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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)

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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
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11
modules/calib3d/src/calibration.cpp
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@ -3187,7 +3187,7 @@ static Mat prepareDistCoeffs(Mat& distCoeffs0, int rtype)
return distCoeffs; return distCoeffs;
} }
} } // namespace cv
void cv::Rodrigues(InputArray _src, OutputArray _dst, OutputArray _jacobian) void cv::Rodrigues(InputArray _src, OutputArray _dst, OutputArray _jacobian)
@ -3361,7 +3361,8 @@ double cv::calibrateCamera( InputArrayOfArrays _objectPoints,
if( !(flags & CALIB_RATIONAL_MODEL) ) if( !(flags & CALIB_RATIONAL_MODEL) )
distCoeffs = distCoeffs.rows == 1 ? distCoeffs.colRange(0, 5) : distCoeffs.rowRange(0, 5); distCoeffs = distCoeffs.rows == 1 ? distCoeffs.colRange(0, 5) : distCoeffs.rowRange(0, 5);
size_t i, nimages = _objectPoints.total(); int i;
size_t nimages = _objectPoints.total();
CV_Assert( nimages > 0 ); CV_Assert( nimages > 0 );
Mat objPt, imgPt, npoints, rvecM((int)nimages, 3, CV_64FC1), tvecM((int)nimages, 3, CV_64FC1); Mat objPt, imgPt, npoints, rvecM((int)nimages, 3, CV_64FC1), tvecM((int)nimages, 3, CV_64FC1);
collectCalibrationData( _objectPoints, _imagePoints, noArray(), collectCalibrationData( _objectPoints, _imagePoints, noArray(),
@ -3373,10 +3374,10 @@ double cv::calibrateCamera( InputArrayOfArrays _objectPoints,
double reprojErr = cvCalibrateCamera2(&c_objPt, &c_imgPt, &c_npoints, imageSize, double reprojErr = cvCalibrateCamera2(&c_objPt, &c_imgPt, &c_npoints, imageSize,
&c_cameraMatrix, &c_distCoeffs, &c_rvecM, &c_cameraMatrix, &c_distCoeffs, &c_rvecM,
&c_tvecM, flags ); &c_tvecM, flags );
_rvecs.create(nimages, 1, CV_64FC3); _rvecs.create((int)nimages, 1, CV_64FC3);
_tvecs.create(nimages, 1, CV_64FC3); _tvecs.create((int)nimages, 1, CV_64FC3);
for( i = 0; i < nimages; i++ ) for( i = 0; i < (int)nimages; i++ )
{ {
_rvecs.create(3, 1, CV_64F, i, true); _rvecs.create(3, 1, CV_64F, i, true);
_tvecs.create(3, 1, CV_64F, i, true); _tvecs.create(3, 1, CV_64F, i, true);

4
modules/features2d/src/descriptors.cpp
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@ -376,13 +376,13 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector<
channelKeypoints[ci].insert( channelKeypoints[ci].begin(), keypoints.begin(), keypoints.end() ); channelKeypoints[ci].insert( channelKeypoints[ci].begin(), keypoints.begin(), keypoints.end() );
// Use class_id member to get indices into initial keypoints vector // Use class_id member to get indices into initial keypoints vector
for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ ) for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
channelKeypoints[ci][ki].class_id = ki; channelKeypoints[ci][ki].class_id = (int)ki;
descriptorExtractor->compute( opponentChannels[ci], channelKeypoints[ci], channelDescriptors[ci] ); descriptorExtractor->compute( opponentChannels[ci], channelKeypoints[ci], channelDescriptors[ci] );
idxs[ci].resize( channelKeypoints[ci].size() ); idxs[ci].resize( channelKeypoints[ci].size() );
for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ ) for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
{ {
idxs[ci][ki] = ki; idxs[ci][ki] = (int)ki;
} }
std::sort( idxs[ci].begin(), idxs[ci].end(), KP_LessThan(channelKeypoints[ci]) ); std::sort( idxs[ci].begin(), idxs[ci].end(), KP_LessThan(channelKeypoints[ci]) );
} }

