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/features2d/include/opencv2/features2d/features2d.hpp

@@ -1965,7 +1965,7 @@ public:
     virtual bool empty() const;
 
 protected:
-	virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+    virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
 
     RTreeClassifier classifier_;
     static const int BORDER_SIZE = 16;

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;

modules/imgproc/src/spilltree.cpp

@@ -93,7 +93,7 @@ icvFarthestNode( CvSpillTreeNode* node,
     }
   return result;
 }
-   
+
 // clone a new tree node
 static inline CvSpillTreeNode*
 icvCloneSpillTreeNode( CvSpillTreeNode* node )

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,
@@ -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 );
 }

samples/c/find_obj.cpp

@@ -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 );

samples/c/find_obj_calonder.cpp

@@ -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;
     }
 

samples/c/find_obj_ferns.cpp

@@ -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 _image = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
-    resize(_image, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
+    help();
 
+    Mat object = imread( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
+    Mat scene = imread( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
 
-    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;
 }

samples/cpp/chamfer.cpp

@@ -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;
 }

samples/cpp/houghlines.cpp

@@ -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;
 }