xdebna01
/
spajanie_snimkov_uprava_jasu
mirror of https://github.com/debnarpavol/spajanie_snimkov_uprava_jasu
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

added template matching; updated neigbouring function

Browse Source
main
Pavol Debnar 2 years ago
parent d535d24082
commit 3025a56f36
2 changed files with 836 additions and 106 deletions
Show Stats Download Patch File Download Diff File

778
src/pointbase.cpp
Unescape View File

@ -67,7 +67,9 @@ class PointBase {
//cout << "x: " << coords["depthPos"]["x"].asDouble() << std::endl;
//cout << "y: " << coords["depthPos"]["y"].asDouble() << std::endl;
Point2d newPoint = Point2d(coords["depthPos"]["x"].asDouble() , coords["depthPos"]["y"].asDouble());
newPoint.x = fmod(newPoint.x,360.0) ;
pathMap[newPoint] = filePath;
points.push_back(newPoint);
imgWidthDeg = coords["rectangle"]["w"].asDouble();
@ -166,13 +168,13 @@ class PointBase {
double xDifference = inputPoint.x - points[i].x;
/*
if(abs(yDifference)<15 && (xDifference<45) && (xDifference>0))
{
resultVector.push_back(points[i]);
toDoVector.push_back(points[i]);
points.erase(points.begin()+i);
continue;
}
@ -182,6 +184,7 @@ class PointBase {
resultVector.push_back(points[i]);
toDoVector.push_back(points[i]);
points.erase(points.begin()+i);
continue;
}
@ -191,7 +194,7 @@ class PointBase {
resultVector.push_back(points[i]);
toDoVector.push_back(points[i]);
points.erase(points.begin()+i);
continue;
}
@ -201,7 +204,16 @@ class PointBase {
resultVector.push_back(points[i]);
toDoVector.push_back(points[i]);
points.erase(points.begin()+i);
continue;
}*/
if(abs(yDifference)<0.9*imgHeightDeg && abs(xDifference)<0.8*imgWidthDeg)
{
resultVector.push_back(points[i]);
toDoVector.push_back(points[i]);
points.erase(points.begin()+i);
continue;
}
}
return resultVector;
@ -381,10 +393,19 @@ class PointBase {
void cumdump(int imgWidth,int imgHeight)
void cumdump(int imgW,int imgH)
{ //sift takes too long if images are large, so we shrink them
int imgWidth = imgW;
int imgHeight = imgH;
if (imgWidth > 1000)
{
imgWidth = int(0.4*imgWidth);
imgHeight = int(0.4*imgHeight);
}
int placeCounter =0;
int roiCounter = 0;
int tempMatchCounter =0;
std::vector <double> edges;
edges = getEdgeValues();
@ -393,10 +414,10 @@ class PointBase {
double minY=edges[2];
double maxY=edges[3];
int windowHeight = (int)(maxY*(imgHeight/imgHeightDeg))+imgHeight;
int windowHeight = (int)(maxY*(imgHeight/imgHeightDeg))+imgHeight*2;
int windowWidth = (int)((360/imgWidthDeg)*imgWidth) + imgWidth;
Mat img = Mat::zeros(windowHeight,windowWidth,CV_8UC3);
Mat img = Mat::zeros(windowHeight,windowWidth,CV_8UC1);
rectangle(img,Point(3,3),Point(15,15),Scalar(128,128, 0),2,2,0);
int imgIdx = 0;
@ -494,7 +515,13 @@ class PointBase {
pngPath = pngPath + "png";
cout << "file: " << pngPath << std::endl;
