void SegmentTrack::drawMap()
{
Mat img, img_scaled;
// For drawing purposes convert positive values to 255, zero to 0.
// Resulting drawing will be black and white existence map drawing
this->M.convertTo(img, CV_8U, 255, 0);
cvtColor(img,img,CV_GRAY2BGR);
drawCursor(img);
scaleUpMap(img, img_scaled, mapScaleFactor, mapScaleFactor);
emit showTrackMap(mat2QImage(img_scaled));
}
void QImage2MatDialog::mat2qImageShow(QString& imagePath)
{
cv::Mat mat = cv::imread(_imageFilePath.toStdString(), IMREAD_UNCHANGED);
qimageShow(mat2QImage(mat), ui->graphicsView_mat);
}
//Calculates matching cost and show matching results on the window
float GraphMatch::drawMatches(vector<NodeSig> ns1, vector<NodeSig> ns2,
Mat img1, Mat img2)
{
Mat assignment, cost;
//Construct [rowsxcols] cost matrix using pairwise
//distance between node signature
int rows = ns1.size();
int cols = ns2.size();
int** cost_matrix = (int**)calloc(rows,sizeof(int*));
for(int i = 0; i < rows; i++)
{
cost_matrix[i] = (int*)calloc(cols,sizeof(int));
for(int j = 0; j < cols; j++)
{
//Multiplied by constant because hungarian algorithm accept integer defined cost
//matrix. We later divide by constant for correction
cost_matrix[i][j] = GraphMatch::calcN2NDistance(ns1[i], ns2[j])*MULTIPLIER_1000;
}
}
hungarian_problem_t p;
// initialize the hungarian_problem using the cost matrix
hungarian_init(&p, cost_matrix , rows, cols, HUNGARIAN_MODE_MINIMIZE_COST);
// solve the assignment problem
hungarian_solve(&p);
//Convert results to OpenCV format
assignment = array2Mat8U(p.assignment,rows,cols);
cost = array2Mat32F(cost_matrix,rows,cols);
//Divide for correction
cost = cost / MULTIPLIER_1000;
//free variables
hungarian_free(&p);
// Calculate match score and
// show matching results given assignment and cost matrix
Mat img_merged;
float matching_cost = 0;
//Concatenate two images
hconcat(img1, img2, img_merged);
//Get non-zero indices which defines the optimal match(assignment)
vector<Point> nonzero_locs;
findNonZero(assignment,nonzero_locs);
//Draw optimal match
for(int i = 0; i < nonzero_locs.size(); i++)
{
Point p1 = ns1[nonzero_locs[i].y].center;
Point p2 = ns2[nonzero_locs[i].x].center+Point(img1.size().width,0);
circle(img_merged,p1,MATCH_CIRCLE_RADIUS,MATCH_LINE_COLOR);
circle(img_merged,p2,MATCH_CIRCLE_RADIUS,MATCH_LINE_COLOR);
line(img_merged,p1,p2,MATCH_LINE_COLOR,MATCH_LINE_WIDTH);
float dist = cost.at<float>(nonzero_locs[i].y, nonzero_locs[i].x);
matching_cost = matching_cost + dist;
//Draw cost on match image
stringstream ss;
ss << dist;
string cost_str = ss.str();
Point center_pt((p1.x + p2.x) / 2.0, (p1.y + p2.y) / 2.0);
//putText(img_merged, cost_str, center_pt, cv::FONT_HERSHEY_SIMPLEX, 1.0, Scalar(255, 0, 0), 1);
}
//Show matching results image on the window
emit showMatchImage(mat2QImage(img_merged));
return matching_cost;
}