//特征匹配
void SiftMatch::on_matchButton_clicked()
{
//若用户勾选了水平排列按钮
if(ui->radioButton_horizontal->isChecked())
{
//将2幅图片合成1幅图片,img1在左，img2在右
stacked = stack_imgs_horizontal(img1, img2);//合成图像，显示经距离比值法筛选后的匹配结果
}
else//用户勾选了垂直排列按钮
{
verticalStackFlag = true;//垂直排列标识设为true
//将2幅图片合成1幅图片,img1在上，img2在下
stacked = stack_imgs( img1, img2 );//合成图像，显示经距离比值法筛选后的匹配结果
}
//根据图1的特征点集feat1建立k-d树，返回k-d树根给kd_root
kd_root = kdtree_build( feat1, n1 );
Point pt1,pt2;//连线的两个端点
double d0,d1;//feat2中每个特征点到最近邻和次近邻的距离
int matchNum = 0;//经距离比值法筛选后的匹配点对的个数
//遍历特征点集feat2，针对feat2中每个特征点feat，选取符合距离比值条件的匹配点，放到feat的fwd_match域中
for(int i = 0; i < n2; i++ )
{
feat = feat2+i;//第i个特征点的指针
//在kd_root中搜索目标点feat的2个最近邻点，存放在nbrs中，返回实际找到的近邻点个数
int k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );//feat与最近邻点的距离的平方
d1 = descr_dist_sq( feat, nbrs[1] );//feat与次近邻点的距离的平方
//若d0和d1的比值小于阈值NN_SQ_DIST_RATIO_THR，则接受此匹配，否则剔除
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{ //将目标点feat和最近邻点作为匹配点对
pt2 = Point( cvRound( feat->x ), cvRound( feat->y ) );//图2中点的坐标
pt1 = Point( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );//图1中点的坐标(feat的最近邻点)
if(verticalStackFlag)//垂直排列
pt2.y += img1->height;//由于两幅图是上下排列的，pt2的纵坐标加上图1的高度，作为连线的终点
else
pt2.x += img1->width;//由于两幅图是左右排列的，pt2的横坐标加上图1的宽度，作为连线的终点
cvLine( stacked, pt1, pt2, CV_RGB(255,0,255), 1, 8, 0 );//画出连线
matchNum++;//统计匹配点对的个数
feat2[i].fwd_match = nbrs[0];//使点feat的fwd_match域指向其对应的匹配点
}
}
free( nbrs );//释放近邻数组
}
qDebug()<<tr("经距离比值法筛选后的匹配点对个数：")<<matchNum<<endl;
//显示并保存经距离比值法筛选后的匹配图
//cvNamedWindow(IMG_MATCH1);//创建窗口
//cvShowImage(IMG_MATCH1,stacked);//显示
//保存匹配图
QString name_match_DistRatio = name1;//文件名，原文件名去掉序号后加"_match_DistRatio"
cvSaveImage(name_match_DistRatio.replace( name_match_DistRatio.lastIndexOf(".",-1)-1 , 1 , "_match_DistRatio").toAscii().data(),stacked);
//利用RANSAC算法筛选匹配点,计算变换矩阵H，
//无论img1和img2的左右顺序，H永远是将feat2中的特征点变换为其匹配点，即将img2中的点变换为img1中的对应点
H = ransac_xform(feat2,n2,FEATURE_FWD_MATCH,lsq_homog,4,0.01,homog_xfer_err,3.0,&inliers,&n_inliers);
//若能成功计算出变换矩阵，即两幅图中有共同区域
if( H )
{
qDebug()<<tr("经RANSAC算法筛选后的匹配点对个数：")<<n_inliers<<endl;
// //输出H矩阵
// for(int i=0;i<3;i++)
// qDebug()<<cvmGet(H,i,0)<<cvmGet(H,i,1)<<cvmGet(H,i,2);
if(verticalStackFlag)//将2幅图片合成1幅图片,img1在上，img2在下
stacked_ransac = stack_imgs( img1, img2 );//合成图像，显示经RANSAC算法筛选后的匹配结果
else//将2幅图片合成1幅图片,img1在左，img2在右
stacked_ransac = stack_imgs_horizontal(img1, img2);//合成图像，显示经RANSAC算法筛选后的匹配结果
//img1LeftBound = inliers[0]->fwd_match->x;//图1中匹配点外接矩形的左边界
//img1RightBound = img1LeftBound;//图1中匹配点外接矩形的右边界
//img2LeftBound = inliers[0]->x;//图2中匹配点外接矩形的左边界
//img2RightBound = img2LeftBound;//图2中匹配点外接矩形的右边界
int invertNum = 0;//统计pt2.x > pt1.x的匹配点对的个数，来判断img1中是否右图
//遍历经RANSAC算法筛选后的特征点集合inliers，找到每个特征点的匹配点，画出连线
for(int i=0; i<n_inliers; i++)
{
feat = inliers[i];//第i个特征点
pt2 = Point(cvRound(feat->x), cvRound(feat->y));//图2中点的坐标
pt1 = Point(cvRound(feat->fwd_match->x), cvRound(feat->fwd_match->y));//图1中点的坐标(feat的匹配点)
//qDebug()<<"pt2:("<<pt2.x<<","<<pt2.y<<")--->pt1:("<<pt1.x<<","<<pt1.y<<")";//输出对应点对
/*找匹配点区域的边界
if(pt1.x < img1LeftBound) img1LeftBound = pt1.x;
if(pt1.x > img1RightBound) img1RightBound = pt1.x;
if(pt2.x < img2LeftBound) img2LeftBound = pt2.x;
if(pt2.x > img2RightBound) img2RightBound = pt2.x;//*/
//统计匹配点的左右位置关系，来判断图1和图2的左右位置关系
if(pt2.x > pt1.x)
invertNum++;
