Point<Dim> KDTree<Dim>::findNearestNeighbor(const Point<Dim> & query, int lo, int hi, int dim) const{
if(lo >= hi ) return points[lo]; //base case
int mid = (hi+lo)/2; //calculate the minPoint index
Point<Dim> temp;
Point<Dim> curr = points[mid];
dim = dim%Dim;
int direction=0;
if(smallerDimVal(query, points[mid], dim)){
direction = 0;
temp = findNearestNeighbor(query, lo, mid-1, dim+1); //left half
}
else{
direction =1;
temp = findNearestNeighbor(query, mid+1, hi, dim+1); //recusively call the function on the left half
}
Point<Dim> best;
if(shouldReplace(query, temp, curr) ) best= curr;
else best = temp;
int dimD = (curr[dim] - query[dim])*(curr[dim] - query[dim]);
int bestCurr = squareDistance(best, query);
if(dimD > bestCurr) return best;
else{
Point<Dim> t;
if(direction == 0)
t = findNearestNeighbor(query, mid+1, hi, dim+1);
else t = findNearestNeighbor(query, lo, mid-1,dim+1);
if(shouldReplace(query,t, best)) return best;
else return t;
}
}
std::string EigenfaceRecognizer::recognize(cv::Mat &face)
{
//int i, nTestFaces = 0; // the number of test images
//CvMat * trainPersonNumMat = 0; // the person numbers during training
//float * projectedTestFace = 0;
IplImage faceToRecognize = (IplImage) face;
//IplImage *faceToRecognize = cvLoadImage("s1/1.pgm", CV_LOAD_IMAGE_GRAYSCALE);
float *projectedTestImage = (float *)cvAlloc(sizeof(float) * numEigenvalues);
//project the test image onto the PCA subspace
cvEigenDecomposite(
&faceToRecognize,
numEigenvalues,
eigenVectors,
0, 0,
averageImage,
projectedTestImage);
int nearest = findNearestNeighbor(projectedTestImage);
//take threshold into account, mark as not recognized if not found!
return recognitionDescriptor[nearest].second;
}
//识别阶段代码
void recognize()
{
int i, nTestFaces = 0; // 测试人脸数
CvMat * trainPersonNumMat = 0; // 训练阶段的人脸数
float * projectedTestFace = 0;
// 加载测试图像，并返回测试人脸数
nTestFaces = loadFaceImgArray("test.txt");
printf("%d test faces loaded\n", nTestFaces);
// 加载保存在.xml文件中的训练结果
if( !loadTrainingData( &trainPersonNumMat ) ) return;
//
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
//将测试图像投影到子空间中
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
printf("nearest = %d, Truth = %d\n", nearest, truth);
}
}
void recognize()
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray("test.txt");
printf("%d test faces loaded\n", nTestFaces);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
//project the test image onto the PCA subspace
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
printf("nearest = %d, Truth = %d\n", nearest, truth);
}
}
/**
* 顔を認識して、該当する人のIDを返す
*/
int EigenFace::recognize(IplImage* testFace)
{
// 事前加工
IplImage* resizedFaceImage = preProcess(testFace);
float * projectedTestFace = 0;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
int iNearest, nearest;
// project the test image onto the PCA subspace
cvEigenDecomposite(
resizedFaceImage,
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace);
nearest = trainPersonNumMat->data.i[iNearest];
cvReleaseImage(&resizedFaceImage);
return nearest;
}
//////////////////////////////////
// recognize()
//返回本次训练通过的图片数
int faceRecognition::recognize()
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray(testFileListPath);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return 0;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
//处理输出结果,记录一次训练中正确匹配的图片数
int count=0;
for(i=0; i<nTestFaces; i++)
{ int iNearest, nearest, truth;
// project the test image onto the PCA subspace
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
// AfxMessageBox("nearest = %d, Truth = %d", nearest, truth);
if(nearest==truth)
count++;
}//for
return count;
}
Point<Dim> KDTree<Dim>::findNearestNeighbor(const Point<Dim> & query) const
{
return findNearestNeighbor(query, 0, points.size()-1, 0);
/**
* @todo Implement this function!
*/
}
int main(int argc, char* argv[])
{
if(argc < 3)
{
std::cerr << "Expected two command line arguments" << std::endl;
}
// The first command line argument is the name of the file,
// the second is the prototype file and the third is the unknown file
char* prototype_file = argv[1];
char* unknown_file = argv[2];
std::vector<std::vector<double>> prototypes = parsePrototypes(prototype_file);
findNearestNeighbor(prototypes, unknown_file);
return 0;
}
//get intersection points
void get_intr_points(MyPointCloud& source_mpc, MyPointCloud& sample_mpc, float search_r, int* intr_points_num){
*intr_points_num=0;
PointCloudPtr source_cloud(new PointCloud);
MyPointCloud2PointCloud(source_mpc, source_cloud);
PointCloudPtr sample_cloud(new PointCloud);
MyPointCloud2PointCloud(sample_mpc, sample_cloud);
PointCloudPtr intr_cloud(new PointCloud);
float resolution = 0.005f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);
octree.setInputCloud (source_cloud);
octree.addPointsFromInputCloud ();
for(int i=0;i<sample_mpc.mypoints.size();i++){
// Neighbors within radius search
std::vector<int> pointIdxRadiusSearch;
std::vector<float> pointRadiusSquaredDistance;
float radius = search_r;
if (octree.radiusSearch(sample_cloud->at(i), radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
{
if(pointIdxRadiusSearch.size()>0){
PointCloudPtr cloud_tem(new PointCloud);
for (size_t j = 0; j < pointIdxRadiusSearch.size (); ++j){
cloud_tem->push_back(source_cloud->points[ pointIdxRadiusSearch[j]]);
}
Point finded_pt;
if(findNearestNeighbor(cloud_tem, intr_cloud, sample_cloud->at(i), finded_pt)){
intr_cloud->push_back(finded_pt);
(*intr_points_num)+=1;
}
}
}
}
}
// Recognize the face in each of the test images given, and compare the results with the truth.
void recognizeFileList(char *szFileTest)
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
char *answer;
int nCorrect = 0;
int nWrong = 0;
double timeFaceRecognizeStart;
double tallyFaceRecognizeTime;
float confidence;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray(szFileTest);
printf("%d test faces loaded\n", nTestFaces);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
timeFaceRecognizeStart = (double)cvGetTickCount(); // Record the timing.
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
// project the test image onto the PCA subspace
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace, &confidence);
truth = personNumTruthMat->data.i[i];
if(iNearest!=-1)
{
nearest = trainPersonNumMat->data.i[iNearest];
}
else
{
nearest = 0;
}
if (nearest == truth) {
answer = "Correct";
nCorrect++;
}
else {
answer = "WRONG!";
nWrong++;
}
printf("nearest = %d, Truth = %d (%s). Confidence = %f\n", nearest, truth, answer, confidence);
}
tallyFaceRecognizeTime = (double)cvGetTickCount() - timeFaceRecognizeStart;
if (nCorrect+nWrong > 0) {
printf("TOTAL ACCURACY: %d%% out of %d tests.\n", nCorrect * 100/(nCorrect+nWrong), (nCorrect+nWrong));
printf("TOTAL TIME: %.1fms average.\n", tallyFaceRecognizeTime/((double)cvGetTickFrequency() * 1000.0 * (nCorrect+nWrong) ) );
}
}
