Added ASIFT (to activate, check parameter Feature2D/Affine) (fixed issue 27)
git-svn-id: http://find-object.googlecode.com/svn/trunk/find_object@385 620bd6b2-0a58-f614-fd9a-1bd335dccda9
This commit is contained in:
matlabbe
parent
dcf406429e
commit
629b2aefaa
@ -102,6 +102,7 @@ void showUsage()
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" --objects \"path\" Directory of the objects to detect.\n"
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" --config \"path\" Path to configuration file (default: %s).\n"
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" --scene \"path\" Path to a scene image file.\n"
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" --debug Show debug log.\n"
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" --params Show all parameters.\n"
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" --My/Parameter \"value\" Set find-Object's parameter (look --params for parameters' name).\n"
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" It will override the one in --config. Example to set 4 threads:\n"
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@ -209,6 +210,13 @@ int main(int argc, char* argv[])
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guiMode = false;
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continue;
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}
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if(strcmp(argv[i], "-debug") == 0 ||
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strcmp(argv[i], "--debug") == 0)
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{
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ULogger::setPrintWhere(true);
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ULogger::setLevel(ULogger::kDebug);
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continue;
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}
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if(strcmp(argv[i], "-help") == 0 ||
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strcmp(argv[i], "--help") == 0)
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{
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@ -42,6 +42,7 @@ public:
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enum TimeStamp{
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kTimeKeypointDetection,
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kTimeDescriptorExtraction,
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kTimeSkewAffine,
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kTimeIndexing,
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kTimeMatching,
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kTimeHomography,
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@ -53,6 +53,13 @@ class DescriptorExtractor;
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class FINDOBJECT_EXP FindObject : public QObject
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{
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Q_OBJECT;
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public:
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static void affineSkew(float tilt,
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float phi,
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const cv::Mat & image,
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cv::Mat & skewImage,
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cv::Mat & skewMask,
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cv::Mat & Ai);
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public:
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FindObject(QObject * parent = 0);
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@ -97,6 +97,8 @@ class FINDOBJECT_EXP Settings
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PARAMETER(Feature2D, 1Detector, QString, "7:Dense;Fast;GFTT;MSER;ORB;SIFT;Star;SURF;BRISK" , "Keypoint detector.");
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PARAMETER(Feature2D, 2Descriptor, QString, "3:Brief;ORB;SIFT;SURF;BRISK;FREAK", "Keypoint descriptor.");
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PARAMETER(Feature2D, 3MaxFeatures, int, 0, "Maximum features per image. If the number of features extracted is over this threshold, only X features with the highest response are kept. 0 means all features are kept.");
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PARAMETER(Feature2D, 4Affine, bool, false, "(ASIFT) Extract features on multiple affine transformations of the image.");
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PARAMETER(Feature2D, 5AffineCount, int, 6, "(ASIFT) Higher the value, more affine transformations will be done.");
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PARAMETER(Feature2D, Brief_bytes, int, 32, "Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.");
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@ -205,7 +207,7 @@ class FINDOBJECT_EXP Settings
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PARAMETER(General, autoStartCamera, bool, false, "Automatically start the camera when the application is opened.");
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PARAMETER(General, autoUpdateObjects, bool, true, "Automatically update objects on every parameter changes, otherwise you would need to press \"Update objects\" on the objects panel.");
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PARAMETER(General, nextObjID, uint, 1, "Next object ID to use.");
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PARAMETER(General, imageFormats, QString, "*.png *.jpg *.bmp *.tiff *.ppm", "Image formats supported.");
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PARAMETER(General, imageFormats, QString, "*.png *.jpg *.bmp *.tiff *.ppm *.pgm", "Image formats supported.");
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PARAMETER(General, videoFormats, QString, "*.avi *.m4v *.mp4", "Video formats supported.");
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PARAMETER(General, mirrorView, bool, true, "Flip the camera image horizontally (like all webcam applications).");
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PARAMETER(General, invertedSearch, bool, true, "Instead of matching descriptors from the objects to those in a vocabulary created with descriptors extracted from the scene, we create a vocabulary from all the objects' descriptors and we match scene's descriptors to this vocabulary. It is the inverted search mode.");
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@ -283,7 +285,9 @@ public:
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KeypointDetector(cv::FeatureDetector * featureDetector);
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KeypointDetector(GPUFeature2D * gpuFeature2D);
