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海思3516跑opencv2.4.9的find_obj
本帖最后由 ccsutbs 于 2017-9-12 10:37 编辑
近来无事,学习下opencv!
环境ubunut16.04 opencv2.4.9 海思3516D
首先下载opencv2.4.9.zip,cmake-gui
1.unzip opencv2.4.9.zaip
2.cd opencv
3.mkdir build
4.mkdir _install
5.cmake-gui
<这里需要注意:选择arm-hisiv300-linux arm-hisiv300-linux-gcc arm-hisiv300-linux-g++ >
查询错误信息,提到了CUDA,百度查了下,是某显卡相关库,对于arm-linux交叉编译没有用。故在列表中取消与之相关的的项,重新点击configure Generate
6.cd build
7.make
修改CMakeCache.txt
//Flags used by the linker.
CMAKE_EXE_LINKER_FLAGS:STRING= -lpthread -lrt
继续make
8.make install
9.cd _install
10.mkdir test
11.vi find_obg.cpp
/*
* A Demo to OpenCV Implementation of SURF
* Further Information Refer to "SURF: Speed-Up Robust Feature"
* Author: Liu Liu
* liuliu.1987+[email]opencv@gmail.com[/email]
*/
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include
#include
#include
using namespace std;
using namespace cv;
static void help()
{
printf(
"This program demonstrated the use of the SURF Detector and Descriptor using\n"
"either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
"on planar objects.\n"
"Usage:\n"
"./find_obj , default is box.png and box_in_scene.png\n\n");
return;
}
// define whether to use approximate nearest-neighbor search
#define USE_FLANN
#ifdef USE_FLANN
static void
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
const CvSeq*, const CvSeq* imageDescriptors, vector& 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(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(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(0);
float* dists_ptr = m_dists.ptr(0);
for (int i=0;i
if (dists_ptr[2*i]<0.6*dists_ptr[2*i+1]) {
ptpairs.push_back(i);
ptpairs.push_back(indices_ptr[2*i]);
}
}
}
#else
static 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;
}
static 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;
}
static void
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector& 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);
}
}
}
#endif
/* a rough implementation for object location */
static 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 ptpairs;
vector 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 = (int)(ptpairs.size()/2);
if( n < 4 )
return 0;
pt1.resize(n);
pt2.resize(n);
for( i = 0; i < n; i++ )
{
pt1 = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))->pt;
pt2 = ((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.x, y = src_corners.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 = 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";
cv::initModule_nonfree();
help();
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",
object_filename, scene_filename );
exit(-1);
}
CvMemStorage* storage = cvCreateMemStorage(0);
#if 0
cvNamedWindow("Object", 1);
cvNamedWindow("Object Correspond", 1);
#endif
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_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 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 );
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] );
}
#if 1
cvShowImage( "Object Correspond", correspond );
#endif
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 );
}
#if 0
cvShowImage( "Object", object_color );
#endif
cvWaitKey(0);
#if 0
cvDestroyWindow("Object");
cvDestroyWindow("Object Correspond");
#endif
return 0;
}
vi Makefile
CC = arm-hisiv300-linux-g++
DEMOTAR = find_obj
DEMOOBJ = find_obj.o
CFLAGS += -g -Wall -I/home/t/Desktop/open/opencv-2.4.9/_install/include
LDFLAGS += -L/home/t/Desktop/open/opencv-2.4.9/_install/lib -Wl,-Bdynamic -lopencv_objdetect -lopencv_features2d -lopencv_highgui -lopencv_calib3d -lopencv_nonfree -lopencv_imgproc -lopencv_legacy -lopencv_core -lopencv_flann -lopencv_gpu -lopencv_ml -lopencv_ocl -lopencv_photo -lopencv_stitching -lopencv_video -lopencv_videostab -lpthread
%.o: %.cpp
@echo "[Compiling] $< ..."
@$(CC) $(CFLAGS) -c $<
all: $(DEMOTAR)
$(DEMOTAR):$(DEMOOBJ)
@$(CC) -o $@ $^ $(LDFLAGS)
.PHONY : clean
clean:
rm -rf $(DEMOOBJ) $(DEMOTAR)
将_install下的lib目录拷贝到终端,运行 find_obg<图片自己拷贝>
发现cpu占用率99.6%,时间18812.3ms. <还有错误提示,是跟Windows显示有关的>
近来无事,学习下opencv!