if(verticalStackFlag)//垂直排列
pt2.y += img1->height;//由于两幅图是上下排列的，pt2的纵坐标加上图1的高度，作为连线的终点
else//水平排列
pt2.x += img1->width;//由于两幅图是左右排列的，pt2的横坐标加上图1的宽度，作为连线的终点
cvLine(stacked_ransac,pt1,pt2,CV_RGB(255,0,255),1,8,0);//在匹配图上画出连线
}
//绘制图1中包围匹配点的矩形
//cvRectangle(stacked_ransac,cvPoint(img1LeftBound,0),cvPoint(img1RightBound,img1->height),CV_RGB(0,255,0),2);
//绘制图2中包围匹配点的矩形
//cvRectangle(stacked_ransac,cvPoint(img1->width+img2LeftBound,0),cvPoint(img1->width+img2RightBound,img2->height),CV_RGB(0,0,255),2);
//cvNamedWindow(IMG_MATCH2);//创建窗口
//cvShowImage(IMG_MATCH2,stacked_ransac);//显示经RANSAC算法筛选后的匹配图
//保存匹配图
QString name_match_RANSAC = name1;//文件名，原文件名去掉序号后加"_match_RANSAC"
cvSaveImage(name_match_RANSAC.replace( name_match_RANSAC.lastIndexOf(".",-1)-1 , 1 , "_match_RANSAC").toAscii().data(),stacked_ransac);
/*程序中计算出的变换矩阵H用来将img2中的点变换为img1中的点，正常情况下img1应该是左图，img2应该是右图。
此时img2中的点pt2和img1中的对应点pt1的x坐标的关系基本都是：pt2.x < pt1.x
若用户打开的img1是右图，img2是左图，则img2中的点pt2和img1中的对应点pt1的x坐标的关系基本都是：pt2.x > pt1.x
所以通过统计对应点变换前后x坐标大小关系，可以知道img1是不是右图。
如果img1是右图，将img1中的匹配点经H的逆阵H_IVT变换后可得到img2中的匹配点*/
//若pt2.x > pt1.x的点的个数大于内点个数的80%，则认定img1中是右图
if(invertNum > n_inliers * 0.8)
{
qDebug()<<tr("img1中是右图");
CvMat * H_IVT = cvCreateMat(3, 3, CV_64FC1);//变换矩阵的逆矩阵
//求H的逆阵H_IVT时，若成功求出，返回非零值
if( cvInvert(H,H_IVT) )
{
// //输出H_IVT
// for(int i=0;i<3;i++)
// qDebug()<<cvmGet(H_IVT,i,0)<<cvmGet(H_IVT,i,1)<<cvmGet(H_IVT,i,2);
cvReleaseMat(&H);//释放变换矩阵H，因为用不到了
H = cvCloneMat(H_IVT);//将H的逆阵H_IVT中的数据拷贝到H中
cvReleaseMat(&H_IVT);//释放逆阵H_IVT
//将img1和img2对调
IplImage * temp = img2;
img2 = img1;
img1 = temp;
//cvShowImage(IMG1,img1);
//cvShowImage(IMG2,img2);
ui->mosaicButton->setEnabled(true);//激活全景拼接按钮
}
else//H不可逆时，返回0
{
cvReleaseMat(&H_IVT);//释放逆阵H_IVT
QMessageBox::warning(this,tr("警告"),tr("变换矩阵H不可逆"));
}
}
else
ui->mosaicButton->setEnabled(true);//激活全景拼接按钮
}
else //无法计算出变换矩阵，即两幅图中没有重合区域
{
QMessageBox::warning(this,tr("警告"),tr("两图中无公共区域"));
}
ui->radioButton_horizontal->setEnabled(false);//禁用排列方向选择按钮
ui->radioButton_vertical->setEnabled(false);
ui->matchButton->setEnabled(false);//禁用特征匹配按钮
}
int main(int argc, char *argv[])
{
IplImage* img1, * img2, * stacked1, *stacked2;
char stemp[1024];
// printf("Reading images: %s and %s\n",argv[1],argv[2]);
if(argc != 3) {printf("\n\nUsage: getsift [image1.jpg] [image2.jpg]\n\n"); exit(0);}
img1=read_jpeg_file(argv[1]);
img2=read_jpeg_file(argv[2]);
stacked1 = stack_imgs( img1, img2 );
stacked2 = stack_imgs( img1, img2 );
struct feature* feat1, * feat2, * feat;
struct feature** nbrs;
struct feature** RANnb;
struct kd_node* kd_root;
CvPoint pt1, pt2;
double d0, d1;
int n1, n2, k, i,j, m = 0, n=0;
printf("SIFT Features Extraction: %s\n", argv[1]);
n1 = sift_features( img1, &feat1 );
printf("Numbers of Features from %s: %d\n",argv[1], n1);
printf("SIFT Features Extraction: %s\n", argv[2]);
n2 = sift_features( img2, &feat2 );
printf("Numbers of Features from %s: %d\n",argv[2], n2);
sprintf(stemp,"%s.sift.jpg",argv[1]);
draw_keypoint( img1, feat1, n1 );
write_jpeg_file(stemp,img1);
sprintf(stemp,"%s.sift.jpg",argv[2]);
draw_keypoint( img2, feat2, n2 );
write_jpeg_file(stemp,img2);
FILE * feat1file;
FILE * feat2file;
feat1file=fopen("features1.txt","w+");
for(i=0;i<n1;i++)
{
fprintf(feat1file,"(%lf,%lf): {",(feat1+i)->x,(feat1+i)->y);
for(j=0;j<FEATURE_MAX_D;j++)
fprintf(feat1file,"% lf ",(feat1+i)->descr[j]);
fprintf(feat1file,"}\n");
}
printf("coordinate and descriptor of %s keypoints have been written in featfile1.txt\n",argv[1]);
feat2file=fopen("features2.txt","w+");
for(i=0;i<n2;i++)
{
fprintf(feat2file,"(%lf,%lf): {",(feat2+i)->x,(feat2+i)->y);
for(j=0;j<FEATURE_MAX_D;j++)
fprintf(feat2file,"% lf ",(feat2+i)->descr[j]);
fprintf(feat2file,"}\n");
}
printf("coordinate and descriptor of %s keypoints have been written in featfile2.txt\n",argv[2]);
kd_root = kdtree_build( feat2, n2 );
for( i = 0; i < n1; i++ )
{
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{
pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
pt2.y += img1->height;
cvLine( stacked1, pt1, pt2, CV_RGB(255,0,255), 1, 8, 0 );
m++;
feat1[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
printf("Found %d total matches\n", m );
write_jpeg_file("matches.jpg",stacked1);
CvMat* H;
int number=0;
H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.25, homog_xfer_err, 27.0, &RANnb, &number );
for( i = 0; i < number; i++ )
{
pt1 = cvPoint( cvRound( RANnb[i]->x ), cvRound( RANnb[i]->y ) );
pt2 = cvPoint( cvRound( RANnb[i]->fwd_match->x ), cvRound( RANnb[i]->fwd_match->y ) );
pt2.y += img1->height;
cvLine( stacked2, pt1, pt2, CV_RGB(255,0,255), 1, 8, 0 );
n++;
}
printf("Found %d total matches after RANSAC\n", n );
write_jpeg_file("matches.ransac.jpg",stacked2);
cvReleaseImage( &img1 );
cvReleaseImage( &img2 );
kdtree_release( kd_root );
free( feat1 );
free( feat2 );
return 0;
}
int main( int argc, char** argv )
{
//IplImage* img1, * img2, * stacked;
struct feature* feat1, * feat2, * feat;
struct feature** nbrs;
struct kd_node* kd_root;
//CvPoint pt1, pt2;
double d0, d1;
int n1, n2, k, i, m = 0;
/*
if( argc != 3 )
fatal_error( "usage: %s <img1> <img2>", argv[0] );
img1 = cvLoadImage( argv[1], 1 );
if( ! img1 )
fatal_error( "unable to load image from %s", argv[1] );
img2 = cvLoadImage( argv[2], 1 );
if( ! img2 )
fatal_error( "unable to load image from %s", argv[2] );
stacked = stack_imgs( img1, img2 );
*/
char *query_img=argv[1];
char* match_img=argv[2];
fprintf( stderr, "Finding features in %s...\n", argv[1] );
//n1 = sift_features( img1, &feat1 );
n1=import_features(match_img, FEATURE_LOWE, &feat1);
fprintf( stderr, "Finding features in %s...\n", argv[2] );
//n2 = sift_features( img2, &feat2 );
n2=import_features(query_img, FEATURE_LOWE, &feat2);
kd_root = kdtree_build( feat2, n2 );
int cnt1=0, cnt2=0,cnt3=0;
for( i = 0; i < n1; i++ )
{
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
cnt1++;
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
cnt2++;
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{
//pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
//pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
//pt2.y += img1->height;
//cvLine( stacked, pt1, pt2, CV_RGB(255,0,255), 1, 8, 0 );
m++;
feat1[i].fwd_match = nbrs[0];
cnt3++;
}
}
free( nbrs );
}
fprintf( stderr, "cnt1: %d cnt2: %d cnt3:%d \n", cnt1, cnt2, cnt3 );
fprintf( stderr, "Found %d total matches\n", m );
//display_big_img( stacked, "Matches" );
//cvWaitKey( 0 );
/*
UNCOMMENT BELOW TO SEE HOW RANSAC FUNCTION WORKS
Note that this line above:
feat1[i].fwd_match = nbrs[0];
is important for the RANSAC function to work.
