OpenCV  4.10.0
Open Source Computer Vision
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Capture Sinusoidal pattern tutorial

Goal

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)
  • Unwrap the previous phase map.

Code

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#include <vector>
#include <iostream>
#include <fstream>
#include <opencv2/core.hpp>
using namespace cv;
using namespace std;
static const char* keys =
{
"{@width | | Projector width}"
"{@height | | Projector height}"
"{@periods | | Number of periods}"
"{@setMarkers | | Patterns with or without markers}"
"{@horizontal | | Patterns are horizontal}"
"{@methodId | | Method to be used}"
"{@outputPatternPath | | Path to save patterns}"
"{@outputWrappedPhasePath | | Path to save wrapped phase map}"
"{@outputUnwrappedPhasePath | | Path to save unwrapped phase map}"
"{@outputCapturePath | | Path to save the captures}"
"{@reliabilitiesPath | | Path to save reliabilities}"
};
static void help()
{
cout << "\nThis example generates sinusoidal patterns" << endl;
cout << "To call: ./example_structured_light_createsinuspattern <width> <height>"
" <number_of_period> <set_marker>(bool) <horizontal_patterns>(bool) <method_id>"
" <output_captures_path> <output_pattern_path>(optional) <output_wrapped_phase_path> (optional)"
" <output_unwrapped_phase_path>" << endl;
}
int main(int argc, char **argv)
{
if( argc < 2 )
{
help();
return -1;
}
// Retrieve parameters written in the command line
CommandLineParser parser(argc, argv, keys);
params.width = parser.get<int>(0);
params.height = parser.get<int>(1);
params.nbrOfPeriods = parser.get<int>(2);
params.setMarkers = parser.get<bool>(3);
params.horizontal = parser.get<bool>(4);
params.methodId = parser.get<int>(5);
String outputCapturePath = parser.get<String>(6);
params.shiftValue = static_cast<float>(2 * CV_PI / 3);
params.nbrOfPixelsBetweenMarkers = 70;
String outputPatternPath = parser.get<String>(7);
String outputWrappedPhasePath = parser.get<String>(8);
String outputUnwrappedPhasePath = parser.get<String>(9);
String reliabilitiesPath = parser.get<String>(10);
structured_light::SinusoidalPattern::create(makePtr<structured_light::SinusoidalPattern::Params>(params));
vector<Mat> patterns;
Mat shadowMask;
Mat unwrappedPhaseMap, unwrappedPhaseMap8;
Mat wrappedPhaseMap, wrappedPhaseMap8;
//Generate sinusoidal patterns
sinus->generate(patterns);
VideoCapture cap(CAP_PVAPI);
if( !cap.isOpened() )
{
cout << "Camera could not be opened" << endl;
return -1;
}
cap.set(CAP_PROP_PVAPI_PIXELFORMAT, CAP_PVAPI_PIXELFORMAT_MONO8);
namedWindow("pattern", WINDOW_NORMAL);
setWindowProperty("pattern", WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
imshow("pattern", patterns[0]);
cout << "Press any key when ready" << endl;
waitKey(0);
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(300);
cap >> img[count];
count += 1;
}
}
cout << "press enter when ready" << endl;
bool loop = true;
while ( loop )
{
char c = (char) waitKey(0);
if( c == 10 )
{
loop = false;
}
}
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
*/
vector<Mat> captures;
if( i == nbrOfImages - 2 )
{
captures.push_back(img[i]);
captures.push_back(img[i-1]);
captures.push_back(img[i+1]);
}
else if( i == nbrOfImages - 1 )
{
captures.push_back(img[i]);
captures.push_back(img[i-1]);
captures.push_back(img[i-2]);
}
else
{
captures.push_back(img[i]);
captures.push_back(img[i+1]);
captures.push_back(img[i+2]);
}
sinus->computePhaseMap(captures, wrappedPhaseMap, shadowMask);
if( camSize.height == -1 )
{
camSize.height = img[i].rows;
camSize.width = img[i].cols;
paramsUnwrapping.height = camSize.height;
paramsUnwrapping.width = camSize.width;
phaseUnwrapping =
phase_unwrapping::HistogramPhaseUnwrapping::create(paramsUnwrapping);
}
sinus->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, camSize, shadowMask);
phaseUnwrapping->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, shadowMask);
Mat reliabilities, reliabilities8;
phaseUnwrapping->getInverseReliabilityMap(reliabilities);
reliabilities.convertTo(reliabilities8, CV_8U, 255,128);
ostringstream tt;
tt << i;
imwrite(reliabilitiesPath + tt.str() + ".png", reliabilities8);
unwrappedPhaseMap.convertTo(unwrappedPhaseMap8, CV_8U, 1, 128);
wrappedPhaseMap.convertTo(wrappedPhaseMap8, CV_8U, 255, 128);
if( !outputUnwrappedPhasePath.empty() )
{
ostringstream name;
name << i;
imwrite(outputUnwrappedPhasePath + "_FTP_" + name.str() + ".png", unwrappedPhaseMap8);
}
