generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Set stricter warning rules for gcc

Browse Source
This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
Download Patch File Download Diff File Expand all files Collapse all files

72
samples/cpp/letter_recog.cpp
View File

@@ -7,24 +7,24 @@
*/
void help()
static void help()
{
printf("\nThe sample demonstrates how to train Random Trees classifier\n"
"(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
"\n"
"We use the sample database letter-recognition.data\n"
"from UCI Repository, here is the link:\n"
"\n"
"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
"UCI Repository of machine learning databases\n"
"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
"Irvine, CA: University of California, Department of Information and Computer Science.\n"
"\n"
"The dataset consists of 20000 feature vectors along with the\n"
"responses - capital latin letters A..Z.\n"
"The first 16000 (10000 for boosting)) samples are used for training\n"
"and the remaining 4000 (10000 for boosting) - to test the classifier.\n"
"======================================================\n");
printf("\nThe sample demonstrates how to train Random Trees classifier\n"
"(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
"\n"
"We use the sample database letter-recognition.data\n"
"from UCI Repository, here is the link:\n"
"\n"
"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
"UCI Repository of machine learning databases\n"
"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
"Irvine, CA: University of California, Department of Information and Computer Science.\n"
"\n"
"The dataset consists of 20000 feature vectors along with the\n"
"responses - capital latin letters A..Z.\n"
"The first 16000 (10000 for boosting)) samples are used for training\n"
"and the remaining 4000 (10000 for boosting) - to test the classifier.\n"
"======================================================\n");
printf("\nThis is letter recognition sample.\n"
"The usage: letter_recog [-data <path to letter-recognition.data>] \\\n"
" [-save <output XML file for the classifier>] \\\n"
@@ -312,7 +312,7 @@ int build_boost_classifier( char* data_filename,
}
temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F );
weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F );
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
@@ -548,7 +548,7 @@ int build_knearest_classifier( char* data_filename, int K )
}
}
printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100,
printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100,
(float)accuracy / (nsamples_all - ntrain_samples) / K * 100);
delete[] true_results;
@@ -674,15 +674,15 @@ int build_svm_classifier( char* data_filename )
for (int j = ntrain_samples; j < nsamples_all; j++)
{
float *s = data->data.fl + j * var_count;
for (int i = 0; i < var_count; i++)
{
{
sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
}
true_results[j - ntrain_samples] = responses->data.fl[j];
}
CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
printf("Classification (may take a few minutes)...\n");
svm.predict(&sample, result);
@@ -692,9 +692,9 @@ int build_svm_classifier( char* data_filename )
if (result->data.fl[i] == true_results[i])
true_resp++;
}
printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
cvReleaseMat( &train_resp );
cvReleaseMat( &result );
cvReleaseMat( &data );
@@ -738,17 +738,17 @@ int main( int argc, char *argv[] )
method = 2;
}
else if ( strcmp(argv[i], "-knearest") == 0)
{
method = 3;
}
else if ( strcmp(argv[i], "-nbayes") == 0)
{
method = 4;
}
else if ( strcmp(argv[i], "-svm") == 0)
{
method = 5;
}
{
method = 3;
}
else if ( strcmp(argv[i], "-nbayes") == 0)
{
method = 4;
}
else if ( strcmp(argv[i], "-svm") == 0)
{
method = 5;
}
else
break;
}
@@ -768,7 +768,7 @@ int main( int argc, char *argv[] )
build_svm_classifier( data_filename ):
-1) < 0)
{
help();
help();
}
return 0;
}