Mat imgSmall = imread(pngPath,IMREAD_COLOR);
Mat imgSmall = imread(pngPath,IMREAD_GRAYSCALE);
//resizing
cout << "w:" <<imgWidth << " h: " <<imgHeight <<"\n";
resize(imgSmall, imgSmall, Size(imgWidth, imgHeight), INTER_LINEAR);
Ptr<CLAHE> clahe = cv::createCLAHE(4.0, cv::Size(8,8));
clahe->apply(imgSmall,imgSmall);
cout<< cv::typeToString( imgSmall.type() );
cout <<minX <<"\n";
@ -505,7 +532,14 @@ class PointBase {
cout << (int)round(coords.y)*(int)(imgHeight/imgHeightDeg)<< "\n";
//first image is added to result img, others will be stitched
if (qIdx==0) imgSmall.copyTo(img(cv::Rect((int)round(coords.x)*(int)(imgWidth/imgWidthDeg),(int)round(coords.y)*(int)(imgHeight/imgHeightDeg),imgSmall.cols, imgSmall.rows)));
if (qIdx==0)
{ cout << "AM TRYING TO PLACE TO \n \n";
cout << "imgHeightDeg" << imgHeightDeg <<"\n";
placeCounter++;
cout << "X: " << (int)round(coords.x)*(int)(imgWidth/imgWidthDeg) << " Y:" << (int)round(coords.y)*(int)(imgHeight/imgHeightDeg) << " W: " <<imgSmall.cols << " H: " << imgSmall.rows << "\n";
imgSmall.copyTo(img(cv::Rect((int)round(coords.x)*(int)(imgWidth/imgWidthDeg),(int)round(coords.y)*(int)(imgHeight/imgHeightDeg),imgSmall.cols, imgSmall.rows)));
}
else {
//default ROI location and size
int xLoc=(int)round(coords.x)*(int)(imgWidth/imgWidthDeg) - imgWidth; //=normalized x-coord. minus 1 width of image
@ -520,6 +554,7 @@ class PointBase {
if (yLoc + yHeight > windowHeight) yHeight = windowHeight - yLoc;
//create the ROI
cout << "AM DOING ROI \n \n";
Mat roi;
roi = img(Rect(xLoc, yLoc, xWitdth, yHeight));
/*imshow("roi",roi);
@ -561,7 +596,7 @@ class PointBase {
drawMatches(roi,keypointsROI,imgSmall,keypointsImg,matches,imageMatches);
cv::namedWindow("matches");
cv::imshow("matches",imageMatches);
cv::waitKey(0);
//cv::waitKey(0);
@ -596,7 +631,9 @@ class PointBase {
cout<<"TU BY MALI BYT MATCHE " <<'\n';
std::vector< DMatch > good_matches;
//float ratio = 0.4; // As in Lowe's paper; can be tuned
for (float ratio = 0.4; good_matches.size()<2; ratio+=0.05)
/*
//this is continous worsening of the ratio in order to get good matches - after 0.4 the results were getting kinda bad tho
for (float ratio = 0.4; good_matches.size()<3; ratio+=0.05)
{
good_matches.clear();
cout << "RATIO: " <<ratio <<"\n";
@ -607,15 +644,20 @@ class PointBase {
{
good_matches.push_back(matches[i][0]);
cout<<"GOOD MATCH DISTANCE " << matches[i][0].distance <<'\n';
}/*
else{
cout<< "BAD MATCH \n";
}*/
}
}
}
*/
float ratio =0.4;
for (int i = 0; i < matches.size(); ++i)
{
//if there are no good matches, try adaptive thresholding
if (matches[i][0].distance < ratio * matches[i][1].distance)
{
good_matches.push_back(matches[i][0]);
cout<<"GOOD MATCH DISTANCE " << matches[i][0].distance <<'\n';
}
}
@ -634,8 +676,81 @@ class PointBase {
//find a transformation based on good matches
