3dpcp/.svn/pristine/5d/5d5aa0784271d0773bf3ae3abec0d78473bbaf24.svn-base
2012-10-15 19:05:06 +02:00

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/*
* feature implementation
*
* Copyright (C) HamidReza Houshiar
*
* Released under the GPL version 3.
*
*/
#include "slam6d/fbr/feature.h"
using namespace std;
namespace fbr{
feature::feature(){
fDetectorMethod = SIFT_DET;
fDescriptorMethod = SIFT_DES;
fFiltrationMethod = DISABLE_FILTER;
}
feature::feature(feature_detector_method detector, feature_descriptor_method descriptor, feature_filtration_method filtration){
fDetectorMethod = detector;
fDescriptorMethod = descriptor;
fFiltrationMethod = filtration;
}
void feature::featureDetection(cv::Mat pImage, feature_detector_method method, cv::Mat rImage, feature_filtration_method fMethod){
fDetectorMethod = method;
fFiltrationMethod = fMethod;
switch(fDetectorMethod){
//Detect the keypoints using SURF Detector
case SURF_DET:{
double minHessian = 400;
cv::SurfFeatureDetector detector(minHessian);
detector.detect(pImage, keypoints);
break;
}
//Detect the keypoints using SIFT Detector
case SIFT_DET:{
cv::SiftFeatureDetector detector;
detector.detect(pImage, keypoints);
break;
}
//Detect the keypoints using ORB Detector
case ORB_DET:{
cv::OrbFeatureDetector detector;
detector.detect(pImage, keypoints);
break;
}
//Detect the keypoints using FAST Detector
case FAST_DET:{
cv::FastFeatureDetector detector;
detector.detect(pImage, keypoints);
break;
}
//Detect the keypoints using STAR Detector
case STAR_DET:{
cv::StarFeatureDetector detector;
detector.detect(pImage, keypoints);
break;
}
}
featureFiltration(pImage, rImage);
}
void feature::featureDetection(cv::Mat pImage, feature_detector_method method){
cv::Mat rImage;
featureDetection(pImage, method, rImage, fFiltrationMethod);
}
void feature::featureDetection(cv::Mat pImage){
featureDetection(pImage, fDetectorMethod);
}
void feature::featureDescription(cv::Mat pImage, feature_descriptor_method method){
fDescriptorMethod = method;
if(keypoints.size() == 0)
featureDetection(pImage);
switch(fDescriptorMethod){
case SURF_DES:{
//Create descriptor using SURF
cv::SurfDescriptorExtractor extractor;
extractor.compute(pImage, keypoints, descriptors);
break;
}
case SIFT_DES:{
//Create descriptor using SIFT
cv::SiftDescriptorExtractor extractor;
extractor.compute(pImage, keypoints, descriptors);
break;
}
case ORB_DES:{
//Create descriptor using ORB
cv::OrbDescriptorExtractor extractor;
extractor.compute(pImage, keypoints, descriptors);
break;
}
}
}
void feature::featureDescription(cv::Mat pImage){
featureDescription(pImage, fDescriptorMethod);
}
feature_detector_method feature::getDetectorMethod(){
return fDetectorMethod;
}
feature_descriptor_method feature::getDescriptorMethod(){
return fDescriptorMethod;
}
feature_filtration_method feature::getFeatureFiltrationMethod(){
return fFiltrationMethod;
}
//check for the keypoints vector not to be empty
vector<cv::KeyPoint> feature::getFeatures(){
return keypoints;
}
//check for the descriptor Mat not to be empty
cv::Mat feature::getDescriptors(){
return descriptors;
}
void feature::setFeatures(vector<cv::KeyPoint> keypoint){
keypoints = keypoint;
}
void feature::setDescriptors(cv::Mat descriptor){
descriptors = descriptor;
}
void feature::getDescription(description_method method){
if(method == FEATURE_DESCRIPTION)
cout<<"fDetectorMethod: "<<featureDetectorMethodToString(fDetectorMethod)<<", number of detected features: "<<keypoints.size()<<", feature filtration method: "<<featureFiltrationMethodToString(fFiltrationMethod)<<"."<<endl;
else if(method == DESCRIPTOR_DESCRIPTION)
cout<<"fDescriptorMethod: "<<featureDescriptorMethodToString(fDescriptorMethod)<<"."<<endl;
else
cout<<"fDetectorMethod: "<<featureDetectorMethodToString(fDetectorMethod)<<", number of detected features: "<<keypoints.size()<<", feature filtration method: "<<featureFiltrationMethodToString(fFiltrationMethod)<<", fDescriptorMethod: "<<featureDescriptorMethodToString(fDescriptorMethod)<<"."<<endl;
cout<<endl;
}
unsigned int feature::getNumberOfFeatures(){
return keypoints.size();
}
void feature::featureFiltration(cv::Mat pImage, cv::Mat rImage){
vector<cv::KeyPoint> filteredKeypoints;
if(fFiltrationMethod == OCCLUSION){
for(unsigned int i = 0; i < keypoints.size(); i++){
int x, y;
x = keypoints[i].pt.x;
y = keypoints[i].pt.y;
float range[8];
if(rImage.at<float>(y,x) != 0){
range[0] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x+1);
range[1] = rImage.at<float>(y,x)-rImage.at<float>(y,x+1);
range[2] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x+1);
range[3] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x);
range[4] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x-1);
range[5] = rImage.at<float>(y,x)-rImage.at<float>(y,x-1);
range[6] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x-1);
range[7] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x);
int count=0;
for(unsigned int j = 0; j < 8; j++){
if(range[j] < 20)
count++;
}
if(count == 8)
filteredKeypoints.push_back(keypoints[i]);
}
}
}else if(fFiltrationMethod == STANDARD_DEVIATION){
for(unsigned int i = 0; i < keypoints.size(); i++){
int x, y;
x = keypoints[i].pt.x;
y = keypoints[i].pt.y;
float range[9];
if(rImage.at<float>(y,x) != 0){
range[0] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x+1);
range[1] = rImage.at<float>(y,x)-rImage.at<float>(y,x+1);
range[2] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x+1);
range[3] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x);
range[4] = rImage.at<float>(y,x)-rImage.at<float>(y-1,x-1);
range[5] = rImage.at<float>(y,x)-rImage.at<float>(y,x-1);
range[6] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x-1);
range[7] = rImage.at<float>(y,x)-rImage.at<float>(y+1,x);
range[8] = rImage.at<float>(y,x)-rImage.at<float>(y,x);
double t_std, r=0, temp=0;
for(unsigned int j = 0; j < 9; j++)
r += range[j];
r /= 9;
for(unsigned int j = 0; j < 9; j++)
temp += ((range[j] - r) * (range[j] - r));
t_std = sqrt(temp/9);
if(t_std < 0.1)
filteredKeypoints.push_back(keypoints[i]);
}
}
}
if(fFiltrationMethod != DISABLE_FILTER)
keypoints = filteredKeypoints;
}
}