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void detect(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints);
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void detect(const cv::Mat & image,
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std::vector<cv::KeyPoint> & keypoints,
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const cv::Mat & mask = cv::Mat());
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private:
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cv::FeatureDetector * featureDetector_;
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@ -202,61 +202,275 @@ std::vector<cv::KeyPoint> limitKeypoints(const std::vector<cv::KeyPoint> & keypo
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return kptsKept;
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}
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// taken from ASIFT example https://github.com/Itseez/opencv/blob/master/samples/python2/asift.py
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// affine - is an affine transform matrix from skew_img to img
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void FindObject::affineSkew(
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float tilt,
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float phi,
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const cv::Mat & image,
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cv::Mat & skewImage,
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cv::Mat & skewMask,
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cv::Mat & Ai)
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{
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float h = image.rows;
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float w = image.cols;
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cv::Mat A = cv::Mat::zeros(2,3,CV_32FC1);
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A.at<float>(0,0) = A.at<float>(1,1) = 1;
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skewMask = cv::Mat::ones(h, w, CV_8U) * 255;
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if(phi != 0.0)
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{
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phi = phi*CV_PI/180.0f; // deg2rad
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float s = std::sin(phi);
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float c = std::cos(phi);
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cv::Mat A22 = (cv::Mat_<float>(2, 2) <<
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c, -s,
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s, c);
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cv::Mat cornersIn = (cv::Mat_<float>(4, 2) <<
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0,0,
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w,0,
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w,h,
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0,h);
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cv::Mat cornersOut = cornersIn * A22.t();
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cv::Rect rect = cv::boundingRect(cornersOut.reshape(2,4));
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A = (cv::Mat_<float>(2, 3) <<
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c, -s, -rect.x,
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s, c, -rect.y);
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cv::warpAffine(image, skewImage, A, cv::Size(rect.width, rect.height), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
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}
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else
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{
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skewImage = image;
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}
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if(tilt != 1.0)
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{
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float s = 0.8*std::sqrt(tilt*tilt-1);
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cv::Mat out, out2;
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cv::GaussianBlur(skewImage, out, cv::Size(0, 0), s, 0.01);
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cv::resize(out, out2, cv::Size(0, 0), 1.0/tilt, 1.0, cv::INTER_NEAREST);
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skewImage = out2;
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A.row(0) /= tilt;
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}
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if(phi != 0.0 || tilt != 1.0)
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{
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cv::Mat mask = skewMask;
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cv::warpAffine(mask, skewMask, A, skewImage.size(), cv::INTER_NEAREST);
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}
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cv::invertAffineTransform(A, Ai);
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}
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class AffineExtractionThread : public QThread
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{
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public:
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AffineExtractionThread(
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KeypointDetector * detector,
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DescriptorExtractor * extractor,
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const cv::Mat & image,
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float tilt,
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float phi) :
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detector_(detector),
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extractor_(extractor),
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image_(image),
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tilt_(tilt),
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phi_(phi),
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timeSkewAffine_(0),
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timeDetection_(0),
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timeExtraction_(0)
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{
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UASSERT(detector && extractor);
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}
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const cv::Mat & image() const {return image_;}
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const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
