本文整理汇总了C++中TickMeter::stop方法的典型用法代码示例。如果您正苦于以下问题:C++ TickMeter::stop方法的具体用法?C++ TickMeter::stop怎么用?C++ TickMeter::stop使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类TickMeter
的用法示例。
在下文中一共展示了TickMeter::stop方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
int main(int argc, const char* argv[])
{
if (argc != 2)
return -1;
const std::string fname(argv[1]);
cv::namedWindow("CPU", cv::WINDOW_NORMAL);
cv::namedWindow("GPU", cv::WINDOW_OPENGL);
cv::cuda::setGlDevice();
cv::Mat frame;
cv::VideoCapture reader(fname);
cv::cuda::GpuMat d_frame;
cv::Ptr<cv::cudacodec::VideoReader> d_reader = cv::cudacodec::createVideoReader(fname);
TickMeter tm;
std::vector<double> cpu_times;
std::vector<double> gpu_times;
for (;;)
{
tm.reset();
tm.start();
if (!reader.read(frame))
break;
tm.stop();
cpu_times.push_back(tm.getTimeMilli());
tm.reset();
tm.start();
if (!d_reader->nextFrame(d_frame))
break;
tm.stop();
gpu_times.push_back(tm.getTimeMilli());
cv::imshow("CPU", frame);
cv::imshow("GPU", d_frame);
if (cv::waitKey(3) > 0)
break;
}
if (!cpu_times.empty() && !gpu_times.empty())
{
std::cout << std::endl << "Results:" << std::endl;
std::sort(cpu_times.begin(), cpu_times.end());
std::sort(gpu_times.begin(), gpu_times.end());
double cpu_avg = std::accumulate(cpu_times.begin(), cpu_times.end(), 0.0) / cpu_times.size();
double gpu_avg = std::accumulate(gpu_times.begin(), gpu_times.end(), 0.0) / gpu_times.size();
std::cout << "CPU : Avg : " << cpu_avg << " ms FPS : " << 1000.0 / cpu_avg << std::endl;
std::cout << "GPU : Avg : " << gpu_avg << " ms FPS : " << 1000.0 / gpu_avg << std::endl;
}
return 0;
}
示例2: detectAndDrawObjects
void detectAndDrawObjects( Mat& image, LatentSvmDetector& detector, const vector<Scalar>& colors, float overlapThreshold, int numThreads )
{
vector<LatentSvmDetector::ObjectDetection> detections;
TickMeter tm;
tm.start();
detector.detect( image, detections, overlapThreshold, numThreads);
tm.stop();
cout << "Detection time = " << tm.getTimeSec() << " sec" << endl;
const vector<string> classNames = detector.getClassNames();
CV_Assert( colors.size() == classNames.size() );
for( size_t i = 0; i < detections.size(); i++ )
{
const LatentSvmDetector::ObjectDetection& od = detections[i];
rectangle( image, od.rect, colors[od.classID], 3 );
}
// put text over the all rectangles
for( size_t i = 0; i < detections.size(); i++ )
{
const LatentSvmDetector::ObjectDetection& od = detections[i];
putText( image, classNames[od.classID], Point(od.rect.x+4,od.rect.y+13), FONT_HERSHEY_SIMPLEX, 0.55, colors[od.classID], 2 );
}
}
示例3: readDatabase
bool ObjectRecognition::readDatabase(const string& dir, vector<Mat>& databaseDescriptors, vector<string>& files)
{
TickMeter tm;
tm.start();
getdir(dir,files);
string outString = "Start Reading Directory.png";
cout << outString << endl;
string extention = ".png";
vector<string>::iterator it = files.begin();
for (unsigned int i = 0;i < files.size();i++)
{
if ( files[i].size() > 4 && files[i].compare( files[i].size() - 4, 4 , extention) == 0)
{
Mat img = imread( dir + files[i] , CV_LOAD_IMAGE_GRAYSCALE );
//if( img.empty() ) cout << "Database descriptor " << files[i] << " can not be read or has no information." << endl;
//cout << files[i] << "\tRows" << img.rows << "\t Cols" << img.cols << "\t Type/Depth: " << img.depth() << endl;
img.assignTo(img, 5);
databaseDescriptors.push_back( img );
}
it++;
}
tm.stop();
cout << "End reading directory in " << tm.getTimeMilli() << " ms, of size " << DB.size() << endl;
return true;
}
示例4: main