//we do not need a Homography, since we deal with affine transformations (no viewport transf. are expected)
//Mat H = findHomography( Mat(obj), Mat(scene), RANSAC );
Mat H = estimateAffinePartial2D( Mat(obj), Mat(scene), noArray(),RANSAC );
Mat H;
if(good_matches.size()>=3) H = estimateAffinePartial2D( Mat(obj), Mat(scene), noArray(),RANSAC );
//here we check for errors - either there were not enough good matches, OR an affine transformation could not be estimated
//we use template matching if errors occured
if(good_matches.size()<3 || H.empty())
{
tempMatchCounter++;
Mat workRoi, workImg;
workRoi = roi.clone();
workImg = imgSmall.clone();
GaussianBlur(roi, workRoi, Size(3, 3), 0);
GaussianBlur(imgSmall, workImg, Size(3, 3), 0);
Canny(workRoi, workRoi, 100, 200, 3, false);
Canny(workImg, workImg, 100, 200, 3, false);
Mat result;
double min_val, max_val;
Point min_loc, max_loc;
matchTemplate(workRoi,workImg,result,TM_CCOEFF);
minMaxLoc(result,&min_val,&max_val,&min_loc,&max_loc);
Mat roiMask, roiDT, imgWarpedMask,imgDT,roiMaskFinal,imgMaskFinal,imgNew;
threshold(roi,roiMask,1,255,CV_8U);
imgWarpedMask = Mat::zeros(roi.rows,roi.cols,CV_8U);
imgWarpedMask(Rect(max_loc.x,max_loc.y,imgSmall.cols,imgSmall.rows))=1.0;
distanceTransform(roiMask,roiDT,DIST_L2, 3);
distanceTransform(imgWarpedMask,imgDT,DIST_L2, 3);
normalize(roiDT, roiDT, 0.0, 1.0, NORM_MINMAX);
normalize(imgDT, imgDT, 0.0, 1.0, NORM_MINMAX);
cv::divide(roiDT,(roiDT+imgDT),roiMaskFinal);
cv::divide(imgDT,(roiDT+imgDT),imgMaskFinal);
normalize(roiMaskFinal, roiMaskFinal, 0.0, 255.0, NORM_MINMAX);
normalize(imgMaskFinal, imgMaskFinal, 0.0, 255.0, NORM_MINMAX);
imgNew = Mat::zeros(roi.rows,roi.cols,CV_8U);
imgSmall.copyTo(imgNew(Rect(max_loc.x , max_loc.y , imgSmall.cols,imgSmall.rows)));
roiMaskFinal.convertTo(roiMaskFinal,CV_8UC1);
imgMaskFinal.convertTo(imgMaskFinal,CV_8UC1);
roi.convertTo(roi,CV_32FC1);
imgNew.convertTo(imgNew,CV_32FC1);
roiMaskFinal.convertTo(roiMaskFinal,CV_32FC1);
imgMaskFinal.convertTo(imgMaskFinal,CV_32FC1);
multiply(roiMaskFinal,roi,roi);
cout<<cv::typeToString(imgMaskFinal.type())<< "imgMaskFinal TYPE \n";
cout<<cv::typeToString(imgNew.type())<< "imgNew TYPE \n";
multiply(imgMaskFinal,imgNew,imgNew);
cv::add(roi,imgNew, roi);
normalize(roi, roi, 0.0, 255.0, NORM_MINMAX);
//imgSmall.copyTo(roi(Rect(max_loc.x , max_loc.y , imgSmall.cols,imgSmall.rows)));
roi.copyTo(img(Rect(xLoc, yLoc, xWitdth, yHeight)));
}
else
{
roiCounter++;
cv::Mat resultWarp;
cv::Mat resultBlend,roiMask,imgWarpedMask,overlapMask,overlapDST,roiDT, imgDT;
//warpPerspective(imgSmall,roi,H,cv::Size(roi.cols,roi.rows));
@ -645,7 +760,7 @@ class PointBase {
imshow("imgSmall", imgSmall);
imshow( "resultWarp", resultWarp );
imshow("roi", roi);
cv::waitKey(0);
//cv::waitKey(0);
//cv::addWeighted( roi, 0.5, resultWarp, 0.5, 0.0, resultBlend);
@ -654,13 +769,14 @@ class PointBase {
threshold(roi,roiMask,1,255,CV_8U);
threshold(resultWarp,imgWarpedMask,1,255,CV_8U);
multiply(roiMask,imgWarpedMask,overlapMask);
//we need to change type in order for img multiplication to work in OpenCV
cv::cvtColor(overlapMask,overlapMask,COLOR_BGR2GRAY);
/*cv::cvtColor(overlapMask,overlapMask,COLOR_BGR2GRAY);
cv::cvtColor(roiMask,roiMask,COLOR_BGR2GRAY);
cv::cvtColor(imgWarpedMask,imgWarpedMask,COLOR_BGR2GRAY);
cv::cvtColor(roi,roi,COLOR_BGR2GRAY);
cv::cvtColor(resultWarp,resultWarp,COLOR_BGR2GRAY);
cv::cvtColor(resultWarp,resultWarp,COLOR_BGR2GRAY);*/
//the blending function is DistanceTransform(img1)/(DT(img1)+DT(img2)) - DT is created from the masks
distanceTransform(roiMask,roiDT,DIST_L2, 3);
@ -710,7 +826,7 @@ class PointBase {
normalize(resultRoi, resultRoi, 0.0, 1.0, NORM_MINMAX);
imshow("resultRoi", resultRoi);
cv::waitKey(0);
//cv::waitKey(0);
//cv::cvtColor(roiMask,roiMask,COLOR_GRAY2BGR);
//cout<<cv::typeToString(roiMask.type())<< "img TYPE \n";
@ -721,12 +837,14 @@ class PointBase {
normalize(resultRoi, resultRoi, 0.0, 255.0, NORM_MINMAX);
cv::cvtColor(resultRoi,resultRoi,COLOR_GRAY2BGR);
//cv::cvtColor(resultRoi,resultRoi,COLOR_GRAY2BGR);
resultRoi.copyTo(img(Rect(xLoc, yLoc, xWitdth, yHeight)));
imwrite("AAAAAAA.png", img);
destroyAllWindows();
}
//cout<<CV_VERSION;
}
@ -737,6 +855,7 @@ class PointBase {
}
logFile.close();
string resultName = "result" + to_string(imgIdx) +".png";
cout<<"ROIS: " <<roiCounter << "\n PlacedImgs: " <<placeCounter <<"\n tempMatches: " << tempMatchCounter <<'\n';
//imshow("fag",img);
//waitKey(0);
//imwrite(resultName, img);
@ -775,4 +894,615 @@ class PointBase {
}
};
void patternMatchTry(int imgW,int imgH)
{
int imgWidth = imgW;
int imgHeight = imgH;
if (imgWidth > 1000)
{
imgWidth = int(0.4*imgWidth);
imgHeight = int(0.4*imgHeight);
}
std::vector <double> edges;
edges = getEdgeValues();
double minX=edges[0];
double maxX=edges[1];
double minY=edges[2];
double maxY=edges[3];
int windowHeight = (int)(maxY*(imgHeight/imgHeightDeg))+imgHeight*2;
int windowWidth = (int)((360/imgWidthDeg)*imgWidth) + imgWidth;
Mat img = Mat::zeros(windowHeight,windowWidth,CV_8UC1);
rectangle(img,Point(3,3),Point(15,15),Scalar(128,128, 0),2,2,0);
int imgIdx = 0;
//main loop
while (!points.empty())
{
//take the first point from the left and find adjts. to it
Point2d p = points[0];
vector<Point2d> stitchQueue;
stitchQueue.push_back(p);
points.erase(points.begin());
vector<Point2d> temp = getAdjacents(p); //find adj. of first point - inits toDo vector
if (toDoVector.empty()) //if first point has no adjts. - log him as singleton
{
ofstream singletonsFile("singletons.log", ios_base::app);
if(!singletonsFile)
{
singletonsFile << "x: " << p.x << std::endl;
