Fix build of samples

samples/cpp/dbt_face_detection.cpp

@@ -1,104 +0,0 @@
-#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID)
-
-#include <opencv2/imgproc/imgproc.hpp>  // Gaussian Blur
-#include <opencv2/core/core.hpp>        // Basic OpenCV structures (cv::Mat, Scalar)
-#include <opencv2/highgui/highgui.hpp>  // OpenCV window I/O
-#include <opencv2/features2d/features2d.hpp>
-#include <opencv2/contrib/detection_based_tracker.hpp>
-
-#include <stdio.h>
-#include <string>
-#include <vector>
-
-using namespace std;
-using namespace cv;
-
-const string WindowName = "Face Detection example";
-
-class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
-{
-    public:
-        CascadeDetectorAdapter(cv::Ptr<cv::CascadeClassifier> detector):
-            IDetector(),
-            Detector(detector)
-        {
-            CV_Assert(!detector.empty());
-        }
-
-        void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects)
-        {
-            Detector->detectMultiScale(Image, objects, scaleFactor, minNeighbours, 0, minObjSize, maxObjSize);
-        }
-
-        virtual ~CascadeDetectorAdapter()
-        {}
-
-    private:
-        CascadeDetectorAdapter();
-        cv::Ptr<cv::CascadeClassifier> Detector;
- };
-
-int main(int , char** )
-{
-    namedWindow(WindowName);
-
-    VideoCapture VideoStream(0);
-
-    if (!VideoStream.isOpened())
-    {
-        printf("Error: Cannot open video stream from camera\n");
-        return 1;
-    }
-
-    std::string cascadeFrontalfilename = "../../data/lbpcascades/lbpcascade_frontalface.xml";
-    cv::Ptr<cv::CascadeClassifier> cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
-    cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = new CascadeDetectorAdapter(cascade);
-
-    cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
-    cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = new CascadeDetectorAdapter(cascade);
-
-    DetectionBasedTracker::Parameters params;
-    DetectionBasedTracker Detector(MainDetector, TrackingDetector, params);
-
-    if (!Detector.run())
-    {
-        printf("Error: Detector initialization failed\n");
-        return 2;
-    }
-
-    Mat ReferenceFrame;
-    Mat GrayFrame;
-    vector<Rect> Faces;
-
-    while(true)
-    {
-        VideoStream >> ReferenceFrame;
-        cvtColor(ReferenceFrame, GrayFrame, COLOR_RGB2GRAY);
-        Detector.process(GrayFrame);
-        Detector.getObjects(Faces);
-
-        for (size_t i = 0; i < Faces.size(); i++)
-        {
-            rectangle(ReferenceFrame, Faces[i], CV_RGB(0,255,0));
-        }
-
-        imshow(WindowName, ReferenceFrame);
-
-        if (cvWaitKey(30) >= 0) break;
-    }
-
-    Detector.stop();
-
-    return 0;
-}
-
-#else
-
-#include <stdio.h>
-int main()
-{
-    printf("This sample works for UNIX or ANDROID only\n");
-    return 0;
-}
-
-#endif

samples/cpp/detection_based_tracker_sample.cpp

@@ -43,6 +43,8 @@
 #define LOGE(...) do{} while(0)
 #endif
 
+
+
 using namespace cv;
 using namespace std;
 
@@ -61,31 +63,9 @@ static void usage()
     LOGE0("\t       (e.g.\"opencv/data/lbpcascades/lbpcascade_frontalface.xml\" ");
 }
 
-class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
-{
-    public:
-        CascadeDetectorAdapter(cv::Ptr<cv::CascadeClassifier> detector):
-            Detector(detector)
-        {
-            CV_Assert(!detector.empty());
-        }
-
-        void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects)
-        {
-            Detector->detectMultiScale(Image, objects, 1.1, 3, 0, minObjSize, maxObjSize);
-        }
-        virtual ~CascadeDetectorAdapter()
-        {}
-
-    private:
-        CascadeDetectorAdapter();
-        cv::Ptr<cv::CascadeClassifier> Detector;
- };
-
 static int test_FaceDetector(int argc, char *argv[])
 {
-    if (argc < 4)
-    {
+    if (argc < 4) {
         usage();
         return -1;
     }
@@ -100,14 +80,12 @@ static int test_FaceDetector(int argc, char *argv[])
     vector<Mat> images;
     {
         char filename[256];
-        for(int n=1; ; n++)
-        {
+        for(int n=1; ; n++) {
             snprintf(filename, sizeof(filename), filepattern, n);
             LOGD("filename='%s'", filename);
             Mat m0;
             m0=imread(filename);
-            if (m0.empty())
-            {
+            if (m0.empty()) {
                 LOGI0("Cannot read the file --- break");
                 break;
             }
@@ -116,15 +94,10 @@ static int test_FaceDetector(int argc, char *argv[])
         LOGD("read %d images", (int)images.size());
     }
 