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const cv::Mat & descriptors() const {return descriptors_;}
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int timeSkewAffine() const {return timeSkewAffine_;}
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int timeDetection() const {return timeDetection_;}
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int timeExtraction() const {return timeExtraction_;}
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protected:
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virtual void run()
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{
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QTime timeStep;
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timeStep.start();
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cv::Mat skewImage, skewMask, Ai;
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FindObject::affineSkew(tilt_, phi_, image_, skewImage, skewMask, Ai);
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timeSkewAffine_=timeStep.restart();
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//Detect features
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detector_->detect(skewImage, keypoints_, skewMask);
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if(keypoints_.size())
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{
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int maxFeatures = Settings::getFeature2D_3MaxFeatures();
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if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
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{
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keypoints_ = limitKeypoints(keypoints_, maxFeatures);
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}
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timeDetection_=timeStep.restart();
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//Extract descriptors
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extractor_->compute(skewImage, keypoints_, descriptors_);
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timeExtraction_=timeStep.restart();
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// Transform points to original image coordinates
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for(unsigned int i=0; i<keypoints_.size(); ++i)
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{
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cv::Mat p = (cv::Mat_<float>(3, 1) << keypoints_[i].pt.x, keypoints_[i].pt.y, 1);
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cv::Mat pa = Ai * p;
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keypoints_[i].pt.x = pa.at<float>(0,0);
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keypoints_[i].pt.y = pa.at<float>(1,0);
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}
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}
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else
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{
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timeDetection_=timeStep.restart();
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}
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}
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private:
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KeypointDetector * detector_;
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DescriptorExtractor * extractor_;
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cv::Mat image_;
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float tilt_;
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float phi_;
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std::vector<cv::KeyPoint> keypoints_;
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cv::Mat descriptors_;
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int timeSkewAffine_;
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int timeDetection_;
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int timeExtraction_;
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};
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class ExtractFeaturesThread : public QThread
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{
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public:
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ExtractFeaturesThread(int objectId, const cv::Mat & image) :
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ExtractFeaturesThread(
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KeypointDetector * detector,
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DescriptorExtractor * extractor,
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int objectId,
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const cv::Mat & image) :
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detector_(detector),
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extractor_(extractor),
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objectId_(objectId),
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image_(image)
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image_(image),
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timeSkewAffine_(0),
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timeDetection_(0),
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timeExtraction_(0)
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{
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UASSERT(detector && extractor);
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}
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int objectId() const {return objectId_;}
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const cv::Mat & image() const {return image_;}
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const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
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const cv::Mat & descriptors() const {return descriptors_;}
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int timeSkewAffine() const {return timeSkewAffine_;}
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int timeDetection() const {return timeDetection_;}
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int timeExtraction() const {return timeExtraction_;}
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protected:
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virtual void run()
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{
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QTime time;
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time.start();
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UINFO("Extracting descriptors from object %d...", objectId_);
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KeypointDetector * detector = Settings::createKeypointDetector();