if( !outputWrappedPhasePath.empty() )
{
ostringstream name;
name << i;
imwrite(outputWrappedPhasePath + "_FTP_" + name.str() + ".png", wrappedPhaseMap8);
}
}
break;
case structured_light::PSP:
case structured_light::FAPS:
for( int i = 0; i < nbrOfImages - 2; ++i )
{
vector<Mat> captures;
captures.push_back(img[i]);
captures.push_back(img[i+1]);
captures.push_back(img[i+2]);
sinus->computePhaseMap(captures, wrappedPhaseMap, shadowMask);
if( camSize.height == -1 )
{
camSize.height = img[i].rows;
camSize.width = img[i].cols;
paramsUnwrapping.height = camSize.height;
paramsUnwrapping.width = camSize.width;
phaseUnwrapping =
phase_unwrapping::HistogramPhaseUnwrapping::create(paramsUnwrapping);
}
sinus->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, camSize, shadowMask);
unwrappedPhaseMap.convertTo(unwrappedPhaseMap8, CV_8U, 1, 128);
wrappedPhaseMap.convertTo(wrappedPhaseMap8, CV_8U, 255, 128);
phaseUnwrapping->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, shadowMask);
Mat reliabilities, reliabilities8;
phaseUnwrapping->getInverseReliabilityMap(reliabilities);
reliabilities.convertTo(reliabilities8, CV_8U, 255,128);
ostringstream tt;
tt << i;
imwrite(reliabilitiesPath + tt.str() + ".png", reliabilities8);
if( !outputUnwrappedPhasePath.empty() )
{
ostringstream name;
name << i;
if( params.methodId == structured_light::PSP )
imwrite(outputUnwrappedPhasePath + "_PSP_" + name.str() + ".png", unwrappedPhaseMap8);
else
imwrite(outputUnwrappedPhasePath + "_FAPS_" + name.str() + ".png", unwrappedPhaseMap8);
}
if( !outputWrappedPhasePath.empty() )
{
ostringstream name;
name << i;
if( params.methodId == structured_light::PSP )
imwrite(outputWrappedPhasePath + "_PSP_" + name.str() + ".png", wrappedPhaseMap8);
else
imwrite(outputWrappedPhasePath + "_FAPS_" + name.str() + ".png", wrappedPhaseMap8);
}
if( !outputCapturePath.empty() )
{
ostringstream name;
name << i;
if( params.methodId == structured_light::PSP )
imwrite(outputCapturePath + "_PSP_" + name.str() + ".png", img[i]);
else
imwrite(outputCapturePath + "_FAPS_" + name.str() + ".png", img[i]);
if( i == nbrOfImages - 3 )
{
if( params.methodId == structured_light::PSP )
{
ostringstream nameBis;
nameBis << i+1;
ostringstream nameTer;
nameTer << i+2;
imwrite(outputCapturePath + "_PSP_" + nameBis.str() + ".png", img[i+1]);
imwrite(outputCapturePath + "_PSP_" + nameTer.str() + ".png", img[i+2]);
}
else
{
ostringstream nameBis;
nameBis << i+1;
ostringstream nameTer;
nameTer << i+2;
imwrite(outputCapturePath + "_FAPS_" + nameBis.str() + ".png", img[i+1]);
imwrite(outputCapturePath + "_FAPS_" + nameTer.str() + ".png", img[i+2]);
}
}
}
}
break;
default:
cout << "error" << endl;
}
cout << "done" << endl;
if( !outputPatternPath.empty() )
{
for( int i = 0; i < 3; ++ i )
{
ostringstream name;
name << i + 1;
imwrite(outputPatternPath + name.str() + ".png", patterns[i]);
}
}
loop = true;
while( loop )
{
char key = (char) waitKey(0);
if( key == 27 )
{
loop = false;
}
}
return 0;
}
Designed for command line parsing.
Definition utility.hpp:820
n-dimensional dense array class
Definition mat.hpp:812
void convertTo(OutputArray m, int rtype, double alpha=1, double beta=0) const
Converts an array to another data type with optional scaling.
Template class for specifying the size of an image or rectangle.
Definition types.hpp:335
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:731
std::string String
Definition cvstd.hpp:151
std::shared_ptr< _Tp > Ptr
Definition cvstd_wrapper.hpp:23
#define CV_8U
Definition interface.h:73
#define CV_PI
Definition cvdef.h:380
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.
void setWindowProperty(const String &winname, int prop_id, double prop_value)
Changes parameters of a window dynamically.
CV_EXPORTS_W bool imwrite(const String &filename, InputArray img, const std::vector< int > &params=std::vector< int >())
Saves an image to a specified file.
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
PyParams params(const std::string &tag, const std::string &model, const std::string &weights, const std::string &device)
"black box" representation of the file storage associated with a file on disk.
Definition core.hpp:102
STL namespace.
Parameters of phaseUnwrapping constructor.
Definition histogramphaseunwrapping.hpp:79
int width
Definition histogramphaseunwrapping.hpp:81
int height
Definition histogramphaseunwrapping.hpp:82
Parameters of SinusoidalPattern constructor.
Definition sinusoidalpattern.hpp:83

