Goal
In this tutorial you will learn how to use the GrayCodePattern class to:
- Generate a Gray code pattern.
- Project the Gray code pattern.
- Capture the projected Gray code pattern.
It is important to underline that GrayCodePattern class actually implements the 3DUNDERWORLD algorithm described in [124] , which is based on a stereo approach: we need to capture the projected pattern at the same time from two different views if we want to reconstruct the 3D model of the scanned object. Thus, an acquisition set consists of the images captured by each camera for each image in the pattern sequence.
Code
#include <iostream>
#include <stdio.h>
static const char* keys =
{ "{@path | | Path of the folder where the captured pattern images will be save }"
"{@proj_width | | Projector width }"
"{@proj_height | | Projector height }" };
static void help()
{
cout << "\nThis example shows how to use the \"Structured Light module\" to acquire a graycode pattern"
"\nCall (with the two cams connected):\n"
"./example_structured_light_cap_pattern <path> <proj_width> <proj_height> \n"
<< endl;
}
int main(
int argc,
char** argv )
{
params.width = parser.get<int>( 1 );
params.height = parser.get<int>( 2 );
if( path.empty() || params.width < 1 || params.height < 1 )
{
help();
return -1;
}
vector<Mat> pattern;
graycode->generate( pattern );
cout << pattern.size() << " pattern images + 2 images for shadows mask computation to acquire with both cameras"
<< endl;
graycode->getImagesForShadowMasks( black, white );
pattern.push_back( black );
namedWindow( "Pattern Window", WINDOW_NORMAL );
resizeWindow( "Pattern Window", params.width, params.height );
moveWindow( "Pattern Window", params.width + 316, -20 );
setWindowProperty( "Pattern Window", WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN );
if( !cap1.isOpened() )
{
cout << "cam1 not opened!" << endl;
help();
return -1;
}
if( !cap2.isOpened() )
{
cout << "cam2 not opened!" << endl;
help();
return -1;
}
cap1.set( CAP_PROP_SETTINGS, 1 );
cap2.set( CAP_PROP_SETTINGS, 1 );
int i = 0;
while( i < (int) pattern.size() )
{
cout << "Waiting to save image number " << i + 1 << endl << "Press any key to acquire the photo" << endl;
imshow(
"Pattern Window", pattern[i] );
cap1 >> frame1;
cap2 >> frame2;
if( ( frame1.
data ) && ( frame2.
data ) )
{
cout <<
"cam 1 size: " <<
Size( (
int ) cap1.get( CAP_PROP_FRAME_WIDTH ), (
int ) cap1.get( CAP_PROP_FRAME_HEIGHT ) )
<< endl;
cout <<
"cam 2 size: " <<
Size( (
int ) cap2.get( CAP_PROP_FRAME_WIDTH ), (
int ) cap2.get( CAP_PROP_FRAME_HEIGHT ) )
<< endl;
cout << "zoom cam 1: " << cap1.get( CAP_PROP_ZOOM ) << endl << "zoom cam 2: " << cap2.get( CAP_PROP_ZOOM )
<< endl;
cout << "focus cam 1: " << cap1.get( CAP_PROP_FOCUS ) << endl << "focus cam 2: " << cap2.get( CAP_PROP_FOCUS )
<< endl;
cout << "Press enter to save the photo or an other key to re-acquire the photo" << endl;
resize( frame1, tmp,
Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT);
resize( frame2, tmp,
Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT);
bool save1 = false;
bool save2 = false;
if( key == 13 )
{
ostringstream name;
name << i + 1;
save1 =
imwrite( path +
"pattern_cam1_im" + name.str() +
".png", frame1 );
save2 =
imwrite( path +
"pattern_cam2_im" + name.str() +
".png", frame2 );
if( ( save1 ) && ( save2 ) )
{
cout << "pattern cam1 and cam2 images number " << i + 1 << " saved" << endl << endl;
i++;
}
else
{
cout << "pattern cam1 and cam2 images number " << i + 1 << " NOT saved" << endl << endl << "Retry, check the path"<< endl << endl;
}
}
if( key == 27 )
{
cout << "Closing program" << endl;
}
}
else
{
cout << "No frame data, waiting for new frame" << endl;
}
}
return 0;
}
Designed for command line parsing.
Definition utility.hpp:820
n-dimensional dense array class
Definition mat.hpp:812
uchar * data
pointer to the data
Definition mat.hpp:2140
void push_back(const _Tp &elem)
Adds elements to the bottom of the matrix.