singletonsFile << "y: " << p.y << std::endl;
singletonsFile << pathMap[Point2d(p.x,p.y) ] << std::endl;
singletonsFile.close();
}
else
{
ofstream sing;
sing.open("singletons.log",ios_base::app);
sing << "x: " << p.x << std::endl;
sing << "y: " << p.y << std::endl;
sing << pathMap[Point2d(p.x,p.y) ] << std::endl;
sing.close();
}
stitchQueue.pop_back();
continue;
}
//finds adjacencies to every point
while(!toDoVector.empty())
{
Point2d p2 = toDoVector[0];
stitchQueue.push_back(p2);
temp = getAdjacents(p2);
toDoVector.erase(toDoVector.begin());
}
//stitching
// Define object to store the stitched image
/* a funct was created for this, see getEdgeValues()
double minX=999;
double maxX=0;
double minY=900000;
double maxY=0;
for (Point2d coords: stitchQueue)
{
if (coords.x < minX) minX=coords.x;
if (coords.x > maxX) maxX=coords.x;
if (coords.y < minY) minY=coords.y;
if (coords.y > maxY) maxY=coords.y;
}
*/
int xDiff = (int)round(maxX)-(int)round(minX);
int yDiff = (int)round(maxY)-(int)round(minY);
cout <<xDiff <<"\n";
cout <<yDiff <<"\n";
//img creation put to beginning of funcion
/*
Mat img = Mat::zeros((int)(maxY*(imgHeight/imgHeightDeg))+imgHeight,(int)((360/imgWidthDeg)*imgWidth) + imgWidth,CV_8UC3);
rectangle(img,Point(3,3),Point(15,15),Scalar(128,128, 0),2,2,0);
*/
//open stream for logging
string logFileName = to_string(imgIdx) + ".log";
ofstream logFile(logFileName);
int qIdx = 0;
for (Point2d coords: stitchQueue)
{
//log which images are in which result file
logFile << "x: " << coords.x << std::endl;
logFile << "y: " << coords.y << std::endl;
logFile << pathMap[Point2d(coords.x,coords.y) ] << std::endl;
//log to stdout
cout << "x: " << coords.x << std::endl;
cout << "y: " << coords.y << std::endl;
string jsonPath = pathMap[coords];
string pngPath = jsonPath.erase(jsonPath.length()-4);
pngPath = pngPath + "png";
cout << "file: " << pngPath << std::endl;
Mat imgSmall = imread(pngPath,IMREAD_GRAYSCALE);
resize(imgSmall, imgSmall, Size(imgWidth, imgHeight), INTER_LINEAR);
Ptr<CLAHE> clahe = cv::createCLAHE(4.0, cv::Size(8,8));
clahe->apply(imgSmall,imgSmall);
cout<< cv::typeToString( imgSmall.type() );
cout <<minX <<"\n";
cout <<minY <<"\n";
//cout << (int)round(coords.x)-(int)round(minX)<< "\n";
//cout << (int)round(coords.y)-(int)round(minY)<< "\n";
cout << (int)round(coords.y)*(int)(imgHeight/imgHeightDeg)<< "\n";
//first image is added to result img, others will be stitched
if (qIdx==0)
{
imgSmall.copyTo(img(cv::Rect((int)round(coords.x)*(int)(imgWidth/imgWidthDeg),(int)round(coords.y)*(int)(imgHeight/imgHeightDeg),imgSmall.cols, imgSmall.rows)));
cout<<"DINGUS WAS PLACED \n";
}
else {
//default ROI location and size
int xLoc=(int)round(coords.x)*(int)(imgWidth/imgWidthDeg) - imgWidth; //=normalized x-coord. minus 1 width of image
int yLoc=(int)round(coords.y)*(int)(imgHeight/imgHeightDeg) - imgHeight;