Expalantion

First, the sinusoidal patterns must be generated. SinusoidalPattern class parameters have to be set by the user:

  • projector width and height
  • number of periods in the patterns
  • set cross markers in the patterns (used to convert relative phase map to absolute phase map)
  • patterns direction (horizontal or vertical)
  • phase shift value (usually set to 2pi/3 to enable a cyclical system)
  • number of pixels between two consecutive markers on the same row/column
  • id of the method used to compute the phase map (FTP = 0, PSP = 1, FAPS = 2)

The user can also choose to save the patterns and the phase map.

params.width = parser.get<int>(0);
params.height = parser.get<int>(1);
params.nbrOfPeriods = parser.get<int>(2);
params.setMarkers = parser.get<bool>(3);
params.horizontal = parser.get<bool>(4);
params.methodId = parser.get<int>(5);
params.shiftValue = static_cast<float>(2 * CV_PI / 3);
params.nbrOfPixelsBetweenMarkers = 70;
String outputPatternPath = parser.get<String>(6);
String outputWrappedPhasePath = parser.get<String>(7);
String outputUnwrappedPhasePath = parser.get<String>(8);
Ptr<structured_light::SinusoidalPattern> sinus = structured_light::SinusoidalPattern::create(params);
// Storage for patterns
vector<Mat> patterns;
//Generate sinusoidal patterns
sinus->generate(patterns);

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.

VideoCapture cap(CAP_PVAPI);
if( !cap.isOpened() )
{
cout << "Camera could not be opened" << endl;
return -1;
}
cap.set(CAP_PROP_PVAPI_PIXELFORMAT, CAP_PVAPI_PIXELFORMAT_MONO8);
namedWindow("pattern", WINDOW_NORMAL);
setWindowProperty("pattern", WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
imshow("pattern", patterns[0]);
cout << "Press any key when ready" << endl;
waitKey(0);

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.

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;
}
}

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 [62]. 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.

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
*/
vector<Mat> captures;
if( i == nbrOfImages - 2 )
{
captures.push_back(img[i]);
captures.push_back(img[i-1]);
captures.push_back(img[i+1]);
}
else if( i == nbrOfImages - 1 )
{
captures.push_back(img[i]);
captures.push_back(img[i-1]);
captures.push_back(img[i-2]);
}
else
{
captures.push_back(img[i]);
captures.push_back(img[i+1]);
captures.push_back(img[i+2]);
}
sinus->computePhaseMap(captures, wrappedPhaseMap, shadowMask);
if( camSize.height == -1 )
{
camSize.height = img[i].rows;
camSize.width = img[i].cols;
}
sinus->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, camSize, shadowMask);
unwrappedPhaseMap.convertTo(unwrappedPhaseMap8, CV_8U, 1, 128);
wrappedPhaseMap.convertTo(wrappedPhaseMap8, CV_8U, 255, 128);
if( !outputUnwrappedPhasePath.empty() )
{
ostringstream name;
name << i;
imwrite(outputUnwrappedPhasePath + "_FTP_" + name.str() + ".png", unwrappedPhaseMap8);
}
if( !outputWrappedPhasePath.empty() )
{
ostringstream name;
name << i;
imwrite(outputWrappedPhasePath + "_FTP_" + name.str() + ".png", wrappedPhaseMap8);
}
}
break;

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.

case structured_light::PSP:
case structured_light::FAPS:
for( int i = 0; i < nbrOfImages - 2; ++i )
{
vector<Mat> captures;
captures.push_back(img[i]);
captures.push_back(img[i+1]);
captures.push_back(img[i+2]);
sinus->computePhaseMap(captures, wrappedPhaseMap, shadowMask);
if( camSize.height == -1 )
{
camSize.height = img[i].rows;
camSize.width = img[i].cols;
}
sinus->unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, camSize, shadowMask);
unwrappedPhaseMap.convertTo(unwrappedPhaseMap8, CV_8U, 1, 128);
wrappedPhaseMap.convertTo(wrappedPhaseMap8, CV_8U, 255, 128);
if( !outputUnwrappedPhasePath.empty() )
{
ostringstream name;
name << i;
if( params.methodId == structured_light::PSP )
imwrite(outputUnwrappedPhasePath + "_PSP_" + name.str() + ".png", unwrappedPhaseMap8);
else
imwrite(outputUnwrappedPhasePath + "_FAPS_" + name.str() + ".png", unwrappedPhaseMap8);
}
if( !outputWrappedPhasePath.empty() )
{
ostringstream name;
name << i;
if( params.methodId == structured_light::PSP )
imwrite(outputWrappedPhasePath + "_PSP_" + name.str() + ".png", wrappedPhaseMap8);
else
imwrite(outputWrappedPhasePath + "_FAPS_" + name.str() + ".png", wrappedPhaseMap8);
}
}
break;