*/
CvMat* H;
//IplImage* xformed;
int inliers=0;
H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01,
homog_xfer_err, 3.0, NULL, &inliers);
cvReleaseMat( &H );
fprintf( stderr, "Found %d total inliers\n", inliers);
/*
if( H )
{
xformed = cvCreateImage( cvGetSize( img2 ), IPL_DEPTH_8U, 3 );
cvWarpPerspective( img1, xformed, H,
CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS,
cvScalarAll( 0 ) );
cvNamedWindow( "Xformed", 1 );
cvShowImage( "Xformed", xformed );
cvWaitKey( 0 );
cvReleaseImage( &xformed );
cvReleaseMat( &H );
}
*/
//cvReleaseImage( &stacked );
//cvReleaseImage( &img1 );
//cvReleaseImage( &img2 );
kdtree_release( kd_root );
free( feat1 );
free( feat2 );
return 0;
}
// Match Thread
void* matchThread(void* matchData)
{
// normal match local variables
struct feature* feat= NULL;
struct feature** nbrs = NULL;
struct kd_node* kd_root = NULL;
double d0, d1;
int k, i, x, y, m = 0;
// arguments passed to thread
char file[25];
char file2[25];
struct mData* temp;
temp = (struct mData*) matchData;
IplImage* img1 = temp->img1;
IplImage* img2 = temp->img2;
int index1 = temp->index1;
int index2 = temp->index2;
CvMat* homography = temp->homography;
sprintf(file, "features/temp%d", index1);
sprintf(file2, "features/temp%d", index2);
struct feature* feat1;
struct feature* feat2;
int numFeatures1 = import_features(file, FEATURE_LOWE, &feat1);
int numFeatures2 = import_features(file2, FEATURE_LOWE, &feat2);
// printf("running the matching function\n");
// matching function
kd_root = kdtree_build( feat2, numFeatures2 );
for( i = 0; i < numFeatures1; i++ ) {
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 ) {
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR ) {
m++;
feat1[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
if (DEBUG)
printf("computing transform\n");
// Compute homography
CvMat* H;
H = ransac_xform(feat1, numFeatures1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01,
homog_xfer_err, 3.0, NULL, NULL);
if (DEBUG)
printf("past ransac_xform\n");
if (!H) {
if (DEBUG)
printf("setting empty homography...\n");
H = cvCreateMat(3, 3, CV_64F);
for (x = 0; x < 3; x++) {
for (y = 0; y < 3; y++) {
cvmSet(H, x, y, 1000000.0);
}
}
}
kdtree_release(kd_root);
if (DEBUG)
printf("setting homography\n");
for (x = 0; x < 3; x++) {
for (y = 0; y < 3; y++) {
double tempValue = cvmGet(H, x, y);
cvmSet(homography, x, y, tempValue);
}
}
if (DEBUG)
printf("past setting homography\n");
free(feat1);
free(feat2);
pthread_exit(NULL);
}
int match( const char * img1fname, const char * img2fname, CvMat **H )
{
double scale = 10.0;
IplImage* img1, * img2, * img1orig, * img2orig;
struct feature* feat1, * feat2, * feat;
struct feature** nbrs;
struct kd_node* kd_root;
CvPoint pt1, pt2;
double d0, d1;
int n1, n2, k, i, m = 0;
img1orig = cvLoadImage( img1fname, CV_LOAD_IMAGE_COLOR );
if( ! img1orig )
fatal_error( "unable to load image from %s", img1fname );
img1 = cvCreateImage( cvSize( (int)(img1orig->width/scale), (int)(img1orig->height/scale) ), img1orig->depth, img1orig->nChannels );
cvResize( img1orig, img1, CV_INTER_AREA );
cvReleaseImage( &img1orig );
img2orig = cvLoadImage( img2fname, CV_LOAD_IMAGE_COLOR );
if( ! img2orig )
fatal_error( "unable to load image from %s", img2fname );
img2 = cvCreateImage( cvSize( (int)(img2orig->width/scale), (int)(img2orig->height/scale) ), img2orig->depth, img2orig->nChannels );
cvResize( img2orig, img2, CV_INTER_AREA );
cvReleaseImage( &img2orig );
fprintf( stdout, "Finding features in %s...\n", img1fname );
n1 = sift_features( img1, &feat1 );
fprintf( stdout, "Finding features in %s...\n", img2fname );
n2 = sift_features( img2, &feat2 );
cvReleaseImage( &img1 );
cvReleaseImage( &img2 );
kd_root = kdtree_build( feat2, n2 );
for( i = 0; i < n1; i++ ) {
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 ) {
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR ) {
/*
pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
pt2.y += img1->height;
*/
m++;
feat1[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
// scale feature coordinates back in to original image size
for( i = 0; i < n2; i++ ) {
feat2[i].img_pt.x *= scale;
feat2[i].img_pt.y *= scale;
}
for( i = 0; i < n1; i++ ) {
feat1[i].img_pt.x *= scale;
feat1[i].img_pt.y *= scale;
}
fprintf( stdout, "Found %d total matches\n", m );
{
// CvMat* H;
*H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01,
homog_xfer_err, 3.0, NULL, NULL );
if(*H != NULL) {
printf("Perspective transform:\n");
for( i = 0; i < 3; i++ ) {
printf("%0.6f %0.6f %0.6f\n", (*H)->data.db[3*i+0], (*H)->data.db[3*i+1], (*H)->data.db[3*i+2]);
}
}
else {
printf("Unable to compute transform\n");
}
/*
CvMat * Hinv = cvCreateMat(3,3,CV_64FC1);
cvInvert( *H, Hinv, CV_LU );
printf("Inverse transform:\n");
for( i = 0; i < 3; i++ ) {
printf("%0.6f %0.6f %0.6f\n", Hinv->data.db[3*i+0], Hinv->data.db[3*i+1], Hinv->data.db[3*i+2]);
}
cvReleaseMat( &Hinv );
*/
/*
if( H ) {
IplImage* xformed;
img2orig = cvLoadImage( img2fname, CV_LOAD_IMAGE_COLOR );
xformed = cvCreateImage( cvGetSize( img2orig ), IPL_DEPTH_8U, 3 );
cvReleaseImage( &img2orig );
img1orig = cvLoadImage( img1fname, CV_LOAD_IMAGE_COLOR);
cvWarpPerspective( img1orig, xformed, H,
CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS,
cvScalarAll( 0 ) );
cvSaveImage( "xformed.jpg", xformed );
cvReleaseImage( &img1orig );
cvReleaseImage( &xformed );
// cvReleaseMat( &H );
}
*/
}
kdtree_release( kd_root );
free( feat1 );
free( feat2 );
return 0;
}
int main( int argc, char** argv )
{
IplImage* img1, * img2, * stacked;
struct feature* feat1, * feat2, * feat;
struct feature** nbrs;
struct kd_node* kd_root;
CvPoint pt1, pt2;
double d0, d1;
int n1, n2, k, i, m = 0;
int match_cnt = 0;
//CvMat *im1_mask = cvCreateMat( img1->height, img1->width, CV_64FC1 );
//CvMat *im2_mask = cvCreateMat( img2->height, img2->width, CV_64FC1 );
//cvSet( im1_mask, cvScalar( 1, 0, 0, 0 ), NULL );
//cvSet( im2_mask, cvScalar( 1, 0, 0, 0 ), NULL );
if( argc != 3 )
fatal_error( "usage: %s <img1> <img2>", argv[0] );
img1 = cvLoadImage( argv[1], 1 );
if( ! img1 )
fatal_error( "unable to load image from %s", argv[1] );
img2 = cvLoadImage( argv[2], 1 );
if( ! img2 )
fatal_error( "unable to load image from %s", argv[2] );
stacked = stack_imgs( img1, img2 );
fprintf( stderr, "Finding features in %s...\n", argv[1] );
n1 = sift_features( img1, &feat1 );
fprintf( stderr, "Finding features in %s...\n", argv[2] );
n2 = sift_features( img2, &feat2 );
kd_root = kdtree_build( feat2, n2 );
for( i = 0; i < n1; i++ )
{
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{
if( m >= 2000 ) break;
pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
pt2.y += img1->height;
cvLine( stacked, pt1, pt2, CV_RGB(255,0,255), 1, 8, 0 );
m++;
feat1[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
fprintf( stderr, "Found %d total matches\n", m );
display_big_img( stacked, "Matches" );
cvWaitKey( 0 );
/*********************************************************************************************************/
CvMat* H;
IplImage* xformed;
H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01, homog_xfer_err, 3.0, NULL, NULL );
/* output H */
printf("xform Homography Matrix is :\n");
printMat( H );
/* get the size of new image */
double XDATA[2];
double YDATA[2];
CvSize new_size;
CvSize im1_size = cvGetSize( img1 );
CvSize im2_size = cvGetSize( img2 );
new_size = get_Stitched_Size( im1_size, im2_size, H, XDATA, YDATA );
/*declare the mask*/
CvMat *im1_mask = cvCreateMat( new_size.height, new_size.width, CV_64FC1 );
CvMat *im2_mask = cvCreateMat( new_size.height, new_size.width, CV_64FC1 );
CvMat *im1_tempMask = cvCreateMat( im1_size.height, im1_size.width, CV_64FC1 );
CvMat *im2_tempMask = cvCreateMat( im2_size.height, im2_size.width, CV_64FC1 );
cvSet( im1_tempMask, cvScalar(1,0,0,0), NULL );
cvSet( im2_tempMask, cvScalar(1,0,0,0), NULL );
/* get translation Matrix for Aligning */
CvMat *T = cvCreateMat( 3, 3, CV_64FC1 );
double tx = 0;
double ty = 0;
if( XDATA[0] < 0 )
{
tx = -XDATA[0] ;
printf("tx = %f\n", tx );
}
if( YDATA[0] < 0 )
{
ty = -YDATA[0];
printf("ty = %f\n", ty );
}
cvmSet( T, 0, 0, 1 );
cvmSet( T, 0, 2, tx );
cvmSet( T, 1, 1, 1 );
cvmSet( T, 1, 2, ty );
cvmSet( T, 2, 2, 1 );
printf("T Matrix:\n");
printMat( T );
/* Transform and Align image2 */
cvGEMM( T, H, 1, NULL, 0, H, 0 );
printMat( H );
xformed = cvCreateImage( new_size, IPL_DEPTH_8U, 3 );
cvWarpPerspective( img1, xformed, H, CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS, cvScalarAll( 0 ) );
cvNamedWindow( "Xformed1", 1 );
cvShowImage( "Xformed1", xformed );
cvWaitKey( 0 );
cvDestroyWindow("Xformed1");
//cvSaveImage("im2.png", xformed);
cvWarpPerspective( im1_tempMask, im1_mask, H, CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS, cvScalarAll( 0 ) );
//cvSaveImage("im1_mask.png", im1_mask);
cvNamedWindow( "im1_mask", 1 );
cvShowImage( "im1_mask", im1_mask );
cvWaitKey( 0 );
cvDestroyWindow("im1_mask");
/* Align image1 to bound */
cvWarpPerspective( im2_tempMask, im2_mask, T, CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS, cvScalarAll( 0 ) );
//cvSaveImage( "im2_mask.png", im2_mask );
cvNamedWindow( "im12_mask", 1 );
cvShowImage( "im2_mask", im2_mask );
cvWaitKey( 0 );
cvDestroyWindow("im2_mask");
cvSetImageROI( xformed, cvRect( tx, ty, img2->width, img2->height ) );
cvCopy( img2, xformed, NULL );
IplImage* huaijin = cvCreateImage( new_size, IPL_DEPTH_8U, 3 );
cvWarpPerspective( img2, huaijin, T, CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS, cvScalarAll( 0 ) );
cvNamedWindow( "im12_mask_i", 1 );
cvShowImage( "im2_mask_i", huaijin);
cvWaitKey( 0 );
cvDestroyWindow("im2_mask_i");
cvResetImageROI( xformed );
//cvSaveImage( "re.png", xformed );
/* composite */
cvNamedWindow( "Xformed", 1 );
cvShowImage( "Xformed", xformed );
cvWaitKey( 0 );
cvDestroyWindow("Xformed");
/* */
cvReleaseImage( &xformed );
cvReleaseMat( &H );
cvReleaseMat( &im1_tempMask );
cvReleaseMat( &im2_tempMask );
cvReleaseMat( &T );
cvReleaseMat ( &im1_mask );
cvReleaseMat ( &im2_mask );
cvReleaseImage( &stacked );
cvReleaseImage( &img1 );
cvReleaseImage( &img2 );
kdtree_release( kd_root );
free( feat1 );
free( feat2 );
/****************************************************
* using RANSAC algorithm get the Homegraphy Matrix
* get T image1-->image2
*
* n_pts the number of points for estimating parameters
*
*
*
* create unique indics of matchs : get_randi( int j )
*
*
* ******slove***********
* 1.create the coff matrix
* 2.Ah=0 -->decomposit A = UDV^T using gsl
* 3
* [ v19 v29 v39 ... v99 ]
* h = -------------------------
* v99
*
* ***************************************************/
/*
CvMat *H1 = cvCreateMat( 3, 3, CV_64FC1 );
CvMat *inliers_mask = cvCreateMat( m, 1, CV_64FC1 );
RANSAC_Homography( m, pts1, pts2, H1, inliers_mask );
printf("my code Homography Matrix is :\n");
for ( i = 0; i < H->rows; i++ ){
for( k = 0; k < H->cols; k++ ){
printf("%f ",cvmGet( H1, i, k ));
}
printf("\n");
}
cvReleaseMat( &H1 );
cvReleaseMat( &inliers_mask );*/
/***********************************************
* composit image1 & image2
* 1) transform image2 to image
*
* 2)***stitched image bounds****
* W = max( [size(im1,2) size(im1,2)-XDATA(1) size(im2,2) size(im2,2)+XDATA(1)] );
* H = max( [size(im1,1) size(im1,1)-YDATA(1) size(im2,1) size(im2,1)+YDATA(1)] );
*
* 3)*** Align image1 to bound ***
*
* 4)*** Align image2 to bound ***
*
* 5)*** Check size of bounds ***
*
* 6)*** combine both images ***
* im1_mask
* im2_mask
* im1_part_mask
* im2_part_mask
* com_part_mask
* stitched_image
*
* 7)****copy im2 transformed to ROI of stitching plan ( just a idear )
*
************************************************/
void compositImages( IplImage *im1, IplImage *im2, CvMat *H )
{
/* 1.create a plan */
/* 2.transform im2 & im2_mask */
/* 3.emstime translation of im2 --T cp im1 -->plan with cvRect( tx, ty) */
/* 4.cp tranformed im2 to plan with im2_mask where im2_mask has the same size with plan */
}
return 0;
}
int main( int argc, char** argv )
{
int KDTREE_BBF_MAX_NN_CHKS = 200;
float NN_SQ_DIST_RATIO_THR = 0.49;
const CvScalar color = cvScalar( 255, 255, 0 );
