mirror of
https://github.com/opencv/opencv.git
synced 2024-11-28 21:20:18 +08:00
309 lines
10 KiB
C++
309 lines
10 KiB
C++
/*
|
|
* A Demo to OpenCV Implementation of SURF
|
|
* Further Information Refer to "SURF: Speed-Up Robust Feature"
|
|
* Author: Liu Liu
|
|
* liuliu.1987+opencv@gmail.com
|
|
*/
|
|
|
|
#include <cv.h>
|
|
#include <highgui.h>
|
|
#include <ctype.h>
|
|
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
|
|
#include <iostream>
|
|
#include <vector>
|
|
|
|
using namespace std;
|
|
|
|
|
|
// define whether to use approximate nearest-neighbor search
|
|
#define USE_FLANN
|
|
|
|
|
|
IplImage *image = 0;
|
|
|
|
double
|
|
compareSURFDescriptors( const float* d1, const float* d2, double best, int length )
|
|
{
|
|
double total_cost = 0;
|
|
assert( length % 4 == 0 );
|
|
for( int i = 0; i < length; i += 4 )
|
|
{
|
|
double t0 = d1[i] - d2[i];
|
|
double t1 = d1[i+1] - d2[i+1];
|
|
double t2 = d1[i+2] - d2[i+2];
|
|
double t3 = d1[i+3] - d2[i+3];
|
|
total_cost += t0*t0 + t1*t1 + t2*t2 + t3*t3;
|
|
if( total_cost > best )
|
|
break;
|
|
}
|
|
return total_cost;
|
|
}
|
|
|
|
|
|
int
|
|
naiveNearestNeighbor( const float* vec, int laplacian,
|
|
const CvSeq* model_keypoints,
|
|
const CvSeq* model_descriptors )
|
|
{
|
|
int length = (int)(model_descriptors->elem_size/sizeof(float));
|
|
int i, neighbor = -1;
|
|
double d, dist1 = 1e6, dist2 = 1e6;
|
|
CvSeqReader reader, kreader;
|
|
cvStartReadSeq( model_keypoints, &kreader, 0 );
|
|
cvStartReadSeq( model_descriptors, &reader, 0 );
|
|
|
|
for( i = 0; i < model_descriptors->total; i++ )
|
|
{
|
|
const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
|
|
const float* mvec = (const float*)reader.ptr;
|
|
CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
|
|
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
|
|
if( laplacian != kp->laplacian )
|
|
continue;
|
|
d = compareSURFDescriptors( vec, mvec, dist2, length );
|
|
if( d < dist1 )
|
|
{
|
|
dist2 = dist1;
|
|
dist1 = d;
|
|
neighbor = i;
|
|
}
|
|
else if ( d < dist2 )
|
|
dist2 = d;
|
|
}
|
|
if ( dist1 < 0.6*dist2 )
|
|
return neighbor;
|
|
return -1;
|
|
}
|
|
|
|
void
|
|
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
|
|
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector<int>& ptpairs )
|
|
{
|
|
int i;
|
|
CvSeqReader reader, kreader;
|
|
cvStartReadSeq( objectKeypoints, &kreader );
|
|
cvStartReadSeq( objectDescriptors, &reader );
|
|
ptpairs.clear();
|
|
|
|
for( i = 0; i < objectDescriptors->total; i++ )
|
|
{
|
|
const CvSURFPoint* kp = (const CvSURFPoint*)kreader.ptr;
|
|
const float* descriptor = (const float*)reader.ptr;
|
|
CV_NEXT_SEQ_ELEM( kreader.seq->elem_size, kreader );
|
|
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
|
|
int nearest_neighbor = naiveNearestNeighbor( descriptor, kp->laplacian, imageKeypoints, imageDescriptors );
|
|
if( nearest_neighbor >= 0 )
|
|
{
|
|
ptpairs.push_back(i);
|
|
ptpairs.push_back(nearest_neighbor);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
void
|
|
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
|
|
const CvSeq*, const CvSeq* imageDescriptors, vector<int>& ptpairs )
|
|
{
|
|
int length = (int)(objectDescriptors->elem_size/sizeof(float));
|
|
|
|
cv::Mat m_object(objectDescriptors->total, length, CV_32F);
|
|
cv::Mat m_image(imageDescriptors->total, length, CV_32F);