+    DetectionBasedTracker::Parameters params;
     std::string cascadeFrontalfilename=cascadefile;
-    cv::Ptr<cv::CascadeClassifier> cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
-    cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = new CascadeDetectorAdapter(cascade);
-
-    cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
-    cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = new CascadeDetectorAdapter(cascade);
 
-    DetectionBasedTracker::Parameters params;
-    DetectionBasedTracker fd(MainDetector, TrackingDetector, params);
+    DetectionBasedTracker fd(cascadeFrontalfilename, params);
 
     fd.run();
 
@@ -135,13 +108,12 @@ static int test_FaceDetector(int argc, char *argv[])
     double freq=getTickFrequency();
 
     int num_images=images.size();
-    for(int n=1; n <= num_images; n++)
-    {
+    for(int n=1; n <= num_images; n++) {
         int64 tcur=getTickCount();
         int64 dt=tcur-tprev;
         tprev=tcur;
         double t_ms=((double)dt)/freq * 1000.0;
-        LOGD("\n\nSTEP n=%d        from prev step %f ms\n", n, t_ms);
+        LOGD("\n\nSTEP n=%d        from prev step %f ms\n\n", n, t_ms);
         m=images[n-1];
         CV_Assert(! m.empty());
         cvtColor(m, gray, CV_BGR2GRAY);
@@ -151,8 +123,11 @@ static int test_FaceDetector(int argc, char *argv[])
         vector<Rect> result;
         fd.getObjects(result);
 
-        for(size_t i=0; i < result.size(); i++)
-        {
+
+
+
+
+        for(size_t i=0; i < result.size(); i++) {
             Rect r=result[i];
             CV_Assert(r.area() > 0);
             Point tl=r.tl();
@@ -161,14 +136,14 @@ static int test_FaceDetector(int argc, char *argv[])
             rectangle(m, tl, br, color, 3);
         }
     }
-
-    char outfilename[256];
-    for(int n=1; n <= num_images; n++)
     {
-        snprintf(outfilename, sizeof(outfilename), outfilepattern, n);
-        LOGD("outfilename='%s'", outfilename);
-        m=images[n-1];
-        imwrite(outfilename, m);
+        char outfilename[256];
+        for(int n=1; n <= num_images; n++) {
+            snprintf(outfilename, sizeof(outfilename), outfilepattern, n);
+            LOGD("outfilename='%s'", outfilename);
+            m=images[n-1];
+            imwrite(outfilename, m);
+        }
     }
 
     fd.stop();
@@ -176,6 +151,8 @@ static int test_FaceDetector(int argc, char *argv[])
     return 0;
 }
 
+
+
 int main(int argc, char *argv[])
 {
     return test_FaceDetector(argc, argv);