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keypoints_.clear();
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descriptors_ = cv::Mat();
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detector->detect(image_, keypoints_);
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delete detector;
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if(keypoints_.size())
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QTime timeStep;
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timeStep.start();
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if(!Settings::getFeature2D_4Affine())
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{
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int maxFeatures = Settings::getFeature2D_3MaxFeatures();
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if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
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{
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int previousCount = (int)keypoints_.size();
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keypoints_ = limitKeypoints(keypoints_, maxFeatures);
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UINFO("obj=%d, %d keypoints removed, (kept %d), min/max response=%f/%f", objectId_, previousCount-(int)keypoints_.size(), (int)keypoints_.size(), keypoints_.size()?keypoints_.back().response:0.0f, keypoints_.size()?keypoints_.front().response:0.0f);
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}
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keypoints_.clear();
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descriptors_ = cv::Mat();
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detector_->detect(image_, keypoints_);
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DescriptorExtractor * extractor = Settings::createDescriptorExtractor();
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extractor->compute(image_, keypoints_, descriptors_);
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delete extractor;
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if((int)keypoints_.size() != descriptors_.rows)
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if(keypoints_.size())
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{
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UERROR("obj=%d kpt=%d != descriptors=%d", objectId_, (int)keypoints_.size(), descriptors_.rows);
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int maxFeatures = Settings::getFeature2D_3MaxFeatures();
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if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
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{
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int previousCount = (int)keypoints_.size();
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keypoints_ = limitKeypoints(keypoints_, maxFeatures);
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UDEBUG("obj=%d, %d keypoints removed, (kept %d), min/max response=%f/%f", objectId_, previousCount-(int)keypoints_.size(), (int)keypoints_.size(), keypoints_.size()?keypoints_.back().response:0.0f, keypoints_.size()?keypoints_.front().response:0.0f);
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}
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timeDetection_+=timeStep.restart();
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extractor_->compute(image_, keypoints_, descriptors_);
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timeExtraction_+=timeStep.restart();
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if((int)keypoints_.size() != descriptors_.rows)
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{
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UERROR("obj=%d kpt=%d != descriptors=%d", objectId_, (int)keypoints_.size(), descriptors_.rows);
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}
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}
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else
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{
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timeDetection_+=timeStep.restart();
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UWARN("no features detected in object %d !?!", objectId_);
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}
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}
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else
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{
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UWARN("no features detected in object %d !?!", objectId_);
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//ASIFT
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std::vector<float> tilts;
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std::vector<float> phis;
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tilts.push_back(1.0f);
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phis.push_back(0.0f);
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int nTilt = Settings::getFeature2D_5AffineCount();
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for(int t=1; t<nTilt; ++t)
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{
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float tilt = std::pow(2.0f, 0.5f*float(t));
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float inc = 72.0f / float(tilt);
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for(float phi=0.0f; phi<180.0f; phi+=inc)
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{
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tilts.push_back(tilt);
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phis.push_back(phi);
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}
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}
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//multi-threaded
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unsigned int threadCounts = Settings::getGeneral_threads();
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if(threadCounts == 0)
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{
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threadCounts = tilts.size();
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}
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for(unsigned int i=0; i<tilts.size(); i+=threadCounts)
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{
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QVector<AffineExtractionThread*> threads;
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for(unsigned int k=i; k<i+threadCounts && k<tilts.size(); ++k)
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{
