {
vector<vector<Vec2f>> hist = vector<vector<Vec2f>>(res);
float step = 2 * max_r / (float)res;
for (size_t ii = 0; ii < lines.size(); ii++)
{
float r = lines[ii][0];
int index = floor((max_r + r) / step);
hist[index].push_back(lines[ii]);
}
return hist;
}
vector<vector<Vec2f>> main_rs(vector<Vec2f> lines, float max_r) {
int res = 100;
vector<vector<Vec2f>> hist = r_hist(lines, res, max_r);
vector<vector<Vec2f>> main_rs;
for (auto batch : hist) {
if (batch.size() > 0.7 * lines.size() / res) {
main_rs.push_back(batch);
}
}
return main_rs;
}
vector<Vec2f> average_lines(vector<vector<Vec2f>> hist) {
vector<Vec2f> average_lines;
for (auto batch : hist) {
Vec2f acc_line = accumulate(batch.begin(), batch.end(), Vec2f(0, 0), sum_line);
acc_line[0] /= batch.size();
acc_line[1] /= batch.size();
average_lines.push_back(acc_line);
}
return average_lines;
}
array<Point2f, 4> quadrant(array<vector<Vec2f>, 2> batches) {
}
int main()
{
Mat src;
src = imread("../data/draught1.jpg");
resize(src, src, Size(800, 800));
imshow("source", src);
Mat src_gray, dst, cdst;
cvtColor(src, src_gray, COLOR_BGR2GRAY);
GaussianBlur(src_gray, src_gray, Size(11, 11), 0);
Canny(src_gray, dst, 50, 100, 3);
cvtColor(dst, cdst, 0);
vector<Vec2f> lines = get_lines(dst);
float max_r = sqrt(src.rows * src.rows + src.cols * src.cols);
vector<Vec2f> real_lines;
for (auto line : lines) {
if (abs(line[0]) < max_r) real_lines.push_back(line);
}
lines = real_lines;
cout << ">>> computing main direction batches..." << endl;
//draw_lines(lines, cdst);
vector<vector<Vec2f>> lines_by_dir = main_directions(lines);
array<vector<Vec2f>, 2> clusters_dir = cluster_directions(lines_by_dir);
//draw_lines(clusters_dir[0], cdst);
cout << ">>> computing main rs batches..." << endl;
for (int ii = 0; ii < 2; ii++) {
vector<vector<Vec2f>> lines_by_r = main_rs(clusters_dir[ii], max_r);
clusters_dir[ii] = average_lines(lines_by_r);
}
draw_lines(clusters_dir[0], cdst);
draw_lines(clusters_dir[1], cdst);
//print_hist(lines_by_dir);
//draw_batches(lines_by_dir, cdst);
waitKey();
return 0;
}