samples/gpu/generalized_hough.cpp

@@ -26,41 +26,41 @@ static Mat loadImage(const string& name)
 int main(int argc, const char* argv[])
 {
     CommandLineParser cmd(argc, argv,
-        "{ image i        | pic1.png  | input image }"
-        "{ template t     | templ.png | template image }"
-        "{ scale s        |           | estimate scale }"
-        "{ rotation r     |           | estimate rotation }"
-        "{ gpu            |           | use gpu version }"
-        "{ minDist        | 100       | minimum distance between the centers of the detected objects }"
-        "{ levels         | 360       | R-Table levels }"
-        "{ votesThreshold | 30        | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"
-        "{ angleThresh    | 10000     | angle votes treshold }"
-        "{ scaleThresh    | 1000      | scale votes treshold }"
-        "{ posThresh      | 100       | position votes threshold }"
-        "{ dp             | 2         | inverse ratio of the accumulator resolution to the image resolution }"
-        "{ minScale       | 0.5       | minimal scale to detect }"
-        "{ maxScale       | 2         | maximal scale to detect }"
-        "{ scaleStep      | 0.05      | scale step }"
-        "{ minAngle       | 0         | minimal rotation angle to detect in degrees }"
-        "{ maxAngle       | 360       | maximal rotation angle to detect in degrees }"
-        "{ angleStep      | 1         | angle step in degrees }"
-        "{ maxSize        | 1000      | maximal size of inner buffers }"
-        "{ help h ?       |           | print help message }"
+        "{ i | image          | pic1.png  | input image }"
+        "{ t | template       | templ.png | template image }"
+        "{ s | scale          |           | estimate scale }"
+        "{ r | rotation       |           | estimate rotation }"
+        "{   | gpu            |           | use gpu version }"
+        "{   | minDist        | 100       | minimum distance between the centers of the detected objects }"
+        "{   | levels         | 360       | R-Table levels }"
+        "{   | votesThreshold | 30        | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"
+        "{   | angleThresh    | 10000     | angle votes treshold }"
+        "{   | scaleThresh    | 1000      | scale votes treshold }"
+        "{   | posThresh      | 100       | position votes threshold }"
+        "{   | dp             | 2         | inverse ratio of the accumulator resolution to the image resolution }"
+        "{   | minScale       | 0.5       | minimal scale to detect }"
+        "{   | maxScale       | 2         | maximal scale to detect }"
+        "{   | scaleStep      | 0.05      | scale step }"
+        "{   | minAngle       | 0         | minimal rotation angle to detect in degrees }"
+        "{   | maxAngle       | 360       | maximal rotation angle to detect in degrees }"
+        "{   | angleStep      | 1         | angle step in degrees }"
+        "{   | maxSize        | 1000      | maximal size of inner buffers }"
+        "{ h | help           |           | print help message }"
     );
 
-    cmd.about("This program demonstrates arbitary object finding with the Generalized Hough transform.");
+    //cmd.about("This program demonstrates arbitary object finding with the Generalized Hough transform.");
 
-    if (cmd.has("help"))
+    if (cmd.get<bool>("help"))
     {
-        cmd.printMessage();
+        cmd.printParams();
         return 0;
     }
 
     const string templName = cmd.get<string>("template");
     const string imageName = cmd.get<string>("image");
-    const bool estimateScale = cmd.has("scale");
-    const bool estimateRotation = cmd.has("rotation");
-    const bool useGpu = cmd.has("gpu");
+    const bool estimateScale = cmd.get<bool>("scale");
+    const bool estimateRotation = cmd.get<bool>("rotation");
+    const bool useGpu = cmd.get<bool>("gpu");
     const double minDist = cmd.get<double>("minDist");
     const int levels = cmd.get<int>("levels");
     const int votesThreshold = cmd.get<int>("votesThreshold");
@@ -76,12 +76,6 @@ int main(int argc, const char* argv[])
     const double angleStep = cmd.get<double>("angleStep");
     const int maxSize = cmd.get<int>("maxSize");
 
-    if (!cmd.check())
-    {
-        cmd.printErrors();
-        return -1;
-    }
-
     Mat templ = loadImage(templName);
     Mat image = loadImage(imageName);
 

samples/gpu/performance/tests.cpp

@@ -364,7 +364,7 @@ TEST(BruteForceMatcher)
 
     // Init GPU matcher
 
-    gpu::BFMatcher_GPU d_matcher(NORM_L2);
+    gpu::BruteForceMatcher_GPU_base d_matcher(gpu::BruteForceMatcher_GPU_base::L2Dist);
 
     gpu::GpuMat d_query(query);
     gpu::GpuMat d_train(train);

samples/gpu/surf_keypoint_matcher.cpp

@@ -57,7 +57,7 @@ int main(int argc, char* argv[])
     cout << "FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl;
 
     // matching descriptors
-    BFMatcher_GPU matcher(NORM_L2);
+    gpu::BruteForceMatcher_GPU_base matcher(gpu::BruteForceMatcher_GPU_base::L2Dist);
     GpuMat trainIdx, distance;
     matcher.matchSingle(descriptors1GPU, descriptors2GPU, trainIdx, distance);
 
@@ -69,7 +69,7 @@ int main(int argc, char* argv[])
     surf.downloadKeypoints(keypoints2GPU, keypoints2);
     surf.downloadDescriptors(descriptors1GPU, descriptors1);
     surf.downloadDescriptors(descriptors2GPU, descriptors2);
-    BFMatcher_GPU::matchDownload(trainIdx, distance, matches);
+    BruteForceMatcher_GPU_base::matchDownload(trainIdx, distance, matches);
 
     // drawing the results
     Mat img_matches;