In this tutorial, you will learn how to use the phase unwrapping module to unwrap two-dimensional phase maps. The implementation is based on @cite histogramUnwrapping.
Code
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@include phase_unwrapping/samples/unwrap.cpp
Explanation
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To use this example, wrapped phase map values should be stored in a yml file as CV_32FC1 Mat, under the name "phaseValues". Path to the data and a name to save the unwrapped phase map must be set in the command line. The results are saved with floating point precision in a yml file and as an 8-bit image for visualization purpose.
Some parameters can be chosen by the user:
- histThresh is a parameter used to divide the histogram in two parts. Bins before histThresh are smaller than the ones after histThresh. (Default value is 3*pi*pi).
- nbrOfSmallBins is the number of bins between 0 and histThresh. (Default value is 10).
- nbrOfLargeBins is the number of bins between histThresh and 32*pi*pi. (Default value is 5).
The wrapped phase map is unwrapped and the result is saved in a yml file. We can also get the reliabilities map for visualization purpose. The unwrapped phase map and the reliabilities map are converted to 8-bit images in order to be saved as png files.
In this tutorial, you will learn how to use the sinusoidal pattern class to:
- Generate sinusoidal patterns.
- Project the generated patterns.
- Capture the projected patterns.
- Compute a wrapped phase map from these patterns using three different algorithms (Fourier Transform Profilometry, Phase Shifting Profilometry, Fourier-assisted Phase Shifting Profilometry)
The number of patterns is always equal to three, no matter the method used to compute the phase map. Those three patterns are projected in a loop which is fine since the system is cyclical.
Once the patterns have been generated, the camera is opened and the patterns are projected, using fullscreen resolution. In this tutorial, a prosilica camera is used to capture gray images. When the first pattern is displayed by the projector, the user can press any key to start the projection sequence.
In this tutorial, 30 images are projected so, each of the three patterns is projected ten times.
The "while" loop takes care of the projection process. The captured images are stored in a vector of Mat. There is a 30 ms delay between two successive captures.
When the projection is done, the user has to press "Enter" to start computing the phase maps.
@code{.cpp}
int nbrOfImages = 30;
int count = 0;
vector<Mat> img(nbrOfImages);
Size camSize(-1, -1);
while( count < nbrOfImages )
{
for(int i = 0; i < (int)patterns.size(); ++i )
{
imshow("pattern", patterns[i]);
waitKey(30);
cap >> img[count];
count += 1;
}
}
cout << "press enter when ready" << endl;
bool loop = true;
while ( loop )
{
char c = waitKey(0);
if( c == 10 )
{
loop = false;
}
}
@endcode
The phase maps are ready to be computed according to the selected method.
For FTP, a phase map is computed for each projected pattern, but we need to compute the shadow mask from three successive patterns, as explained in @cite faps. Therefore, three patterns are set in a vector called captures. Care is taken to fill this vector with three patterns, especially when we reach the last captures. The unwrapping algorithm needs to know the size of the captured images so, we make sure to give it to the "unwrapPhaseMap" method.
The phase maps are converted to 8-bit images in order to save them as png.
@code{.cpp}
switch(params.methodId)
{
case structured_light::FTP:
for( int i = 0; i < nbrOfImages; ++i )
{
/*We need three images to compute the shadow mask, as described in the reference paper
* even if the phase map is computed from one pattern only
For PSP and FAPS, three projected images are used to compute a single phase map. These three images are set in "captures", a vector working as a FIFO.Here again, phase maps are converted to 8-bit images in order to save them as png.