int xWitdth=3*imgWidth;
int yHeight=3*imgHeight;
//edge cases
if ((int)round(coords.x)*(int)(imgWidth/imgWidthDeg) - imgWidth < 0) xLoc=0;
if ((int)round(coords.y)*(int)(imgHeight/imgHeightDeg) - imgHeight < 0) yLoc = 0;
if (xLoc + xWitdth > windowWidth) xWitdth = windowWidth - xLoc;
if (yLoc + yHeight > windowHeight) yHeight = windowHeight - yLoc;
//create the ROI
Mat roi; //we dont need to apply CLAHE to ROI, since it consists of images which have clahe already applied
roi = img(Rect(xLoc, yLoc, xWitdth, yHeight));
Mat roiEdit, imgSmallEdit;
roiEdit = roi.clone();
imgSmallEdit = imgSmall.clone();
GaussianBlur(roi, roiEdit, Size(3, 3), 0);
GaussianBlur(imgSmall, imgSmallEdit, Size(3, 3), 0);
Canny(roiEdit, roiEdit, 100, 200, 3, false);
Canny(imgSmallEdit, imgSmallEdit, 100, 200, 3, false);
//vector<Mat> rectHolder;
//Mat rect1,rect2,rect3,rect4,rect5;
int rectH = imgSmall.rows, rectW = imgSmall.cols;
/*
Point2d coord1 = Point2d(int(imgWidth*0.1),int(imgHeight*0.1));
Point2d coord2 = Point2d(int(imgWidth*0.1),int(imgHeight*0.7));
Point2d coord3 = Point2d(int(imgWidth*0.5),int(imgHeight*0.5));
Point2d coord4 = Point2d(int(imgWidth*0.7),int(imgHeight*0.2));
Point2d coord5 = Point2d(int(imgWidth*0.7),int(imgHeight*0.7));
*/
//vector<Point2d> coordRects;
/*
coordRects.push_back(coord1);
coordRects.push_back(coord2);
coordRects.push_back(coord3);
coordRects.push_back(coord4);
coordRects.push_back(coord5);
*/
/*
vector<Point2d> coordRects;
vector<Mat> rects;
for (int y= 0; y<imgSmall.rows - rectH; y=y+10)
{
for (int x=0 ; x< imgSmall.cols - rectW; x = x+10)
{
Point2d currentPoint = Point2d(x,y);
coordRects.push_back(currentPoint);
rects.push_back(imgSmallEdit(Rect(currentPoint.x,currentPoint.y,rectW,rectH)));
}
}
cout << "RECTS DONE \n";
vector<Mat> results;
double min_val, max_val;
Point min_loc, max_loc;
double globMax = 0.0;
int minIDX = 0;
Mat result;
for (int i = 0; i < rects.size(); i++)
{
matchTemplate(roiEdit,rects[i],result,TM_CCOEFF);
results.push_back(result);
minMaxLoc(result,&min_val,&max_val,&min_loc,&max_loc);
if (max_val>globMax)
{
globMax=max_val;
minIDX = i;
}
}
minMaxLoc(results[minIDX],&min_val,&max_val,&min_loc,&max_loc);
cout << "SIZE:"<< results[minIDX].size() << '\n';
cout << "IDX:" << minIDX << "\n";
*/
//min_loc is the top left of the best of the five sub-rois
//the top left of where to put the small img is min_loc - coordrects[minIDX]
Mat result;
double min_val, max_val;
Point min_loc, max_loc;
matchTemplate(roiEdit,imgSmallEdit,result,TM_CCOEFF);
minMaxLoc(result,&min_val,&max_val,&min_loc,&max_loc);
//debug stuff
Mat roiWithLocation, chosenSmallRoiImg;
imshow("matchResult",result);
roiWithLocation= roiEdit.clone();
chosenSmallRoiImg = imgSmallEdit.clone();
