generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Debug message is turned off

Browse Source
This commit is contained in:
Andrey Kamaev 2012-06-29 08:47:38 +00:00
parent 0584ac525b
commit 2b6b6f12e2
1 changed files with 18 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
modules/stitching/src/matchers.cpp
View File

@ -273,14 +273,14 @@ namespace cv {
namespace detail {
void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
{
{
find(image, features);
features.img_size = image.size();
}
void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features, const vector<Rect> &rois)
{
{
vector<ImageFeatures> roi_features(rois.size());
size_t total_kps_count = 0;
int total_descriptors_height = 0;
@ -294,8 +294,8 @@ void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features, cons
features.img_size = image.size();
features.keypoints.resize(total_kps_count);
features.descriptors.create(total_descriptors_height,
roi_features[0].descriptors.cols,
features.descriptors.create(total_descriptors_height,
roi_features[0].descriptors.cols,
roi_features[0].descriptors.type());
int kp_idx = 0;
@ -332,14 +332,14 @@ SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int
{
detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
if( detector_.empty() || extractor_.empty() )
CV_Error( CV_StsNotImplemented, "OpenCV was built without SURF support" );
detector_->set("hessianThreshold", hess_thresh);
detector_->set("nOctaves", num_octaves);
detector_->set("nOctaveLayers", num_layers);
extractor_->set("nOctaves", num_octaves_descr);
extractor_->set("nOctaveLayers", num_layers_descr);
}
@ -403,17 +403,17 @@ void OrbFeaturesFinder::find(const Mat &image, ImageFeatures &features)
int xr = (c+1) * gray_image.cols / grid_size.width;
int yr = (r+1) * gray_image.rows / grid_size.height;
LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
<< " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
<< " yl=" << yl << ", yr=" << yr << ", "
<< " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
<< "gray_image.dims=" << gray_image.dims << "\n");
// LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
// << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
// << " yl=" << yl << ", yr=" << yr << ", "
// << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
// << "gray_image.dims=" << gray_image.dims << "\n");
Mat gray_image_part=gray_image(Range(yl, yr), Range(xl, xr));
LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
<< " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
<< " gray_image_part.dims=" << gray_image_part.dims << ", "
<< " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
// LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
// << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
// << " gray_image_part.dims=" << gray_image_part.dims << ", "
// << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
(*orb)(gray_image_part, Mat(), points, descriptors);
@ -583,11 +583,11 @@ void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFea
if (matches_info.inliers_mask[i])
matches_info.num_inliers++;
// These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching
// These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching
// using Invariant Features"
matches_info.confidence = matches_info.num_inliers / (8 + 0.3 * matches_info.matches.size());
// Set zero confidence to remove matches between too close images, as they don't provide
// Set zero confidence to remove matches between too close images, as they don't provide
// additional information anyway. The threshold was set experimentally.
matches_info.confidence = matches_info.confidence > 3. ? 0. : matches_info.confidence;