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threads.push_back(new AffineExtractionThread(detector_, extractor_, image_, tilts[k], phis[k]));
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threads.back()->start();
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}
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for(int k=0; k<threads.size(); ++k)
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{
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threads[k]->wait();
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keypoints_.insert(keypoints_.end(), threads[k]->keypoints().begin(), threads[k]->keypoints().end());
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descriptors_.push_back(threads[k]->descriptors());
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timeSkewAffine_ += threads[k]->timeSkewAffine();
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timeDetection_ += threads[k]->timeDetection();
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timeExtraction_ += threads[k]->timeExtraction();
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}
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}
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}
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UINFO("%d descriptors extracted from object %d (in %d ms)", descriptors_.rows, objectId_, time.elapsed());
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}
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private:
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KeypointDetector * detector_;
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DescriptorExtractor * extractor_;
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int objectId_;
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cv::Mat image_;
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std::vector<cv::KeyPoint> keypoints_;
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cv::Mat descriptors_;
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int timeSkewAffine_;
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int timeDetection_;
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int timeExtraction_;
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};
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void FindObject::updateObjects()
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@ -278,7 +492,7 @@ void FindObject::updateObjects()
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QVector<ExtractFeaturesThread*> threads;
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for(int k=i; k<i+threadCounts && k<objectsList.size(); ++k)
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{
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threads.push_back(new ExtractFeaturesThread(objectsList.at(k)->id(), objectsList.at(k)->image()));
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threads.push_back(new ExtractFeaturesThread(detector_, extractor_, objectsList.at(k)->id(), objectsList.at(k)->image()));
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threads.back()->start();
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}
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@ -677,30 +891,15 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
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grayscaleImg = image;
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}
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QTime time;
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time.start();
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// EXTRACT KEYPOINTS
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detector_->detect(grayscaleImg, info.sceneKeypoints_);
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info.timeStamps_.insert(DetectionInfo::kTimeKeypointDetection, time.restart());
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bool emptyScene = info.sceneKeypoints_.size() == 0;
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if(info.sceneKeypoints_.size())
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{
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int maxFeatures = Settings::getFeature2D_3MaxFeatures();
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if(maxFeatures > 0 && (int)info.sceneKeypoints_.size() > maxFeatures)
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{
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info.sceneKeypoints_ = limitKeypoints(info.sceneKeypoints_, maxFeatures);
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}
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// EXTRACT DESCRIPTORS
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extractor_->compute(grayscaleImg, info.sceneKeypoints_, info.sceneDescriptors_);
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if((int)info.sceneKeypoints_.size() != info.sceneDescriptors_.rows)
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{
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UERROR("kpt=%d != descriptors=%d", (int)info.sceneKeypoints_.size(), info.sceneDescriptors_.rows);
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}
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}
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info.timeStamps_.insert(DetectionInfo::kTimeDescriptorExtraction, time.restart());
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// DETECT FEATURES AND EXTRACT DESCRIPTORS
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ExtractFeaturesThread extractThread(detector_, extractor_, -1, grayscaleImg);
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extractThread.start();
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extractThread.wait();
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info.sceneKeypoints_ = extractThread.keypoints();
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info.sceneDescriptors_ = extractThread.descriptors();
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info.timeStamps_.insert(DetectionInfo::kTimeKeypointDetection, extractThread.timeDetection());
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info.timeStamps_.insert(DetectionInfo::kTimeDescriptorExtraction, extractThread.timeExtraction());
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info.timeStamps_.insert(DetectionInfo::kTimeSkewAffine, extractThread.timeSkewAffine());
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bool consistentNNData = (vocabulary_->size()!=0 && vocabulary_->wordToObjects().begin().value()!=-1 && Settings::getGeneral_invertedSearch()) ||
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((vocabulary_->size()==0 || vocabulary_->wordToObjects().begin().value()==-1) && !Settings::getGeneral_invertedSearch());
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@ -713,6 +912,8 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