rectangle(roiWithLocation,max_loc,Point(max_loc.x+rectW,max_loc.y+rectH),Scalar(128,128, 0),2,2,0);
rectangle(chosenSmallRoiImg,max_loc,Point(rectW,rectH),Scalar(128,128, 0),2,2,0);
imshow("roiWithLocation",roiWithLocation);
imshow("chosenSmallRoiImg",chosenSmallRoiImg);
//cout<<"HOW MANY RECTS " <<rects.size() <<"\n";
cout<<"Location in roi X:" << max_loc.x << " Y " << max_loc.y << "\n";
//imshow("chosen smallROI", rects[minIDX]);
Mat roiMask, roiDT, imgWarpedMask,imgDT,roiMaskFinal,imgMaskFinal,imgNew;
threshold(roi,roiMask,1,255,CV_8U);
imgWarpedMask = Mat::zeros(roi.rows,roi.cols,CV_8U);
imgWarpedMask(Rect(max_loc.x,max_loc.y,rectW,rectH))=1.0;
distanceTransform(roiMask,roiDT,DIST_L2, 3);
distanceTransform(imgWarpedMask,imgDT,DIST_L2, 3);
normalize(roiDT, roiDT, 0.0, 1.0, NORM_MINMAX);
normalize(imgDT, imgDT, 0.0, 1.0, NORM_MINMAX);
cv::divide(roiDT,(roiDT+imgDT),roiMaskFinal);
cv::divide(imgDT,(roiDT+imgDT),imgMaskFinal);
normalize(roiMaskFinal, roiMaskFinal, 0.0, 255.0, NORM_MINMAX);
normalize(imgMaskFinal, imgMaskFinal, 0.0, 255.0, NORM_MINMAX);
imgNew = Mat::zeros(roi.rows,roi.cols,CV_8U);
imgSmall.copyTo(imgNew(Rect(max_loc.x , max_loc.y , rectW,rectH)));
roiMaskFinal.convertTo(roiMaskFinal,CV_8UC1);
imgMaskFinal.convertTo(imgMaskFinal,CV_8UC1);
roi.convertTo(roi,CV_32FC1);
imgNew.convertTo(imgNew,CV_32FC1);
roiMaskFinal.convertTo(roiMaskFinal,CV_32FC1);
imgMaskFinal.convertTo(imgMaskFinal,CV_32FC1);
multiply(roiMaskFinal,roi,roi);
cout<<cv::typeToString(imgMaskFinal.type())<< "imgMaskFinal TYPE \n";
cout<<cv::typeToString(imgNew.type())<< "imgNew TYPE \n";
multiply(imgMaskFinal,imgNew,imgNew);
cv::add(roi,imgNew, roi);
normalize(roi, roi, 0.0, 1.0, NORM_MINMAX);
imshow("roiafter",roi);
//uncomment this and comment from neareast Mat in order to disable blending
//imgSmall.copyTo(roi(Rect(max_loc.x , max_loc.y , rectW,rectH)));
//imshow("roiafter",roi);
normalize(roi, roi, 0.0, 255.0, NORM_MINMAX);
roi.copyTo(img(Rect(xLoc, yLoc, xWitdth, yHeight)));
imwrite("roiafter.jpg",roi);
waitKey(0);
//cout<<CV_VERSION;
}
//if(qIdx==2) break;
qIdx++;
}
logFile.close();
string resultName = "result" + to_string(imgIdx) +".png";
//imshow("fag",img);
//waitKey(0);
//imwrite(resultName, img);
//imgIdx++;
//stitching
/*
cout << "Stitching" << std::endl;
Stitcher::Status status = stitcher->stitch(imgs, pano);
if (status != Stitcher::OK)
{
// Check if images could not be stitched
// status is OK if images are stitched successfully
cout << "Can't stitch images\n";
}
else
{
string resultName = "result" + to_string(imgIdx) +".jpg";
// Store a new image stitched from the given
//set of images as "result.jpg"
imwrite(resultName, pano);
// Show the result
imshow(resultName, pano);
waitKey(0);
}
*/
}
imwrite("result0GIOJSDFI.png", img);
}
};

BIN
src/test
View File

Binary file not shown.
Write Preview
Loading…
Cancel
Save