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/* Copyright (c) 2012 Patrick Ruoff
*
* Permission to use, copy, modify, and/or distribute this software for any
* purpose with or without fee is hereby granted, provided that the above
* copyright notice and this permission notice appear in all copies.
*/
#include "point_extractor.h"
#include <QDebug>
using namespace cv;
using namespace std;
PointExtractor::PointExtractor(){
//if (!AllocConsole()){}
//else SetConsoleTitle("debug");
//freopen("CON", "w", stdout);
//freopen("CON", "w", stderr);
}
// ----------------------------------------------------------------------------
std::vector<Vec2f> PointExtractor::extract_points(Mat& frame)
{
const int W = frame.cols;
const int H = frame.rows;
// convert to grayscale
Mat frame_gray;
cvtColor(frame, frame_gray, cv::COLOR_RGB2GRAY);
int min_size = s.min_point_size;
int max_size = s.max_point_size;
unsigned int region_size_min = 3.14*min_size*min_size/4.0;
unsigned int region_size_max = 3.14*max_size*max_size/4.0;
// testing indicates threshold difference of 45 from lowest to highest
// that's applicable to poor lighting conditions.
static constexpr int diff = 20;
static constexpr int steps = 10;
static constexpr int successes = 8;
int thres = s.threshold;
struct blob {
double max_radius;
std::vector<cv::Vec2d> pos;
std::vector<double> confids;
cv::Vec2d effective_pos() const
{
double x = 0;
double y = 0;
double norm = 0;
for (unsigned i = 0; i < pos.size(); i++)
{
x += pos[i][0] * confids[i];
y += pos[i][1] * confids[i];
norm += confids[i];
}
cv::Vec2d ret(x, y);
ret *= 1./norm;
//qDebug() << "ret" << ret[0] << ret[1] << "norm" << norm << "count" << pos.size();
return ret;
}
};
struct simple_blob
{
double radius;
cv::Vec2d pos;
double confid;
bool taken;
simple_blob(double radius, const cv::Vec2d& pos, double confid) : radius(radius), pos(pos), confid(confid), taken(false)
{
//qDebug() << "radius" << radius << "pos" << pos[0] << pos[1] << "confid" << confid;
}
bool inside(const simple_blob& other)
{
cv::Vec2d tmp = pos - other.pos;
double p = sqrt(1e-4 + tmp.dot(tmp));
return p < radius;
}
static std::vector<blob> merge(std::vector<simple_blob>& blobs)
{
std::vector<blob> ret;
for (unsigned i = 0; i < blobs.size(); i++)
{
auto& b = blobs[i];
if (b.taken)
continue;
b.taken = true;
blob b_;
b_.pos.push_back(b.pos);
b_.confids.push_back(b.confid);
b_.max_radius = b.radius;
for (unsigned j = i+1; j < blobs.size(); j++)
{
auto& b2 = blobs[j];
if (b2.taken)
continue;
if (b.inside(b2) || b2.inside(b))
{
b2.taken = true;
b_.pos.push_back(b2.pos);
b_.confids.push_back(b2.confid);
b_.max_radius = std::max(b_.max_radius, b2.radius);
}
}
if (b_.pos.size() >= successes)
ret.push_back(b_);
}
return ret;
}
};
// mask for everything that passes the threshold (or: the upper threshold of the hysteresis)
Mat frame_bin = cv::Mat::zeros(H, W, CV_8U);
const int min = std::max(0, thres - diff/2);
const int max = std::min(255, thres + diff/2);
const int step = std::max(1, diff / steps);
std::vector<simple_blob> blobs;
// this code is based on OpenCV SimpleBlobDetector
for (int i = min; i < max; i += step)
{
Mat frame_bin_;
threshold(frame_gray, frame_bin_, i, 255, THRESH_BINARY);
frame_bin.setTo(170, frame_bin_);
std::vector<std::vector<cv::Point>> contours;
cv::findContours(frame_bin_, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
for (auto& c : contours)
{
auto m = cv::moments(cv::Mat(c));
const double area = m.m00;
if (area == 0.)
continue;
cv::Vec2d pos(m.m10 / m.m00, m.m01 / m.m00);
if (area < region_size_min || area > region_size_max)
continue;
double radius = 0;
for (auto& k : c)
{
cv::Vec2d pos_(k.x, k.y);
cv::Vec2d tmp = pos_ - pos;
radius = std::max(radius, sqrt(1e-2 + tmp.dot(tmp)));
}
double confid = 1;
{
double denominator = std::sqrt(std::pow(2 * m.mu11, 2) + std::pow(m.mu20 - m.mu02, 2));
const double eps = 1e-2;
if (denominator > eps)
{
double cosmin = (m.mu20 - m.mu02) / denominator;
double sinmin = 2 * m.mu11 / denominator;
double cosmax = -cosmin;
double sinmax = -sinmin;
double imin = 0.5 * (m.mu20 + m.mu02) - 0.5 * (m.mu20 - m.mu02) * cosmin - m.mu11 * sinmin;
double imax = 0.5 * (m.mu20 + m.mu02) - 0.5 * (m.mu20 - m.mu02) * cosmax - m.mu11 * sinmax;
confid = imin / imax;
}
}
blobs.push_back(simple_blob(radius, pos, confid));
}
}
// clear old points
points.clear();
for (auto& b : simple_blob::merge(blobs))
{
auto pos = b.effective_pos();
Vec2f p((pos[0] - W/2)/W, -(pos[1] - H/2)/W);
points.push_back(p);
}
// draw output image
vector<Mat> channels;
channels.push_back(frame_gray + frame_bin);
channels.push_back(frame_gray - frame_bin);
channels.push_back(frame_gray - frame_bin);
merge(channels, frame);
return points;
}
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