int main(int argc, char** argv) {
using namespace std;
using namespace cv;
VideoCapture cap(0);
if (!cap.isOpened())
exit(1);
if (argc > 2) {
cap.set(CV_CAP_PROP_FRAME_WIDTH, atoi(argv[1]));
cap.set(CV_CAP_PROP_FRAME_HEIGHT, atoi(argv[2]));
}
CascadeClassifier cascade;
if (!cascade.load("haarcascade_frontalface_default.xml"))
exit(2);
const char* name = basename(argv[0]);
namedWindow(name);
for (int frame = 1;; frame++) {
static double mean = 0;
TickMeter tm;
Mat img, gray;
tm.start();
cap >> img;
cvtColor(img, gray, CV_BGR2GRAY);
equalizeHist(gray, gray);
vector<Rect> objects;
cascade.detectMultiScale(gray, objects, 1.2, 9,
CV_HAAR_DO_CANNY_PRUNING);
typedef vector<Rect>::const_iterator RCI;
for (RCI i = objects.begin(); i != objects.end(); ++i) {
Point center(cvRound(i->x+i->width/2),cvRound(i->y+i->height/2));
int radius = cvRound(i->width / 2);
circle(img, center, radius, Scalar(128,255,128), 2, 8, 0);
}
imshow(name, img);
tm.stop();
mean += tm.getTimeMilli();
if (frame % 25 == 0) {
printf("avg detect time: %.2f ms\n", mean / 25);
mean = 0;
}
switch (waitKey(10)) {
case 'q': case 27:
exit(0);
break;
}
}
}
示例5: matchDescriptors
static void matchDescriptors( const Mat& queryDescriptors, const vector<Mat>& trainDescriptors,
vector<DMatch>& matches, Ptr<DescriptorMatcher>& descriptorMatcher )
{
cout << "< Set train descriptors collection in the matcher and match query descriptors to them..." << endl;
TickMeter tm;
tm.start();
descriptorMatcher->add( trainDescriptors );
descriptorMatcher->train();
tm.stop();
double buildTime = tm.getTimeMilli();
tm.start();
descriptorMatcher->match( queryDescriptors, matches );
tm.stop();
double matchTime = tm.getTimeMilli();
CV_Assert( queryDescriptors.rows == (int)matches.size() || matches.empty() );
cout << "Number of matches: " << matches.size() << endl;
cout << "Build time: " << buildTime << " ms; Match time: " << matchTime << " ms" << endl;
cout << ">" << endl;
}
示例6: loadImageDB
bool ObjectRecognition::loadImageDB()
{
TickMeter tm;
tm.start();
vector<string> files;
getdir(DBdirName,files);
string extention = ".png";
vector<string>::iterator it = files.begin();
vector<Mat> descriptorDatabase;
for (unsigned int i = 0;i < files.size();i++)
{
if ( files[i].size() > 4 && files[i].compare( files[i].size() - 4, 4 , extention) == 0)
{
DBobj DBentry;
DBentry.name = files[i];
DBentry.img = imread( DBdirName + files[i] );
if( DBentry.img.empty() ) cout << "Image: " << files[i] << " can not be read or has no information." << endl;
DBentry.img.assignTo(DBentry.img, CV_8U);
//cout << files[i] << "\tRows" << DBentry.img.rows << "\t Cols" << DBentry.img.cols << "\t Type/Depth: " << DBentry.img.depth() << endl;
detectKeypointsSingle(DBentry.img, DBentry.keypoints );
//cout << files[i] << "\t# Keypoints:" << DBentry.keypoints.size() << endl;
if (DBentry.keypoints.size() > 9)
{
computeDescriptorsSingle(DBentry.img, DBentry.keypoints, DBentry.description);
//cout << files[i] << "\t# of Descriptors: " << DBentry.description.rows << "\t# of Dimensions for descriptor: " << DBentry.description.cols
// << "\tType/depth: " << DBentry.description.type() << " | " << DBentry.description.depth() << endl;
descriptorDatabase.push_back(DBentry.description);
DB.push_back( DBentry );
}
}
it++;
}
// Add Database to matcher program.
matcher->add(descriptorDatabase);
matcher->train();
tm.stop();
cout << "End reading directory in " << tm.getTimeMilli() << " ms, of size " << DB.size() << endl;
return true;
}
示例7: main
//.........这里部分代码省略.........
