cv::vector<pocket> PointLocator::infer(cv::vector<cv::KeyPoint> orangeKeyPoints, cv::vector<cv::KeyPoint> greenKeyPoints,
cv::vector<cv::KeyPoint> purpleKeyPoints, cv::vector<cv::KeyPoint> pinkKeyPoints)
{
//Define vector of pocket points to be passed
cv::vector<pocket> pockets;
//There should be a maximum of 2 points per colour. If there is more, reduce.
//This depends on the quality of test video results.
//Right now it just takes the first 2 points in vector to prevent crashes.
//Takes only one point for orange and purple since they are side pockets
//TODO if needed later.
if (orangeKeyPoints.size() > 1){
orangeKeyPoints.erase(orangeKeyPoints.begin() + 1, orangeKeyPoints.end());
}
if (greenKeyPoints.size() > 2){
greenKeyPoints.erase(greenKeyPoints.begin() + 2, greenKeyPoints.end());
}
if (purpleKeyPoints.size() > 1){
purpleKeyPoints.erase(purpleKeyPoints.begin() + 1, purpleKeyPoints.end());
}
if (pinkKeyPoints.size() > 3){
pinkKeyPoints.erase(pinkKeyPoints.begin() + 3, pinkKeyPoints.end());
}
//Returns a vector of pocket type
pockets = labelPockets(orangeKeyPoints, greenKeyPoints, purpleKeyPoints, pinkKeyPoints);
return pockets;
}
cv::vector< double > getMomentMatchBasedProbs( const cv::vector< cv::vector< cv::Point > > &contours )
{
cv::vector< double > probs( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{
probs[i] = 1.0 - fmin( matchShapes( contours[i], matchContour_, CV_CONTOURS_MATCH_I2, 0.0 ) / matchThreshold_, 1.0 );
}
return( probs );
}
cv::vector< double > getSurfMatchBasedProbs( const cv::vector< cv::vector< cv::Point > > &contours, cv_bridge::CvImagePtr cvPtr )
{
cv::vector< double > probs( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{
probs[i] = getSingleSurfProb( contours[i], cvPtr, i );
}
return( probs );
}
int GrayCodes::grayToDec(cv::vector<bool> gray)//convert a gray code sequence to a decimal number
{
int dec = 0;
bool tmp = gray[0];
if(tmp)
dec += (int) pow((float)2, int(gray.size() - 1));
for(int i = 1; i < gray.size(); i++){
tmp=Utilities::XOR(tmp,gray[i]);
if(tmp)
dec+= (int) pow((float)2,int (gray.size() - i - 1) );
}
return dec;
}
cv::vector<cv::DMatch> Matching::getSymetryMatches(
const cv::vector<cv::DMatch> &matches1, const cv::vector<cv::DMatch> &matches2){
cv::vector<cv::DMatch> symmetryMatches;
for(int i = 0; i < matches1.size(); i++){
for (int j = 0; j < matches2.size(); j++){
if(matches1[i].queryIdx == matches2[j].trainIdx && matches1[i].trainIdx == matches2[j].queryIdx ){
symmetryMatches.push_back(cv::DMatch(matches1[i].queryIdx, matches1[i].trainIdx, matches1[i].distance));
break;
}
}
}
return symmetryMatches;
}
// 透視投影変換行列の推定
void calcProjectionMatrix(cv::vector<cv::Point3d>& op, cv::vector<cv::Point2d>& ip, cv::Mat& dst)
{
cv::Mat A;
A.create(cv::Size(12, op.size()*2), CV_64FC1);
for (int i = 0, j = 0; i < op.size()*2; i+=2, ++j)
{
A.at<double>(i, 0) = 0.0;
A.at<double>(i, 1) = 0.0;
A.at<double>(i, 2) = 0.0;
A.at<double>(i, 3) = 0.0;
A.at<double>(i, 4) = -op[j].x;
A.at<double>(i, 5) = -op[j].y;
A.at<double>(i, 6) = -op[j].z;
A.at<double>(i, 7) = -1.0;
A.at<double>(i, 8) = ip[j].y*op[j].x;
A.at<double>(i, 9) = ip[j].y*op[j].y;
A.at<double>(i, 10) = ip[j].y*op[j].z;
A.at<double>(i, 11) = ip[j].y;
A.at<double>(i+1, 0) = op[j].x;
A.at<double>(i+1, 1) = op[j].y;
A.at<double>(i+1, 2) = op[j].z;
A.at<double>(i+1, 3) = 1.0;
A.at<double>(i+1, 4) = 0.0;
A.at<double>(i+1, 5) = 0.0;
A.at<double>(i+1, 6) = 0.0;
A.at<double>(i+1, 7) = 0.0;
A.at<double>(i+1, 8) = -ip[j].x*op[j].x;
A.at<double>(i+1, 9) = -ip[j].x*op[j].y;
A.at<double>(i+1, 10) = -ip[j].x*op[j].z;
A.at<double>(i+1, 11) = -ip[j].x;
}
cv::Mat pvect;
cv::SVD::solveZ(A, pvect);
cv::Mat pm(3, 4, CV_64FC1);
for (int i = 0; i < 12; i++)
{
pm.at<double>(i/4, i%4) = pvect.at<double>( i );
}
dst = pm;
}
int findChessboard(cv::vector<cv::Mat> &rgb, cv::vector<cv::Mat> &depth,
cv::vector<cv::vector<cv::vector<cv::Point2f> > > &imagePoints,
const cv::Size patternSize,
const int &fileNum){
for(int i = 0; i < rgb.size(); ++i){
cout << i << endl;
if( cv::findChessboardCorners( rgb[i],
patternSize,
imagePoints[0][i],
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE
) &&
cv::findChessboardCorners( depth[i],
patternSize,
imagePoints[1][i] ,
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE
) ) {
std::cout << " ... All corners found." << std::endl;
cv::cornerSubPix(rgb[i], imagePoints[0][i], cv::Size(11,11), cv::Size(-1,-1),
cv::TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
30, 0.01));
cv::cornerSubPix(depth[i], imagePoints[1][i], cv::Size(11,11), cv::Size(-1,-1),
cv::TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
30, 0.01));
// 検出点を描画する
cv::drawChessboardCorners( rgb[i], patternSize, ( cv::Mat )( imagePoints[0][i] ), true );
cv::drawChessboardCorners( depth[i], patternSize, ( cv::Mat )( imagePoints[1][i] ), true );
cv::imshow( "rgb", rgb[i] );
cv::imshow("depth", depth[i]);
cv::waitKey( 100 );
} else {
std::cout << " ... at least 1 corner not found." << std::endl;
rgb.erase(rgb.begin() + i);
depth.erase(depth.begin() + i);
imagePoints[0].erase(imagePoints[0].begin() + i);
imagePoints[1].erase(imagePoints[1].begin() + i);
cout << rgb.size() << endl;;
// fileNum--;
i--;
cv::waitKey( 100 );
}
}
return rgb.size();
}
void RemoveNoise::removeOutlier(cv::vector<cv::Point2f>& start,
cv::vector<cv::Point2f>& end) const
{
float averageNorm = 0.0f;
for(auto startIter=start.begin(),endIter=end.begin();
startIter!=start.end(); startIter++,endIter++)
{
averageNorm += cv::norm(*startIter - *endIter);
}
averageNorm /= start.size();
for(auto startIter=start.begin(), endIter=end.begin();
startIter!=start.end(); /* look at the end of for */)
{
if(cv::norm(*startIter - *endIter) > threshNorm * averageNorm){
startIter = start.erase(startIter);
endIter = end.erase(endIter);
continue;
}
startIter++, endIter++;
}
}
/**
* Another function to validate ellipses. Based on having an error measure of
* points of contours with respect to their respective position
* on the fitted ellipse.
*/
bool MyEllipses::errorMeasureEllipse(cv::RotatedRect anEllipse, cv::vector<cv::Point> setOfPoints){
/* Equation of an ellipse
(x-h)^2/a^2 + (y-k)^2/b^2 = 1
where,
(h,k) are the coordinates of the center of the ellipse
a is the length of the semi-major or minor axis
b is the length of the semi-major or minor axis
*/
float h = anEllipse.center.x;
float k = anEllipse.center.y;
float a = anEllipse.size.width / 2;
float b = anEllipse.size.height / 2;
float diff = 0;
int size = setOfPoints.size();
for( int i = 0; i < size; i++){
float xx = (float) (setOfPoints[i].x) - h;
float yy = (float) (setOfPoints[i].y) - k;
float common = a*b/(float) (sqrt((double) ((b*xx)*(b*xx) + (a*yy)*(a*yy))));
float xIntersection = xx*common;
float yIntersection = yy*common;
//distance = sqrt((xx-x)2 + (yy-y)2)
float currentDiff = (float) sqrt((xx-xIntersection)*(xx-xIntersection) + (yy-yIntersection)*(yy-yIntersection));
diff = diff + currentDiff;
}
diff = diff/size;
return diff < 100;
}
void drawDetections(const cv::vector<cv::Point2f>& detections, const cv::Scalar& color, cv::Mat image)
{
for (size_t i = 0; i < detections.size(); ++i)
{
circle(image, detections[i], 3, color, 1, 8, 0);
}
}
bool VisualFeatureExtraction::cutFeatures(cv::vector<cv::KeyPoint> &kpts,
cv::Mat &features, unsigned short maxFeats) const {
// store hash values in a map
std::map<size_t, unsigned int> keyp_hashes;
cv::vector<cv::KeyPoint>::iterator itKeyp;
cv::Mat sorted_features;
unsigned int iLine = 0;
for (itKeyp = kpts.begin(); itKeyp < kpts.end(); itKeyp++, iLine++)
keyp_hashes[(*itKeyp).hash()] = iLine;
// sort values according to the response
std::sort(kpts.begin(), kpts.end(), greater_than_response());
// create a new descriptor matrix with the sorted keypoints
sorted_features.create(0, features.cols, features.type());
sorted_features.reserve(features.rows);
for (itKeyp = kpts.begin(); itKeyp < kpts.end(); itKeyp++)
sorted_features.push_back(features.row(keyp_hashes[(*itKeyp).hash()]));
features = sorted_features.clone();
// select the first maxFeats features
if (kpts.size() > maxFeats) {
vector<KeyPoint> cutKpts(kpts.begin(), kpts.begin() + maxFeats);
kpts = cutKpts;
features = features.rowRange(0, maxFeats).clone();
}
return 0;
}
bool RemoveNoise::isEnoughAllVector(cv::vector<cv::Point2f>& start) const
{
int count = (int) start.size();
if(count < threshNum) return false;
return true;
}
/* function AverageLine */
void lineAverage(cv::vector<cv::Vec2f> lines, cv::Mat& src)
{
float rho=0,theta=0;
for( size_t i = 0; i < lines.size(); i++ )
{
rho += lines[i][0];theta += lines[i][1];
}
rho/=lines.size();theta/=lines.size();
cv::Point pt1, pt2;
double a = cos(theta), b = sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
//cv::line( src, pt1, pt2, cv::Scalar(80,10,55), 3, CV_AA);
float rho1=0,rho2=0,theta1=0,theta2=0;
float i1=0,i2=0;
for( size_t i = 0; i < lines.size(); i++ )
{
if (lines[i][1]>theta)
{ rho1 += lines[i][0];theta1 += lines[i][1];i1++;}
else
{ rho2 += lines[i][0];theta2 += lines[i][1];i2++;}
}
rho1/=i1;theta1/=i1;
a = cos(theta1), b = sin(theta1);
x0 = a*rho1, y0 = b*rho1;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line( src, pt1, pt2, cv::Scalar(0,100,255), 3, CV_AA);
rho2/=i2;theta2/=i2;
a = cos(theta2), b = sin(theta2);
x0 = a*rho2, y0 = b*rho2;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line( src, pt1, pt2, cv::Scalar(0,100,0), 3, CV_AA);
}
int findChessboards(
cv::vector<cv::Mat> &lefts, cv::vector<cv::Mat> &rights,
cv::vector<cv::vector<cv::vector<cv::Point2f>>> &imagePoints,
const cv::Size patternSize, const int &fileNum) {
for (size_t i = 0; i < lefts.size(); ++i) {
if (cv::findChessboardCorners(
lefts[i], patternSize, imagePoints[0][i],
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE) &&
cv::findChessboardCorners(
rights[i], patternSize, imagePoints[1][i],
CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE)) {
cv::cornerSubPix(
lefts[i], imagePoints[0][i], cv::Size(11, 11), cv::Size(-1, -1),
cv::TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 30, 0.01));
cv::cornerSubPix(
rights[i], imagePoints[1][i], cv::Size(11, 11), cv::Size(-1, -1),
cv::TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 30, 0.01));
cv::drawChessboardCorners(
lefts[i], patternSize, (cv::Mat)(imagePoints[0][i]), true);
cv::drawChessboardCorners(
rights[i], patternSize, (cv::Mat)(imagePoints[1][i]), true);
cv::imshow("Left", lefts[i]);
cv::imshow("Right", rights[i]);
} else {
std::cout << "cannot find all corners" << std::endl;
lefts.erase(lefts.begin() + i);
rights.erase(rights.begin() + i);
imagePoints[0].erase(imagePoints[0].begin() + i);
imagePoints[1].erase(imagePoints[1].begin() + i);
i--;
}
cv::waitKey(100);
}
return lefts.size();
}
/**
* A function that takes a list of ellipses with their respective quality and returns the best one.
*/
cv::RotatedRect MyEllipses::getBestEllipse(cv::vector<cv::RotatedRect> ellipses, cv::vector<double> qualityOfEllipses){
int size = ellipses.size(); std::cout<<size;
double maxQuality = 0;
int index = 0;
for(int i = 0; i < size; i++){
if( qualityOfEllipses[i]>maxQuality ){
maxQuality = qualityOfEllipses[i];
index = i;
}
}
return ellipses[index];
}
/*
* Takes the vector of detected contours from and image and determines whether the points of a contour are clustered around an endpoint making the contour invalid.
* Any contours deemed to be invalid are not included in the returned vector containing contours deemed valid
*
*@param contours - a vector containing all the detected contours
*@param lines - a vector containing all detected straight lines
*
*@return - a vector of valid contours
*/
cv::vector< cv::vector<cv::Point> > ImageProcessor::removeRedundantContours(cv::vector< cv::vector<cv::Point> > & contours, cv::vector<cv::Vec4i> lines){
cv::vector< cv::vector<cv::Point> > valid_contours; //a vector to contain all contours that are determined to be valid
for(int i = 0; i < (int)contours.size(); i++){
float invalid_point_count = 0.0f; //count of points in a contour that are clustering around an endpoint
cv::vector<cv::Point> contour_vec = contours[i];
for(int k = 0; k < (int)contour_vec.size(); k++){
//compute the distance between each point in the contour and the endpoints of each straight line
cv::Point point = contour_vec[k];
for(int j = 0; j < (int)lines.size(); j++){
//distances of countour point from each endpoint
double dist1 = distance(point, cv::Point(lines[j][0], lines[j][1]));
double dist2 = distance(point, cv::Point(lines[j][2], lines[j][3]));
//distance between both endpoints of line[j]
double endpoint_dist = distance(cv::Point(lines[j][0], lines[j][1]), cv::Point(lines[j][2], lines[j][3]));
double contour_inLine_dist = dist1 + dist2;
//if the distance between the contour point and a line endpoint, then increment the number of detected invalid points
if(dist1 < MAX_DIST || dist2 < MAX_DIST || (contour_inLine_dist - endpoint_dist) < 1.0 ){
invalid_point_count++;
break;
}
}
}
//compute the percentage of invalid points in the contour, current contour is valid and pushed onto back of valid_contour vector
//if less than 10% of the points are found to be invalid.
float invalid_percentage = invalid_point_count / (float) contour_vec.size();
if(invalid_percentage < PERCENTAGE){
valid_contours.push_back(contours[i]);
}
}
return valid_contours;
}
cv::vector< double > getAreaBasedProbs( const cv::vector< cv::vector< cv::Point > > &contours )
{
int largestContour = -1;
double maxArea = 0.0;
cv::vector< double > probs( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{
probs[i] = cv::contourArea( contours[i] );
if( maxArea < probs[i] )
{
maxArea = probs[i];
largestContour = i;
}
}
for( int i = 0; i < contours.size(); i++ )
{
probs[i] /= probs[largestContour];
}
return( probs );
}
void AGPathDetection::detectPaths(cv::vector<cv::vector<AGImage>> &imagesMatrix)
{
this->testingMode = false;
// this->testSelectedImage(imagesMatrix[1][0]);
// this->testSelectedImage(imagesMatrix[1][0]);
for (int x = 0; x < imagesMatrix.size(); x++) {
for (int y = 0; y < imagesMatrix.front().size(); y++) {
Mat skeleton;
this->prepareForPathDetecting(imagesMatrix[x][y].image, skeleton);
this->searchForPathsInImageUsingSkeleton(imagesMatrix[x][y], skeleton);
this->testDetectedPathsInImage(imagesMatrix[x][y]);
}
}
}
//Remove pink pocket from vector that is between 2 green points.
void PointLocator::removePinkCandidate(cv::vector<cv::KeyPoint> &pinkKeyPoints, cv::KeyPoint firstPocket, cv::KeyPoint secondPocket){
//First check that there are actually pink pocket points
if (!pinkKeyPoints.empty()){
float distance = -1;
int min = 0;
cv::KeyPoint middlePoint;
middlePoint.pt.x = (firstPocket.pt.x + secondPocket.pt.x) / 2;
middlePoint.pt.y = (firstPocket.pt.y + secondPocket.pt.y) / 2;
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = distBetweenKeyPoints(pinkKeyPoints[i], middlePoint);
if ((distance + 1) < epsilon || newDistance < distance){
distance = newDistance;
min = i;
}
}
pinkKeyPoints.erase(pinkKeyPoints.begin() + min, pinkKeyPoints.begin() + min + 1);
}
}
//ランダムに6点を抽出
void get_six_points(cv::vector<cv::Point2d>& calib_p, cv::vector<cv::Point3d>& calib_P, cv::vector<cv::Point2d>& src_p, cv::vector<cv::Point3d>& src_P)
{
int i=0;
srand(time(NULL)); /* 乱数の初期化 */
cv::Vector<int> exists;
while(i <= 6){
int maxValue = (int)src_p.size();
int v = rand() % maxValue;
bool e2=false;
for(int s=0; s<i; s++){
if(exists[s] == v) e2 = true;
}
if(!e2){
exists.push_back(v);
calib_P.push_back(src_P[v]);
calib_p.push_back(src_p[v]);
i++;
}
}
}
/*
* Convert the points that contain contour coordinates and store them in a Vec4i data type
*
*@param contours - vector containining contour information
*
*@return the contour information as a vector of Vec4i data types
*/
cv::vector<cv::Vec4i> ImageProcessor::pointsToVec4i(const cv::vector< cv::vector<cv::Point> > & contours){
cv::vector<cv::Vec4i> vector;
for(int i = 0; i < (int)contours.size(); i++){
cv::vector<cv::Point> contour = contours[i];
for(int j = 1; j < (int)contour.size();j++){
cv::Point p1, p2;
cv::Vec4i vec;
p1 = contour[j - 1];
p2 = contour[j];
vec[0] = p1.x;
vec[1] = p1.y;
vec[2] = p2.x;
vec[3] = p2.y;
vector.push_back(vec);
}
}
return vector;
}
//元画像の特徴点と、再計算した特徴点の誤差を求める
double inspection_error_value(cv::Mat& cameraMat, cv::vector<cv::Point3d>& P, cv::vector<cv::Point2d>& groundTruth)
{
if(cameraMat.cols != 4 || cameraMat.rows != 3){
return 0.0;
}
cv::vector<cv::Point2d> p;
for(int i=0; i<(int)P.size(); i++){
double x = (cameraMat.at<double>(0,0)*P[i].x + cameraMat.at<double>(0,1)*P[i].y + cameraMat.at<double>(0,2)*P[i].z + cameraMat.at<double>(0,3))
/ (cameraMat.at<double>(2,0)*P[i].x + cameraMat.at<double>(2,1)*P[i].y + cameraMat.at<double>(2,2)*P[i].z + cameraMat.at<double>(2,3));
double y = (cameraMat.at<double>(1,0)*P[i].x + cameraMat.at<double>(1,1)*P[i].y + cameraMat.at<double>(1,2)*P[i].z + cameraMat.at<double>(1,3))
/ (cameraMat.at<double>(2,0)*P[i].x + cameraMat.at<double>(2,1)*P[i].y + cameraMat.at<double>(2,2)*P[i].z + cameraMat.at<double>(2,3));
p.push_back(cv::Point2d(x,y));
}
double sum = 0.0;
for(int i=0; i<(int)p.size(); i++){
double error = pow(pow(groundTruth[i].x-p[i].x,2.0)+pow(groundTruth[i].y-p[i].y,2.0),0.5);
sum = sum + error;
}
return sum/(int)p.size();
}
cv::vector<pocket> PointLocator::labelPockets(cv::vector<cv::KeyPoint> orangeKeyPoints, cv::vector<cv::KeyPoint> greenKeyPoints,
cv::vector<cv::KeyPoint> purpleKeyPoints, cv::vector<cv::KeyPoint> pinkKeyPoints){
//Define vector of pocket points to be passed
cv::vector<pocket> pockets(4);
int pocketCount = 0;
int realPocketCount = 0;
bool pinkTop = true;
bool pinkLeft = true;
bool pinkRight = true;
if (greenKeyPoints.size() + orangeKeyPoints.size() + pinkKeyPoints.size() + purpleKeyPoints.size() >= 4){
defPerspective = true;
}
//Select green pockets: Case 1: 2 green pockets in view
if (greenKeyPoints.size() == 2){
//Step 1: If only green pockets are seen, select destination locations based on their x values.
if (orangeKeyPoints.size() == 0 && purpleKeyPoints.size() == 0){
if (greenKeyPoints[0].pt.x < greenKeyPoints[1].pt.x){
pockets[0].pocketPoints = greenKeyPoints[0];
pockets[1].pocketPoints = greenKeyPoints[1];
}
else{
pockets[0].pocketPoints = greenKeyPoints[1];
pockets[1].pocketPoints = greenKeyPoints[0];
}
}
//Step 2: If green end pockets and if both purple and orange side pockets are in view
//Is there more logic we can use to make sure this is right? Right now it is same as just orange pockets logic.
else if (orangeKeyPoints.size() > 0 && purpleKeyPoints.size() > 0){
float distGreen0ToOrange = distBetweenKeyPoints(greenKeyPoints[0], orangeKeyPoints[0]);
float distGreen1ToOrange = distBetweenKeyPoints(greenKeyPoints[1], orangeKeyPoints[0]);
if (distGreen0ToOrange > distGreen1ToOrange){
pockets[0].pocketPoints = greenKeyPoints[0];
pockets[1].pocketPoints = greenKeyPoints[1];
}
else{
pockets[0].pocketPoints = greenKeyPoints[1];
pockets[1].pocketPoints = greenKeyPoints[0];
}
}
//Step 3: If green end pockets and if only the orange side pocket is in view
else if (orangeKeyPoints.size() > 0){
float distGreen0ToOrange = distBetweenKeyPoints(greenKeyPoints[0], orangeKeyPoints[0]);
float distGreen1ToOrange = distBetweenKeyPoints(greenKeyPoints[1], orangeKeyPoints[0]);
if (distGreen0ToOrange > distGreen1ToOrange){
pockets[0].pocketPoints = greenKeyPoints[0];
pockets[1].pocketPoints = greenKeyPoints[1];
}
else{
pockets[0].pocketPoints = greenKeyPoints[1];
pockets[1].pocketPoints = greenKeyPoints[0];
}
}
//Step 4: If green end pockets and if only the purple side pocket is in view
else if (purpleKeyPoints.size() > 0){
float distGreen0ToPurple = distBetweenKeyPoints(greenKeyPoints[0], purpleKeyPoints[0]);
float distGreen1ToPurple = distBetweenKeyPoints(greenKeyPoints[1], purpleKeyPoints[0]);
if (distGreen0ToPurple < distGreen1ToPurple){
pockets[0].pocketPoints = greenKeyPoints[0];
pockets[1].pocketPoints = greenKeyPoints[1];
}
else{
pockets[0].pocketPoints = greenKeyPoints[1];
pockets[1].pocketPoints = greenKeyPoints[0];
}
}
//Removes pink keypoint candidate which is between green pockets. (Co-linear)
removePinkCandidate(pinkKeyPoints, pockets[0].pocketPoints, pockets[1].pocketPoints);
pinkTop = false;
//Puts the pockets destination locations in since top left pocket will always be pockets[0]
pockets[0].xLocation = xLeft;
pockets[0].yLocation = yTop;
pockets[1].xLocation = xRight;
pockets[1].yLocation = yTop;
//Updates Pocket count
pocketCount = 2;
realPocketCount = 2;
}
//Step 5: Select green pockets: Case 2: 1 green pocket in view
if (greenKeyPoints.size() == 1){
pockets[0].pocketPoints = greenKeyPoints[0];
if (orangeKeyPoints.size() > 0 && purpleKeyPoints.size() > 0){
float distToOrange = distBetweenKeyPoints(greenKeyPoints[0], orangeKeyPoints[0]);
float distToPurple = distBetweenKeyPoints(greenKeyPoints[0], purpleKeyPoints[0]);
if (distToOrange < distToPurple){
pockets[0].xLocation = xRight;
pockets[0].yLocation = yTop;
}
else{
pockets[0].xLocation = xLeft;
pockets[0].yLocation = yTop;
}
}
else if (orangeKeyPoints.size() > 0){
pockets[0].xLocation = xRight;
pockets[0].yLocation = yTop;
}
else if (purpleKeyPoints.size() > 0){
pockets[0].xLocation = xLeft;
pockets[0].yLocation = yTop;
}
//Updates Pocket count
pocketCount = 1;
realPocketCount = 1;
}
//Update orange and purple pockets after green pockets are in so we know that green pockets are first in vector.
if (orangeKeyPoints.size() > 0){
pockets[pocketCount].pocketPoints = orangeKeyPoints[0];
pockets[pocketCount].xLocation = xRight;
pockets[pocketCount].yLocation = yMid;
pocketCount++;
realPocketCount++;
}
if (purpleKeyPoints.size() > 0){
pockets[pocketCount].pocketPoints = purpleKeyPoints[0];
pockets[pocketCount].xLocation = xLeft;
pockets[pocketCount].yLocation = yMid;
pocketCount++;
realPocketCount++;
}
//Removes pink candidates between green and orange and pink pockets
if (greenKeyPoints.size() == 2){
if (orangeKeyPoints.size() > 0){
removePinkCandidate(pinkKeyPoints, pockets[1].pocketPoints, orangeKeyPoints[0]);
pinkRight = false;
}
if (purpleKeyPoints.size() > 0){
removePinkCandidate(pinkKeyPoints, pockets[0].pocketPoints, purpleKeyPoints[0]);
pinkLeft = false;
}
}
else if (greenKeyPoints.size() == 1){
int removeLocation = 0;
if (orangeKeyPoints.size() > 0 && purpleKeyPoints.size() > 0){
float distToOrange = distBetweenKeyPoints(orangeKeyPoints[0], pockets[0].pocketPoints);
float distToPurple = distBetweenKeyPoints(purpleKeyPoints[0], pockets[0].pocketPoints);
if (distToOrange > distToPurple){
removeLocation = 2;
}
else{
removeLocation = 1;
}
}
else if (orangeKeyPoints.size() > 0 && (removeLocation == 0 || removeLocation == 1)){
removePinkCandidate(pinkKeyPoints, pockets[0].pocketPoints, orangeKeyPoints[0]);
pinkRight = false;
}
if (purpleKeyPoints.size() > 0 && (removeLocation == 0 || removeLocation == 2)){
removePinkCandidate(pinkKeyPoints, pockets[0].pocketPoints, purpleKeyPoints[0]);
pinkLeft = false;
}
}
//Adds pink pockets to list of pockets based on other pockets identified.
while (!pinkKeyPoints.empty() && pockets[3].xLocation == NULL && pocketCount < 4){
//Find the pink marker closest to the first pocket in list.
//It is structured so this is always the right marker to choose because of elimination of markers from candidate list.
float distance = -1;
int min = 0;
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = distBetweenKeyPoints(pinkKeyPoints[i], pockets[0].pocketPoints);
if ((distance + 1) < epsilon || newDistance < distance){
distance = newDistance;
min = i;
}
}
pockets[pocketCount].pocketPoints = pinkKeyPoints[min];
//
if (pinkTop){
pockets[pocketCount].xLocation = xMid;
pockets[pocketCount].yLocation = yTop;
pocketCount++;
pinkTop = false;
}
else if (pinkLeft){
pockets[pocketCount].xLocation = xLeft;
pockets[pocketCount].yLocation = yMidTop;
pocketCount++;
pinkLeft = false;
}
else if (pinkRight){
pockets[pocketCount].xLocation = xRight;
pockets[pocketCount].yLocation = yMidTop;
pocketCount++;
pinkRight = false;
}
//Remove pink marker from candidate list
pinkKeyPoints.erase(pinkKeyPoints.begin() + min, pinkKeyPoints.begin() + min + 1);
}
//Use the pink marker furthest to the left
/*if ((pocketCount == 2 || pocketCount == 3) && !pinkKeyPoints.empty()){
//Determine which pink side marker is being used.
//Should be marker closest along line between first two pockets.
float distance = -1;
int min = 0;
cv::Vec2f line = lineEqn(pockets[0].pocketPoints.pt.x, pockets[0].pocketPoints.pt.y, pockets[1].pocketPoints.pt.x, pockets[1].pocketPoints.pt.y);
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = pinkKeyPoints[i].pt.x;
if ((distance + 1) < epsilon || newDistance < distance){
distance = newDistance;
min = i;
}
}
pockets[pocketCount].pocketPoints = pinkKeyPoints[min];
pockets[pocketCount].xLocation = xLeft;
pockets[pocketCount].yLocation = yMidTop;
pinkKeyPoints.erase(pinkKeyPoints.begin() + min, pinkKeyPoints.begin() + min + 1);
pocketCount++;
}*/
//If 2 or 3 pockets are picked up, use any pink side marker
/*if (pocketCount == 2 || pocketCount == 3){
//Determine which pink side marker is being used.
//Should be marker closest along line between first two pockets.
float distance = -1;
int min = 0;
cv::Vec2f line = lineEqn(pockets[0].pocketPoints.pt.x, pockets[0].pocketPoints.pt.y, pockets[1].pocketPoints.pt.x, pockets[1].pocketPoints.pt.y);
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = distPointToLine(pinkKeyPoints[i].pt.x, pinkKeyPoints[i].pt.y, line);
if ((distance + 1) < epsilon || newDistance < distance){
distance = newDistance;
min = i;
}
}
pockets[pocketCount].pocketPoints = pinkKeyPoints[min];
pockets[pocketCount].xLocation = (pockets[0].xLocation + pockets[1].xLocation) / 2;
pockets[pocketCount].yLocation = (pockets[0].yLocation + pockets[1].yLocation) / 2;
pocketCount++;
}*/
//If 2 pockets are picked up, use a pink marker not linearly dependent with the pockets.
//This is accomplished by finding the pink marker furthest from the line.
/*if (realPocketCount == 2){
//Determine which pink side marker is being used.
//Should be marker furthest along line between pockets.
float distance = 0;
int max = 0;
cv::Vec2f line = lineEqn(pockets[0].pocketPoints.pt.x, pockets[0].pocketPoints.pt.y, pockets[1].pocketPoints.pt.x, pockets[1].pocketPoints.pt.y);
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = distPointToLine(pinkKeyPoints[i].pt.x, pinkKeyPoints[i].pt.y, line);
if ( newDistance > distance){
distance = newDistance;
max = i;
}
pockets[pocketCount].pocketPoints = pinkKeyPoints[max];
//Remove pink Keypoint so it doesn't get used as 4th point in the transform.
if (!pinkKeyPoints.empty()){
pinkKeyPoints.erase(pinkKeyPoints.begin() + max, pinkKeyPoints.begin() + max + 1);
}
pocketCount++;
//Need to determine coordinates for point in perspective transform
addNonLinearPointLocation(pockets);
}
}*/
/*//If 3 pockets are picked up, use any pink side marker
if (pocketCount == 3){
//Determine which pink side marker is being used.
//Should be marker closest along line between first two pockets.
float distance = -1;
int min = 0;
cv::Vec2f line = lineEqn(pockets[0].pocketPoints.pt.x, pockets[0].pocketPoints.pt.y, pockets[1].pocketPoints.pt.x, pockets[1].pocketPoints.pt.y);
for (int i = 0; i < pinkKeyPoints.size(); i++){
float newDistance = distPointToLine(pinkKeyPoints[i].pt.x, pinkKeyPoints[i].pt.y, line);
if ((distance + 1) < epsilon || newDistance < distance){
distance = newDistance;
min = i;
}
pockets[pocketCount].pocketPoints = pinkKeyPoints[min];
if (!pinkKeyPoints.empty()){
pinkKeyPoints.erase(pinkKeyPoints.begin() + min);
}
//Need to determine coordinates for point in perspective transform
//First calculate distance from both known pocket points
float distToPocket0 = distBetweenKeyPoints(pockets[0].pocketPoints, pockets[pocketCount - 1].pocketPoints);
float distToPocket1 = distBetweenKeyPoints(pockets[1].pocketPoints, pockets[pocketCount - 1].pocketPoints);
//For the case where both pockets are top pockets, pink pocket must be directly below these.
if (inferPurple && inferOrange){
pockets[pocketCount].yLocation = yMidTop;
if (distToPocket0 < distToPocket1)
pockets[pocketCount].xLocation = xLeft;
else
pockets[pocketCount].xLocation = xRight;
}
if (!inferOrange){
if (distToPocket0 < distToPocket1){
pockets[pocketCount].xLocation = xMid;
pockets[pocketCount].yLocation = yTop;
}
else{
//May get here in test video on accident
//Then logic is broken.
pockets[pocketCount].xLocation = xRight;
pockets[pocketCount].yLocation = yMidBot;
}
}
if (!inferPurple){
if (distToPocket0 < distToPocket1){
pockets[pocketCount].xLocation = xMid;
pockets[pocketCount].yLocation = yTop;
}
else{
//Should never get here in test video
pockets[pocketCount].xLocation = xRight;
pockets[pocketCount].yLocation = yMidBot;
}
}
//Increase pocket count once all locations are set.
pocketCount++;
}
}*/
/*while (pockets.size() >= 2 && pockets.size() < 4 && !pinkKeyPoints.empty()){
if (pinkKeyPoints.size() > 0){
int i = 0;
float distance = -1;
else{
//Find equation for line
for (int j = 0; j < pinkKeyPoints.size(); j++){
float newDistance = sqrt();
if (distance == 0 || newDistance < distance){
}
}
pockets[pocketCount] = pinkKeyPoints(i);
if (!pinkKeyPoints.empty()){
pinkKeyPoints.erase(pinkKeyPoints.begin() + i);
}
pocketCount++;
}
}
}*/
/*if (pocketCount == 3){
cv::KeyPoint tempPoint = cv::KeyPoint();
tempPoint.pt.x = (pockets[0].pocketPoints.pt.x + pockets[1].pocketPoints.pt.x) / 2;
tempPoint.pt.y = (pockets[0].pocketPoints.pt.y + pockets[1].pocketPoints.pt.y) / 2;
pockets[pocketCount].pocketPoints = tempPoint;
pockets[pocketCount].xLocation = (pockets[0].xLocation + pockets[1].xLocation) / 2;
pockets[pocketCount].yLocation = (pockets[0].yLocation + pockets[1].yLocation) / 2;
pocketCount++;
}*/
return pockets;
}
cv::Mat MSFM::MSFMSurfaceO2(cv::Mat& image, cv::vector<cv::Point>& initials, cv::Point2d &h) {
cv::Mat u_surface = MAX_VAL*cv::Mat::ones(image.rows, image.cols, CV_64FC1);
cv::Mat state = cv::Mat::zeros(image.rows, image.cols, CV_8UC1);
std::multimap<double, cv::Point> trial_set;
std::map<int, std::multimap<double, cv::Point>::iterator> mapa_trial;
std::multimap<double, cv::Point>::iterator trial_set_it;
std::map<int, std::multimap<double, cv::Point>::iterator>::iterator mapa_trial_it;
std::pair<double, cv::Point> pr_trial;
std::pair<int, std::multimap<double, cv::Point>::iterator> pr_mapa;
int key, i;
cv::Point winner, neigh;
// Initialization
for (i = 0; i < (int) initials.size(); i++) {
key = initials[i].y + image.rows*initials[i].x;
if (mapa_trial.find(key) == mapa_trial.end()) {
distance2M(initials[i]) = 0.0;
state2M(initials[i]) = P_TRIAL;
pr_trial = std::pair<double, cv::Point>(0.0, initials[i]);
trial_set_it = trial_set.insert(pr_trial);
pr_mapa = std::pair<int, std::multimap<double, cv::Point>::iterator>(key, trial_set_it);
mapa_trial.insert(pr_mapa);
}
}
// LOOP
while (!trial_set.empty()) {
trial_set_it = trial_set.begin();
key = trial_set_it->second.y + image.rows*trial_set_it->second.x;
mapa_trial_it = mapa_trial.find(key);
if (mapa_trial_it == mapa_trial.end()) {
printf("ERROR: bad map alloc");
exit(-1);
}
if (mapa_trial_it->second != trial_set_it) {
printf("ERROR: bad trial/map alloc");
exit(-1);
}
winner = trial_set_it->second;
trial_set.erase(trial_set_it);
mapa_trial.erase(mapa_trial_it);
state2M(winner) = P_ALIVE;
// UPWIND PROCEDURE
for (int i=-1; i<2; i+=2) {
neigh = cv::Point(winner.x + i, winner.y);
if (contains2M(neigh))
this->StencilS1O2(image, u_surface, state, trial_set, mapa_trial, neigh, h);
neigh = cv::Point(winner.x, winner.y + i);
if (contains2M(neigh))
this->StencilS1O2(image, u_surface, state, trial_set, mapa_trial, neigh, h);
neigh = cv::Point(winner.x + i, winner.y + i);
if (contains2M(neigh))
this->StencilS2O2(image, u_surface, state, trial_set, mapa_trial, neigh, h);
neigh = cv::Point(winner.x - i, winner.y + i);
if (contains2M(neigh))
this->StencilS2O2(image, u_surface, state, trial_set, mapa_trial, neigh, h);
}
}
return u_surface;
}