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Image_Processing.cpp
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Image_Processing.cpp
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#include "Image_Processing.h"
#include "openglscene.h"
#include "P3P.h"
#include "pPOSIT.h"
#include <vector>
using std::vector;
/** Image_Processing()
* \brief Constructor. Calls initAttributes() and checks OpenCV backend.
*
* This is the version using OpenCV 2.0 or higher C++ interface.
*
* @see initAttributes()
*
*/
Image_Processing::Image_Processing()
{
if(initAttributes() < 0)
cerr << "\n\n\nError initializing OpenCV backend. Aborting program" << endl;
Calib_Threshold = 150;
//Create the POSIT object with the model points
p = new pPOSIT( cvCreatePOSITObject( &modelPoints[0],
modelPoints.size()),
srcImagePoints,
fl,
cvTermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 100, 1.0e-4f) );
q = new P3P( ObjectPoints,
ImagePoints,
initialGuess,
fl );
findROI();
srand( time(NULL) );
}
/** ~Image_Processing()
* \brief Destructor.
*
*/
Image_Processing::~Image_Processing() { }
void Image_Processing::openCAM()
{
if(vdo_flag) {
capture.release();
}
if(!capture.open(0)) {
QMessageBox::critical(0,"Open Camera","Could not open camera for capture!");
qDebug() << "ERROR: Could not open camera for capture!";
return;
}
else {
capture >> cframe;
// width and height of frames in the video stream
frame_size = cframe.size();
// brightness, contrast, saturation and hue of image
brightness = capture.get( CV_CAP_PROP_BRIGHTNESS );
contrast = capture.get( CV_CAP_PROP_CONTRAST );
saturation = capture.get( CV_CAP_PROP_SATURATION );
hue = capture.get( CV_CAP_PROP_HUE );
fps = 100;
qDebug() << "Input Frame Size: " << frame_size.width << "x" << frame_size.height << endl;
}
vdo_flag = false;
img_flag = false;
cam_flag = true;
if (XML_To_Mat()) //Added by yang
{
//Enable checkBox_undistort
undistort_flag = true; //actually should enable checkbox and let user to select
}
}
void Image_Processing::openVideoFile(QString fname)
{
if(vdo_flag)
if(current_vdo_file == fname)
return;
capture.release();
if(!capture.open(fname.toStdString())) {
QMessageBox::critical(0,"Open Camera","Could not open video file!");
qDebug() << "ERROR: Could not open video file!";
return;
}
qDebug() << "Opening video file " << fname;
capture >> cframe;
fps = capture.get(CV_CAP_PROP_FPS);
qDebug() << "FPS: " << fps;
if(fps < 1.0)
fps = 25.0;
qDebug() << "FPS: " << fps;
current_vdo_file = fname;
vdo_flag = true;
img_flag = false;
cam_flag = false;
}
void Image_Processing::openImageFile(QString fname)
{
capture.release();
current_img_file = fname;
cframe = imread(fname.toStdString());
qDebug() << "Opening image file " << fname;
vdo_flag = false;
img_flag = true;
cam_flag = false;
}
/** initAttributes()
* \brief Initialization of all relevant data structures as needed.
*
*
*/
int Image_Processing::initAttributes()
{
cam_flag = false;
vdo_flag = false;
img_flag = false;
draw_autocalib = false;
draw_features = false;
draw_pose = false;
cali_in_progress = 0; //added by yang
undistort_flag = 0; //added by yang
frame_size = Size(640,480);
if(fps < 1.0)
fps = 100.0;
fl = 1000.0;
CUBE_SIZE = 10;
centerpoint = Point2f(frame_size.width/2,frame_size.height/2);
//Create the model points
modelPoints.push_back(cvPoint3D32f(0.0f, 0.0f, 0.0f)); //The first must be (0,0,0)
modelPoints.push_back(cvPoint3D32f(22.0f, -7.5f, 0.0f));
modelPoints.push_back(cvPoint3D32f(22.0f, 7.5f, 0.0f));
modelPoints.push_back(cvPoint3D32f(30.0f, 0.0f, 0.0f));
//Create the image points
srcImagePoints.push_back( cvPoint2D32f( 0, 0 ) );
srcImagePoints.push_back( cvPoint2D32f( -20, 20 ) );
srcImagePoints.push_back( cvPoint2D32f( -20, -20 ) );
srcImagePoints.push_back( cvPoint2D32f( 20, 0 ) );
// gripper object points, not the CUBE, e.g.
ObjectPoints.push_back(Point3f(0, 0, 0));
ObjectPoints.push_back(Point3f(22, -7.5, 0));
ObjectPoints.push_back(Point3f(22, 7.5, 0));
ImagePoints.push_back(Point2i(320,240)); // first point in the middle
ImagePoints.push_back(Point2i(300,230));
ImagePoints.push_back(Point2i(300,250));
initialGuess.push_back(0);
initialGuess.push_back(0);
initialGuess.push_back(300);
return 0;
}
/** captureFrame( int mode )
* \brief Captures a single frame and applies optional image filters
*
* The important part is to
* add an alpha channel to the output frame that is accessed by the
* other parts of the system in order for it to be quickly transformed
* into a QImage.
*
* @param int mode of operation
* @see qtopencv
*/
int Image_Processing::captureFrame(bool a_mode, bool p_mode, bool e_mode)
{
autocalib_mode = a_mode;
pose_mode = p_mode;
extract_mode = e_mode;
if(!(cam_flag || vdo_flag || img_flag))
return -1;
if( cam_flag ) {
capture >> cframe; //cframe is a Mat type
}
if( vdo_flag ) {
if( capture.grab() ) {
capture.retrieve(cframe);
}
else {
capture.set( CV_CAP_PROP_POS_AVI_RATIO, 0 ); // repeat video if at end
capture >> cframe;
}
}
if( img_flag ) {
cframe = imread(current_img_file.toStdString());
}
// do deinterlacing
// you need to convert the Mat cframe to an IplImage*
IplImage fullframe = cframe;
IplImage * field1 = cvCreateImage( cvSize(cframe.cols,cframe.rows/2),IPL_DEPTH_8U,3);
IplImage * field2 = cvCreateImage( cvSize(cframe.cols,cframe.rows/2),IPL_DEPTH_8U,3);
cvDeInterlace(&fullframe, field1, field2);
Mat temp = cvarrToMat(field2);
resize(temp,cframe, cframe.size(), 0, 0, INTER_LINEAR);
cvReleaseImage(&field1);
cvReleaseImage(&field2);
// necessary for conversion to QImage later. We need an alpha
// channel.
split(cframe, channels);
channels.push_back(Mat(cframe.size(), CV_8UC1, Scalar(255)));
merge(channels, cframe);
if (cali_in_progress){
uframe = cframe;
dframe = caldframe;
}
else if(undistort_flag) {
undistort(cframe, uframe,intrinsic_matrix, distortion_coeffs);
dframe = uframe;
}
else {
uframe = cframe;
dframe = cframe;
}
if(autocalib_mode)
doAutoCalibration();
if(extract_mode)
//FindROI();
SaveContours();
if(pose_mode)
pEstimate();
return 0;
}
void Image_Processing::doCalibration()
{
cout << "Image_Processing::doCalibration() is running.." << endl;
// cvNamedWindow( "Image View", 1 );
// // IplImage *view = 0, *view_gray = 0;
// IplImage fullframe = cframe;
// IplImage *view = &fullframe;
// cvShowImage( "Image View", view );
// //cvReleaseImage( &view );
// capture.release();
//char * argv[5] = {"", "-w", "4", "-h", "4"};
//persudo_main(5, argv, &cframe, &caldframe, &camera_matrix, &distortion_coeffs);
cali_in_progress = 1;
calimain();
cali_in_progress = 0;
}
int Image_Processing::find_blob()
{
cout << "find_blob() is running..." << endl;
/* IplImage* out = cvCreateImage( cframe.size(), IPL_DEPTH_8U, 1 );
IplImage src = cframe;
IplImage* r = cvCreateImage( cvGetSize(&src), IPL_DEPTH_8U, 1 );
IplImage* g = cvCreateImage( cvGetSize(&src), IPL_DEPTH_8U, 1 );
IplImage* b = cvCreateImage( cvGetSize(&src), IPL_DEPTH_8U, 1 );
IplImage* a = cvCreateImage( cvGetSize(&src), IPL_DEPTH_8U, 1 );
IplImage* t = cvCreateImage( cvGetSize(&src), IPL_DEPTH_8U, 1 );
// Split image onto the color planes
cvSplit( &src, r, g, b, a );
Mat hframe;
// Perform histogram equalization
equalizeHist( r, hframe );
//cvThreshold(g, t, 100, 100, CV_THRESH_TRUNC);
cvThreshold(r, t, 50, 200, CV_THRESH_BINARY);
cvSmooth(t,a,CV_MEDIAN,5);*/
Mat hframe, tframe;
vector< Mat > chans(cframe.channels());
split(cframe, chans);
//Adjust ROI -- Make a rectangle
Rect roi(120, 0, 480, 300);
//Point a cv::Mat header at it (no allocation is done)
Mat image_roi = chans.at(0)(roi);
equalizeHist( image_roi, hframe );
threshold(chans.at(0),tframe, Calib_Threshold, 254, CV_THRESH_BINARY);
cout << "Threshold" << Calib_Threshold << endl;
medianBlur( tframe, tframe, 5);
//namedWindow("Threshold"); //<<==============================
//imshow("Threshold", tframe); //<<==============================
//namedWindow("Histogram"); //<<==============================
//imshow("Histogram", hframe); //<<==============================
Mat CTframe = tframe;
vector< vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(tframe, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
//Mat outframe = Mat::zeros(cframe.rows, cframe.cols, CV_8UC3);
RotatedRect rect;
//Find the largest blob;
unsigned int s = 6;
int j = -1;
for( unsigned int i = 0; i < contours.size(); ++i ) {
if(contours.at(i).size() > s) {
j = i;
s = contours.at(i).size();
}
}
if (j>=0)
{
if(contours.at(j).size() > 6) {
//for debug only
//--------------------------------
cout << "Contours found: " << contours.size() << endl;
Mat c = Mat(contours.at(j));
rect = fitEllipse(c);
centerpoint = rect.center;
if(draw_autocalib) {
ellipse(dframe, rect, CV_RGB(0,255,0),1,8);
drawContours(dframe, contours, -1, CV_RGB(0,0,255), 2, CV_AA, hierarchy);//<<=========================
circle(dframe, centerpoint, 4, CV_RGB(0,255,0), 2, 8);
//draw center cross lines
Point pt1=centerpoint, pt2=centerpoint;
pt1.x -= 20; pt2.x +=20; line(dframe, pt1, pt2, CV_RGB(0,255,0), 1);
pt1=pt2=centerpoint;
pt1.y-=20; pt2.y+=20; line(dframe, pt1, pt2, CV_RGB(0,255,0), 1);
}
cout << "New Principal Point: (" << centerpoint.x << "," << centerpoint.y << ")" << endl;
}
else
cout << "No contours found!" << endl;
}//if (j>=0)
//namedWindow("Contour"); //<<==============================
//drawContours( dframe, contours, -1, CV_RGB(255,0,0), 2, CV_AA, hierarchy );
//drawContours(CTframe, contours, -1, CV_RGB(255,255,255), 1, CV_AA, hierarchy);
//imshow("Contour", CTframe); //<<==============================
if(draw_autocalib) {
Point ref1, ref2;
ref1.x=0; ref1.y=480/2; ref2.x=640; ref2.y=480/2; line(dframe, ref1, ref2, CV_RGB(255,255,0), 1, 8);
ref1.x=640/2; ref1.y=0; ref2.x=640/2; ref2.y=480; line(dframe, ref1, ref2, CV_RGB(255,255,0), 1, 8);
if (undistort_flag)
{
cx = intrinsic_matrix.at<double>(0,2);
cy = intrinsic_matrix.at<double>(1,2);
ref1.x=cx-10; ref1.y=cy; ref2.x=cx+10; ref2.y=cy; line(dframe, ref1, ref2, CV_RGB(255,255,0), 1, 8);
ref1.x=cx; ref1.y=cy-10; ref2.x=cx; ref2.y=cy+10; line(dframe, ref1, ref2, CV_RGB(255,255,0), 1, 8);
}
}
// namedWindow("Threshold");
// imshow("Threshold", outframe);
return 0;
}
void Image_Processing::doAutoCalibration()
{
cout << "Image_Processing::doAutoCalibration() is running222...111" << endl;
find_blob();
}
void Image_Processing::doUndistort(bool flag)
{
cout << "Image_Processing::doUndistort() is running..." << endl;
undistort_flag = flag;
}
