generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

removed trailing backspaces, reduced number of warnings (under MSVC2010 x64) for size_t to int conversion, added handling of samples launch without parameters (should not have abnormal termination if there was no paramaters supplied)

Browse Source
This commit is contained in:
Vladimir Dudnik
2011-06-17 06:31:54 +00:00
parent 092beae2d5
commit 6e38b6aaed
12 changed files with 289 additions and 267 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
samples/c/facedetect.cpp
View File

@@ -20,7 +20,7 @@ void help()
"see facedetect.cmd for one call:\n"
"./facedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"../../data/haarcascades/haarcascade_eye.xml\" --scale=1.3 \n"
"Hit any key to quit.\n"
"Using OpenCV version %s\n" << CV_VERSION << "\n" << endl;
"Using OpenCV version " << CV_VERSION << "\n" << endl;
}
void detectAndDraw( Mat& img,
@@ -49,7 +49,7 @@ int main( int argc, const char** argv )
for( int i = 1; i < argc; i++ )
{
cout << "Processing " << i << " " << argv[i] << endl;
cout << "Processing " << i << " " << argv[i] << endl;
if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 )
{
cascadeName.assign( argv[i] + cascadeOptLen );
@@ -111,7 +111,7 @@ int main( int argc, const char** argv )
if( capture )
{
cout << "In capture ..." << endl;
cout << "In capture ..." << endl;
for(;;)
{
IplImage* iplImg = cvQueryFrame( capture );
@@ -130,12 +130,13 @@ int main( int argc, const char** argv )
}
waitKey(0);
_cleanup_:
cvReleaseCapture( &capture );
}
else
{
cout << "In image read" << endl;
cout << "In image read" << endl;
if( !image.empty() )
{
detectAndDraw( image, cascade, nestedCascade, scale );
@@ -239,6 +240,6 @@ void detectAndDraw( Mat& img,
radius = cvRound((nr->width + nr->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
}
cv::imshow( "result", img );
}
cv::imshow( "result", img );
}

46
samples/c/find_obj.cpp
View File

@@ -16,14 +16,13 @@
using namespace std;
void help()
{
printf(
"This program demonstrated the use of the SURF Detector and Descriptor using\n"
"either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
"on planar objects.\n"
"Call:\n"
"./find_obj [<object_filename default box.png> <scene_filename default box_in_scene.png>]\n\n"
);
printf(
"This program demonstrated the use of the SURF Detector and Descriptor using\n"
"either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
"on planar objects.\n"
"Usage:\n"
"./find_obj <object_filename> <scene_filename>, default is box.png and box_in_scene.png\n\n");
return;
}
// define whether to use approximate nearest-neighbor search
@@ -214,8 +213,19 @@ int main(int argc, char** argv)
const char* object_filename = argc == 3 ? argv[1] : "box.png";
const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
CvMemStorage* storage = cvCreateMemStorage(0);
help();
IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
if( !object || !image )
{
fprintf( stderr, "Can not load %s and/or %s\n",
object_filename, scene_filename );
exit(-1);
}
CvMemStorage* storage = cvCreateMemStorage(0);
cvNamedWindow("Object", 1);
cvNamedWindow("Object Correspond", 1);
@@ -232,30 +242,24 @@ int main(int argc, char** argv)
{{255,255,255}}
};
IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
if( !object || !image )
{
fprintf( stderr, "Can not load %s and/or %s\n"
"Usage: find_obj [<object_filename> <scene_filename>]\n",
object_filename, scene_filename );
exit(-1);
}
IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
cvCvtColor( object, object_color, CV_GRAY2BGR );
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
CvSeq* objectKeypoints = 0, *objectDescriptors = 0;
CvSeq* imageKeypoints = 0, *imageDescriptors = 0;
int i;
CvSURFParams params = cvSURFParams(500, 1);
double tt = (double)cvGetTickCount();
cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
printf("Object Descriptors: %d\n", objectDescriptors->total);
cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
printf("Image Descriptors: %d\n", imageDescriptors->total);
tt = (double)cvGetTickCount() - tt;
printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));
CvPoint src_corners[4] = {{0,0}, {object->width,0}, {object->width, object->height}, {0, object->height}};
CvPoint dst_corners[4];
IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 );