环境ubunut16.04 opencv2.4.9 海思3516D
首先下载opencv2.4.9.zip,cmake-gui
1.unzip opencv2.4.9.zaip
2.cd opencv
3.mkdir build
4.mkdir _install
5.cmake-gui
<这里需要注意:选择arm-hisiv300-linux arm-hisiv300-linux-gcc arm-hisiv300-linux-g++ >
查询错误信息,提到了CUDA,百度查了下,是某显卡相关库,对于arm-linux交叉编译没有用。故在列表中取消与之相关的的项,重新点击configure Generate
6.cd build
7.make
修改CMakeCache.txt
//Flags used by the linker.
CMAKE_EXE_LINKER_FLAGS:STRING= -lpthread -lrt
继续make
8.make install
9.cd _install
10.mkdir test
11.vi find_obg.cpp
/*
* A Demo to OpenCV Implementation of SURF
* Further Information Refer to "SURF: Speed-Up Robust Feature"
* Author: Liu Liu
* liuliu.1987+[email]opencv@gmail.com[/email]
*/
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include
#include
#include
using namespace std;
using namespace cv;
static void help()
{
printf(
"This program demonstrated the use of the SURF Detector and Descriptor using\n"
"either FLANN (fast approx nearst neighbor classification) or brute force matching\n"
"on planar objects.\n"
"Usage:\n"
"./find_obj
return;
}
// define whether to use approximate nearest-neighbor search
#define USE_FLANN
#ifdef USE_FLANN
static void
flannFindPairs( const CvSeq*, const CvSeq* objectDescriptors,
const CvSeq*, const CvSeq* imageDescriptors, vector
{
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
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
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
float* dists_ptr = m_dists.ptr
for (int i=0;i
ptpairs.push_back(i);
ptpairs.push_back(indices_ptr[2*i]);
}
}
}
#else
static 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;
}
static 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;
}
static void
findPairs( const CvSeq* objectKeypoints, const CvSeq* objectDescriptors,
const CvSeq* imageKeypoints, const CvSeq* imageDescriptors, vector
{
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);
}
}
}
#endif
/* a rough implementation for object location */
static 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
vector
CvMat _pt1, _pt2;
int i, n;
#ifdef USE_FLANN
flannFindPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#else
findPairs( objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, ptpairs );
#endif
n = (int)(ptpairs.size()/2);
if( n < 4 )
return 0;
pt1.resize(n);
pt2.resize(n);
for( i = 0; i < n; i++ )
{
pt1 = ((CvSURFPoint*)cvGetSeqElem(objectKeypoints,ptpairs[i*2]))->pt;
pt2 = ((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.x, y = src_corners.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 = 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";
cv::initModule_nonfree();
help();
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",
object_filename, scene_filename );
exit(-1);
}
CvMemStorage* storage = cvCreateMemStorage(0);
#if 0
cvNamedWindow("Object", 1);
cvNamedWindow("Object Correspond", 1);
#endif
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_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
#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 );
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] );
}
#if 1
cvShowImage( "Object Correspond", correspond );
#endif
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 );
}
#if 0
cvShowImage( "Object", object_color );
#endif
cvWaitKey(0);
#if 0
cvDestroyWindow("Object");
cvDestroyWindow("Object Correspond");
#endif
return 0;
}
vi Makefile
CC = arm-hisiv300-linux-g++
DEMOTAR = find_obj
DEMOOBJ = find_obj.o
CFLAGS += -g -Wall -I/home/t/Desktop/open/opencv-2.4.9/_install/include
LDFLAGS += -L/home/t/Desktop/open/opencv-2.4.9/_install/lib -Wl,-Bdynamic -lopencv_objdetect -lopencv_features2d -lopencv_highgui -lopencv_calib3d -lopencv_nonfree -lopencv_imgproc -lopencv_legacy -lopencv_core -lopencv_flann -lopencv_gpu -lopencv_ml -lopencv_ocl -lopencv_photo -lopencv_stitching -lopencv_video -lopencv_videostab -lpthread
%.o: %.cpp
@echo "[Compiling] $< ..."
@$(CC) $(CFLAGS) -c $<
all: $(DEMOTAR)
$(DEMOTAR):$(DEMOOBJ)
@$(CC) -o $@ $^ $(LDFLAGS)
.PHONY : clean
clean:
rm -rf $(DEMOOBJ) $(DEMOTAR)
将_install下的lib目录拷贝到终端,运行 find_obg<图片自己拷贝>
发现cpu占用率99.6%,时间18812.3ms. <还有错误提示,是跟Windows显示有关的>
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