update help corresponding single format and update some samples through using CommandLineParser class

samples/c/adaptiveskindetector.cpp

@@ -331,7 +331,7 @@ void help()
             "./adaptiveskindetector [--fileMask]=<path to file, which are used in mask \n"
             "           [--firstFrame]=<first frame number \n"
             "           [--lastFrame]=<last frame number> \n"
-            "                       if at least one parameter doesn't specified, it will try to use default webcam \n"
+            "if at least one parameter doesn't specified, it will try to use default webcam \n"
             "Expample: \n"
             "         --fileMask = /home/user_home_directory/work/opencv/samples/c/temp_%%05d.jpg --firstFrame=0  --lastFrame=1000 \n");
 }

samples/c/contours.c

@@ -8,7 +8,7 @@ void help()
 			"functions cvCreateMemStorage() and cvDrawContours().\n"
 			"It also shows the use of a trackbar to control contour retrieval.\n"
 			"\n"
-			"Call:\n"
+            "Usage :\n"
 			"./contours\n");
 }
 

samples/c/find_obj.cpp

@@ -22,9 +22,9 @@ 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"
+            "Usage :\n"
             "./find_obj [--object_filename]=<object_filename, box.png as default> \n"
-                    "[--scene_filename]=<scene_filename box_in_scene.png as default>]\n\n"
+            "       [--scene_filename]=<scene_filename box_in_scene.png as default>]\n\n"
             );
 }
 

samples/c/find_obj_calonder.cpp

@@ -11,14 +11,17 @@ using namespace cv;
 
 void help()
 {
-    cout << "This program shows the use of the Calonder point descriptor classifier"
+    printf("\n This program shows the use of the Calonder point descriptor classifier \n"
-    		"SURF is used to detect interest points, Calonder is used to describe/match these points\n"
+           "SURF is used to detect interest points, Calonder is used to describe/match these points \n"
-    		"Format:" << endl <<
+           "Usage: \n"
-            "   classifier_file(to write) test_image file_with_train_images_filenames(txt)" <<
+           "./find_obj_calonder --classifier_file=<classifier file, there is no default classifier file. You should create it at first and when you can use it for test> \n"
-            "   or" << endl <<
+           "        --test_image=<image file for test, lena.jpg as default> \n"
-            "   classifier_file(to read) test_image"
+           "        [--train_container]=<txt file with train images filenames> \n"
-    		"Using OpenCV version %s\n" << CV_VERSION << "\n"
+           "Example: \n"
-            << endl;
+           "    --classifier_file=test_classifier --test_image=lena.jpg --train_container=one_way_train_images.txt \n"
+           "                the test_classifier is created here using --train_container and tested witn --test_image at the end \n"
+           "    --classifier_file=test_classifier --test_image=lena.jpg \n"
+           "                the test classifier is tested here using lena.jpg \n");
 }
 /*
  * Generates random perspective transform of image
@@ -144,18 +147,27 @@ void testCalonderClassifier( const string& classifierFilename, const string& img
     waitKey();
 }
 
-int main( int argc, char **argv )
+int main( int argc, const char **argv )
 {
-    if( argc != 4 && argc != 3 )
+    help();
+
+    CommandLineParser parser(argc, argv);
+
+    string classifierFileName = parser.get<string>("classifier_file");
+    string testImageFileName = parser.get<string>("test_image", "lena.jpg");
+    string trainContainerFileName = parser.get<string>("train_container");
+
+    if( classifierFileName.empty())
     {
+        printf("\n Can't find classifier file, please select file for --classifier_file parameter \n");
     	help();
         return -1;
     }
 
-    if( argc == 4 )
+    if( !trainContainerFileName.empty())
-        trainCalonderClassifier( argv[1], argv[3] );
+        trainCalonderClassifier( classifierFileName.c_str(), trainContainerFileName.c_str() );
 
-    testCalonderClassifier( argv[1], argv[2] );
+    testCalonderClassifier( classifierFileName.c_str(), testImageFileName.c_str() );
 
     return 0;
 }

samples/c/find_obj_ferns.cpp

@@ -16,7 +16,7 @@ void help()
             "descriptor classifier"
             "Usage:\n"
             "./find_obj_ferns [--object_filename]=<object_filename, box.png as default> \n"
-            "[--scene_filename]=<scene_filename box_in_scene.png as default>]\n\n");
+            "           [--scene_filename]=<scene_filename box_in_scene.png as default>] \n");
 }
 
 int main(int argc, const char** argv)