siftsim[ID] = similarities::compareDescriptors(siftmap[ID], imgSiftDescriptors);
orbsim[ID] = 0;//similarities::compareDescriptors(orbmap[ID], imgOrbDescriptors);
fastsim[ID] = 0;//similarities::compareDescriptors(fastmap[ID], imgFastDescriptors);
}
map<vector<float>, int> top;
bool gotone = false;
typedef map<vector<float>, int>::iterator iter;
// Choose the best ones!
for (map<vector<float>, Mat>::iterator i = imagemap.begin(); i != imagemap.end(); ++i)
{
vector<float> ID = i->first;
int sim = /* gssim[ID] + 0.5*bwsim[ID] + */ 5*surfsim[ID] + 0.3*siftsim[ID] + orbsim[ID] + fastsim[ID];
// cout << surfsim[ID] << "\t";
// cout << siftsim[ID] << "\t";
// cout << orbsim[ID] << "\t";
// cout << fastsim[ID] << endl;
if (!gotone)
{
top[ID] = sim;
gotone = true;
}
iter it = top.begin();
iter end = top.end();
int max_value = it->second;
vector<float> max_ID = it->first;
for( ; it != end; ++it)
{
int current = it->second;
if(current > max_value)
{
max_value = it->second;
max_ID = it->first;
}
}
// cout << "Sim: " << sim << "\tmax_value: " << max_value << endl;
if (top.size() < numtoreturn)
top[ID] = sim;
else
{
if (sim < max_value)
{
top[ID] = sim;
top.erase(max_ID);
}
}
}
tm.stop();
double s = tm.getTimeSec();
cout << ">\n<\n Writing top " << numtoreturn << " images ..." << endl;
int count = 1;
namedWindow("Image");
namedWindow("Match");
namedWindow("ImageBW");
namedWindow("MatchBW");
namedWindow("ImageGS");
namedWindow("MatchGS");
imshow("Image", image);
imshow("ImageBW", bwimage);
imshow("ImageGS", gsimage);
vector<KeyPoint> currentPoints;
for (iter i = top.begin(); i != top.end(); ++i)
{
vector<float> ID = i->first;
cout << " Score: "<< i->second << "\tGrayScale: " << gssim[ID] << "\tBW: " << bwsim[ID] << " \tSURF: " << surfsim[ID] << "\tSIFT: " << siftsim[ID] << endl;
string fn = "Sim_" + boost::to_string(count) + "_" + boost::to_string(i->second) + ".png";
imwrite(fn, imagemap[ID]);
count++;
normalize(bwmap[ID], bwmap[ID], 0, 255, NORM_MINMAX, CV_64F);
normalize(gsmap[ID], gsmap[ID], 0, 255, NORM_MINMAX, CV_64F);
imshow("Match", imagemap[ID]);
imshow("MatchBW", bwmap[ID]);
imshow("MatchGS", gsmap[ID]);
waitKey(0);
}
cout << ">\nComparisons took " << s << " seconds for " << imagemap.size() << " images ("
<< (int) imagemap.size()/s << " images per second)." << endl;
return 0;
}
示例8: bingQdpmRocTest
void bingQdpmRocTest(vector<string> &dirs,
int windowLimit = -1, double timeLimitMs = -1, float ratioThreshold = -1)
{
size_t imageCount = 0;
size_t personCount = 0;
size_t matchCount = 0;
vector<ScoreTp> pScores;
TickMeter tm;
vector<std::string>::const_iterator it = dirs.begin();
char buf[512];
for (; it != dirs.end(); it++) {
string dir = *it;
DataSetVOC voc(dir, true, true);
voc.loadAnnotations();
const size_t testNum = voc.testSet.size();
const char *imgPath =_S(voc.imgPathW);
// Objectness
double base = 2;
double intUionThr = 0.5;
int W = 8;
int NSS = 2;
#ifdef WINDOW_GUESS
Objectness objNess(voc, base, intUionThr, W, NSS);
objNess.loadTrainedModel(TRAIN_MODEL);
#endif
// LSVM DPM
string dpmPersonModel = "../ExtraData/latentsvmXml/person.xml";
vector<string> models;
models.push_back(dpmPersonModel);
QUniLsvmDetector detector(models);
float overlapThreshold = 0.2f;
if (ratioThreshold > 0)
detector.setRatioThreshold(ratioThreshold);
printf("%d: \n", testNum);
for (int i = 0; i < testNum; i++) {
const vector<Vec4i> &boxesGT = voc.gtTestBoxes[i];
const size_t gtNumCrnt = boxesGT.size();
if (gtNumCrnt <= 0)
continue;
imageCount++;
personCount += gtNumCrnt;
Mat image = imread(format(imgPath, _S(voc.testSet[i])));
if (image.ptr() == NULL) {
fprintf(stderr, "No JPG Image !\n");
exit(1);
}
int numPerSz = 130;
ValStructVec<float, Vec4i> boxes;
double preObj = tm.getTimeMilli();
double objTime = 0.;
#ifdef WINDOW_GUESS // window guess
tm.start();
objNess.getObjBndBoxes(image, boxes, numPerSz);
tm.stop();
objTime = tm.getTimeMilli() - preObj;
#endif
double localTimeLimitMs = timeLimitMs;
if (timeLimitMs > 0) {
localTimeLimitMs -= objTime;
if (localTimeLimitMs < 0.)
