| OpenCV 4.3.0 Open Source Computer Vision

Quasi dense Stereo

## Goal

In this tutorial you will learn how to

- Configure a QuasiDenseStero object
- Compute dense Stereo correspondences.

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <opencv2/stereo.hpp>
using namespace cv;
using namespace std;
int main()
{
cv::Mat rightImg, leftImg;
leftImg = imread("./imgLeft.png", IMREAD_COLOR);
rightImg = imread("./imgRight.png", IMREAD_COLOR);
cv::Size frameSize = leftImg.size();
Ptr<stereo::QuasiDenseStereo> stereo = stereo::QuasiDenseStereo::create(frameSize);
stereo->process(leftImg, rightImg);
uint8_t displvl = 80;
cv::Mat disp;
disp = stereo->getDisparity(displvl);
cv::namedWindow("disparity map");
cv::imshow("disparity map", disp);
cv::namedWindow("right channel");
cv::namedWindow("left channel");
cv::imshow("left channel", leftImg);
cv::imshow("right channel", rightImg);
vector<stereo::Match> matches;
stereo->getDenseMatches(matches);
std::ofstream dense("./dense.txt", std::ios::out);
for (uint i=0; i< matches.size(); i++)
{
dense << matches[i].p0 << matches[i].p1 << endl;
}
dense.close();
cv::waitKey(0);
return 0;
}

## Explanation:

The program loads a stereo image pair.

After importing the images.

cv::Mat rightImg, leftImg;
leftImg = imread("./imgLeft.png", IMREAD_COLOR);
rightImg = imread("./imgRight.png", IMREAD_COLOR);

We need to know the frame size of a single image, in order to create an instance of a `QuasiDesnseStereo` object.

cv::Size frameSize = leftImg.size();
Ptr<stereo::QuasiDenseStereo> stereo = stereo::QuasiDenseStereo::create(frameSize);

Because we didn't specify the second argument in the constructor, the `QuasiDesnseStereo` object will load default parameters.

We can then pass the imported stereo images in the process method like this

stereo->process(leftImg, rightImg);

The process method contains most of the functionality of the class and does two main things.

- Computes a sparse stereo based in "Good Features to Track" and "pyramidal Lucas-Kanade" flow algorithm
- Based on those sparse stereo points, densifies the stereo correspondences using Quasi Dense Stereo method.

After the execution of `process()` we can display the disparity Image of the stereo.

uint8_t displvl = 80;
cv::Mat disp;
disp = stereo->getDisparity(displvl);
cv::namedWindow("disparity map");
cv::imshow("disparity map", disp);

At this point we can also extract all the corresponding points using `getDenseMatches()` method and export them in a file.

vector<stereo::Match> matches;
stereo->getDenseMatches(matches);
std::ofstream dense("./dense.txt", std::ios::out);
for (uint i=0; i< matches.size(); i++)
{
dense << matches[i].p0 << matches[i].p1 << endl;
}
dense.close();