2
modules/features2d/src/orb.cpp
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@ -127,7 +127,7 @@ HarrisResponse::HarrisResponse(const cv::Mat& image, double k) :
dX_offsets_.resize(7 * 9); dX_offsets_.resize(7 * 9);
dY_offsets_.resize(7 * 9); dY_offsets_.resize(7 * 9);
std::vector<int>::iterator dX_offsets = dX_offsets_.begin(), dY_offsets = dY_offsets_.begin(); std::vector<int>::iterator dX_offsets = dX_offsets_.begin(), dY_offsets = dY_offsets_.begin();
unsigned int image_step = image.step1(); unsigned int image_step = (unsigned int)image.step1();
for (size_t y = 0; y <= 6 * image_step; y += image_step) for (size_t y = 0; y <= 6 * image_step; y += image_step)
{ {
int dX_offset = y + 2, dY_offset = y + 2 * image_step; int dX_offset = y + 2, dY_offset = y + 2 * image_step;

3
samples/c/facedetect.cpp
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@ -20,7 +20,7 @@ void help()
"see facedetect.cmd for one call:\n" "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" "./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" "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, void detectAndDraw( Mat& img,
@ -130,6 +130,7 @@ int main( int argc, const char** argv )
} }
waitKey(0); waitKey(0);
_cleanup_: _cleanup_:
cvReleaseCapture( &capture ); cvReleaseCapture( &capture );
} }

36
samples/c/find_obj.cpp
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@ -20,10 +20,9 @@ void help()
"This program demonstrated the use of the SURF Detector and Descriptor using\n" "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" "either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
"on planar objects.\n" "on planar objects.\n"
"Call:\n" "Usage:\n"
"./find_obj [<object_filename default box.png> <scene_filename default box_in_scene.png>]\n\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 // 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* object_filename = argc == 3 ? argv[1] : "box.png";
const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png"; const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
CvMemStorage* storage = cvCreateMemStorage(0);
help(); 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", 1);
cvNamedWindow("Object Correspond", 1); cvNamedWindow("Object Correspond", 1);
@ -232,30 +242,24 @@ int main(int argc, char** argv)
{{255,255,255}} {{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); IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
cvCvtColor( object, object_color, CV_GRAY2BGR ); cvCvtColor( object, object_color, CV_GRAY2BGR );
CvSeq *objectKeypoints = 0, *objectDescriptors = 0; CvSeq* objectKeypoints = 0, *objectDescriptors = 0;
CvSeq *imageKeypoints = 0, *imageDescriptors = 0; CvSeq* imageKeypoints = 0, *imageDescriptors = 0;
int i; int i;
CvSURFParams params = cvSURFParams(500, 1); CvSURFParams params = cvSURFParams(500, 1);
double tt = (double)cvGetTickCount(); double tt = (double)cvGetTickCount();
cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params ); cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
printf("Object Descriptors: %d\n", objectDescriptors->total); printf("Object Descriptors: %d\n", objectDescriptors->total);
cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params ); cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
printf("Image Descriptors: %d\n", imageDescriptors->total); printf("Image Descriptors: %d\n", imageDescriptors->total);
tt = (double)cvGetTickCount() - tt; tt = (double)cvGetTickCount() - tt;
printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.)); 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 src_corners[4] = {{0,0}, {object->width,0}, {object->width, object->height}, {0, object->height}};
CvPoint dst_corners[4]; CvPoint dst_corners[4];
IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 ); IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 );

11
samples/c/find_obj_calonder.cpp
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@ -16,10 +16,13 @@ void help()
"Format:" << endl << "Format:" << endl <<
" classifier_file(to write) test_image file_with_train_images_filenames(txt)" << " classifier_file(to write) test_image file_with_train_images_filenames(txt)" <<
" or" << endl << " or" << endl <<
" classifier_file(to read) test_image" " classifier_file(to read) test_image" << "\n" << endl <<
"Using OpenCV version %s\n" << CV_VERSION << "\n" "Using OpenCV version " << CV_VERSION << "\n" << endl;
<< endl;
return;
} }
/* /*
* Generates random perspective transform of image * 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); Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
for( size_t mi = 0; mi < matches.size(); mi++ ) 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; matchesMask[mi] = 1;
} }

39
samples/c/find_obj_ferns.cpp
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@ -12,38 +12,41 @@ using namespace cv;
void help() void help()
{ {
printf( "This program shows the use of the \"fern\" plannar PlanarObjectDetector point\n" printf( "This program shows the use of the \"fern\" plannar PlanarObjectDetector point\n"
"descriptor classifier" "descriptor classifier\n"
"Usage:\n" "Usage:\n"
"./find_obj_ferns [<object_filename default: box.png> <scene_filename default:box_in_scene.png>]\n" "./find_obj_ferns <object_filename> <scene_filename>, default: box.png and box_in_scene.png\n\n");
"\n"); return;
} }
int main(int argc, char** argv) int main(int argc, char** argv)
{ {
int i;
const char* object_filename = argc > 1 ? argv[1] : "box.png"; const char* object_filename = argc > 1 ? argv[1] : "box.png";
const char* scene_filename = argc > 2 ? argv[2] : "box_in_scene.png"; const char* scene_filename = argc > 2 ? argv[2] : "box_in_scene.png";
int i;
help(); help();
cvNamedWindow("Object", 1);
cvNamedWindow("Image", 1);
cvNamedWindow("Object Correspondence", 1);
Mat object = imread( object_filename, CV_LOAD_IMAGE_GRAYSCALE ); Mat object = imread( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
Mat image; Mat scene = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
double imgscale = 1; if( !object.data || !scene.data )
Mat _image = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
resize(_image, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
if( !object.data || !image.data )
{ {
fprintf( stderr, "Can not load %s and/or %s\n" fprintf( stderr, "Can not load %s and/or %s\n",
"Usage: find_obj_ferns [<object_filename> <scene_filename>]\n",
object_filename, scene_filename ); object_filename, scene_filename );
exit(-1); 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); Size patchSize(32, 32);
LDetector ldetector(7, 20, 2, 2000, patchSize.width, 2); LDetector ldetector(7, 20, 2, 2000, patchSize.width, 2);
ldetector.setVerbose(true); ldetector.setVerbose(true);
@ -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, 2, Scalar(0,0,255), -1 );
circle( imageColor, imgKeypoints[i].pt, (1 << imgKeypoints[i].octave)*15, Scalar(0,255,0), 1 ); circle( imageColor, imgKeypoints[i].pt, (1 << imgKeypoints[i].octave)*15, Scalar(0,255,0), 1 );
} }
imwrite("correspond.png", correspond ); imwrite("correspond.png", correspond );
imshow( "Object", objectColor ); imshow( "Object", objectColor );
imshow( "Image", imageColor ); imshow( "Image", imageColor );
waitKey(0); waitKey(0);
return 0; return 0;
} }

65
samples/cpp/build3dmodel.cpp
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@ -13,14 +13,15 @@
using namespace cv; using namespace cv;
using namespace std; using namespace std;
void myhelp() void help()
{ {
printf("\nSigh: This program is not complete/will be replaced. \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"
"So: Use this just to see hints of how to use things like Rodrigues\n" " conversions, finding the fundamental matrix, using descriptor\n"
" conversions, finding the fundamental matrix, using descriptor\n" " finding and matching in features2d and using camera parameters\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;
} }
@ -159,14 +160,14 @@ static void findConstrainedCorrespondences(const Mat& _F,
_F.convertTo(Fhdr, CV_32F); _F.convertTo(Fhdr, CV_32F);
matches.clear(); 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; Point2f p1 = keypoints1[i].pt;
double bestDist1 = DBL_MAX, bestDist2 = DBL_MAX; double bestDist1 = DBL_MAX, bestDist2 = DBL_MAX;
int bestIdx1 = -1, bestIdx2 = -1; int bestIdx1 = -1, bestIdx2 = -1;
const float* d1 = descriptors1.ptr<float>(i); 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; Point2f p2 = keypoints2[j].pt;
double e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) + double e = p2.x*(F[0]*p1.x + F[1]*p1.y + F[2]) +
@ -224,8 +225,8 @@ static void findConstrainedCorrespondences(const Mat& _F,
continue; continue;
double threshold = bestDist1/ratio; double threshold = bestDist1/ratio;
const float* d22 = descriptors2.ptr<float>(bestIdx1); const float* d22 = descriptors2.ptr<float>(bestIdx1);
size_t i1 = 0; int i1 = 0;
for( ; i1 < keypoints1.size(); i1++ ) for( ; i1 < (int)keypoints1.size(); i1++ )
{ {
if( i1 == i ) if( i1 == i )
continue; continue;
@ -440,7 +441,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
alldescriptorsVec.resize(prev + delta); alldescriptorsVec.resize(prev + delta);
std::copy(buf.ptr<float>(), buf.ptr<float>() + delta, std::copy(buf.ptr<float>(), buf.ptr<float>() + delta,
alldescriptorsVec.begin() + prev); alldescriptorsVec.begin() + prev);
dstart.push_back(dstart.back() + keypoints.size()); dstart.push_back(dstart.back() + (int)keypoints.size());
Mat R, t; Mat R, t;
unpackPose(poseList[i], R, t); unpackPose(poseList[i], R, t);
@ -454,7 +455,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
} }
} }
Mat alldescriptors(alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F, Mat alldescriptors((int)alldescriptorsVec.size()/descriptorSize, descriptorSize, CV_32F,
&alldescriptorsVec[0]); &alldescriptorsVec[0]);
printf("\nOk. total images = %d. total keypoints = %d\n", printf("\nOk. total images = %d. total keypoints = %d\n",
@ -516,8 +517,8 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
//model.points.push_back(objpt); //model.points.push_back(objpt);
pairs[Pair2i(i1+dstart[i], i2+dstart[j])] = 1; pairs[Pair2i(i1+dstart[i], i2+dstart[j])] = 1;
pairs[Pair2i(i2+dstart[j], i1+dstart[i])] = 1; pairs[Pair2i(i2+dstart[j], i1+dstart[i])] = 1;
keypointsIdxMap[Pair2i(i,i1)] = 1; keypointsIdxMap[Pair2i((int)i,i1)] = 1;
keypointsIdxMap[Pair2i(j,i2)] = 1; keypointsIdxMap[Pair2i((int)j,i2)] = 1;
//CV_Assert(e1 < 5 && e2 < 5); //CV_Assert(e1 < 5 && e2 < 5);
//Scalar color(rand()%256,rand()%256, rand()%256); //Scalar color(rand()%256,rand()%256, rand()%256);
//circle(img1, keypoints1[i1].pt, 2, color, -1, CV_AA); //circle(img1, keypoints1[i1].pt, 2, color, -1, CV_AA);
@ -551,7 +552,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
printf("\nOk. Total classes (i.e. 3d points) = %d\n", nclasses ); 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.didx.resize(nclasses);
model.points.resize(nclasses); model.points.resize(nclasses);
@ -614,26 +615,22 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
int main(int argc, char** argv) 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* intrinsicsFilename = 0;
const char* modelName = 0; const char* modelName = 0;
const char* detectorName = "SURF"; const char* detectorName = "SURF";
const char* descriptorExtractorName = "SURF"; const char* descriptorExtractorName = "SURF";
vector<Point3f> modelBox; vector<Point3f> modelBox;
vector<string> imageList; vector<string> imageList;
vector<Rect> roiList; vector<Rect> roiList;
vector<Vec6f> poseList; vector<Vec6f> poseList;
if(argc < 3)
{
help();
return -1;
}
for( int i = 1; i < argc; i++ ) for( int i = 1; i < argc; i++ )
{ {
if( strcmp(argv[i], "-i") == 0 ) if( strcmp(argv[i], "-i") == 0 )
@ -646,19 +643,21 @@ int main(int argc, char** argv)
descriptorExtractorName = argv[++i]; descriptorExtractorName = argv[++i];
else else
{ {
help();
printf("Incorrect option\n"); printf("Incorrect option\n");
puts(help); return -1;
return 0;
} }
} }
if( !intrinsicsFilename || !modelName ) if( !intrinsicsFilename || !modelName )
{ {
printf("Some of the required parameters are missing\n"); printf("Some of the required parameters are missing\n");
puts(help); help();
return 0; return -1;
} }
triangulatePoint_test();
Mat cameraMatrix, distCoeffs; Mat cameraMatrix, distCoeffs;
Size calibratedImageSize; Size calibratedImageSize;
readCameraMatrix(intrinsicsFilename, cameraMatrix, distCoeffs, calibratedImageSize); readCameraMatrix(intrinsicsFilename, cameraMatrix, distCoeffs, calibratedImageSize);
@ -670,8 +669,8 @@ int main(int argc, char** argv)
if(!readModelViews( modelIndexFilename, modelBox, imageList, roiList, poseList)) if(!readModelViews( modelIndexFilename, modelBox, imageList, roiList, poseList))
{ {
printf("Can not read the model. Check the parameters and the working directory\n"); printf("Can not read the model. Check the parameters and the working directory\n");
puts(help); help();
return 0; return -1;
} }
PointModel model; PointModel model;