//Add by Yang Lei 2010.11.23
int Image_Processing::calimain()
{
using namespace cv;
//===========================================
const int number_of_boards = 4; //number of boards
const int board_dt = 20; //Wait 20 frames per chessboard view
const int board_w = 4;
const int board_h = 4;
static int number_of_corners = board_w * board_h;
static Size board_size(board_w, board_h);
// VideoCapture capture(0);
// if (!capture.isOpened())
// return -1;
// cvNamedWindow("Calibration");
//ALLOCATE STORAGE
static Mat object_points(number_of_boards*number_of_corners, 3, CV_32FC1);
static Mat image_points(number_of_boards*number_of_corners, 2, CV_32FC1);
static Mat point_counts(number_of_boards, 1, CV_32SC1);
//static Mat intrinsic_matrix(3, 3, CV_32FC1);
//static Mat distortion_coeffs(5, 1, CV_32FC1);
//static Point corners(number_of_corners);
static vector<Point2f> corners(number_of_corners);
static int boards_captured = 0;
static int step, frame = 0;
//===========================================
// Step 1. Capture enough boards =
//===========================================
Mat gray_image(cframe.size(), 8, 1);
// Capture corner views loop until we have got number_of_boards
// sucessful captures (All corners on the board are found)
//
int speedcounter = 0;
while (boards_captured < number_of_boards)
{
waitKey(5); //yield CPU to other threads;
//Draw corners
caldframe = cframe;
//Print text on output image frame
char text[50];
if (speedcounter > 60)
{speedcounter = 1;}
sprintf(text, "%d/%d boards captured. %d", boards_captured, number_of_boards, speedcounter++);
putText(caldframe, text, Point(20,30), FONT_HERSHEY_SIMPLEX, 1, CV_RGB(255,0,0),
2, 5, false);
//Skip every board_dt frames to allow user to move chessboard
if(0 == frame++ % board_dt)
{
bool found = false;
//Find chessboard corners
found = findChessboardCorners(
cframe, board_size, corners,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FILTER_QUADS
);
//Get Subpixel accuracy on those corners
cvtColor(cframe, gray_image, CV_BGR2GRAY);
Size winSize(11,11);
Size zeroZone(-1,-1);
cornerSubPix(gray_image, corners,
winSize, zeroZone,
TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 30, 0.1)
);
//Draw chessboard
Mat cornerMat = Mat(corners);
if((cornerMat.cols == 0) || (cornerMat.rows == 0))
{
cout << "No Points found!" << endl;
}
else
{
drawChessboardCorners(caldframe, board_size, cornerMat, found);
}
//ShowImage("Calibration", image);
//If we got a good board, add it to our data
if(found)
{
step = boards_captured * number_of_corners;
for (int i = step, j=0; j<number_of_corners; ++i, ++j)
{
image_points.at<float>(i,0) = corners[j].x;
image_points.at<float>(i,1) = corners[j].y;
object_points.at<float>(i,0) = j/board_w;
object_points.at<float>(i,1) = j%board_w;
object_points.at<float>(i,2) = 0.0f;
}
point_counts.at<int>(boards_captured,0) = number_of_corners;
boards_captured++;
cout << boards_captured << " of " << number_of_boards << " boadrs captured!" << endl;
}
} //end skip board_dt between chessboard capture
}//end of collection while loop
//Allocate Matrices according to how many chessboards found
//==========================================
// Step 2. Data format exchange =
//==========================================
//At this point we have all of the chessboard corners we need.
