removed trailing backspaces, reduced number of warnings (under MSVC2010 x64) for…

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)

modules/calib3d/src/calibration.cpp

@@ -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);

modules/features2d/src/descriptors.cpp

@@ -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]) );
     }

modules/features2d/src/orb.cpp

@@ -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;

samples/c/facedetect.cpp

@@ -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 );
     }

samples/c/find_obj.cpp

@@ -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 );

samples/c/find_obj_calonder.cpp

@@ -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;
     }
 

samples/c/find_obj_ferns.cpp

@@ -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;
-    
-    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);
@@ -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;
 }

samples/cpp/build3dmodel.cpp

@@ -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;

samples/cpp/chamfer.cpp

@@ -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;
 }

samples/cpp/houghlines.cpp

@@ -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())
     {
+        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);
@@ -57,6 +58,7 @@ int main(int argc, char** argv)
     imshow("detected lines", cdst);
 
     waitKey();
+
     return 0;
 }