int CUtil_Sift::GetSiftFile( const char* imagefile,const char* siftfile )
{
int intvls = SIFT_INTVLS;
double sigma = SIFT_SIGMA;
double contr_thr = 0.2;
int curv_thr = SIFT_CURV_THR;
int img_dbl = SIFT_IMG_DBL;
int descr_width = SIFT_DESCR_WIDTH;
int descr_hist_bins = SIFT_DESCR_HIST_BINS;
IplImage* img;
struct feature* features;
int n = 0;
if ( !imagefile || !siftfile )
{
return -1;
}
if ( strlen(imagefile)<1 || strlen(siftfile)<1 )
{
return -1;
}
img = cvLoadImage( imagefile,1);
if( !img )
{
return -1;
}
fprintf(stderr,"load image %s\n",imagefile);
n = _sift_features( img, &features, intvls, sigma, contr_thr, curv_thr,
img_dbl, descr_width, descr_hist_bins );
export_features( (char*)siftfile, features, n );
return n;
}
int main( int argc, char** argv )
{
IplImage* img;
struct feature* features;
int n = 0;
fprintf( stderr, "Finding SIFT features...\n" );
img = cvLoadImage( img_file_name, 1 );
if( ! img )
{
fprintf( stderr, "unable to load image from %s", img_file_name );
exit( 1 );
}
n = _sift_features( img, &features, intvls, sigma, contr_thr, curv_thr,
img_dbl, descr_width, descr_hist_bins );
fprintf( stderr, "Found %d features.\n", n );
if( display )
{
draw_features( img, features, n );
cvNamedWindow( img_file_name, 1 );
cvShowImage( img_file_name, img );
cvWaitKey( 0 );
}
if( out_file_name != NULL )
export_features( out_file_name, features, n );
if( out_img_name != NULL )
cvSaveImage( out_img_name, img );
return 0;
}
/**
Finds SIFT features in an image using default parameter values. All
detected features are stored in the array pointed to by \a feat.
@param img the image in which to detect features
@param feat a pointer to an array in which to store detected features
@return Returns the number of features stored in \a feat or -1 on failure
@see _sift_features()
*/
int sift_features( IplImage* img, struct feature** feat )
{
//调用_sift_features()函数进行特征点检测
return _sift_features( img, feat, SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR,
SIFT_CURV_THR, SIFT_IMG_DBL, SIFT_DESCR_WIDTH,
SIFT_DESCR_HIST_BINS );
}
int main2( int argc, char** argv )
{
IplImage* img;
struct feature* features;
int n = 0;
arg_parse( argc, argv );
fprintf( stderr, "Finding SIFT features...\n" );
img = cvLoadImage( img_file_name, 1 );
if( ! img )
fatal_error( "unable to load image from %s", img_file_name );
n = _sift_features( img, &features, intvls, sigma, contr_thr, curv_thr,
img_dbl, descr_width, descr_hist_bins );
fprintf( stderr, "Found %d features.\n", n );
if( display )
{
draw_features( img, features, n );
display_big_img( img, img_file_name );
cvWaitKey( 0 );
}
if( out_file_name != NULL )
export_features( out_file_name, features, n );
if( out_img_name != NULL )
cvSaveImage( out_img_name, img, NULL );
cvReleaseImage( &img );
return 0;
}
// [ref] ${OPENSIFT_HOME}/src/siftfeat.c
void extract_feature()
{
#if 1
const std::string in_img_file_name("./data/feature_analysis/sift/beaver.png");
const std::string out_sift_file_name("./data/feature_analysis/sift/beaver.sift");
const std::string out_img_file_name;
#elif 0
const std::string in_img_file_name("./data/feature_analysis/sift/marker_pen_2.bmp");
const std::string out_sift_file_name("./data/feature_analysis/sift/marker_pen_2.sift");
const std::string out_img_file_name;
#endif
const int display = 1;
const int intvls = SIFT_INTVLS;
const double sigma = SIFT_SIGMA;
const double contr_thr = SIFT_CONTR_THR;
const int curv_thr = SIFT_CURV_THR;
const int img_dbl = SIFT_IMG_DBL;
const int descr_width = SIFT_DESCR_WIDTH;
const int descr_hist_bins = SIFT_DESCR_HIST_BINS;
std::cout << "finding SIFT features..." << std::endl;
IplImage *img = cvLoadImage(in_img_file_name.c_str(), 1);
if (!img)
{
std::cout <<"unable to load image from " << in_img_file_name << std::endl;
return;
}
struct feature *features;
const int n = _sift_features(img, &features, intvls, sigma, contr_thr, curv_thr, img_dbl, descr_width, descr_hist_bins);
std::cout << "found " << n << " features." << std::endl;
if (display)
{
draw_features(img, features, n);
cvNamedWindow(in_img_file_name.c_str(), 1);
cvShowImage(in_img_file_name.c_str(), img);
cvWaitKey(0);
}
if (!out_sift_file_name.empty())
export_features((char *)out_sift_file_name.c_str(), features, n);
if (!out_img_file_name.empty())
cvSaveImage(out_img_file_name.c_str(), img);
}
/**
* Apply SIFT algorithm to an image
*
* @return 0 on failure, nonzero on success
*/
int sift(const char *path, struct feature **features, int *num_features)
{
IplImage *img;
static const int MAX_SIZE = 800;
img = cvLoadImage(path, CV_LOAD_IMAGE_COLOR);
if(!img) {
fprintf(stderr, "%s: I/O error\n", path);
return 0;
}
if(img->width > MAX_SIZE || img->height > MAX_SIZE) {
IplImage *scaled = cvCreateImage(cvSize(img->width > MAX_SIZE ? MAX_SIZE : img->width,
img->height > MAX_SIZE ? MAX_SIZE : img->height),
img->depth, img->nChannels);
if(!scaled) {
fprintf(stderr, "%s: cvCreateImage failed (out of memory?)\n", path);
cvReleaseImage(&img);
return 0;
}
cvResize(img, scaled, CV_INTER_LINEAR);
cvReleaseImage(&img);
img = scaled;
}
(*num_features) = _sift_features(img, features, SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTRAST_THRESHOLD,
SIFT_CURV_THR, SIFT_IMG_DBL, SIFT_DESCR_WIDTH,
SIFT_DESCR_HIST_BINS);
cvReleaseImage(&img);
if(!(*features) || !(*num_features)) {
fprintf(stderr, "%s: No features found\n", path);
return 0;
}
return 1;
}
bool BagOfFeatures::extractSIFTFeatures(int lvls = SIFT_INTVLS,
double sigma = SIFT_SIGMA,
double thresh1 = SIFT_CONTR_THR,
int thresh2 = SIFT_CURV_THR,
int dbl = SIFT_IMG_DBL,
int width = SIFT_DESCR_WIDTH,
int bins = SIFT_DESCR_HIST_BINS)
{
if(numFeatures)
return false;
int i, j;
int train, valid, test, label;
int count;
char fileName[256];
IplImage *dataImage = NULL;
struct feature *siftFeatures = NULL;
descrSize = width*width*bins;
// For each object class
for(i = 0; i < numClasses; i++)
{
// Get the distribution of data
data[i].getDataInfo(train, valid, test, label);
// Extrain the features of the training set
// For each training image
for(j = 0; j < train; j++)
{
// Get the image from the data list
strcpy(fileName, data[i].getDataList(j));
cout << "Loading training image: " << fileName << endl;
dataImage = cvLoadImage(fileName);
IplImage *dataGray = cvCreateImage(cvSize(dataImage->width, dataImage->height), 8, 1);
// Convert to grayscale
cvCvtColor(dataImage, dataGray, CV_BGR2GRAY);
IplImage *resized = preProcessImages(dataGray, 80, 200);
// Extract the sift features from the images
//Default: SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR, SIFT_CURV_THR,
// SIFT_IMG_DBL, SIFT_DESCR_WIDTH, SIFT_DESCR_HIST_BINS
count = _sift_features(resized, &siftFeatures, lvls, sigma, thresh1, thresh2, dbl, width, bins);
cout << "Found " << count << " SIFT interest points" << endl;
// Keep a running total of features for training purposes
numFeatures += count;
// Copy the descriptors into the feature set
copySIFTPts(trainObject[i].featureSet[j], siftFeatures, count, descrSize);
// Release Memory
free(siftFeatures);
cvReleaseImage(&dataImage);
cvReleaseImage(&dataGray);
cvReleaseImage(&resized);
}
// Extrain the features of the validation set
// For each validation image
for(j = 0; j < valid; j++)
{
// Get the image from the data list
strcpy(fileName, data[i].getDataList(j+train));
cout << "Loading validation image: " << fileName << endl;
dataImage = cvLoadImage(fileName);
IplImage *dataGray = cvCreateImage(cvSize(dataImage->width, dataImage->height), 8, 1);
// Convert to grayscale
cvCvtColor(dataImage, dataGray, CV_BGR2GRAY);
IplImage *resized = preProcessImages(dataGray, 80, 200);
// Extract the sift features from the images
//Default: SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR, SIFT_CURV_THR,
// SIFT_IMG_DBL, SIFT_DESCR_WIDTH, SIFT_DESCR_HIST_BINS
count = _sift_features(resized, &siftFeatures, lvls, sigma, thresh1, thresh2, dbl, width, bins);
cout << "Found " << count << " SIFT interest points" << endl;
// Copy the descriptors into the feature set
copySIFTPts(validObject[i].featureSet[j], siftFeatures, count, descrSize);
// Release Memory
free(siftFeatures);
cvReleaseImage(&dataImage);
cvReleaseImage(&dataGray);
cvReleaseImage(&resized);
}
// Extrain the features of the test set
// For each test image
for(j = 0; j < test; j++)
{
// Get the image from the data list
strcpy(fileName, data[i].getDataList(j+train+valid));
cout << "Loading test image: " << fileName << endl;
dataImage = cvLoadImage(fileName);
IplImage *dataGray = cvCreateImage(cvSize(dataImage->width, dataImage->height), 8, 1);
// Convert to grayscale
cvCvtColor(dataImage, dataGray, CV_BGR2GRAY);
IplImage *resized = preProcessImages(dataGray, 80, 200);
// Extract the sift features from the images
//Default: SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR, SIFT_CURV_THR,
// SIFT_IMG_DBL, SIFT_DESCR_WIDTH, SIFT_DESCR_HIST_BINS
count = _sift_features(resized, &siftFeatures, lvls, sigma, thresh1, thresh2, dbl, width, bins);
cout << "Found " << count << " SIFT interest points" << endl;
// Copy the descriptors into the feature set
copySIFTPts(testObject[i].featureSet[j], siftFeatures, count, descrSize);
// Release Memory
free(siftFeatures);
cvReleaseImage(&dataImage);
cvReleaseImage(&dataGray);
cvReleaseImage(&resized);
}
}
return true;
}