int main(int argc, char **argv)
{
float priors[] = { 1.0, 10.0 }; // Edible vs poisonos weights
CvMat *var_type;
CvMat *data; // jmh add
data = cvCreateMat(20, 30, CV_8U); // jmh add
var_type = cvCreateMat(data->cols + 1, 1, CV_8U);
cvSet(var_type, cvScalarAll(CV_VAR_CATEGORICAL)); // all these vars
// are categorical
CvDTree *dtree;
dtree = new CvDTree;
dtree->train(data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing, CvDTreeParams(8, // max depth
10, // min sample count
0, // regression accuracy: N/A here
true, // compute surrogate split,
// as we have missing data
15, // max number of categories
// (use sub-optimal algorithm for
// larger numbers)
10, // cross-validations
true, // use 1SE rule => smaller tree
true, // throw away the pruned tree branches
priors // the array of priors, the bigger
// p_weight, the more attention
// to the poisonous mushrooms
)
);
dtree->save("tree.xml", "MyTree");
dtree->clear();
dtree->load("tree.xml", "MyTree");
#define MAX_CLUSTERS 5
CvScalar color_tab[MAX_CLUSTERS];
IplImage *img = cvCreateImage(cvSize(500, 500), 8, 3);
CvRNG rng = cvRNG(0xffffffff);
color_tab[0] = CV_RGB(255, 0, 0);
color_tab[1] = CV_RGB(0, 255, 0);
color_tab[2] = CV_RGB(100, 100, 255);
color_tab[3] = CV_RGB(255, 0, 255);
color_tab[4] = CV_RGB(255, 255, 0);
cvNamedWindow("clusters", 1);
for (;;) {
int k, cluster_count = cvRandInt(&rng) % MAX_CLUSTERS + 1;
int i, sample_count = cvRandInt(&rng) % 1000 + 1;
CvMat *points = cvCreateMat(sample_count, 1, CV_32FC2);
CvMat *clusters = cvCreateMat(sample_count, 1, CV_32SC1);
/* generate random sample from multivariate
Gaussian distribution */
for (k = 0; k < cluster_count; k++) {
CvPoint center;
CvMat point_chunk;
center.x = cvRandInt(&rng) % img->width;
center.y = cvRandInt(&rng) % img->height;
cvGetRows(points, &point_chunk,
k * sample_count / cluster_count,
k == cluster_count - 1 ? sample_count :
(k + 1) * sample_count / cluster_count);
cvRandArr(&rng, &point_chunk, CV_RAND_NORMAL,
cvScalar(center.x, center.y, 0, 0),
cvScalar(img->width / 6, img->height / 6, 0, 0));
}
/* shuffle samples */
for (i = 0; i < sample_count / 2; i++) {
CvPoint2D32f *pt1 = (CvPoint2D32f *) points->data.fl +
cvRandInt(&rng) % sample_count;
CvPoint2D32f *pt2 = (CvPoint2D32f *) points->data.fl +
cvRandInt(&rng) % sample_count;
CvPoint2D32f temp;
CV_SWAP(*pt1, *pt2, temp);
}
cvKMeans2(points, cluster_count, clusters,
cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0));
cvZero(img);
for (i = 0; i < sample_count; i++) {
CvPoint2D32f pt = ((CvPoint2D32f *) points->data.fl)[i];
int cluster_idx = clusters->data.i[i];
cvCircle(img, cvPointFrom32f(pt), 2,
color_tab[cluster_idx], CV_FILLED);
}
cvReleaseMat(&points);
cvReleaseMat(&clusters);
cvShowImage("clusters", img);
int key = cvWaitKey(0);
if (key == 27) // 'ESC'
break;
}
}
int main( int argc, char** argv )
{
Mat img;
char file[255];
//total no of training samples
int total_train_samples = 0;
for(int cl=0; cl<nr_classes; cl++)
{
total_train_samples = total_train_samples + train_samples[cl];
}
// Training Data
Mat training_data = Mat(total_train_samples,feature_size,CV_32FC1);
Mat training_label = Mat(total_train_samples,1,CV_32FC1);
// training data .csv file
ofstream trainingDataCSV;
trainingDataCSV.open("./training_data.csv");
int index = 0;
for(int cl=0; cl<nr_classes; cl++)
{
for(int ll=0; ll<train_samples[cl]; ll++)
{
//assign sample label
training_label.at<float>(index+ll,0) = class_labels[cl];
//image feature extraction
sprintf(file, "%s/%d/%d.png", pathToImages, class_labels[cl], ll);
img = imread(file, 1);
if (!img.data)
{
cout << "File " << file << " not found\n";
exit(1);
}
imshow("sample",img);
waitKey(1);
//calculate feature vector
vector<float> feature = ColorHistFeature(img);
for(int ft=0; ft<feature.size(); ft++)
{
training_data.at<float>(index+ll,ft) = feature[ft];
trainingDataCSV<<feature[ft]<<",";
}
trainingDataCSV<<class_labels[cl]<<"\n";
}
index = index + train_samples[cl];
}
trainingDataCSV.close();
/// Decision Tree
// Training
float *priors = NULL;
CvDTreeParams DTParams = CvDTreeParams(25, // max depth
5, // min sample count
0, // regression accuracy: N/A here
false, // compute surrogate split, no missing data
15, // max number of categories (use sub-optimal algorithm for larger numbers)
15, // the number of cross-validation folds
false, // use 1SE rule => smaller tree
false, // throw away the pruned tree branches
priors // the array of priors
);
CvDTree DTree;
DTree.train(training_data,CV_ROW_SAMPLE,training_label,Mat(),Mat(),Mat(),Mat(),DTParams);
// save model
DTree.save("training.model");
// load model
CvDTree DT;
DT.load("training.model");
// test on sample image
string filename = string(pathToImages)+"/test.png";
Mat test_img = imread(filename.c_str());
vector<float> test_feature = ColorHistFeature(test_img);
CvDTreeNode* result_node = DT.predict(Mat(test_feature),Mat(),false);
double predictedClass = result_node->value;
cout<<"predictedClass "<<predictedClass<<"\n";
/*
//CvMLData for calculating error
CvMLData* MLData;
MLData = new CvMLData();
MLData->read_csv("training_data.csv");
MLData->set_response_idx(feature_size);
// MLData->change_var_type(feature_size,CV_VAR_CATEGORICAL);
// calculate training error
float error = DT.calc_error(MLData,CV_TRAIN_ERROR,0);
cout<<"training error "<<error<<"\n";
*/
return 0;
}