17
samples/c/find_obj_calonder.cpp
View File

@@ -12,14 +12,17 @@ using namespace cv;
void help()
{
cout << "This program shows the use of the Calonder point descriptor classifier"
"SURF is used to detect interest points, Calonder is used to describe/match these points\n"
"Format:" << endl <<
"SURF is used to detect interest points, Calonder is used to describe/match these points\n"
"Format:" << endl <<
" classifier_file(to write) test_image file_with_train_images_filenames(txt)" <<
" or" << endl <<
" classifier_file(to read) test_image"
"Using OpenCV version %s\n" << CV_VERSION << "\n"
<< endl;
" classifier_file(to read) test_image" << "\n" << endl <<
"Using OpenCV version " << CV_VERSION << "\n" << endl;
return;
}
/*
* Generates random perspective transform of image
*/
@@ -131,7 +134,7 @@ void testCalonderClassifier( const string& classifierFilename, const string& img
Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
for( size_t mi = 0; mi < matches.size(); mi++ )
{
if( norm(points2[matches[mi].trainIdx] - points1t.at<Point2f>(mi,0)) < 4 ) // inlier
if( norm(points2[matches[mi].trainIdx] - points1t.at<Point2f>((int)mi,0)) < 4 ) // inlier
matchesMask[mi] = 1;
}
@@ -148,7 +151,7 @@ int main( int argc, char **argv )
{
if( argc != 4 && argc != 3 )
{
help();
help();
return -1;
}

