/** @brief Get the number of pattern images needed for the graycode pattern.
*
* @return The number of pattern images needed for the graycode pattern.
*
*/
CV_WRAP
virtualsize_tgetNumberOfPatternImages()const=0;
/** @brief Sets the value for white threshold, needed for decoding.
*
* White threshold is a number between 0-255 that represents the minimum brightness difference required for valid pixels, between the graycode pattern and its inverse images; used in getProjPixel method.
*
* @param value The desired white threshold value.
*
*/
CV_WRAP
virtualvoidsetWhiteThreshold(size_tvalue)=0;
/** @brief Sets the value for black threshold, needed for decoding (shadowsmasks computation).
*
* Black threshold is a number between 0-255 that represents the minimum brightness difference required for valid pixels, between the fully illuminated (white) and the not illuminated images (black); used in computeShadowMasks method.
*
* @param value The desired black threshold value.
*
*/
CV_WRAP
virtualvoidsetBlackThreshold(size_tvalue)=0;
/** @brief Generates the all-black and all-white images needed for shadowMasks computation.
*
* 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 coded 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.
* This method generates these two additional images to project.
*
* @param blackImage The generated all-black CV_8U image, at projector's resolution.
* @param whiteImage The generated all-white CV_8U image, at projector's resolution.
/** @brief For a (x,y) pixel of a camera returns the corresponding projector pixel.
*
* The function decodes each pixel in the pattern images acquired by a camera into their corresponding decimal numbers representing the projector's column and row,
* providing a mapping between camera's and projector's pixel.
*
* @param patternImages The pattern images acquired by the camera, stored in a grayscale vector < Mat >.
* @param x x coordinate of the image pixel.
* @param y y coordinate of the image pixel.
* @param projPix Projector's pixel corresponding to the camera's pixel: projPix.x and projPix.y are the image coordinates of the projector’s pixel corresponding to the pixel being decoded in a camera.
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 @cite UNDERWORLD , 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 structured_light/samples/cap_pattern.cpp
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:
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:
@code{.cpp}
// Generate the all-white and all-black images needed for shadows mask computation
Mat white;
Mat black;
graycode->getImagesForShadowMasks( black, white );
pattern.push_back( white );
pattern.push_back( black );
@endcode
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.
@code{.cpp}
// Setting pattern window on second monitor (the projector's one)
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.
@code{.cpp}
// Open camera number 1, using libgphoto2
VideoCapture cap1( CAP_GPHOTO2 );
if( !cap1.isOpened() )
{
// check if cam1 opened
cout << "cam1 not opened!" << endl;
help();
return -1;
}
// Open camera number 2
VideoCapture cap2( 1 );
if( !cap2.isOpened() )
{
// check if cam2 opened
cout << "cam2 not opened!" << endl;
help();
return -1;
}
@endcode
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.
@code{.cpp}
// Turning off autofocus
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] );
Mat frame1;
Mat frame2;
cap1 >> frame1; // get a new frame from camera 1
cap2 >> frame2; // get a new frame from camera 2
...
}
@endcode
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.
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;
}
}
@endcode
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).
In this tutorial you will learn how to use the *GrayCodePattern* class to:
- Decode a previously acquired Gray code pattern.
- Generate a disparity map.
- Generate a pointcloud.
Code
----
@include structured_light/samples/pointcloud.cpp
Explanation
-----------
First of all the needed parameters must be passed to the program.
The first is the name list of previously acquired pattern images, stored in a .yaml file organized as below:
@code{.cpp}
%YAML:1.0
cam1:
- "/data/pattern_cam1_im1.png"
- "/data/pattern_cam1_im2.png"
..............
- "/data/pattern_cam1_im42.png"
- "/data/pattern_cam1_im43.png"
- "/data/pattern_cam1_im44.png"
cam2:
- "/data/pattern_cam2_im1.png"
- "/data/pattern_cam2_im2.png"
..............
- "/data/pattern_cam2_im42.png"
- "/data/pattern_cam2_im43.png"
- "/data/pattern_cam2_im44.png"
@endcode
For example, the dataset used for this tutorial has been acquired using a projector with a resolution of 1280x800, so 42 pattern images (from number 1 to 42) + 1 white (number 43) and 1 black (number 44) were captured with both the two cameras.
Then the cameras calibration parameters, stored in another .yml file, together with the width and the height of the projector used to project the pattern, and, optionally, the values of white and black tresholds, must be passed to the tutorial program.
In this way, *GrayCodePattern* class parameters can be set up with the width and the height of the projector used during the pattern acquisition and a pointer to a GrayCodePattern object can be created:
If the white and black thresholds are passed as parameters (these thresholds influence the number of decoded pixels), their values can be set, otherwise the algorithm will use the default values.
@code{.cpp}
size_t white_thresh = 0;
size_t black_thresh = 0;
if( argc == 7 )
{
// If passed, setting the white and black threshold, otherwise using default values
white_thresh = parser.get<size_t>( 4 );
black_thresh = parser.get<size_t>( 5 );
graycode->setWhiteThreshold( white_thresh );
graycode->setBlackThreshold( black_thresh );
}
@endcode
At this point, to use the *decode* method of *GrayCodePattern* class, the acquired pattern images must be stored in a vector of vector of Mat.
The external vector has a size of two because two are the cameras: the first vector stores the pattern images captured from the left camera, the second those acquired from the right one. The number of pattern images is obviously the same for both cameras and can be retrieved using the getNumberOfPatternImages() method:
It is important to underline that all the images, the pattern ones, black and white, must be loaded as grayscale images and rectified before being passed to decode method:
In this way the *decode* method can be called to decode the pattern and to generate the corresponding disparity map, computed on the first camera (left):
At this point the point cloud can be generated using the reprojectImageTo3D method, taking care to convert the computed disparity in a CV_32FC1 Mat (decode method computes a CV_64FC1 disparity map):
@code{.cpp}
Mat pointcloud;
disparityMap.convertTo( disparityMap, CV_32FC1 );
reprojectImageTo3D( disparityMap, pointcloud, Q, true, -1 );
@endcode
Then a mask to remove the unwanted background is computed:
