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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;
}
}
} // namespace cv
void cv::Rodrigues(InputArray _src, OutputArray _dst, OutputArray _jacobian)
@ -3361,7 +3361,8 @@ double cv::calibrateCamera( InputArrayOfArrays _objectPoints,
if( !(flags & CALIB_RATIONAL_MODEL) )
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 );
Mat objPt, imgPt, npoints, rvecM((int)nimages, 3, CV_64FC1), tvecM((int)nimages, 3, CV_64FC1);
collectCalibrationData( _objectPoints, _imagePoints, noArray(),
@ -3373,10 +3374,10 @@ double cv::calibrateCamera( InputArrayOfArrays _objectPoints,
double reprojErr = cvCalibrateCamera2(&c_objPt, &c_imgPt, &c_npoints, imageSize,
&c_cameraMatrix, &c_distCoeffs, &c_rvecM,
&c_tvecM, flags );
_rvecs.create(nimages, 1, CV_64FC3);
_tvecs.create(nimages, 1, CV_64FC3);
_rvecs.create((int)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);
_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() );
// Use class_id member to get indices into initial keypoints vector
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] );
idxs[ci].resize( channelKeypoints[ci].size() );
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]) );
}

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);
dY_offsets_.resize(7 * 9);
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)
{
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"
"./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,
@ -130,6 +130,7 @@ int main( int argc, const char** argv )
}
waitKey(0);
_cleanup_:
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"
"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"
);
"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 );

11
samples/c/find_obj_calonder.cpp
View File

@ -16,10 +16,13 @@ void help()
"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;
}

39
samples/c/find_obj_ferns.cpp
View File

@ -12,38 +12,41 @@ using namespace cv;
void help()
{
printf( "This program shows the use of the \"fern\" plannar PlanarObjectDetector point\n"
"descriptor classifier"
"descriptor classifier\n"
"Usage:\n"
"./find_obj_ferns [<object_filename default: box.png> <scene_filename default:box_in_scene.png>]\n"
"\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;
Mat scene = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
double imgscale = 1;
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);
@ -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;
}

65
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;
}
@ -159,14 +160,14 @@ static void findConstrainedCorrespondences(const Mat& _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]) +
@ -224,8 +225,8 @@ 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;
@ -440,7 +441,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
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);
@ -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]);
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);
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);
@ -551,7 +552,7 @@ static void build3dmodel( const Ptr<FeatureDetector>& detector,
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);
@ -614,26 +615,22 @@ 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* detectorName = "SURF";
const char* descriptorExtractorName = "SURF";
vector<Point3f> modelBox;
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 )
@ -646,19 +643,21 @@ int main(int argc, char** argv)
descriptorExtractorName = argv[++i];
else
{
help();
printf("Incorrect option\n");
puts(help);
return 0;
return -1;
}
}
if( !intrinsicsFilename || !modelName )
{
printf("Some of the required parameters are missing\n");
puts(help);
return 0;
help();
return -1;
}
triangulatePoint_test();
Mat cameraMatrix, distCoeffs;
Size calibratedImageSize;
readCameraMatrix(intrinsicsFilename, cameraMatrix, distCoeffs, calibratedImageSize);
@ -670,8 +669,8 @@ int main(int argc, char** argv)
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;

19
samples/cpp/chamfer.cpp
View File

@ -12,18 +12,22 @@ 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;
"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);
@ -41,7 +45,7 @@ int main( int argc, char** argv )
int best = chamerMatching( img, tpl, results, costs );
if( best < 0 )
{
cout << "not found;\n";
cout << "matching not found\n";
return 0;
}
@ -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;
}

12
samples/cpp/houghlines.cpp
View File

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