69
samples/c/find_obj_ferns.cpp
View File

@@ -12,43 +12,46 @@ using namespace cv;
void help()
{
printf( "This program shows the use of the \"fern\" plannar PlanarObjectDetector point\n"
"descriptor classifier"
"Usage:\n"
"./find_obj_ferns [<object_filename default: box.png> <scene_filename default:box_in_scene.png>]\n"
"\n");
"descriptor classifier\n"
"Usage:\n"
"./find_obj_ferns <object_filename> <scene_filename>, default: box.png and box_in_scene.png\n\n");
return;
}
int main(int argc, char** argv)
{
int i;
const char* object_filename = argc > 1 ? argv[1] : "box.png";
const char* scene_filename = argc > 2 ? argv[2] : "box_in_scene.png";
int i;
help();
cvNamedWindow("Object", 1);
cvNamedWindow("Image", 1);
cvNamedWindow("Object Correspondence", 1);
Mat object = imread( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
Mat image;
double imgscale = 1;
Mat scene = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
Mat _image = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
resize(_image, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
if( !object.data || !image.data )
if( !object.data || !scene.data )
{
fprintf( stderr, "Can not load %s and/or %s\n"
"Usage: find_obj_ferns [<object_filename> <scene_filename>]\n",
fprintf( stderr, "Can not load %s and/or %s\n",
object_filename, scene_filename );
exit(-1);
}
double imgscale = 1;
Mat image;
resize(scene, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
cvNamedWindow("Object", 1);
cvNamedWindow("Image", 1);
cvNamedWindow("Object Correspondence", 1);
Size patchSize(32, 32);
LDetector ldetector(7, 20, 2, 2000, patchSize.width, 2);
ldetector.setVerbose(true);
PlanarObjectDetector detector;
vector<Mat> objpyr, imgpyr;
int blurKSize = 3;
double sigma = 0;
@@ -56,10 +59,10 @@ int main(int argc, char** argv)
GaussianBlur(image, image, Size(blurKSize, blurKSize), sigma, sigma);
buildPyramid(object, objpyr, ldetector.nOctaves-1);
buildPyramid(image, imgpyr, ldetector.nOctaves-1);
vector<KeyPoint> objKeypoints, imgKeypoints;
PatchGenerator gen(0,256,5,true,0.8,1.2,-CV_PI/2,CV_PI/2,-CV_PI/2,CV_PI/2);
PatchGenerator gen(0,256,5,true,0.8,1.2,-CV_PI/2,CV_PI/2,-CV_PI/2,CV_PI/2);
string model_filename = format("%s_model.xml.gz", object_filename);
printf("Trying to load %s ...\n", model_filename.c_str());
FileStorage fs(model_filename, FileStorage::READ);
@@ -76,7 +79,7 @@ int main(int argc, char** argv)
ldetector.getMostStable2D(object, objKeypoints, 100, gen);
printf("Done.\nStep 2. Training ferns-based planar object detector ...\n");
detector.setVerbose(true);
detector.train(objpyr, objKeypoints, patchSize.width, 100, 11, 10000, ldetector, gen);
printf("Done.\nStep 3. Saving the model to %s ...\n", model_filename.c_str());
if( fs.open(model_filename, FileStorage::WRITE) )
@@ -84,7 +87,7 @@ int main(int argc, char** argv)
}
printf("Now find the keypoints in the image, try recognize them and compute the homography matrix\n");
fs.release();
vector<Point2f> dst_corners;
Mat correspond( object.rows + image.rows, std::max(object.cols, image.cols), CV_8UC3);
correspond = Scalar(0.);
@@ -92,20 +95,20 @@ int main(int argc, char** argv)
cvtColor(object, part, CV_GRAY2BGR);
part = Mat(correspond, Rect(0, object.rows, image.cols, image.rows));
cvtColor(image, part, CV_GRAY2BGR);
vector<int> pairs;
Mat H;
double t = (double)getTickCount();
objKeypoints = detector.getModelPoints();
ldetector(imgpyr, imgKeypoints, 300);
std::cout << "Object keypoints: " << objKeypoints.size() << "\n";
std::cout << "Image keypoints: " << imgKeypoints.size() << "\n";
bool found = detector(imgpyr, imgKeypoints, H, dst_corners, &pairs);
t = (double)getTickCount() - t;
printf("%gms\n", t*1000/getTickFrequency());
if( found )
{
for( i = 0; i < 4; i++ )
@@ -116,14 +119,14 @@ int main(int argc, char** argv)
Point(r2.x, r2.y+object.rows), Scalar(0,0,255) );
}
}
for( i = 0; i < (int)pairs.size(); i += 2 )
{
line( correspond, objKeypoints[pairs[i]].pt,
imgKeypoints[pairs[i+1]].pt + Point2f(0,(float)object.rows),
Scalar(0,255,0) );
}
imshow( "Object Correspondence", correspond );
Mat objectColor;
cvtColor(object, objectColor, CV_GRAY2BGR);
@@ -139,10 +142,12 @@ int main(int argc, char** argv)
circle( imageColor, imgKeypoints[i].pt, 2, Scalar(0,0,255), -1 );
circle( imageColor, imgKeypoints[i].pt, (1 << imgKeypoints[i].octave)*15, Scalar(0,255,0), 1 );
}
imwrite("correspond.png", correspond );
imshow( "Object", objectColor );
imshow( "Image", imageColor );
waitKey(0);
return 0;
}

227
samples/cpp/build3dmodel.cpp
View File

@@ -13,14 +13,15 @@
using namespace cv;
using namespace std;
void myhelp()
void help()
{
printf("\nSigh: This program is not complete/will be replaced. \n"
"So: Use this just to see hints of how to use things like Rodrigues\n"
" conversions, finding the fundamental matrix, using descriptor\n"
" finding and matching in features2d and using camera parameters\n"
);
printf("\nSigh: This program is not complete/will be replaced. \n"
"So: Use this just to see hints of how to use things like Rodrigues\n"
" conversions, finding the fundamental matrix, using descriptor\n"
" finding and matching in features2d and using camera parameters\n"
"Usage: build3dmodel -i <intrinsics_filename>\n"
"\t[-d <detector>] [-de <descriptor_extractor>] -m <model_name>\n\n");
return;
}
@@ -33,12 +34,12 @@ static bool readCameraMatrix(const string& filename,
fs["image_height"] >> calibratedImageSize.height;
fs["distortion_coefficients"] >> distCoeffs;
fs["camera_matrix"] >> cameraMatrix;
if( distCoeffs.type() != CV_64F )
distCoeffs = Mat_<double>(distCoeffs);
if( cameraMatrix.type() != CV_64F )
cameraMatrix = Mat_<double>(cameraMatrix);
return true;
}
@@ -154,19 +155,19 @@ static void findConstrainedCorrespondences(const Mat& _F,
{
float F[9]={0};
int dsize = descriptors1.cols;
Mat Fhdr = Mat(3, 3, CV_32F, F);
_F.convertTo(Fhdr, CV_32F);
matches.clear();
for( size_t i = 0; i < keypoints1.size(); i++ )
for( int i = 0; i < (int)keypoints1.size(); i++ )
{
Point2f p1 = keypoints1[i].pt;
double bestDist1 = DBL_MAX, bestDist2 = DBL_MAX;
int bestIdx1 = -1, bestIdx2 = -1;
const float* d1 = descriptors1.ptr<float>(i);
for( size_t j = 0; j < keypoints2.size(); j++ )
for( int j = 0; j < (int)keypoints2.size(); j++ )
{
Point2f p2 = keypoints2[j].pt;
double e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) +
@@ -177,7 +178,7 @@ static void findConstrainedCorrespondences(const Mat& _F,
const float* d2 = descriptors2.ptr<float>(j);
double dist = 0;
int k = 0;
for( ; k <= dsize - 8; k += 8 )
{
float t0 = d1[k] - d2[k], t1 = d1[k+1] - d2[k+1];
@@ -186,11 +187,11 @@ static void findConstrainedCorrespondences(const Mat& _F,
float t6 = d1[k+6] - d2[k+6], t7 = d1[k+7] - d2[k+7];
dist += t0*t0 + t1*t1 + t2*t2 + t3*t3 +
t4*t4 + t5*t5 + t6*t6 + t7*t7;
if( dist >= bestDist2 )
break;
}
if( dist < bestDist2 )
{
for( ; k < dsize; k++ )
@@ -198,7 +199,7 @@ static void findConstrainedCorrespondences(const Mat& _F,
float t = d1[k] - d2[k];
dist += t*t;
}
if( dist < bestDist1 )
{
bestDist2 = bestDist1;
@@ -213,7 +214,7 @@ static void findConstrainedCorrespondences(const Mat& _F,
}
}
}
if( bestIdx1 >= 0 && bestDist1 < bestDist2*ratio )
{
Point2f p2 = keypoints1[bestIdx1].pt;
@@ -224,21 +225,21 @@ static void findConstrainedCorrespondences(const Mat& _F,
continue;
double threshold = bestDist1/ratio;
const float* d22 = descriptors2.ptr<float>(bestIdx1);
size_t i1 = 0;
for( ; i1 < keypoints1.size(); i1++ )
int i1 = 0;
for( ; i1 < (int)keypoints1.size(); i1++ )
{
if( i1 == i )
continue;
Point2f p1 = keypoints1[i1].pt;
const float* d11 = descriptors1.ptr<float>(i1);
double dist = 0;
e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) +
p2.y*(F[3]*p1.x + F[4]*p1.y + F[5]) +
F[6]*p1.x + F[7]*p1.y + F[8];
if( fabs(e) > eps )
continue;
for( int k = 0; k < dsize; k++ )
{
float t = d11[k] - d22[k];
@@ -246,7 +247,7 @@ static void findConstrainedCorrespondences(const Mat& _F,
if( dist >= threshold )
break;
}
if( dist < threshold )
break;
}
@@ -334,7 +335,7 @@ void triangulatePoint_test(void)
randu(tvec1, Scalar::all(-10), Scalar::all(10));
randu(rvec2, Scalar::all(-10), Scalar::all(10));
randu(tvec2, Scalar::all(-10), Scalar::all(10));
randu(objptmat, Scalar::all(-10), Scalar::all(10));
double eps = 1e-2;
randu(deltamat1, Scalar::all(-eps), Scalar::all(eps));
@@ -343,10 +344,10 @@ void triangulatePoint_test(void)
Mat_<float> cameraMatrix(3,3);
double fx = 1000., fy = 1010., cx = 400.5, cy = 300.5;
cameraMatrix << fx, 0, cx, 0, fy, cy, 0, 0, 1;
projectPoints(Mat(objpt)+Mat(delta1), rvec1, tvec1, cameraMatrix, Mat(), imgpt1);
projectPoints(Mat(objpt)+Mat(delta2), rvec2, tvec2, cameraMatrix, Mat(), imgpt2);
vector<Point3f> objptt(n);
vector<Point2f> pts(2);
vector<Mat> Rv(2), tv(2);
@@ -390,10 +391,10 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
PointModel& model )
{
int progressBarSize = 10;
const double Feps = 5;
const double DescriptorRatio = 0.7;
vector<vector<KeyPoint> > allkeypoints;
vector<int> dstart;
vector<float> alldescriptorsVec;
@@ -402,23 +403,23 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
int descriptorSize = 0;
Mat descriptorbuf;
Set2i pairs, keypointsIdxMap;
model.points.clear();
model.didx.clear();
dstart.push_back(0);
size_t nimages = imageList.size();
size_t nimagePairs = (nimages - 1)*nimages/2 - nimages;
printf("\nComputing descriptors ");
// 1. find all the keypoints and all the descriptors
for( size_t i = 0; i < nimages; i++ )
{
Mat img = imread(imageList[i], 1), gray;
cvtColor(img, gray, CV_BGR2GRAY);
vector<KeyPoint> keypoints;
detector->detect(gray, keypoints);
descriptorExtractor->compute(gray, keypoints, descriptorbuf);
@@ -426,7 +427,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
for( size_t k = 0; k < keypoints.size(); k++ )
keypoints[k].pt += roiofs;
allkeypoints.push_back(keypoints);
Mat buf = descriptorbuf;
if( !buf.isContinuous() || buf.type() != CV_32F )
{
@@ -434,41 +435,41 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
descriptorbuf.convertTo(buf, CV_32F);
}
descriptorSize = buf.cols;
size_t prev = alldescriptorsVec.size();
size_t delta = buf.rows*buf.cols;
alldescriptorsVec.resize(prev + delta);
std::copy(buf.ptr<float>(), buf.ptr<float>() + delta,
alldescriptorsVec.begin() + prev);
dstart.push_back(dstart.back() + keypoints.size());
dstart.push_back(dstart.back() + (int)keypoints.size());
Mat R, t;
unpackPose(poseList[i], R, t);
Rs.push_back(R);
ts.push_back(t);
if( (i+1)*progressBarSize/nimages > i*progressBarSize/nimages )
{
putchar('.');
fflush(stdout);
}
}
Mat alldescriptors(alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F,
Mat alldescriptors((int)alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F,
&alldescriptorsVec[0]);
printf("\nOk. total images = %d. total keypoints = %d\n",
(int)nimages, alldescriptors.rows);
printf("\nFinding correspondences ");
int pairsFound = 0;
vector<Point2f> pts_k(2);
vector<Mat> Rs_k(2), ts_k(2);
//namedWindow("img1", 1);
//namedWindow("img2", 1);
// 2. find pairwise correspondences
for( size_t i = 0; i < nimages; i++ )
for( size_t j = i+1; j < nimages; j++ )
@@ -477,47 +478,47 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
const vector<KeyPoint>& keypoints2 = allkeypoints[j];
Mat descriptors1 = alldescriptors.rowRange(dstart[i], dstart[i+1]);
Mat descriptors2 = alldescriptors.rowRange(dstart[j], dstart[j+1]);
Mat F = getFundamentalMat(Rs[i], ts[i], Rs[j], ts[j], cameraMatrix);
findConstrainedCorrespondences( F, keypoints1, keypoints2,
descriptors1, descriptors2,
pairwiseMatches, Feps, DescriptorRatio );
//pairsFound += (int)pairwiseMatches.size();
//Mat img1 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)i), 1);
//Mat img2 = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)j), 1);
//double avg_err = 0;
for( size_t k = 0; k < pairwiseMatches.size(); k++ )
{
int i1 = pairwiseMatches[k][0], i2 = pairwiseMatches[k][1];
pts_k[0] = keypoints1[i1].pt;
pts_k[1] = keypoints2[i2].pt;
Rs_k[0] = Rs[i]; Rs_k[1] = Rs[j];
ts_k[0] = ts[i]; ts_k[1] = ts[j];
Point3f objpt = triangulatePoint(pts_k, Rs_k, ts_k, cameraMatrix);
vector<Point3f> objpts;
objpts.push_back(objpt);
vector<Point2f> imgpts1, imgpts2;
projectPoints(Mat(objpts), Rs_k[0], ts_k[0], cameraMatrix, Mat(), imgpts1);
projectPoints(Mat(objpts), Rs_k[1], ts_k[1], cameraMatrix, Mat(), imgpts2);
double e1 = norm(imgpts1[0] - keypoints1[i1].pt);
double e2 = norm(imgpts2[0] - keypoints2[i2].pt);
if( e1 + e2 > 5 )
continue;
pairsFound++;
//model.points.push_back(objpt);
pairs[Pair2i(i1+dstart[i], i2+dstart[j])] = 1;
pairs[Pair2i(i2+dstart[j], i1+dstart[i])] = 1;
keypointsIdxMap[Pair2i(i,i1)] = 1;
keypointsIdxMap[Pair2i(j,i2)] = 1;
keypointsIdxMap[Pair2i((int)i,i1)] = 1;
keypointsIdxMap[Pair2i((int)j,i2)] = 1;
//CV_Assert(e1 < 5 && e2 < 5);
//Scalar color(rand()%256,rand()%256, rand()%256);
//circle(img1, keypoints1[i1].pt, 2, color, -1, CV_AA);
@@ -527,41 +528,41 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
//imshow("img1", img1);
//imshow("img2", img2);
//waitKey();
if( (i+1)*progressBarSize/nimagePairs > i*progressBarSize/nimagePairs )
{
putchar('.');
fflush(stdout);
}
}
printf("\nOk. Total pairs = %d\n", pairsFound );
// 3. build the keypoint clusters
vector<Pair2i> keypointsIdx;
Set2i::iterator kpidx_it = keypointsIdxMap.begin(), kpidx_end = keypointsIdxMap.end();
for( ; kpidx_it != kpidx_end; ++kpidx_it )
keypointsIdx.push_back(kpidx_it->first);
printf("\nClustering correspondences ");
vector<int> labels;
int nclasses = partition( keypointsIdx, labels, EqKeypoints(&dstart, &pairs) );
printf("\nOk. Total classes (i.e. 3d points) = %d\n", nclasses );
model.descriptors.create(keypointsIdx.size(), descriptorSize, CV_32F);
model.descriptors.create((int)keypointsIdx.size(), descriptorSize, CV_32F);
model.didx.resize(nclasses);
model.points.resize(nclasses);
vector<vector<Pair2i> > clusters(nclasses);
for( size_t i = 0; i < keypointsIdx.size(); i++ )
clusters[labels[i]].push_back(keypointsIdx[i]);
// 4. now compute 3D points corresponding to each cluster and fill in the model data
printf("\nComputing 3D coordinates ");
int globalDIdx = 0;
for( int k = 0; k < nclasses; k++ )
{
@@ -575,7 +576,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
int imgidx = clusters[k][i].first, ptidx = clusters[k][i].second;
Mat dstrow = model.descriptors.row(globalDIdx);
alldescriptors.row(dstart[imgidx] + ptidx).copyTo(dstrow);
model.didx[k][i] = globalDIdx++;
pts_k[i] = allkeypoints[imgidx][ptidx].pt;
Rs_k[i] = Rs[imgidx];
@@ -583,7 +584,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
}
Point3f objpt = triangulatePoint(pts_k, Rs_k, ts_k, cameraMatrix);
model.points[k] = objpt;
if( (i+1)*progressBarSize/nclasses > i*progressBarSize/nclasses )
{
putchar('.');
@@ -600,10 +601,10 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
{
img = imread(format("%s/frame%04d.jpg", model.name.c_str(), (int)i), 1);
projectPoints(Mat(model.points), Rs[i], ts[i], cameraMatrix, Mat(), imagePoints);
for( int k = 0; k < (int)imagePoints.size(); k++ )
circle(img, imagePoints[k], 2, Scalar(0,255,0), -1, CV_AA, 0);
imshow("Test", img);
int c = waitKey();
if( c == 'q' || c == 'Q' )
@@ -614,75 +615,73 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
int main(int argc, char** argv)
{
triangulatePoint_test();
const char* help = "Usage: build3dmodel -i <intrinsics_filename>\n"
"\t[-d <detector>] [-de <descriptor_extractor>] -m <model_name>\n\n";
if(argc < 3)
{
puts(help);
myhelp();
return 0;
}
const char* intrinsicsFilename = 0;
const char* modelName = 0;
const char* modelName = 0;
const char* detectorName = "SURF";
const char* descriptorExtractorName = "SURF";
vector<Point3f> modelBox;
vector<string> imageList;
vector<Rect> roiList;
vector<Vec6f> poseList;
vector<string> imageList;
vector<Rect> roiList;
vector<Vec6f> poseList;
if(argc < 3)
{
help();
return -1;
}
for( int i = 1; i < argc; i++ )
{
if( strcmp(argv[i], "-i") == 0 )
intrinsicsFilename = argv[++i];
else if( strcmp(argv[i], "-m") == 0 )
modelName = argv[++i];
else if( strcmp(argv[i], "-d") == 0 )
intrinsicsFilename = argv[++i];
else if( strcmp(argv[i], "-m") == 0 )
modelName = argv[++i];
else if( strcmp(argv[i], "-d") == 0 )
detectorName = argv[++i];
else if( strcmp(argv[i], "-de") == 0 )
descriptorExtractorName = argv[++i];
else
{
printf("Incorrect option\n");
puts(help);
return 0;
}
else
{
help();
printf("Incorrect option\n");
return -1;
}
}
if( !intrinsicsFilename || !modelName )
{
printf("Some of the required parameters are missing\n");
puts(help);
return 0;
}
if( !intrinsicsFilename || !modelName )
{
printf("Some of the required parameters are missing\n");
help();
return -1;
}
triangulatePoint_test();
Mat cameraMatrix, distCoeffs;
Size calibratedImageSize;
readCameraMatrix(intrinsicsFilename, cameraMatrix, distCoeffs, calibratedImageSize);
Ptr<FeatureDetector> detector = FeatureDetector::create(detectorName);
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create(descriptorExtractorName);
string modelIndexFilename = format("%s_segm/frame_index.yml", modelName);
if(!readModelViews( modelIndexFilename, modelBox, imageList, roiList, poseList))
{
printf("Can not read the model. Check the parameters and the working directory\n");
puts(help);
return 0;
}
help();
return -1;
}
PointModel model;
model.name = modelName;
build3dmodel( detector, descriptorExtractor, modelBox,
imageList, roiList, poseList, cameraMatrix, model );
string outputModelName = format("%s_model.yml.gz", modelName);
printf("\nDone! Now saving the model ...\n");
writeModel(outputModelName, modelName, model);
return 0;
}