localTimeLimitMs = 0.;
}
vector<QRect> searchBoxes;
if (windowLimit > 0) {
for (int j = 0; j < (int)boxes.size() && j < windowLimit; j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
} else {
for (int j = 0; j < (int)boxes.size(); j++) {
const Vec4i &bb = boxes[j];
QRect rt(bb[0], bb[1], bb[2], bb[3]);
searchBoxes.push_back(rt);
}
}
tm.start();
detector.setup(image, overlapThreshold, localTimeLimitMs);
tm.stop();
vector<FeatureMapCoord> ftrMapCoords;
#ifdef WINDOW_GUESS
detector.cvtBox2FtrMapCoord(&searchBoxes, &ftrMapCoords);
#else
detector.genFullFtrMapCoord(&ftrMapCoords);
//.........这里部分代码省略.........
示例9: process
Mat Tracker::process(const Mat frame, Stats& stats)
{
TickMeter tm;
vector<KeyPoint> kp;
Mat desc;
tm.start();
detector->detectAndCompute(frame, noArray(), kp, desc);
stats.keypoints = (int)kp.size();
vector< vector<DMatch> > matches;
vector<KeyPoint> matched1, matched2;
matcher->knnMatch(first_desc, desc, matches, 2);
for(unsigned i = 0; i < matches.size(); i++) {
if(matches[i][0].distance < nn_match_ratio * matches[i][1].distance) {
matched1.push_back(first_kp[matches[i][0].queryIdx]);
matched2.push_back( kp[matches[i][0].trainIdx]);
}
}
stats.matches = (int)matched1.size();
Mat inlier_mask, homography;
vector<KeyPoint> inliers1, inliers2;
vector<DMatch> inlier_matches;
if(matched1.size() >= 4) {
homography = findHomography(Points(matched1), Points(matched2),
RANSAC, ransac_thresh, inlier_mask);
}
tm.stop();
stats.fps = 1. / tm.getTimeSec();
if(matched1.size() < 4 || homography.empty()) {
Mat res;
hconcat(first_frame, frame, res);
stats.inliers = 0;
stats.ratio = 0;
return res;
}
for(unsigned i = 0; i < matched1.size(); i++) {
if(inlier_mask.at<uchar>(i)) {
int new_i = static_cast<int>(inliers1.size());
inliers1.push_back(matched1[i]);
inliers2.push_back(matched2[i]);
inlier_matches.push_back(DMatch(new_i, new_i, 0));
}
}
stats.inliers = (int)inliers1.size();
stats.ratio = stats.inliers * 1.0 / stats.matches;
vector<Point2f> new_bb;
perspectiveTransform(object_bb, new_bb, homography);
Mat frame_with_bb = frame.clone();
if(stats.inliers >= bb_min_inliers) {
drawBoundingBox(frame_with_bb, new_bb);
}
Mat res;
drawMatches(first_frame, inliers1, frame_with_bb, inliers2,
inlier_matches, res,
Scalar(255, 0, 0), Scalar(255, 0, 0));
return res;
}
示例10: main
//.........这里部分代码省略.........