ros::init(argc, argv, "SIFT");
ros::NodeHandle n;
ros::Rate rate(33);
ImageConverter ic;
while ( !ic.ready )
{
ros::spinOnce();
rate.sleep();
if ( !ros::ok() )
{
printf("terminated by control_c\n");
return 0;
}
}
string filepath = ros::package::getPath("sift") + "/";
ifstream fin( (filepath + "store.txt").data(), ios::in);
//ifstream fin_main_pic( (filepath + "main_pic.txt").data(), ios::in);
int pic_num = 5;
string find;
//cout << "how many pictures?" << endl;
//cin >> pic_num;
//cout << "which picture?" << endl;
//cin >> find;
time_t rawtime;
struct tm * timeinfo;
time ( &rawtime );
timeinfo = localtime ( &rawtime );
printf ( "The current date/time is: %s", asctime (timeinfo) );
char line[1024] = {0};
string* store = new string [pic_num+1];
string main_pic_name;
int pic = 0;
int find_pic = 0;
while(fin.getline(line, sizeof(line)))
{
stringstream word(line);
word >> store[pic];
store[pic] = filepath + store[pic];
/*if (store[pic] == find)
{
cout << store[pic] << endl;
find_pic = pic;
}*/
pic++;
}
//fin_main_pic.getline(line, sizeof(line));
//stringstream word(line);
//word >> main_pic_name;
//fin_main_pic.clear();
//fin_main_pic.close();
fin.clear();
fin.close();
IplImage* img;
//IplImage* img1;
struct feature* features;//, * features1;
feature** features_all = new feature*[pic];
int* features_num = new int[pic];
for (int i = 0; i < pic; i++ )
features_num[i] = 0;
IplImage** img_all = new IplImage*[pic];
for ( int i = 0; i < pic; i++ )
{
//printf ( "Finding features in template picture %d\n", i );
img_all[i] = cvLoadImage( store[i].data(), 1 );
features_num[i] = sift_features( img_all[i], &features_all[i] );
printf ( "%d features in template picture %d\n", features_num[i], i );
time ( &rawtime );
timeinfo = localtime ( &rawtime );
printf ( "The current date/time is: %s", asctime (timeinfo) );
}
/*
printf ( "Finding features in main picture\n" );
img = cvLoadImage( main_pic_name.data(), 1 );
int n1 = sift_features( img, &features );
printf ( "%d features in main picture\n", n1 );
*/
//cvShowImage( "main", img );
//for (int i = 0; i < n1; i++)
//cvCircle( img, cvPoint(features[i].x, features[i].y), 5, color, 1, 8, 0 );
//cvShowImage( "Foundmain", img );
//cvShowImage( "template", img1 );
//for (int i = 0; i < n2; i++)
//cvCircle( img1, cvPoint(features1[i].x, features1[i].y), 5, color, 1, 8, 0 );
//cvShowImage( "Foundtemplate", img1 );
bool features_catched = false;
while ( ros::ok() )
{
if ( ic.ready == true )
{
ic.ready = false;
*img = ic.curr_image;
int n1 = sift_features( img, &features );
printf ( "%d features in main picture\n", n1 );
time ( &rawtime );
timeinfo = localtime ( &rawtime );
printf ( "The current date/time is: %s", asctime (timeinfo) );
features_catched = false;
for ( int j = 0; j < pic ; j++ )
{
IplImage* stacked;
IplImage* ransac;
struct feature* feat;
struct feature** nbrs;
struct kd_node* kd_root;
CvPoint pt1, pt2;
double d0, d1;
int k, i, m = 0;
CvMat point1_test;
CvMat point2_test;
double point1[3];
double point2[3] = { 0 };
stacked = stack_imgs( img, img_all[j] );
ransac = stack_imgs( img, img_all[j] );
kd_root = kdtree_build( features_all[j], features_num[j] );
for( i = 0; i < n1; i++ )
{
feat = features + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{
//pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
//pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
//pt2.y += img->height;
//cvCircle( stacked, pt1, 3, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
//cvCircle( stacked, pt2, 3, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
//cvLine( stacked, pt1, pt2, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
m++;
features[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
double accounts = m * 100 / (double)features_num[j];
printf( "%d total matches, accounts for %f %%, in pic %d\n", m, accounts, j);
//cvNamedWindow( "Matches", 1 );
//cvShowImage( "Matches", stacked );
time ( &rawtime );
timeinfo = localtime ( &rawtime );
printf ( "The current date/time is: %s", asctime (timeinfo) );