|
|
|
|
|
|
// copy descriptors
|
|
CvSeqReader obj_reader;
|
|
float* obj_ptr = m_object.ptr<float>(0);
|
|
cvStartReadSeq( objectDescriptors, &obj_reader );
|
|
for(int i = 0; i < objectDescriptors->total; i++ )
|
|
{
|
|
const float* descriptor = (const float*)obj_reader.ptr;
|
|
CV_NEXT_SEQ_ELEM( obj_reader.seq->elem_size, obj_reader );
|
|
memcpy(obj_ptr, descriptor, length*sizeof(float));
|
|
obj_ptr += length;
|
|
}
|
|
CvSeqReader img_reader;
|
|
float* img_ptr = m_image.ptr<float>(0);
|
|
cvStartReadSeq( imageDescriptors, &img_reader );
|
|
for(int i = 0; i < imageDescriptors->total; i++ )
|
|
{
|
|
const float* descriptor = (const float*)img_reader.ptr;
|
|
CV_NEXT_SEQ_ELEM( img_reader.seq->elem_size, img_reader );
|
|
memcpy(img_ptr, descriptor, length*sizeof(float));
|
|
img_ptr += length;
|
|
}
|
|
|
|
// find nearest neighbors using FLANN
|
|
cv::Mat m_indices(objectDescriptors->total, 2, CV_32S);
|
|
cv::Mat m_dists(objectDescriptors->total, 2, CV_32F);
|
|
cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4)); // using 4 randomized kdtrees
|
|
flann_index.knnSearch(m_object, m_indices, m_dists, 2, cv::flann::SearchParams(64) ); // maximum number of leafs checked
|
|
|
|
int* indices_ptr = m_indices.ptr<int>(0);
|
|
float* dists_ptr = m_dists.ptr<float>(0);
|
|
for (int i=0;i<m_indices.rows;++i) {
|
|
if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
|
|
ptpairs.push_back(i);
|
|
ptpairs.push_back(indices_ptr[2*i]);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
/* a rough implementation for object location */
|
|
int
|
|
locatePlanarObject( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
|
|
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors,
|
|
const CvPoint src_corners[4], CvPoint dst_corners[4] )
|
|
{
|
|
double h[9];
|
|
CvMat _h = cvMat(3, 3, CV_64F, h);
|
|
vector<int> ptpairs;
|
|
vector<CvPoint2D32f> pt1, pt2;
|
|
CvMat _pt1, _pt2;
|
|
int i, n;
|
|
|
|
#ifdef USE_FLANN
|
|
flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
|
|
#else
|
|
findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
|
|
#endif
|
|
|
|
n = ptpairs.size()/2;
|
|
if( n < 4 )
|
|
return 0;
|
|
|
|
pt1.resize(n);
|
|
pt2.resize(n);
|
|
for( i = 0; i < n; i++ )
|
|
{
|
|
pt1[i] = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))->pt;
|
|
pt2[i] = ((CvSURFPoint*)cvGetSeqElem(imageKeypoints,ptpairs[i*2+1]))->pt;
|
|
}
|
|
|
|
_pt1 = cvMat(1, n, CV_32FC2, &pt1[0] );
|
|
_pt2 = cvMat(1, n, CV_32FC2, &pt2[0] );
|
|
if( !cvFindHomography( &_pt1, &_pt2, &_h, CV_RANSAC, 5 ))
|
|
return 0;
|
|
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
double x = src_corners[i].x, y = src_corners[i].y;
|
|
double Z = 1./(h[6]*x + h[7]*y + h[8]);
|
|
double X = (h[0]*x + h[1]*y + h[2])*Z;
|
|
double Y = (h[3]*x + h[4]*y + h[5])*Z;
|
|
dst_corners[i] = cvPoint(cvRound(X), cvRound(Y));
|
|
}
|
|
|
|
return 1;
|
|
}
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
const char* object_filename = argc == 3 ? argv[1] : "box.png";
|
|
const char* scene_filename = argc == 3 ? argv[2] : "box_in_scene.png";
|
|
|
|
CvMemStorage* storage = cvCreateMemStorage(0);
|
|
|
|
cvNamedWindow("Object", 1);
|
|
cvNamedWindow("Object Correspond", 1);
|
|
|
|
static CvScalar colors[] =
|
|
{
|
|
{{0,0,255}},
|
|
{{0,128,255}},
|
|
{{0,255,255}},
|
|
{{0,255,0}},
|
|
{{255,128,0}},
|
|
{{255,255,0}},
|
|
{{255,0,0}},
|
|
{{255,0,255}},
|
|
{{255,255,255}}
|
|
};
|
|
|
|
IplImage* object = cvLoadImage( object_filename, CV_LOAD_IMAGE_GRAYSCALE );
|
|
IplImage* image = cvLoadImage( scene_filename, CV_LOAD_IMAGE_GRAYSCALE );
|
|
if( !object || !image )
|
|
{
|
|
fprintf( stderr, "Can not load %s and/or %s\n"
|
|
"Usage: find_obj [<object_filename> <scene_filename>]\n",
|
|
object_filename, scene_filename );
|
|
exit(-1);
|
|
}
|
|
IplImage* object_color = cvCreateImage(cvGetSize(object), 8, 3);
|
|
cvCvtColor( object, object_color, CV_GRAY2BGR );
|
|
|
|
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
|
|
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
|
|
int i;
|
|
CvSURFParams params = cvSURFParams(500, 1);
|
|
|
|
double tt = (double)cvGetTickCount();
|
|
cvExtractSURF( object, 0, &objectKeypoints, &objectDescriptors, storage, params );
|
|
printf("Object Descriptors: %d\n", objectDescriptors->total);
|
|
cvExtractSURF( image, 0, &imageKeypoints, &imageDescriptors, storage, params );
|
|
printf("Image Descriptors: %d\n", imageDescriptors->total);
|
|
tt = (double)cvGetTickCount() - tt;
|
|
printf( "Extraction time = %gms\n", tt/(cvGetTickFrequency()*1000.));
|
|
CvPoint src_corners[4] = {{0,0}, {object->width,0}, {object->width, object->height}, {0, object->height}};
|
|
CvPoint dst_corners[4];
|
|
IplImage* correspond = cvCreateImage( cvSize(image->width, object->height+image->height), 8, 1 );
|
|
cvSetImageROI( correspond, cvRect( 0, 0, object->width, object->height ) );
|
|
cvCopy( object, correspond );
|
|
cvSetImageROI( correspond, cvRect( 0, object->height, correspond->width, correspond->height ) );
|
|
cvCopy( image, correspond );
|
|
cvResetImageROI( correspond );
|
|
|
|
#ifdef USE_FLANN
|
|
printf("Using approximate nearest neighbor search\n");
|
|
#endif
|
|
|
|
if( locatePlanarObject( objectKeypoints, objectDescriptors, imageKeypoints,
|
|
imageDescriptors, src_corners, dst_corners ))
|
|
{
|
|
for( i = 0; i < 4; i++ )
|
|
{
|
|
CvPoint r1 = dst_corners[i%4];
|
|
CvPoint r2 = dst_corners[(i+1)%4];
|
|
cvLine( correspond, cvPoint(r1.x, r1.y+object->height ),
|
|
cvPoint(r2.x, r2.y+object->height ), colors[8] );
|
|
}
|
|
}
|
|
vector<int> ptpairs;
|
|
#ifdef USE_FLANN
|
|
flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
|
|
#else
|
|
findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
|
|
#endif
|
|
for( i = 0; i < (int)ptpairs.size(); i += 2 )
|
|
{
|
|
CvSURFPoint* r1 = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, ptpairs[i] );
|
|
CvSURFPoint* r2 = (CvSURFPoint*)cvGetSeqElem( imageKeypoints, ptpairs[i+1] );
|
|
cvLine( correspond, cvPointFrom32f(r1->pt),
|
|
cvPoint(cvRound(r2->pt.x), cvRound(r2->pt.y+object->height)), colors[8] );
|
|
}
|
|
|
|
cvShowImage( "Object Correspond", correspond );
|
|
for( i = 0; i < objectKeypoints->total; i++ )
|
|
{
|
|
CvSURFPoint* r = (CvSURFPoint*)cvGetSeqElem( objectKeypoints, i );
|
|
CvPoint center;
|
|
int radius;
|
|
center.x = cvRound(r->pt.x);
|
|
center.y = cvRound(r->pt.y);
|
|
radius = cvRound(r->size*1.2/9.*2);
|
|
cvCircle( object_color, center, radius, colors[0], 1, 8, 0 );
|
|
}
|
|
cvShowImage( "Object", object_color );
|
|
|
|
cvWaitKey(0);
|
|
|
|
cvDestroyWindow("Object");
|
|
cvDestroyWindow("Object SURF");
|
|
cvDestroyWindow("Object Correspond");
|
|
|
|
return 0;
|
|
}
|