cuda::flip(frame_gpu,frame_gpu,1);
cv::flip(image,image,1);
cuda::cvtColor(frame_gpu,k_rgb_gpu,CV_BGRA2BGR);
convertAndResizeGPU(k_rgb_gpu, gray_gpu, resized_gpu, scaleFactor);
convertAndResizeCPU(image,image,scaleFactor);
TickMeter tm;
tm.start();
//cascade_gpu->setMaxNumObjects(2);
//cascade_gpu->setMaxObjectSize(cv::Size(224,224));
//cascade_gpu->setMinObjectSize(cv::Size(0,0));
cascade_gpu->setFindLargestObject(findLargestObject);
cascade_gpu->setScaleFactor(1.2);
cascade_gpu->setMinNeighbors((filterRects || findLargestObject) ? 4 : 0);
cascade_gpu->detectMultiScale(resized_gpu, facesBuf_gpu);
cascade_gpu->convert(facesBuf_gpu, faces);
for (size_t i = 0; i < faces.size(); ++i)
{
//cout<< "object [" << i << "]: " << faces[i].width << " x " << faces[i].height <<endl;
rectangle(image, faces[i], Scalar(255));
cropRect = Rect(image.cols / 2, image.rows / 2,224,224);
Mat cropImg = image(cropRect).clone();
if(predictObject == true)
{
std::vector<Prediction> predictions = CaffeClassifier.Classify(cropImg,1);
/* Print the top N predictions. */
for (size_t i = 0; i < predictions.size(); ++i)
{
Prediction p = predictions[i];
std::cout << std::fixed << std::setprecision(4) << p.second << " - \"" << p.first << "\"" << std::endl;
}
predictObject = false;
}
}
tm.stop();
double detectionTime = tm.getTimeMilli();
double fps = 1000 / detectionTime;
displayState(image, helpScreen, useGPU, findLargestObject, filterRects, fps,scaleFactor);
imshow("result", image);
char key = (char)waitKey(5);
if (key == 27)
{
break;
}
switch (key)
{
case ' ':
useGPU = !useGPU;
break;
case 'm':
case 'M':
findLargestObject = !findLargestObject;
break;
case 'f':
case 'F':
filterRects = !filterRects;
break;
case '1':
scaleFactor *= 1.05;
break;
case 'q':
case 'Q':
scaleFactor /= 1.05;
break;
case 'h':
case 'H':
helpScreen = !helpScreen;
break;
case 'p':
case 'P':
predictObject = !predictObject;
break;
}
protonect_shutdown = protonect_shutdown || (key > 0 && ((key & 0xFF) == 27)); // shutdown on escape
listener.release(frames);
//libfreenect2::this_thread::sleep_for(libfreenect2::chrono::milliseconds(100));
}
resized_gpu.release();
// TODO: restarting ir stream doesn't work!
// TODO: bad things will happen, if frame listeners are freed before dev->stop() :(
dev->stop();
dev->close();
delete registration;
return 0;
}
示例11: main
/*
* This sample helps to evaluate odometry on TUM datasets and benchmark http://vision.in.tum.de/data/datasets/rgbd-dataset.
* At this link you can find instructions for evaluation. The sample runs some opencv odometry and saves a camera trajectory
* to file of format that the benchmark requires. Saved file can be used for online evaluation.
*/
int main(int argc, char** argv)
{
if(argc != 4)
{
cout << "Format: file_with_rgb_depth_pairs trajectory_file odometry_name [Rgbd or ICP or RgbdICP]" << endl;
return -1;
}
vector<string> timestamps;
vector<Mat> Rts;
const string filename = argv[1];
ifstream file( filename.c_str() );
if( !file.is_open() )
return -1;
char dlmrt = '/';
size_t pos = filename.rfind(dlmrt);
string dirname = pos == string::npos ? "" : filename.substr(0, pos) + dlmrt;
const int timestampLength = 17;
const int rgbPathLehgth = 17+8;
const int depthPathLehgth = 17+10;
float fx = 525.0f, // default
fy = 525.0f,
cx = 319.5f,
cy = 239.5f;
if(filename.find("freiburg1") != string::npos)
setCameraMatrixFreiburg1(fx, fy, cx, cy);
if(filename.find("freiburg2") != string::npos)
setCameraMatrixFreiburg2(fx, fy, cx, cy);
Mat cameraMatrix = Mat::eye(3,3,CV_32FC1);
{
cameraMatrix.at<float>(0,0) = fx;
cameraMatrix.at<float>(1,1) = fy;
cameraMatrix.at<float>(0,2) = cx;
cameraMatrix.at<float>(1,2) = cy;
}
Ptr<OdometryFrame> frame_prev = new OdometryFrame(),
frame_curr = new OdometryFrame();