//Init the intrinsic matrix such that the two focal length
//have a ratio of 1.0
//intrinsic_matrix.at<float>(0,0) = 1.0f;
//intrinsic_matrix.at<float>(1,1) = 1.0f;
vector< vector <Point3f> > obj(boards_captured);
for(uint j = 0, i=0; i<obj.size();++i) {
obj[i].resize(number_of_corners);
for(int k=0; k<number_of_corners; ++k ) {
obj[i][k].x = object_points.at<float>(k+j,0);
obj[i][k].y = object_points.at<float>(k+j,1);
obj[i][k].z = object_points.at<float>(k+j,2);
}
j+=(number_of_corners);
}
vector< vector<Point2f> > img(boards_captured);
for(uint j = 0, i=0; i<img.size();++i) {
img[i].resize(number_of_corners);
for(int k=0; k<number_of_corners; ++k ) {
img[i][k].x = image_points.at<float>(k+j,0);
img[i][k].y = image_points.at<float>(k+j,1);
}
j+=(number_of_corners);
}
//==========================================
// Step 3. Do calibration =
//==========================================
vector<Mat> rvecs, tvecs;
calibrateCamera(obj,
img,
cframe.size(),
intrinsic_matrix,
distortion_coeffs,
rvecs, //rvect
tvecs, //tvect
0);
cout << "Camera calibration done!" << endl;
Mat_To_XML();
return 0;
}
//============================================
// XML =
//============================================
bool Image_Processing::XML_To_Mat()
{
FileStorage fs ("CameraMatrix.yml", FileStorage::READ);//
if (fs.isOpened())
{
fs["Perspective Matrix"] >> intrinsic_matrix;
fs["Distortion Matrix"] >> distortion_coeffs;
cout << "Camera calibration parameters loaded from CameraMatrix.yml" << endl;
QMessageBox::information(0, "XML_To_MAT()", "Camera calibration parameters are loaded from <CameraMatrix.yml>.");
return 1;
}
else
{
return 0;
}
//insert here...
}
int Image_Processing::Mat_To_XML()
{
QDomDocument doc( "CaliParaXML" );
QDomNode root = doc.createElement( "calipara" );
doc.appendChild( root );
//Get perspective parameters:
fx = intrinsic_matrix.at<double>(0,0);
fy = intrinsic_matrix.at<double>(1,1);
cx = intrinsic_matrix.at<double>(0,2);
cy = intrinsic_matrix.at<double>(1,2);
//Get distortion coefficients:
//distCoeffs – The output 5x1 or 1x5 vector of distortion coefficients (k_1, k_2, p_1, p_2[, k_3])
k1 = distortion_coeffs.at<double>(0,0);
k2 = distortion_coeffs.at<double>(0,1);
p1 = distortion_coeffs.at<double>(0,2); //pay attention to the order!
p2 = distortion_coeffs.at<double>(0,3);
k3 = distortion_coeffs.at<double>(0,4);
QDomElement perspective = doc.createElement("Perspective Parameters" );
perspective.setAttribute("Fx", fx);
perspective.setAttribute("Fy", fy);
perspective.setAttribute("Cx", cx);
perspective.setAttribute("Cy", cy);
root.appendChild( perspective );
QDomElement distortion = doc.createElement("Distortion Parameters" );
distortion.setAttribute("K1", k1);
distortion.setAttribute("K2", k2);
distortion.setAttribute("K3", k3);
distortion.setAttribute("P1", p1);
distortion.setAttribute("P2", p2);
root.appendChild( distortion );
QFile file( "CameraPara.xml" );
if( !file.open(QIODevice::WriteOnly) )
return -1;
QTextStream ts( &file );
ts << doc.toString();
file.close();
//==============YML========================
FileStorage fs("CameraMatrix.yml", FileStorage::WRITE);
fs << "Perspective Matrix" << intrinsic_matrix;
fs << "Distortion Matrix" << distortion_coeffs;
return 0;
}
void Image_Processing::pEstimate()
{
#ifdef estP3P
// no armadillo structures outside of P3P, that was the whole point
// of creating a P3P class instead of simply using the PoseLM directly!
vector< Point2i > newImgPts(ImagePoints.size());
vector< Point3f > estPts(ImagePoints.size());
// randomize newImgPts outside of P3P
static int mynumberx = 0;
static int mynumbery = 0;
if((rand() % 10 + 1) > 5) {
mynumberx += rand() % 10 + 1;
mynumbery += rand() % 10 + 1;
}
else {
mynumberx -= rand() % 10 + 1;
mynumbery -= rand() % 10 + 1;
}
cout << "x: " << mynumberx << endl;
cout << "y: " << mynumbery << endl;
cout << "newImgPts:" << endl;
for (size_t i=0;i<newImgPts.size();++i)
{
newImgPts[i].x = ImagePoints.at(i).x + mynumberx;
newImgPts[i].y = ImagePoints.at(i).y + mynumbery;
cout << "\t\t" << newImgPts[i].x << "," << newImgPts[i].y << endl;
}
// set the new image points
q->setImagePoints(newImgPts);
// run the estimator
q->runEstimator();
// return result
estPts = q->getPose();
// print result for testing
//q->printResults();
// register the object points with the estimated points
transMat = q->getRegister(ObjectPoints,estPts);
circle(dframe,newImgPts.at(0),15,CV_RGB(255,0,0));
circle(dframe,newImgPts.at(1),15,CV_RGB(255,0,0));
circle(dframe,newImgPts.at(2),15,CV_RGB(255,0,0));
cout << "Final Transformation: " << endl;
for ( int i=0;i<4;++i)
{
for (int j=0;j<4;++j)
cout << transMat(i,j) << "\t";
cout << endl;
}
cout << endl;
#endif
#ifdef estPOSIT
p->runEstimator(srcImagePoints);
srcImagePoints = p->setImagePoints(srcImagePoints);
cout << srcImagePoints[0].x << " " << srcImagePoints[0].y << endl;
CvVect32f translation_vector = p->getTvec();
CvMatr32f rotation_matrix = p->getRmat();
p->printResults();
//Project the model points with the estimated pose
vector<Point2f> projectedPoints;
for ( size_t p=0; p<modelPoints.size(); p++ )
{
Point3f point3D;
point3D = Point3f((rotation_matrix[0] * modelPoints[p].x +
rotation_matrix[1] * modelPoints[p].y +
rotation_matrix[2] * modelPoints[p].z +
translation_vector[0]),
(rotation_matrix[3] * modelPoints[p].x +
rotation_matrix[4] * modelPoints[p].y +
rotation_matrix[5] * modelPoints[p].z +
translation_vector[1]),
(rotation_matrix[6] * modelPoints[p].x +
rotation_matrix[7] * modelPoints[p].y +
rotation_matrix[8] * modelPoints[p].z +
translation_vector[2]));
Point2f point2D = Point2f( 0.0, 0.0 );
if ( point3D.z != 0 )
{
point2D.x = fl * point3D.x / point3D.z;
point2D.y = fl * point3D.y / point3D.z;
}
projectedPoints.push_back( point2D );
}
//Draw the source image points
int centreX = static_cast<int>( cframe.cols * 0.5 );
int centreY = static_cast<int>( cframe.rows * 0.5 );
if(draw_pose) {
for ( size_t p=0; p<modelPoints.size(); p++ )
circle( cframe, Point( centreX + (int)srcImagePoints[p].x, centreY - (int)srcImagePoints[p].y ), 8, CV_RGB(255,0,0 ) );
//Draw the axes
line( cframe, Point( centreX + (int)projectedPoints[0].x, centreY - (int)projectedPoints[0].y ),
cvPoint( centreX + (int)projectedPoints[1].x, centreY - (int)projectedPoints[1].y ), CV_RGB( 0, 0, 255 ), 2 );
line( cframe, Point( centreX + (int)projectedPoints[0].x, centreY - (int)projectedPoints[0].y ),
cvPoint( centreX + (int)projectedPoints[2].x, centreY - (int)projectedPoints[2].y ), CV_RGB( 255, 0, 0 ), 2 );
line( cframe, Point( centreX + (int)projectedPoints[0].x, centreY - (int)projectedPoints[0].y ),
cvPoint( centreX + (int)projectedPoints[3].x, centreY - (int)projectedPoints[3].y ), CV_RGB( 0, 255, 0 ), 2 );
}
//Draw the projected model points
cout << "\n-.- ESTIMATED IMAGE POINTS -.-\n";
for ( size_t p=0; p<projectedPoints.size(); p++ )
{
if(draw_pose)
circle( cframe, cvPoint( centreX + (int)projectedPoints[p].x, centreY - (int)projectedPoints[p].y ), 3, CV_RGB(255,255,255 ), -1 );
cout << projectedPoints[p].x << ", " << projectedPoints[p].y << " \n";
}
Mat srcPoints(4,2,CV_32F);
for (size_t i=0;i<4;++i)
{
srcPoints.at<float>(i,0) = srcImagePoints[i].x;
srcPoints.at<float>(i,1) = srcImagePoints[i].y;
}
cout << endl;
int method = 0;
double ransaceReprojThreshold = 10;
Mat H(3,3,CV_32F);
H = findHomography(srcPoints, srcPoints, method, ransaceReprojThreshold);
cout<< "Homography Matrix:" << endl;
for (int i=0; i<H.size().height; i++)
cout << H.at<float>(i,0) << " | " << H.at<float>(i,1) << " | " << H.at<float>(i,2) << endl;
cout << endl;
#endif
}
/*
//read in dframe, display on cframe, use iframe for calculations
void Image_Processing::FindROI()
{
//have "load" button to load previously generated calibration
//upon button click, save current frame, analyze for ROI, display ROI on live feed
//tell user to keep end effectors within box and to rotate through all possible positions
//click next button to begin saving contours, click same button to finish
//prompt user to save calibration, recalibrate, or cancel
// user will then have to load previously generated calibration
//display loaded calibration name for each instrument
vector < Mat > channelsP;
vector < vector< Point > > contours;
Mat iframe, bframe;
// this will be the first frame grabbed from the capture to set up a reference
// it will be created upon first click of "new tool - get initial frame"
// this will be used to create our capture rectangle
Size dframe_sizeP = dframe.size(); //frame sizes
iframe = dframe;
//int middle = cframe_size.width/2; //find middle of frame
int middle = 475; //specified since capture isn't centered
//draw line through middle of frame for visual check
if(draw_features)
line( cframe, Point(middle,0), Point(middle,dframe_sizeP.height ), Scalar(0,0,255), 2, CV_AA );
cvtColor(iframe, bframe, CV_RGB2YCrCb ); //convert to grayscale or HLS
split(bframe, channelsP);
GaussianBlur(channelsP[2], gframe, Size (5,5), 4.0, 4.0 , 1);
threshold(gframe, tframe, 100, 255, THRESH_BINARY_INV | THRESH_OTSU ); //needs a SINGLE CHANNEL
// Contours
findContours(tframe,contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE );
Scalar color( 255, 0, 0 );
if(draw_features)
drawContours(cframe, contours, -1, color, 2, 8);
// Classify
int Num_of_Contours = contours.size();
int closest_right = dframe_sizeP.width;
int closest_left = 0;
int max_y = 0;
for (int i=0; i < Num_of_Contours; ++i)
{
int min_x = dframe_sizeP.width;
int max_x = 0;
int contour_length = contours[i].size();
for (int j=0; j < contour_length; ++j)
{
min_x = std::min(min_x,contours[i][j].x);
max_x = std::max(max_x,contours[i][j].x);
max_y = std::max(max_y,contours[i][j].y); //Find y closest to bottom
}
//extremes
if (min_x > middle)
{
closest_right = std::min(closest_right,min_x);
}
if (max_x < middle)
{
closest_left = std::max(closest_left,max_x);
}
}
if(draw_features) {
//draws lines representing the closest points on each contour to the middle
//line( dframe, Point(closest_right,0), Point(closest_right,cframe_size.height ), Scalar(0,255,0), 2, CV_AA );
//line( dframe, Point(closest_left,0), Point(closest_left,cframe_size.height ), Scalar(255,0,0), 2, CV_AA );
//line( dframe, Point(0,max_y), Point(cframe_size.width,max_y ), Scalar(255,255,0), 2, CV_AA );
}
Rect roiL( x_offset, y_offset, 50, 50 );
Rect roiR( closest_right-50, max_y-200, 200, 200 );
Mat dframe_roiL = dframe(roiL);
Mat dframe_roiR = dframe(roiR);
}
*/
void Image_Processing::findROI()
{
// region of interest point values
// Could add slider to adjust x and y instead of
// trying to detect instruments
point_Left_x = 150; // left side of roi
point_Left_y = 175; // top of roi - decreasing moves roi up
offset_Left_x = 100; // width to the right
offset_Left_y = 100; // height going down
point_Right_x = 390; // left side of roi
point_Right_y = 175; // top of roi - decreasing moves roi up
offset_Right_x = 100; // width to the right
offset_Right_y = 100; // height going down
// Specify regions of interest
roiL.x = point_Left_x;
roiL.y = point_Left_y;
roiL.width = offset_Left_x;
roiL.height = offset_Left_y;
roiR.x = point_Right_x;
roiR.y = point_Right_y;
roiR.width = offset_Right_x;
roiR.height = offset_Right_y;
}
void Image_Processing::SaveContours()
{
// needed to specify offset for drawing
vector<Vec4i> hierarchy;
// Left Side Specifics
vector < Mat > channelsL;
vector < vector< Point > > contoursL;
vector < vector< Point > > Left_Contour;
Mat iframeL, bframeL, gframeL, tframeL;
uframe_roiL = uframe(roiL);
iframeL = uframe_roiL;
// Left side - find contours
cvtColor(iframeL, bframeL, CV_RGB2YCrCb ); //convert to grayscale or HLS
split(bframeL, channelsL);
GaussianBlur(channelsL[2], gframeL, Size (5,5), 4.0, 4.0 , 1);
threshold(gframeL, tframeL, 100, 255, THRESH_BINARY_INV | THRESH_OTSU ); //needs a SINGLE CHANNEL
findContours(tframeL,contoursL, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE );
// Find largest contour ---------------------------not working
int Num_of_Left_Contours = contoursL.size();
unsigned int size_L = 0;
for (int i=0; i < Num_of_Left_Contours; ++i)
{
if (contoursL[i].size() >= size_L)
{
Left_Contour[0] = contoursL[i];
size_L = Left_Contour.size();
}
}
// counts idx for each saved contour
static int contour_count = 0;
// saves left contours to hashL
hashL.insert(contour_count,Left_Contour.at(0)); // ---------------------------
// Draw contours for visual check
Scalar colorL( 255, 0, 0 );
if(draw_features)
rectangle(cframe, roiL, Scalar(0,0,255), 1, 8); // Boundary of roiL
drawContours(cframe, Left_Contour, -1, colorL, 2, 8, hierarchy, 2, Point(point_Left_x,point_Left_y));
// Right Side Specifics
vector < Mat > channelsR;
vector < vector< Point > > contoursR;
Mat iframeR, bframeR, gframeR, tframeR;
uframe_roiR = uframe(roiR);
iframeR = uframe_roiR;
// Right side - find contours
cvtColor(iframeR, bframeR, CV_RGB2YCrCb ); //convert to grayscale or HLS
split(bframeR, channelsR);
GaussianBlur(channelsR[2], gframeR, Size (5,5), 4.0, 4.0 , 1);
threshold(gframeR, tframeR, 100, 255, THRESH_BINARY_INV | THRESH_OTSU ); //needs a SINGLE CHANNEL
findContours(tframeR,contoursR, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE );
// saves right contours to hashR
//hashR.insert(contour_count,contoursR.at(0));
// Draw contours
Scalar colorR( 0, 255, 0 );
if(draw_features)
rectangle(cframe, roiR, Scalar(0,0,255), 1, 8); // Boundary of roiR
drawContours(cframe, contoursR, -1, colorR, 2, 8, hierarchy, 2, Point(point_Right_x,point_Right_y));
//increment for next contour
contour_count++;
}
void Image_Processing::AddPoints()