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

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This commit is contained in:
Kirill Kornyakov 2011-05-29 10:01:01 +00:00
parent 2f3b75c841
commit 534ac83b8d
10 changed files with 73 additions and 63 deletions
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2
samples/c/contours.c
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@ -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");
}

2
samples/c/find_obj.cpp
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@ -22,7 +22,7 @@ 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"
);

36
samples/c/find_obj_calonder.cpp
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@ -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"
"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;
"Usage: \n"
"./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"
" --test_image=<image file for test, lena.jpg as default> \n"
" [--train_container]=<txt file with train images filenames> \n"
"Example: \n"
" --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 )
trainCalonderClassifier( argv[1], argv[3] );
if( !trainContainerFileName.empty())
trainCalonderClassifier( classifierFileName.c_str(), trainContainerFileName.c_str() );
testCalonderClassifier( argv[1], argv[2] );
testCalonderClassifier( classifierFileName.c_str(), testImageFileName.c_str() );
return 0;
}

2
samples/c/find_obj_ferns.cpp
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@ -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)

3
samples/c/latentsvmdetect.cpp
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@ -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");
}

4
samples/c/mser_sample.cpp
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@ -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"
"./mser_sample [--image_filename] <path_and_image_filename, default is 'puzzle.png'>\n\n");
"\n Usage: \n"
"./mser_sample [--image_filename] <path_and_image_filename, default is 'puzzle.png'> \n");
}
static CvScalar colors[] =

10
samples/c/one_way_sample.cpp
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@ -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");
printf("Format: \n./one_way_sample <path_to_samples> <image1> <image2>\n");
printf("For example: ./one_way_sample --path=../../../opencv/samples/c --first_image=scene_l.bmp --second_image=scene_r.bmp\n");
"Correspondences are drawn\n"
"Usage: \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;

45
samples/c/tree_engine.cpp
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@ -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"
"where -r <response_column> specified the 0-based index of the response (0 by default)\n"
"-c specifies that the response is categorical (it's ordered by default) and\n"
"<csv filename> is the name of training data file in comma-separated value format\n\n");
"Usage: \n"
" ./tree_engine [--response_column]=<specified the 0-based index of the response, 0 as default> \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"
);
}
@ -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)
{
if(argc < 2)
int main(int argc, const char** argv)
{
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)
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]);
printf("\n Please, select value for --csv_filename key \n");
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)