33
samples/cpp/chamfer.cpp
View File

@@ -9,42 +9,46 @@ using namespace std;
void help()
{
cout <<
"\nThis program demonstrates Chamfer matching -- computing a distance between an \n"
"edge template and a query edge image.\n"
"Call:\n"
"./chamfer [<image edge map> <template edge map>]\n"
"By default\n"
"the inputs are ./chamfer logo_in_clutter.png logo.png\n"<< endl;
cout <<
"\nThis program demonstrates Chamfer matching -- computing a distance between an \n"
"edge template and a query edge image.\n"
"Usage:\n"
"./chamfer <image edge map> <template edge map>,"
" By default the inputs are logo_in_clutter.png logo.png\n" << endl;
return;
}
int main( int argc, char** argv )
{
if( argc != 1 && argc != 3 )
if( argc != 3 )
{
help();
return 0;
}
Mat img = imread(argc == 3 ? argv[1] : "logo_in_clutter.png", 0);
Mat cimg;
cvtColor(img, cimg, CV_GRAY2BGR);
Mat tpl = imread(argc == 3 ? argv[2] : "logo.png", 0);
// if the image and the template are not edge maps but normal grayscale images,
// you might want to uncomment the lines below to produce the maps. You can also
// run Sobel instead of Canny.
// Canny(img, img, 5, 50, 3);
// Canny(tpl, tpl, 5, 50, 3);
vector<vector<Point> > results;
vector<float> costs;
int best = chamerMatching( img, tpl, results, costs );
if( best < 0 )
{
cout << "not found;\n";
cout << "matching not found\n";
return 0;
}
size_t i, n = results[best].size();
for( i = 0; i < n; i++ )
{
@@ -52,7 +56,10 @@ int main( int argc, char** argv )
if( pt.inside(Rect(0, 0, cimg.cols, cimg.rows)) )
cimg.at<Vec3b>(pt) = Vec3b(0, 255, 0);
}
imshow("result", cimg);
waitKey();
return 0;
}

18
samples/cpp/houghlines.cpp
View File

@@ -8,30 +8,31 @@ using namespace std;
void help()
{
cout << "\nThis program demonstrates line finding with the Hough transform.\n"
"Call:\n"
"./houghlines [image_len -- Default is pic1.png\n" << endl;
cout << "\nThis program demonstrates line finding with the Hough transform.\n"
"Usage:\n"
"./houghlines <image_name>, Default is pic1.png\n" << endl;
}
int main(int argc, char** argv)
{
const char* filename = argc >= 2 ? argv[1] : "pic1.png";
Mat src = imread(filename, 0);
if(src.empty())
{
help();
cout << "can not open " << filename << endl;
cout << "Usage: houghlines <image_name>" << endl;
return -1;
}
help();
Mat dst, cdst;
Canny(src, dst, 50, 200, 3);
cvtColor(dst, cdst, CV_GRAY2BGR);
#if 0
vector<Vec2f> lines;
HoughLines(dst, lines, 1, CV_PI/180, 100, 0, 0 );
for( size_t i = 0; i < lines.size(); i++ )
{
float rho = lines[i][0], theta = lines[i][1];
@@ -57,6 +58,7 @@ int main(int argc, char** argv)
imshow("detected lines", cdst);
waitKey();
return 0;
}