CvMat* H = ransac_xform( features, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01, error, 2, NULL, NULL );
if( H )
{
for( i = 0; i < n1; i++ )
{
feat = features + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 )
{
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR )
{
pt1 = cvPoint( cvRound( feat->x ), cvRound( feat->y ) );
pt2 = cvPoint( cvRound( nbrs[0]->x ), cvRound( nbrs[0]->y ) );
pt2.y += img->height;
point1[0] = pt1.x;
point1[1] = pt1.y;
point1[2] = 1.0;
cvInitMatHeader( &point1_test, 3, 1, CV_64FC1, point1, CV_AUTOSTEP );
cvInitMatHeader( &point2_test, 3, 1, CV_64FC1, point2, CV_AUTOSTEP );
cvMatMul( H, &point1_test, &point2_test );
/*if ( abs( point2[0]/point2[2]-pt2.x) < 2 && abs( point2[1]/point2[2]+img->height-pt2.y) < 2 )
{
cvCircle( ransac, pt1, 3, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
cvCircle( ransac, pt2, 3, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
cvLine( ransac, pt1, pt2, cvScalar( (i*10)%255, (i*10)%255, 127 ), 1, 8, 0 );
}*/
// features[i].fwd_match = nbrs[0];
}
}
free( nbrs );
//printf("features catched, going to exit\n");
}
//cvNamedWindow( "Xformed" );
//cvShowImage( "Xformed", ransac );
features_catched = true;
time ( &rawtime );
timeinfo = localtime ( &rawtime );
printf ( "ransac.. The current date/time is: %s", asctime (timeinfo) );
}
//cvWaitKey( 0 );
cvReleaseImage( &ransac );
cvReleaseMat( &H );
//cvDestroyWindow( "main" );
//cvDestroyWindow( "Foundmain" );
//cvDestroyWindow( "template" );
//cvDestroyWindow( "Foundtemplate" );
//cvReleaseImage( &img_all[j] );
cvReleaseImage( &stacked );
kdtree_release( kd_root );
}
if (!features_catched)
{
printf("Sorry, there is no item in the picture\n");
}
else
{
printf("Item catched in the picture!\n");
}
}
ros::spinOnce();
rate.sleep();
}
//cvReleaseImage( &img );
free( features );
for ( int i = 0; i < pic; i++ )
{
free( features_all[i] );
}
free(features_all);
return 0;
}
int main( int argc, char** argv ) {
struct feature* feat1, * feat2, * feat;
struct feature** nbrs;
struct kd_node* kd_root;
double d0, d1;
int n1, n2, k, i, m = 0;
if( argc != 3 )
fatal_error( "usage: %s <keydescr1> <keydescr2>", argv[0] );
n1 = import_features( argv[1], feat_type, &feat1 );
n2 = import_features( argv[2], feat_type, &feat2 );
if( n1 < 0 )
fatal_error( "unable to load key descriptors from %s", argv[1] );
if( n2 < 0 )
fatal_error( "unable to load key descriptors from %s", argv[2] );
printf("%d features presenti nel modello.\n", n1);
printf("%d features presenti nell'immagine.\n", n2);
kd_root = kdtree_build( feat2, n2 );
for( i = 0; i < n1; i++ ) {
feat = feat1 + i;
k = kdtree_bbf_knn( kd_root, feat, 2, &nbrs, KDTREE_BBF_MAX_NN_CHKS );
if( k == 2 ) {
// Controlla
d0 = descr_dist_sq( feat, nbrs[0] );
d1 = descr_dist_sq( feat, nbrs[1] );
if( d0 < d1 * NN_SQ_DIST_RATIO_THR ) {
m++;
feat1[i].fwd_match = nbrs[0];
}
}
free( nbrs );
}
#ifdef RANSAC
{
CvMat* H;
IplImage* xformed;
struct feature ** inliers;
int n_inliers;
H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01, homog_xfer_err, 8.0, &inliers, &n_inliers );
if( H )
{
if ( cvmGet( H, 1, 2 ) < 120.0 &&
cvmGet( H, 0, 2 ) < 120.0 &&
(
fabs(cvmGet( H, 0, 0)) >= 0.000001 ||
fabs(cvmGet( H, 0, 1)) >= 0.000001 ||
fabs(cvmGet( H, 1, 0)) >= 0.000001 ||
fabs(cvmGet( H, 1, 1)) >= 0.000001 ||
fabs(cvmGet( H, 2, 0)) >= 0.000001 ||
fabs(cvmGet( H, 2, 1)) >= 0.000001
)
) {
// TROVATO
printf("RANSAC OK\n");
/*
xformed = cvCreateImage( cvGetSize( img2 ), IPL_DEPTH_8U, 3 );
cvWarpPerspective( img1, xformed, H, CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS, cvScalarAll( 0 ) );
cvNamedWindow( "Xformed", 1 );
cvShowImage( "Xformed", xformed );
cvWaitKey( 0 );
cvReleaseImage( &xformed );
*/
printf("N. inliers: %d\n", n_inliers);
}
else {
// Trovato ma probabilmente la matrice di trasformazione e' sbagliata.
printf("RANSAC FAIL\n");
}
cvReleaseMat( &H );
}
else {
printf("RANSAC FAIL\n");
}
}
#endif
fprintf( stderr, "Found %d total matches\n", m );
kdtree_release( kd_root );
free( feat1 );
free( feat2 );
return 0;
}