Ptr<Odometry> odometry = Algorithm::create<Odometry>("RGBD." + string(argv[3]) + "Odometry");
if(odometry.empty())
{
cout << "Can not create Odometry algorithm. Check the passed odometry name." << endl;
return -1;
}
odometry->set("cameraMatrix", cameraMatrix);
TickMeter gtm;
int count = 0;
for(int i = 0; !file.eof(); i++)
{
string str;
std::getline(file, str);
if(str.empty()) break;
if(str.at(0) == '#') continue; /* comment */
Mat image, depth;
// Read one pair (rgb and depth)
// example: 1305031453.359684 rgb/1305031453.359684.png 1305031453.374112 depth/1305031453.374112.png
#if BILATERAL_FILTER
TickMeter tm_bilateral_filter;
#endif
{
string rgbFilename = str.substr(timestampLength + 1, rgbPathLehgth );
string timestap = str.substr(0, timestampLength);
string depthFilename = str.substr(2*timestampLength + rgbPathLehgth + 3, depthPathLehgth );
image = imread(dirname + rgbFilename);
depth = imread(dirname + depthFilename, -1);
CV_Assert(!image.empty());
CV_Assert(!depth.empty());
CV_Assert(depth.type() == CV_16UC1);
cout << i << " " << rgbFilename << " " << depthFilename << endl;
// scale depth
Mat depth_flt;
depth.convertTo(depth_flt, CV_32FC1, 1.f/5000.f);
#if not BILATERAL_FILTER
depth_flt.setTo(std::numeric_limits<float>::quiet_NaN(), depth == 0);
depth = depth_flt;
#else
tm_bilateral_filter.start();
depth = Mat(depth_flt.size(), CV_32FC1, Scalar(0));
const double depth_sigma = 0.03;
const double space_sigma = 4.5; // in pixels
Mat invalidDepthMask = depth_flt == 0.f;
depth_flt.setTo(-5*depth_sigma, invalidDepthMask);
bilateralFilter(depth_flt, depth, -1, depth_sigma, space_sigma);
depth.setTo(std::numeric_limits<float>::quiet_NaN(), invalidDepthMask);
tm_bilateral_filter.stop();
//.........这里部分代码省略.........
示例12: main
//.........这里部分代码省略.........
Mat image = loadImage(imageName);
Ptr<GeneralizedHough> alg;
if (!full)
{
Ptr<GeneralizedHoughBallard> ballard = useGpu ? cuda::createGeneralizedHoughBallard() : createGeneralizedHoughBallard();
ballard->setMinDist(minDist);
ballard->setLevels(levels);
ballard->setDp(dp);
ballard->setMaxBufferSize(maxBufSize);
ballard->setVotesThreshold(votesThreshold);
alg = ballard;
}
else
{
Ptr<GeneralizedHoughGuil> guil = useGpu ? cuda::createGeneralizedHoughGuil() : createGeneralizedHoughGuil();
guil->setMinDist(minDist);
guil->setLevels(levels);
guil->setDp(dp);
guil->setMaxBufferSize(maxBufSize);
guil->setMinAngle(minAngle);
guil->setMaxAngle(maxAngle);
guil->setAngleStep(angleStep);
guil->setAngleThresh(angleThresh);
guil->setMinScale(minScale);
guil->setMaxScale(maxScale);
guil->setScaleStep(scaleStep);
guil->setScaleThresh(scaleThresh);
guil->setPosThresh(posThresh);
alg = guil;
}
vector<Vec4f> position;
TickMeter tm;
if (useGpu)
{
cuda::GpuMat d_templ(templ);
cuda::GpuMat d_image(image);
cuda::GpuMat d_position;
alg->setTemplate(d_templ);
tm.start();
alg->detect(d_image, d_position);
d_position.download(position);
tm.stop();
}
else
{
alg->setTemplate(templ);
tm.start();
alg->detect(image, position);
tm.stop();
}
cout << "Found : " << position.size() << " objects" << endl;
cout << "Detection time : " << tm.getTimeMilli() << " ms" << endl;
Mat out;
cv::cvtColor(image, out, COLOR_GRAY2BGR);
for (size_t i = 0; i < position.size(); ++i)
{
Point2f pos(position[i][0], position[i][1]);
float scale = position[i][2];
float angle = position[i][3];
RotatedRect rect;
rect.center = pos;
rect.size = Size2f(templ.cols * scale, templ.rows * scale);
rect.angle = angle;
Point2f pts[4];
rect.points(pts);
line(out, pts[0], pts[1], Scalar(0, 0, 255), 3);
line(out, pts[1], pts[2], Scalar(0, 0, 255), 3);
line(out, pts[2], pts[3], Scalar(0, 0, 255), 3);
line(out, pts[3], pts[0], Scalar(0, 0, 255), 3);
}
imshow("out", out);
waitKey();
return 0;
}
示例13: SuperRe
//.........这里部分代码省略.........