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objectsDescriptors_.begin().value().type() == info.sceneDescriptors_.type()) // binary descriptor issue, if the dataTree is not yet updated with modified settings
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{
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success = true;
|
||||
QTime time;
|
||||
time.start();
|
||||
|
||||
QMultiMap<int, int> words;
|
||||
|
||||
@ -778,7 +979,10 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
|
||||
matched = false;
|
||||
}
|
||||
}
|
||||
if(!matched && !Settings::getNearestNeighbor_3nndrRatioUsed() && !Settings::getNearestNeighbor_5minDistanceUsed())
|
||||
if(!matched &&
|
||||
!Settings::getNearestNeighbor_3nndrRatioUsed() &&
|
||||
!Settings::getNearestNeighbor_5minDistanceUsed() &&
|
||||
dists.at<float>(i,0) >= 0.0f)
|
||||
{
|
||||
matched = true; // no criterion, match to the nearest descriptor
|
||||
}
|
||||
@ -1024,7 +1228,7 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
|
||||
{
|
||||
UWARN("Cannot search, objects must be updated");
|
||||
}
|
||||
else if(emptyScene)
|
||||
else if(info.sceneKeypoints_.size() == 0)
|
||||
{
|
||||
// Accept but warn the user
|
||||
UWARN("No features detected in the scene!?!");
|
||||
|
||||
@ -786,6 +786,7 @@ void MainWindow::updateVocabulary()
|
||||
QApplication::processEvents();
|
||||
|
||||
QTime time;
|
||||
time.start();
|
||||
findObject_->updateVocabulary();
|
||||
|
||||
if(findObject_->vocabulary()->size())
|
||||
@ -946,9 +947,13 @@ void MainWindow::update(const cv::Mat & image)
|
||||
if(findObject_->detect(sceneImage_, info))
|
||||
{
|
||||
ui_->label_timeDetection->setNum(info.timeStamps_.value(DetectionInfo::kTimeKeypointDetection, 0));
|
||||
ui_->label_timeSkewAffine->setNum(info.timeStamps_.value(DetectionInfo::kTimeSkewAffine, 0));
|
||||
ui_->label_timeExtraction->setNum(info.timeStamps_.value(DetectionInfo::kTimeDescriptorExtraction, 0));
|
||||
ui_->imageView_source->setData(info.sceneKeypoints_, cvtCvMat2QImage(sceneImage_));
|
||||
ui_->label_timeIndexing->setNum(info.timeStamps_.value(DetectionInfo::kTimeIndexing, 0));
|
||||
if(!findObject_->vocabulary()->size())
|
||||
{
|
||||
ui_->label_timeIndexing->setNum(info.timeStamps_.value(DetectionInfo::kTimeIndexing, 0));
|
||||
}
|
||||
ui_->label_timeMatching->setNum(info.timeStamps_.value(DetectionInfo::kTimeMatching, 0));
|
||||
ui_->label_timeHomographies->setNum(info.timeStamps_.value(DetectionInfo::kTimeHomography, 0));
|
||||
|
||||
@ -1135,7 +1140,13 @@ void MainWindow::update(const cv::Mat & image)
|
||||
}
|
||||
else
|
||||
{
|
||||
this->statusBar()->showMessage(tr("Cannot search, objects must be updated!"));
|
||||
if(findObject_->vocabulary()->size())
|
||||
{
|
||||
this->statusBar()->showMessage(tr("Cannot search, objects must be updated!"));
|
||||
}
|
||||
ui_->label_timeDetection->setNum(info.timeStamps_.value(DetectionInfo::kTimeKeypointDetection, 0));
|
||||
ui_->label_timeSkewAffine->setNum(info.timeStamps_.value(DetectionInfo::kTimeSkewAffine, 0));
|
||||
ui_->label_timeExtraction->setNum(info.timeStamps_.value(DetectionInfo::kTimeDescriptorExtraction, 0));
|
||||
ui_->imageView_source->setData(info.sceneKeypoints_, cvtCvMat2QImage(sceneImage_));
|
||||
}
|
||||
|
||||
|
||||
@ -38,8 +38,6 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#include <opencv2/nonfree/gpu.hpp>
|
||||
#include <opencv2/gpu/gpu.hpp>
|
||||
|
||||
#define VERBOSE 0
|
||||
|
||||
namespace find_object {
|
||||
|
||||
ParametersMap Settings::defaultParameters_;
|
||||
@ -199,7 +197,8 @@ public:
|
||||
virtual ~GPUFeature2D() {}
|
||||
|
||||
virtual void detectKeypoints(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints) = 0;
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
const cv::Mat & mask = cv::Mat()) = 0;
|
||||
|
||||
virtual void computeDescriptors(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
@ -225,12 +224,15 @@ public:
|
||||
}
|
||||
virtual ~GPUSURF() {}
|
||||
|
||||
void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
|
||||
void detectKeypoints(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
const cv::Mat & mask = cv::Mat())
|
||||
{
|
||||
cv::gpu::GpuMat imgGpu(image);
|
||||
cv::gpu::GpuMat maskGpu(mask);
|
||||
try
|
||||
{
|
||||
surf_(imgGpu, cv::gpu::GpuMat(), keypoints);
|
||||
surf_(imgGpu, maskGpu, keypoints);
|
||||
}
|
||||
catch(cv::Exception &e)
|
||||
{
|
||||
@ -292,10 +294,13 @@ public:
|
||||
virtual ~GPUFAST() {}
|
||||
|
||||
protected:
|
||||
void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
|
||||
void detectKeypoints(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
const cv::Mat & mask = cv::Mat())
|
||||
{
|
||||
cv::gpu::GpuMat imgGpu(image);
|
||||
fast_(imgGpu, cv::gpu::GpuMat(), keypoints);
|
||||
cv::gpu::GpuMat maskGpu(mask);
|
||||
fast_(imgGpu, maskGpu, keypoints);
|
||||
}
|
||||
void computeDescriptors( const cv::Mat& image,
|
||||
std::vector<cv::KeyPoint>& keypoints,
|
||||
@ -335,12 +340,15 @@ public:
|
||||
virtual ~GPUORB() {}
|
||||
|
||||
protected:
|
||||
void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
|
||||
void detectKeypoints(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
const cv::Mat & mask = cv::Mat())
|
||||
{
|
||||
cv::gpu::GpuMat imgGpu(image);
|
||||
cv::gpu::GpuMat maskGpu(mask);
|
||||
try
|
||||
{
|
||||
orb_(imgGpu, cv::gpu::GpuMat(), keypoints);
|
||||
orb_(imgGpu, maskGpu, keypoints);
|
||||
}
|
||||
catch(cv::Exception &e)
|
||||
{
|
||||
@ -411,7 +419,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_Dense_initImgBound(),
|
||||
getFeature2D_Dense_varyXyStepWithScale(),
|
||||
getFeature2D_Dense_varyImgBoundWithScale());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 1:
|
||||
@ -422,14 +430,14 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
detectorGPU = new GPUFAST(
|
||||
getFeature2D_Fast_threshold(),
|
||||
getFeature2D_Fast_nonmaxSuppression());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s GPU\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s GPU", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
else
|
||||
{
|
||||
detector = new cv::FastFeatureDetector(
|
||||
getFeature2D_Fast_threshold(),
|
||||
getFeature2D_Fast_nonmaxSuppression());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
}
|
||||
break;
|
||||
@ -443,7 +451,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_GFTT_blockSize(),
|
||||
getFeature2D_GFTT_useHarrisDetector(),
|
||||
getFeature2D_GFTT_k());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 3:
|
||||
@ -459,7 +467,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_MSER_areaThreshold(),
|
||||
getFeature2D_MSER_minMargin(),
|
||||
getFeature2D_MSER_edgeBlurSize());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 4:
|
||||
@ -478,7 +486,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_ORB_patchSize(),
|
||||
getFeature2D_Fast_threshold(),
|
||||
getFeature2D_Fast_nonmaxSuppression());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
else
|
||||
{
|
||||
@ -491,7 +499,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_ORB_WTA_K(),
|
||||
getFeature2D_ORB_scoreType(),
|
||||
getFeature2D_ORB_patchSize());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
}
|
||||
break;
|
||||
@ -504,7 +512,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_SIFT_contrastThreshold(),
|
||||
getFeature2D_SIFT_edgeThreshold(),
|
||||
getFeature2D_SIFT_sigma());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 6:
|
||||
@ -516,7 +524,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_Star_lineThresholdProjected(),
|
||||
getFeature2D_Star_lineThresholdBinarized(),
|
||||
getFeature2D_Star_suppressNonmaxSize());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 7:
|
||||
@ -531,7 +539,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_SURF_extended(),
|
||||
getFeature2D_SURF_keypointsRatio(),
|
||||
getFeature2D_SURF_upright());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
else
|
||||
{
|
||||
@ -541,7 +549,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_SURF_nOctaveLayers(),
|
||||
getFeature2D_SURF_extended(),
|
||||
getFeature2D_SURF_upright());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
}
|
||||
break;
|
||||
@ -552,7 +560,7 @@ KeypointDetector * Settings::createKeypointDetector()
|
||||
getFeature2D_BRISK_thresh(),
|
||||
getFeature2D_BRISK_octaves(),
|
||||
getFeature2D_BRISK_patternScale());
|
||||
if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
default:
|
||||
@ -595,7 +603,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
{
|
||||
extractor = new cv::BriefDescriptorExtractor(
|
||||
getFeature2D_Brief_bytes());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 1:
|
||||
@ -614,7 +622,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_ORB_patchSize(),
|
||||
getFeature2D_Fast_threshold(),
|
||||
getFeature2D_Fast_nonmaxSuppression());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
else
|
||||
{
|
||||
@ -627,7 +635,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_ORB_WTA_K(),
|
||||
getFeature2D_ORB_scoreType(),
|
||||
getFeature2D_ORB_patchSize());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
}
|
||||
break;
|
||||
@ -640,7 +648,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_SIFT_contrastThreshold(),
|
||||
getFeature2D_SIFT_edgeThreshold(),
|
||||
getFeature2D_SIFT_sigma());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 3:
|
||||
@ -655,7 +663,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_SURF_extended(),
|
||||
getFeature2D_SURF_keypointsRatio(),
|
||||
getFeature2D_SURF_upright());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
else
|
||||
{
|
||||
@ -665,7 +673,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_SURF_nOctaveLayers(),
|
||||
getFeature2D_SURF_extended(),
|
||||
getFeature2D_SURF_upright());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
}
|
||||
break;
|
||||
@ -676,7 +684,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_BRISK_thresh(),
|
||||
getFeature2D_BRISK_octaves(),
|
||||
getFeature2D_BRISK_patternScale());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
case 5:
|
||||
@ -687,7 +695,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
|
||||
getFeature2D_FREAK_scaleNormalized(),
|
||||
getFeature2D_FREAK_patternScale(),
|
||||
getFeature2D_FREAK_nOctaves());
|
||||
if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
|
||||
UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
|
||||
}
|
||||
break;
|
||||
default:
|
||||
@ -745,14 +753,14 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
|
||||
case 0:
|
||||
if(strategies.at(index).compare("Linear") == 0)
|
||||
{
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Linear");
|
||||
UDEBUG("type=%s", "Linear");
|
||||
params = new cv::flann::LinearIndexParams();
|
||||
}
|
||||
break;
|
||||
case 1:
|
||||
if(strategies.at(index).compare("KDTree") == 0)
|
||||
{
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "KDTree");
|
||||
UDEBUG("type=%s", "KDTree");
|
||||
params = new cv::flann::KDTreeIndexParams(
|
||||
getNearestNeighbor_KDTree_trees());
|
||||
}
|
||||
@ -772,7 +780,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
|
||||
centers_init = (cvflann::flann_centers_init_t)index;
|
||||
}
|
||||
}
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "KMeans");
|
||||
UDEBUG("type=%s", "KMeans");
|
||||
params = new cv::flann::KMeansIndexParams(
|
||||
getNearestNeighbor_KMeans_branching(),
|
||||
getNearestNeighbor_KMeans_iterations(),
|
||||
@ -795,7 +803,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
|
||||
centers_init = (cvflann::flann_centers_init_t)index;
|
||||
}
|
||||
}
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Composite");
|
||||
UDEBUG("type=%s", "Composite");
|
||||
params = new cv::flann::CompositeIndexParams(
|
||||
getNearestNeighbor_Composite_trees(),
|
||||
getNearestNeighbor_Composite_branching(),
|
||||
@ -807,7 +815,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
|
||||
case 4:
|
||||
if(strategies.at(index).compare("Autotuned") == 0)
|
||||
{
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Autotuned");
|
||||
UDEBUG("type=%s", "Autotuned");
|
||||
params = new cv::flann::AutotunedIndexParams(
|
||||
getNearestNeighbor_Autotuned_target_precision(),
|
||||
getNearestNeighbor_Autotuned_build_weight(),
|
||||
@ -818,7 +826,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
|
||||
case 5:
|
||||
if(strategies.at(index).compare("Lsh") == 0)
|
||||
{
|
||||
if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Lsh");
|
||||
UDEBUG("type=%s", "Lsh");
|
||||
params = new cv::flann::LshIndexParams(
|
||||
getNearestNeighbor_Lsh_table_number(),
|
||||
getNearestNeighbor_Lsh_key_size(),
|
||||
@ -858,7 +866,6 @@ cvflann::flann_distance_t Settings::getFlannDistanceType()
|
||||
}
|
||||
}
|
||||
}
|
||||
if(VERBOSE)printf("Settings::getFlannDistanceType() distance=%d\n", distance);
|
||||
return distance;
|
||||
}
|
||||
|
||||
@ -896,7 +903,7 @@ int Settings::getHomographyMethod()
|
||||
}
|
||||
}
|
||||
}
|
||||
if(VERBOSE)printf("Settings::getHomographyMethod() method=%d\n", method);
|
||||
UDEBUG("method=%d", method);
|
||||
return method;
|
||||
}
|
||||
|
||||
@ -912,15 +919,17 @@ KeypointDetector::KeypointDetector(GPUFeature2D * gpuFeature2D) :
|
||||
{
|
||||
Q_ASSERT(gpuFeature2D_!=0);
|
||||
}
|
||||
void KeypointDetector::detect(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
|
||||
void KeypointDetector::detect(const cv::Mat & image,
|
||||
std::vector<cv::KeyPoint> & keypoints,
|
||||
const cv::Mat & mask)