samples/c/latentsvmdetect.cpp

@@ -17,11 +17,10 @@ void help()
 {
     printf( "This program demonstrated the use of the latentSVM detector.\n"
             "It reads in a trained object model and then uses that to detect the object in an image\n"
-            "Call:\n"
+            "Usage: \n"
             "./latentsvmdetect [--image_filename]=<image_filename, cat.jpg as default> \n"
             "       [--model_filename] = <model_filename, cat.xml as default> \n"
             "       [--threads_number] = <number of threads, -1 as default>\n"
-            "  The defaults for image_filename and model_filename are cat.jpg and cat.xml respectively\n"
             "  Press any key to quit.\n");
 }
 

samples/c/mser_sample.cpp

@@ -16,8 +16,8 @@ void help()
 {
     printf("\nThis program demonstrates the Maximal Extremal Region interest point detector.\n"
            "It finds the most stable (in size) dark and white regions as a threshold is increased.\n"
-           "\nCall:\n"
+           "\n Usage: \n"
-           "./mser_sample [--image_filename] <path_and_image_filename, default is 'puzzle.png'>\n\n");
+           "./mser_sample [--image_filename] <path_and_image_filename, default is 'puzzle.png'> \n");
 }
 
 static CvScalar colors[] = 

samples/c/one_way_sample.cpp

@@ -18,9 +18,13 @@
 void help()
 {
     printf("\nThis program demonstrates the one way interest point descriptor found in features2d.hpp\n"
-           "Correspondences are drawn\n");
+           "Correspondences are drawn\n"
-    printf("Format: \n./one_way_sample <path_to_samples> <image1> <image2>\n");
+           "Usage: \n"
-    printf("For example: ./one_way_sample --path=../../../opencv/samples/c --first_image=scene_l.bmp --second_image=scene_r.bmp\n");
+           "./one_way_sample [--path]=<path_to_samples, '../../../opencv/samples/c' as default> \n"
+           "        [--first_image]=<first image file, scene_l.bmp as default> \n"
+           "        [--second_image]=<second image file, scene_r.bmp as default>\n"
+           "For example: \n"
+           "    ./one_way_sample --path=../../../opencv/samples/c --first_image=scene_l.bmp --second_image=scene_r.bmp \n");
 }
 
 using namespace cv;

samples/c/tree_engine.cpp

@@ -1,8 +1,12 @@
+#include "opencv2/core/core.hpp"
 #include "opencv2/ml/ml.hpp"
 #include "opencv2/core/core_c.h"
 #include <stdio.h>
 #include <map>
 
+using namespace std;
+using namespace cv;
+
 void help()
 {
     printf(
@@ -12,10 +16,11 @@ void help()
         "CvRTrees rtrees;\n"
         "CvERTrees ertrees;\n"
         "CvGBTrees gbtrees;\n"
-		"Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
+        "Usage: \n"
-        "where -r <response_column> specified the 0-based index of the response (0 by default)\n"
+        "       ./tree_engine [--response_column]=<specified the 0-based index of the response, 0 as default> \n"
-        "-c specifies that the response is categorical (it's ordered by default) and\n"
+        "[--categorical_response]=<specifies that the response is categorical, 0-false, 1-true, 0 as default> \n"
-        "<csv filename> is the name of training data file in comma-separated value format\n\n");
+        "[--csv_filename]=<is the name of training data file in comma-separated value format> \n"
+        );
 }
 
 
@@ -59,34 +64,24 @@ void print_result(float train_err, float test_err, const CvMat* _var_imp)
     printf("\n");
 }
 
-int main(int argc, char** argv)
+int main(int argc, const char** argv)
 {
-    if(argc < 2)
-    {
     help();
-        return 0;
-    }
-    const char* filename = 0;
-    int response_idx = 0;
-    bool categorical_response = false;
 
-    for(int i = 1; i < argc; i++)
+    CommandLineParser parser(argc, argv);
+
+    string filename = parser.get<string>("csv_filename");
+    int response_idx = parser.get<int>("response_column", 0);
+    bool categorical_response = (bool)parser.get<int>("categorical_response", 1);
+
+    if(filename.empty())
     {
-        if(strcmp(argv[i], "-r") == 0)
+        printf("\n Please, select value for --csv_filename key \n");
-            sscanf(argv[++i], "%d", &response_idx);
-        else if(strcmp(argv[i], "-c") == 0)
-            categorical_response = true;
-        else if(argv[i][0] != '-' )
-            filename = argv[i];
-        else
-        {
-            printf("Error. Invalid option %s\n", argv[i]);
         help();
         return -1;
     }
-    }
         
-    printf("\nReading in %s...\n\n",filename);
+    printf("\nReading in %s...\n\n",filename.c_str());
     CvDTree dtree;
     CvBoost boost;
     CvRTrees rtrees;
@@ -98,7 +93,7 @@ int main(int argc, char** argv)
     
     CvTrainTestSplit spl( 0.5f );
     
-    if ( data.read_csv( filename ) == 0)
+    if ( data.read_csv( filename.c_str() ) == 0)
     {
         data.set_response_idx( response_idx );
         if(categorical_response)