}
cc++;
}
}*/
//higres = Values(Rect(index, 0, 1, 25))*result;
//if (r<20&&c<21)
//{
char temps[20];
sprintf(temps, "%d ", index);
string s(temps);
fout << s << " ";
/*}*/
}
/*if (r < 20)
{*/
fout << endl;
/*}*/
}
/*************************************/
/*WriteFile(3025, 174, data,OUTPUT);
for (int i = 0; i <3025; i++){
delete[]data[i];
}
delete[]data;*/
/*************************************/
Mat final, finalresult;
imwrite(TEST + "addOriginalResult.png", Result*255.0);
add(lowResImg, Result, Result);
imwrite(TEST + "addResult.png", Result*255.0);
CrCb[0] = Result;
merge(CrCb, finalresult);
cvtColor(finalresult, finalMerge, CV_YCrCb2BGR);
imwrite(TEST + "addfinalResult.png", finalMerge*255.0);
tm.stop();
cout << "count=" << tm.getCounter() << ",process time=" << tm.getTimeSec() << endl;
Sleep(5000);
/*******************test data*********/
Mat Finaltest = finalMerge*255.0;
vector<Mat> testInput;
Mat* inputest = new Mat[traiNo];
for (int i = 0; i < traiNo; i++)
{
inputest[i] = imread(TRAINFILENAME[i],1);
}
for (int i = 0; i < traiNo; i++)
{
Mat a = Mat::zeros(Finaltest.rows, Finaltest.cols + inputest[i].cols, CV_32FC3);
Finaltest.copyTo(a(Rect(0, 0, Finaltest.cols, Finaltest.rows)));
inputest[i].convertTo(inputest[i], CV_32FC3);
inputest[i].copyTo(a(Rect(Finaltest.cols, 0, inputest[i].cols, inputest[i].rows)));
testInput.push_back(a);
}
int *sumtemp = new int[traiNo];
for (int n = 0; n < traiNo; n++)
{
sumtemp[n] = 0;
}
int width = inputImage.cols, height = inputImage.rows;
map<int, Point2d>::iterator itest;
itest = testPoint.begin();
for (it_map = vectorList.begin(); it_map != vectorList.end(); it_map++)
{
int index = it_map->first;
Point2d a = itest->second;
int pic = InWhichPic(index);
sumtemp[pic]++;
Point2d point = Valuse[index];
Point b = Point(a.x + 5, a.y + 5);
Point a1 = Point(point.x + 1 + inputImg.cols, point.y + 1);
Point b1 = Point(point.x + 5 + inputImg.cols, point.y + 5);
rectangle(testInput[pic], a, b, Scalar(a.y * 255 / height, 0, a.x * 255 / width), 1, 8);
rectangle(testInput[pic], a1, b1, Scalar(a.y * 255 / height, 0, a.x * 255 / width), 1, 8);
//line(testInput[pic], a, a1, Scalar(255, 0, 0), 0.1, 8);
itest++;
}
for (int i = 0; i < traiNo; i++)
{
char temps[20];
sprintf(temps, "test_%d.png", i);
string s(temps);
imwrite(TEST + temps, testInput[i]);
}
WriteINTFile(inputImage.rows, inputImage.cols, drawindex, INDEX);
WritePOINTFile(inputImage.rows, inputImage.cols, draw, POINTfile);
for (int n = 0; n < traiNo; n++)
{
fout << sumtemp[n] << endl;
}
fout.close();
/******************test data**********/
//CrCb.clear();
vectorList.clear();
/*cvMerge(Y, Cr, Cb, NULL, frame)
cvtColor(frame, frame, CV_YCrCb2BGR)*/
}
示例14: run
void TaskManager::run(string groundTruthFile)
{
//run on the train data or on the test data
bool useGroundTruth = !groundTruthFile.empty();
cout << "Processing from " << from << " to " << to << endl << endl;
answers.clear();
Answers::type rightAnswers;
if (useGroundTruth)
{
Answers::loadAnswers(groundTruthFile, rightAnswers);
const int inlierLabel = 0;
vector<int> fullPanoramaMask(images_count, inlierLabel);
for (int i = from; i <= to; ++i)
if (rightAnswers.find(i) == rightAnswers.end())
