mirror of
https://github.com/opencv/opencv.git
synced 2024-11-29 22:00:25 +08:00
306 lines
8.2 KiB
C++
306 lines
8.2 KiB
C++
//Calonder descriptor sample
|
|
|
|
#include <cxcore.h>
|
|
#include <cv.h>
|
|
#include <cvaux.h>
|
|
#include <highgui.h>
|
|
#include <vector>
|
|
using namespace std;
|
|
|
|
// Number of training points (set to -1 to use all points)
|
|
const int n_points = -1;
|
|
|
|
//Draw the border of projection of train image calculed by averaging detected correspondences
|
|
const bool draw_border = true;
|
|
|
|
void cvmSet6(CvMat* m, int row, int col, float val1, float val2, float val3, float val4, float val5, float val6)
|
|
{
|
|
cvmSet(m, row, col, val1);
|
|
cvmSet(m, row, col + 1, val2);
|
|
cvmSet(m, row, col + 2, val3);
|
|
cvmSet(m, row, col + 3, val4);
|
|
cvmSet(m, row, col + 4, val5);
|
|
cvmSet(m, row, col + 5, val6);
|
|
}
|
|
|
|
void FindAffineTransform(const vector<CvPoint>& p1, const vector<CvPoint>& p2, CvMat* affine)
|
|
{
|
|
int eq_num = 2*(int)p1.size();
|
|
CvMat* A = cvCreateMat(eq_num, 6, CV_32FC1);
|
|
CvMat* B = cvCreateMat(eq_num, 1, CV_32FC1);
|
|
CvMat* X = cvCreateMat(6, 1, CV_32FC1);
|
|
|
|
for(int i = 0; i < (int)p1.size(); i++)
|
|
{
|
|
cvmSet6(A, 2*i, 0, p1[i].x, p1[i].y, 1, 0, 0, 0);
|
|
cvmSet6(A, 2*i + 1, 0, 0, 0, 0, p1[i].x, p1[i].y, 1);
|
|
cvmSet(B, 2*i, 0, p2[i].x);
|
|
cvmSet(B, 2*i + 1, 0, p2[i].y);
|
|
}
|
|
|
|
cvSolve(A, B, X, CV_SVD);
|
|
|
|
cvmSet(affine, 0, 0, cvmGet(X, 0, 0));
|
|
cvmSet(affine, 0, 1, cvmGet(X, 1, 0));
|
|
cvmSet(affine, 0, 2, cvmGet(X, 2, 0));
|
|
cvmSet(affine, 1, 0, cvmGet(X, 3, 0));
|
|
cvmSet(affine, 1, 1, cvmGet(X, 4, 0));
|
|
cvmSet(affine, 1, 2, cvmGet(X, 5, 0));
|
|
|
|
cvReleaseMat(&A);
|
|
cvReleaseMat(&B);
|
|
cvReleaseMat(&X);
|
|
}
|
|
|
|
void MapVectorAffine(const vector<CvPoint>& p1, vector<CvPoint>& p2, CvMat* transform)
|
|
{
|
|
float a = cvmGet(transform, 0, 0);
|
|
float b = cvmGet(transform, 0, 1);
|
|
float c = cvmGet(transform, 0, 2);
|
|
float d = cvmGet(transform, 1, 0);
|
|
float e = cvmGet(transform, 1, 1);
|
|
float f = cvmGet(transform, 1, 2);
|
|
|
|
for(int i = 0; i < (int)p1.size(); i++)
|
|
{
|
|
float x = a*p1[i].x + b*p1[i].y + c;
|
|
float y = d*p1[i].x + e*p1[i].y + f;
|
|
p2.push_back(cvPoint(x, y));
|
|
}
|
|
}
|
|
|
|
|
|
float CalcAffineReprojectionError(const vector<CvPoint>& p1, const vector<CvPoint>& p2, CvMat* transform)
|
|
{
|
|
vector<CvPoint> mapped_p1;
|
|
MapVectorAffine(p1, mapped_p1, transform);
|
|
float error = 0;
|
|
for(int i = 0; i < (int)p2.size(); i++)
|
|
{
|
|
error += ((p2[i].x - mapped_p1[i].x)*(p2[i].x - mapped_p1[i].x)+(p2[i].y - mapped_p1[i].y)*(p2[i].y - mapped_p1[i].y));
|
|
}
|
|
|
|
error /= p2.size();
|
|
|
|
return error;
|
|
}
|
|
|
|
|
|
int main( int argc, char** argv )
|
|
{
|
|
printf("calonder_sample is under construction\n");
|
|
return 0;
|
|
|
|
IplImage* test_image;
|
|
IplImage* train_image;
|
|
if (argc < 3)
|
|
{
|
|
|
|
test_image = cvLoadImage("box_in_scene.png",0);
|
|
train_image = cvLoadImage("box.png ",0);
|
|
if (!test_image || !train_image)
|
|
{
|
|
printf("Usage: calonder_sample <train_image> <test_image>");
|
|
return 0;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
test_image = cvLoadImage(argv[2],0);
|
|
train_image = cvLoadImage(argv[1],0);
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
if (!train_image)
|
|
{
|
|
printf("Unable to load train image\n");
|
|
return 0;
|
|
}
|
|
|
|
if (!test_image)
|
|
{
|
|
printf("Unable to load test image\n");
|
|
return 0;
|
|
}
|
|
|
|
|
|
|
|
CvMemStorage* storage = cvCreateMemStorage(0);
|
|
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
|
|
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
|
|
CvSURFParams params = cvSURFParams(500, 1);
|
|
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors, storage, params );
|
|
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors, storage, params );
|
|
|
|
|
|
cv::RTreeClassifier detector;
|
|
int patch_width = cv::PATCH_SIZE;
|
|
int patch_height = cv::PATCH_SIZE;
|
|
vector<cv::BaseKeypoint> base_set;
|
|
int i=0;
|
|
CvSURFPoint* point;
|
|
|
|
|
|
for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
|
|
base_set.push_back(cv::BaseKeypoint(point->pt.x,point->pt.y,train_image));
|
|
}
|
|
|
|
//Detector training
|
|
cv::RNG rng( cvGetTickCount() );
|
|
cv::PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,-CV_PI/3,CV_PI/3);
|
|
|
|
printf("RTree Classifier training...\n");
|
|
detector.train(base_set,rng,gen,24,cv::DEFAULT_DEPTH,2000,(int)base_set.size(),detector.DEFAULT_NUM_QUANT_BITS);
|
|
printf("Done\n");
|
|
|
|
float* signature = new float[detector.original_num_classes()];
|
|
float* best_corr;
|
|
int* best_corr_idx;
|
|
if (imageKeypoints->total > 0)
|
|
{
|
|
best_corr = new float[imageKeypoints->total];
|
|
best_corr_idx = new int[imageKeypoints->total];
|
|
}
|
|
|
|
for(i=0; i < imageKeypoints->total; i++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
|
|
int part_idx = -1;
|
|
float prob = 0.0f;
|
|
|
|
|
|
CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,(int)(point->pt.y) - patch_height/2, patch_width, patch_height);
|
|
cvSetImageROI(test_image, roi);
|
|
roi = cvGetImageROI(test_image);
|
|
if(roi.width != patch_width || roi.height != patch_height)
|
|
{
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
}
|
|
else
|
|
{
|
|
cvSetImageROI(test_image, roi);
|
|
IplImage* roi_image = cvCreateImage(cvSize(roi.width, roi.height), test_image->depth, test_image->nChannels);
|
|
cvCopy(test_image,roi_image);
|
|
|
|
detector.getSignature(roi_image, signature);
|
|
|
|
|
|
for (int j = 0; j< detector.original_num_classes();j++)
|
|
{
|
|
if (prob < signature[j])
|
|
{
|
|
part_idx = j;
|
|
prob = signature[j];
|
|
}
|
|
}
|
|
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
|
|
|
|
if (roi_image)
|
|
cvReleaseImage(&roi_image);
|
|
}
|
|
cvResetImageROI(test_image);
|
|
}
|
|
|
|
float min_prob = 0.0f;
|
|
vector<CvPoint> object;
|
|
vector<CvPoint> features;
|
|
|
|
