Goal
In this tutorial you will learn how to
- Configure a QuasiDenseStero object
- Compute dense Stereo correspondences.
#include <fstream>
int main()
{
stereo->process(leftImg, rightImg);
disp =
stereo->getDisparity();
vector<stereo::MatchQuasiDense> 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();
return 0;
}
n-dimensional dense array class
Definition: mat.hpp:811
MatSize size
Definition: mat.hpp:2138
Template class for specifying the size of an image or rectangle.
Definition: types.hpp:335
std::shared_ptr< _Tp > Ptr
Definition: cvstd_wrapper.hpp:23
uint32_t uint
Definition: interface.h:42
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
int waitKey(int delay=0)
Waits for a pressed key.
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
@ IMREAD_COLOR
If set, always convert image to the 3 channel BGR color image.
Definition: imgcodecs.hpp:72
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR)
Loads an image from a file.
GMat stereo(const GMat &left, const GMat &right, const StereoOutputFormat of=StereoOutputFormat::DEPTH_FLOAT32)
Computes disparity/depth map for the specified stereo-pair. The function computes disparity or depth ...
"black box" representation of the file storage associated with a file on disk.
Definition: core.hpp:106
Explanation:
The program loads a stereo image pair.
After importing the images.
We need to know the frame size of a single image, in order to create an instance of a QuasiDesnseStereo
object.
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.
disp =
stereo->getDisparity();
At this point we can also extract all the corresponding points using getDenseMatches()
method and export them in a file.
vector<stereo::MatchQuasiDense> 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();