|
||||
{
|
||||
if(featureDetector_)
|
||||
{
|
||||
featureDetector_->detect(image, keypoints);
|
||||
featureDetector_->detect(image, keypoints, mask);
|
||||
}
|
||||
else // assume GPU
|
||||
{
|
||||
gpuFeature2D_->detectKeypoints(image, keypoints);
|
||||
gpuFeature2D_->detectKeypoints(image, keypoints, mask);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
<x>0</x>
|
||||
<y>0</y>
|
||||
<width>826</width>
|
||||
<height>506</height>
|
||||
<height>523</height>
|
||||
</rect>
|
||||
</property>
|
||||
<property name="windowTitle">
|
||||
@ -344,7 +344,7 @@
|
||||
<x>0</x>
|
||||
<y>0</y>
|
||||
<width>198</width>
|
||||
<height>376</height>
|
||||
<height>393</height>
|
||||
</rect>
|
||||
</property>
|
||||
<layout class="QVBoxLayout" name="verticalLayout_objects">
|
||||
@ -449,83 +449,83 @@
|
||||
<number>0</number>
|
||||
</property>
|
||||
<item row="4" column="1">
|
||||
<widget class="QLabel" name="label_timeMatching">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="3" column="2">
|
||||
<widget class="QLabel" name="label_9">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="4" column="2">
|
||||
<widget class="QLabel" name="label_10">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="3" column="0">
|
||||
<widget class="QLabel" name="label_8">
|
||||
<property name="text">
|
||||
<string>Descriptors indexing</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="3" column="1">
|
||||
<widget class="QLabel" name="label_timeIndexing">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="5" column="1">
|
||||
<widget class="QLabel" name="label_timeMatching">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="4" column="2">
|
||||
<widget class="QLabel" name="label_9">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="5" column="2">
|
||||
<widget class="QLabel" name="label_10">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="4" column="0">
|
||||
<widget class="QLabel" name="label_8">
|
||||
<property name="text">
|
||||
<string>Descriptors indexing</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="6" column="2">
|
||||
<widget class="QLabel" name="label_12">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="7" column="1">
|
||||
<item row="8" column="1">
|
||||
<widget class="QLabel" name="label_minMatchedDistance">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="7" column="0">
|
||||
<item row="8" column="0">
|
||||
<widget class="QLabel" name="label_13">
|
||||
<property name="text">
|
||||
<string>Min matched distance</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="8" column="0">
|
||||
<item row="9" column="0">
|
||||
<widget class="QLabel" name="label_14">
|
||||
<property name="text">
|
||||
<string>Max matched distance</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="8" column="1">
|
||||
<item row="9" column="1">
|
||||
<widget class="QLabel" name="label_maxMatchedDistance">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="5" column="0">
|
||||
<item row="6" column="0">
|
||||
<widget class="QLabel" name="label_11">
|
||||
<property name="text">
|
||||
<string>Homograhies</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="0">
|
||||
<item row="3" column="0">
|
||||
<widget class="QLabel" name="label_2">
|
||||
<property name="text">
|
||||
<string>Descriptors extraction</string>
|
||||
@ -539,14 +539,14 @@
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="1">
|
||||
<item row="3" column="1">
|
||||
<widget class="QLabel" name="label_timeExtraction">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="2">
|
||||
<item row="3" column="2">
|
||||
<widget class="QLabel" name="label_4">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
@ -567,7 +567,7 @@
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="4" column="0">
|
||||
<item row="5" column="0">
|
||||
<widget class="QLabel" name="label_7">
|
||||
<property name="text">
|
||||
<string>Descriptors matching</string>
|
||||
@ -595,80 +595,101 @@
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="9" column="0">
|
||||
<item row="10" column="0">
|
||||
<widget class="QLabel" name="label_17">
|
||||
<property name="text">
|
||||
<string>Vocabulary size</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="9" column="1">
|
||||
<item row="10" column="1">
|
||||
<widget class="QLabel" name="label_vocabularySize">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="10" column="0">
|
||||
<item row="11" column="0">
|
||||
<widget class="QLabel" name="label_18">
|
||||
<property name="text">
|
||||
<string>IP address</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="11" column="0">
|
||||
<item row="12" column="0">
|
||||
<widget class="QLabel" name="label_19">
|
||||
<property name="text">
|
||||
<string>Output detection port</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="10" column="1">
|
||||
<item row="11" column="1">
|
||||
<widget class="QLabel" name="label_ipAddress">
|
||||
<property name="text">
|
||||
<string>0.0.0.0</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="11" column="1">
|
||||
<item row="12" column="1">
|
||||
<widget class="QLabel" name="label_port">
|
||||
<property name="text">
|
||||
<string>0</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="6" column="0">
|
||||
<item row="7" column="0">
|
||||
<widget class="QLabel" name="label_20">
|
||||
<property name="text">
|
||||
<string>Objects detected</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="6" column="1">
|
||||
<item row="7" column="1">
|
||||
<widget class="QLabel" name="label_objectsDetected">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="5" column="1">
|
||||
<item row="6" column="1">
|
||||
<widget class="QLabel" name="label_timeHomographies">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="12" column="0">
|
||||
<item row="13" column="0">
|
||||
<widget class="QLabel" name="label_21">
|
||||
<property name="text">
|
||||
<string>Input image port</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="12" column="1">
|
||||
<item row="13" column="1">
|
||||
<widget class="QLabel" name="label_port_image">
|
||||
<property name="text">
|
||||
<string>NA</string>
|
||||
<string>-</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="0">
|
||||
<widget class="QLabel" name="label_22">
|
||||
<property name="text">
|
||||
<string>Affine transforms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="1">
|
||||
<widget class="QLabel" name="label_timeSkewAffine">
|
||||
<property name="text">
|
||||
<string>000</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
<item row="2" column="2">
|
||||
<widget class="QLabel" name="label_23">
|
||||
<property name="text">
|
||||
<string>ms</string>
|
||||
</property>
|
||||
</widget>
|
||||
</item>
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user