19
samples/cpp/chamfer.cpp
View File

@ -12,18 +12,22 @@ void help()
cout << cout <<
"\nThis program demonstrates Chamfer matching -- computing a distance between an \n" "\nThis program demonstrates Chamfer matching -- computing a distance between an \n"
"edge template and a query edge image.\n" "edge template and a query edge image.\n"
"Call:\n" "Usage:\n"
"./chamfer [<image edge map> <template edge map>]\n" "./chamfer <image edge map> <template edge map>,"
"By default\n" " By default the inputs are logo_in_clutter.png logo.png\n" << endl;
"the inputs are ./chamfer logo_in_clutter.png logo.png\n"<< endl;
return;
} }
int main( int argc, char** argv ) int main( int argc, char** argv )
{ {
if( argc != 1 && argc != 3 ) if( argc != 3 )
{ {
help(); help();
return 0; return 0;
} }
Mat img = imread(argc == 3 ? argv[1] : "logo_in_clutter.png", 0); Mat img = imread(argc == 3 ? argv[1] : "logo_in_clutter.png", 0);
Mat cimg; Mat cimg;
cvtColor(img, cimg, CV_GRAY2BGR); cvtColor(img, cimg, CV_GRAY2BGR);
@ -41,7 +45,7 @@ int main( int argc, char** argv )
int best = chamerMatching( img, tpl, results, costs ); int best = chamerMatching( img, tpl, results, costs );
if( best < 0 ) if( best < 0 )
{ {
cout << "not found;\n"; cout << "matching not found\n";
return 0; return 0;
} }
@ -52,7 +56,10 @@ int main( int argc, char** argv )
if( pt.inside(Rect(0, 0, cimg.cols, cimg.rows)) ) if( pt.inside(Rect(0, 0, cimg.cols, cimg.rows)) )
cimg.at<Vec3b>(pt) = Vec3b(0, 255, 0); cimg.at<Vec3b>(pt) = Vec3b(0, 255, 0);
} }
imshow("result", cimg); imshow("result", cimg);
waitKey(); waitKey();
return 0; return 0;
} }

12
samples/cpp/houghlines.cpp
View File

@ -9,8 +9,8 @@ using namespace std;
void help() void help()
{ {
cout << "\nThis program demonstrates line finding with the Hough transform.\n" cout << "\nThis program demonstrates line finding with the Hough transform.\n"
"Call:\n" "Usage:\n"
"./houghlines [image_len -- Default is pic1.png\n" << endl; "./houghlines <image_name>, Default is pic1.png\n" << endl;
} }
int main(int argc, char** argv) int main(int argc, char** argv)
@ -20,10 +20,11 @@ int main(int argc, char** argv)
Mat src = imread(filename, 0); Mat src = imread(filename, 0);
if(src.empty()) if(src.empty())
{ {
cout << "can not open " << filename << endl;
cout << "Usage: houghlines <image_name>" << endl;
}
help(); help();
cout << "can not open " << filename << endl;
return -1;
}
Mat dst, cdst; Mat dst, cdst;
Canny(src, dst, 50, 200, 3); Canny(src, dst, 50, 200, 3);
cvtColor(dst, cdst, CV_GRAY2BGR); cvtColor(dst, cdst, CV_GRAY2BGR);
@ -57,6 +58,7 @@ int main(int argc, char** argv)
imshow("detected lines", cdst); imshow("detected lines", cdst);
waitKey(); waitKey();
return 0; return 0;
} }