for (int j=0;j<objectKeypoints->total;j++)
|
|
{
|
|
float prob = 0.0f;
|
|
int idx = -1;
|
|
for (i = 0; i<imageKeypoints->total;i++)
|
|
{
|
|
if ((best_corr_idx[i]!=j)||(best_corr[i] < min_prob))
|
|
continue;
|
|
|
|
if (best_corr[i] > prob)
|
|
{
|
|
prob = best_corr[i];
|
|
idx = i;
|
|
}
|
|
}
|
|
if (idx >=0)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,j);
|
|
object.push_back(cvPoint((int)point->pt.x,(int)point->pt.y));
|
|
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,idx);
|
|
features.push_back(cvPoint((int)point->pt.x,(int)point->pt.y));
|
|
}
|
|
}
|
|
if ((int)object.size() > 3)
|
|
{
|
|
CvMat* affine = cvCreateMat(2, 3, CV_32FC1);
|
|
FindAffineTransform(object,features,affine);
|
|
|
|
vector<CvPoint> corners;
|
|
vector<CvPoint> mapped_corners;
|
|
corners.push_back(cvPoint(0,0));
|
|
corners.push_back(cvPoint(0,train_image->height));
|
|
corners.push_back(cvPoint(train_image->width,0));
|
|
corners.push_back(cvPoint(train_image->width,train_image->height));
|
|
MapVectorAffine(corners,mapped_corners,affine);
|
|
|
|
//Drawing the result
|
|
IplImage* result = cvCreateImage(cvSize(test_image->width > train_image->width ? test_image->width : train_image->width,
|
|
train_image->height + test_image->height),
|
|
test_image->depth, test_image->nChannels);
|
|
cvSetImageROI(result,cvRect(0,0,train_image->width, train_image->height));
|
|
cvCopy(train_image,result);
|
|
cvResetImageROI(result);
|
|
cvSetImageROI(result,cvRect(0,train_image->height,test_image->width, test_image->height));
|
|
cvCopy(test_image,result);
|
|
cvResetImageROI(result);
|
|
|
|
for (int i=0;i<(int)features.size();i++)
|
|
{
|
|
cvLine(result,object[i],cvPoint(features[i].x,features[i].y+train_image->height),cvScalar(255));
|
|
}
|
|
|
|
if (draw_border)
|
|
{
|
|
cvLine(result,cvPoint(mapped_corners[0].x, mapped_corners[0].y+train_image->height),
|
|
cvPoint(mapped_corners[1].x, mapped_corners[1].y+train_image->height),cvScalar(150),3);
|
|
cvLine(result,cvPoint(mapped_corners[0].x, mapped_corners[0].y+train_image->height),
|
|
cvPoint(mapped_corners[2].x, mapped_corners[2].y+train_image->height),cvScalar(150),3);
|
|
cvLine(result,cvPoint(mapped_corners[1].x, mapped_corners[1].y+train_image->height),
|
|
cvPoint(mapped_corners[3].x, mapped_corners[3].y+train_image->height),cvScalar(150),3);
|
|
cvLine(result,cvPoint(mapped_corners[2].x, mapped_corners[2].y+train_image->height),
|
|
cvPoint(mapped_corners[3].x, mapped_corners[3].y+train_image->height),cvScalar(150),3);
|
|
}
|
|
|
|
cvSaveImage("Result.jpg",result);
|
|
cvNamedWindow("Result",0);
|
|
cvShowImage("Result",result);
|
|
cvWaitKey();
|
|
cvReleaseMat(&affine);
|
|
cvReleaseImage(&result);
|
|
}
|
|
else
|
|
{
|
|
printf("Unable to find correspondence\n");
|
|
}
|
|
|
|
|
|
|
|
|
|
if (signature)
|
|
delete[] signature;
|
|
if (best_corr)
|
|
delete[] best_corr;
|
|
cvReleaseMemStorage(&storage);
|
|
cvReleaseImage(&train_image);
|
|
cvReleaseImage(&test_image);
|
|
|
|
return 0;
|
|
}
|