rightAnswers.insert(make_pair(i, fullPanoramaMask));
}
int total = to - from + 1;
vector < vector<KruskalGrouper::Grouping> > groupings(total);
float minIncorrectDistance = std::numeric_limits<float>::max();
int minIncorrectDistanceSeriaIdx = -1;
#pragma omp parallel for schedule(dynamic, 5)
for (int i = from; i <= to; ++i)
{
cout << "Seria #" << i << "\t" << endl;
TickMeter time;
time.start();
OnePanoSolver solver(folder, i, cache_folder);
Mat diff;
#if 0 // cross-check only
bool found = solver.launch(groupings[i - from], diff, int());
#else
solver.launch(groupings[i - from], diff);
#endif
time.stop();
cout << "Time: " << time.getTimeSec() << "s" << endl;
if (useGroundTruth)
{
vector<int> right = rightAnswers[i];
for (int j = 0; j < diff.rows; j++)
{
for (int k = j + 1; k < diff.cols; k++)
{
if (right[j] != right[k])
{
CV_Assert(diff.type() == CV_32FC1);
if (diff.at<float> (j, k) <= minIncorrectDistance)
{
minIncorrectDistance = diff.at<float> (j, k);
minIncorrectDistanceSeriaIdx = i;
}
}
}
}
}
}
int bestScore = -1;
double bestThreshold = -1;
const int minPanoSize = 3;
if (useGroundTruth)
{
for (size_t i = 0; i < groupings.size(); i++)
{
int curBestScore = -1;
double curBestThreshold = -1;
for (size_t j = 1; j < groupings[i].size() - 1; j++)
{
double curThreshold = groupings[i][j].threshold;
int curScore = 0;
for (size_t k = 0; k < groupings.size(); k++)
{
vector<int> classes, answer_mask;
KruskalGrouper::group(groupings[k], curThreshold, minPanoSize, classes);
stringstream devNull;
generateOutputMaskFromClasses(classes, answer_mask, devNull);
int score = static_cast<int>(images_count - norm(Mat(answer_mask) - Mat(rightAnswers[from + k]), NORM_L1));
curScore += score;
}
if (curScore > curBestScore)
{
curBestScore = curScore;
curBestThreshold = curThreshold;
}
}
if (curBestScore > bestScore)
{
bestScore = curBestScore;
bestThreshold = curBestThreshold;
//.........这里部分代码省略.........
示例15: getFeaturePyramid
int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps,
const int bx, const int by)
{
IplImage *imgResize;
float step;
unsigned int numStep;
unsigned int maxNumCells;
unsigned int W, H;
if (image->depth == IPL_DEPTH_32F)
{
imgResize = image;
}
else
{
imgResize = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
cvConvert(image, imgResize);
}
W = imgResize->width;
H = imgResize->height;
step = powf(2.0f, 1.0f / ((float) Lambda));
maxNumCells = W / Side_Length;
if (maxNumCells > H / Side_Length)
{
maxNumCells = H / Side_Length;
}
numStep = (int) (logf((float) maxNumCells / (5.0f)) / logf(step)) + 1;
allocFeaturePyramidObject(maps, numStep + Lambda);
#ifdef PROFILE
TickMeter tm;
tm.start();
cout << "(featurepyramid.cpp)getPathOfFeaturePyramid START " << endl;
#endif
uploadImageToGPU1D(imgResize);
getPathOfFeaturePyramidGPUStream(imgResize, step , Lambda, 0,
Side_Length / 2, bx, by, maps);
getPathOfFeaturePyramidGPUStream(imgResize, step, numStep, Lambda,
Side_Length , bx, by, maps);
cleanImageFromGPU1D();
#ifdef PROFILE
tm.stop();
cout << "(featurepyramid.cpp)getPathOfFeaturePyramid END time = "
<< tm.getTimeSec() << " sec" << endl;
#endif
if (image->depth != IPL_DEPTH_32F)
{
cvReleaseImage(&imgResize);
}
return LATENT_SVM_OK;
}