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
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 moveWindow(const String &winname, int x, int y)
Moves the window to the specified position.
void resizeWindow(const String &winname, int width, int height)
Resizes the window to the specified size.
CV_EXPORTS_W bool imwrite(const String &filename, InputArray img, const std::vector< int > ¶ms=std::vector< int >())
Saves an image to a specified file.
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0)
Converts an image from one color space to another.
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
"black box" representation of the file storage associated with a file on disk.
Definition core.hpp:102
Parameters of StructuredLightPattern constructor.
Definition graycodepattern.hpp:77
Explanation
First of all the pattern images to project must be generated. Since the number of images is a function of the projector's resolution, GrayCodePattern class parameters must be set with our projector's width and height. In this way the generate method can be called: it fills a vector of Mat with the computed pattern images:
....
params.width = parser.get<int>( 1 );
params.height = parser.get<int>( 2 );
....
vector<Mat> pattern;
graycode->generate( pattern );
For example, using the default projector resolution (1024 x 768), 40 images have to be projected: 20 for regular color pattern (10 images for the columns sequence and 10 for the rows one) and 20 for the color-inverted pattern, where the inverted pattern images are images with the same structure as the original but with inverted colors. This provides an effective method for easily determining the intensity value of each pixel when it is lit (highest value) and when it is not lit (lowest value) during the decoding step.
Subsequently, to identify shadow regions, the regions of two images where the pixels are not lit by projector's light and thus where there is not code information, the 3DUNDERWORLD algorithm computes a shadow mask for the two cameras views, starting from a white and a black images captured by each camera. So two additional images need to be projected and captured with both cameras:
graycode->getImagesForShadowMasks( black, white );
pattern.push_back( black );
Thus, the final projection sequence is projected as follows: first the column and its inverted sequence, then the row and its inverted sequence and finally the white and black images.
Once the pattern images have been generated, they must be projected using the full screen option: the images must fill all the projection area, otherwise the projector full resolution is not exploited, a condition on which is based 3DUNDERWORLD implementation.
namedWindow( "Pattern Window", WINDOW_NORMAL );
resizeWindow( "Pattern Window", params.width, params.height );
moveWindow( "Pattern Window", params.width + 316, -20 );
setWindowProperty( "Pattern Window", WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN );
At this point the images can be captured with our digital cameras, using libgphoto2 library, recently included in OpenCV: remember to turn on gPhoto2 option in Cmake.list when building OpenCV.
if( !cap1.isOpened() )
{
cout << "cam1 not opened!" << endl;
help();
return -1;
}
if( !cap2.isOpened() )
{
cout << "cam2 not opened!" << endl;
help();
return -1;
}
The two cameras must work at the same resolution and must have autofocus option disabled, maintaining the same focus during all acquisition. The projector can be positioned in the middle of the cameras.
However, before to proceed with pattern acquisition, the cameras must be calibrated. Once the calibration is performed, there should be no movement of the cameras, otherwise a new calibration will be needed.
After having connected the cameras and the projector to the computer, cap_pattern demo can be launched giving as parameters the path where to save the images, and the projector's width and height, taking care to use the same focus and cameras settings of calibration.
At this point, to acquire the images with both cameras, the user can press any key.
cap1.set( CAP_PROP_SETTINGS, 1 );
cap2.set( CAP_PROP_SETTINGS, 1 );
int i = 0;
while( i < (int) pattern.size() )
{
cout << "Waiting to save image number " << i + 1 << endl << "Press any key to acquire the photo" << endl;
imshow( "Pattern Window", pattern[i] );
cap1 >> frame1;
cap2 >> frame2;
...
}
If the captured images are good (the user must take care that the projected pattern is viewed from the two cameras), the user can save them pressing the enter key, otherwise pressing any other key he can take another shot.
if( key == 13 )
{
ostringstream name;
name << i + 1;
save1 = imwrite( path + "pattern_cam1_im" + name.str() + ".png", frame1 );
save2 = imwrite( path + "pattern_cam2_im" + name.str() + ".png", frame2 );
if( ( save1 ) && ( save2 ) )
{
cout << "pattern cam1 and cam2 images number " << i + 1 << " saved" << endl << endl;
i++;
}
else
{
cout << "pattern cam1 and cam2 images number " << i + 1 << " NOT saved" << endl << endl << "Retry, check the path"<< endl << endl;
}
}
The acquistion ends when all the pattern images have saved for both cameras. Then the user can reconstruct the 3D model of the captured scene using the decode method of GrayCodePattern class (see next tutorial).