export to python/java

modules/objdetect/doc/soft_cascade.rst

@@ -39,6 +39,7 @@ Implementation of soft (stageless) cascaded detector. ::
         cv::AlgorithmInfo* info() const;
         virtual bool load(const FileNode& fn);
         virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
+        virtual void detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const;
     };
 
 
@@ -80,10 +81,16 @@ SCascade::detect
 --------------------------
 Apply cascade to an input frame and return the vector of Decection objcts.
 
-.. ocv:function:: bool SCascade::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
+.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const
+
+.. ocv:function:: void SCascade::detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const
 
     :param image: a frame on which detector will be applied.
 
     :param rois: a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
 
-    :param objects: an output array of Detections.
\ No newline at end of file
+    :param objects: an output array of Detections.
+
+    :param rects: an output array of bounding rectangles for detected objects.
+
+    :param confs: an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th configence.
\ No newline at end of file

modules/objdetect/include/opencv2/objdetect/objdetect.hpp

@@ -490,7 +490,7 @@ protected:
 
 
 // Implementation of soft (stageless) cascaded detector.
-class CV_EXPORTS SCascade : public Algorithm
+class CV_EXPORTS_W SCascade : public Algorithm
 {
 public:
 
@@ -539,24 +539,27 @@ public:
     // Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
     // Param scales is a number of scales from minScale to maxScale.
     // Param rejfactor is used for NMS.
-    SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejfactor = 1);
+    CV_WRAP SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejfactor = 1);
 
-    virtual ~SCascade();
+    CV_WRAP virtual ~SCascade();
 
     cv::AlgorithmInfo* info() const;
 
     // Load cascade from FileNode.
     // Param fn is a root node for cascade. Should be <cascade>.
-    virtual bool load(const FileNode& fn);
+    CV_WRAP virtual bool load(const FileNode& fn);
 
     // Load cascade config.
-    virtual void read(const FileNode& fn);
+    CV_WRAP virtual void read(const FileNode& fn);
 
     // Return the vector of Decection objcts.
     // Param image is a frame on which detector will be applied.
     // Param rois is a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
     // Param objects is an output array of Detections
     virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
+    // Param rects is an output array of bounding rectangles for detected objects.
+    // Param confs is an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th configence.
+    CV_WRAP virtual void detect(InputArray image, InputArray rois, OutputArray rects, OutputArray confs) const;
 
 private:
     void detectNoRoi(const Mat& image, std::vector<Detection>& objects) const;

modules/objdetect/src/softcascade.cpp

@@ -500,4 +500,27 @@ void cv::SCascade::detect(cv::InputArray _image, cv::InputArray _rois, std::vect
              }
          }
     }
+}
+
+void cv::SCascade::detect(InputArray _image, InputArray _rois,  OutputArray _rects, OutputArray _confs) const
+{
+    std::vector<Detection> objects;
+    detect( _image, _rois, objects);
+
+    _rects.create(1, objects.size(), CV_32SC4);
+    cv::Mat_<cv::Rect> rects = (cv::Mat_<cv::Rect>)_rects.getMat();
+    cv::Rect* rectPtr = rects.ptr<cv::Rect>(0);
+
+    _confs.create(1, objects.size(), CV_32F);
+    cv::Mat confs = _confs.getMat();
+    float* confPtr = rects.ptr<float>(0);
+
+    typedef std::vector<Detection>::const_iterator IDet;
+
+    int i = 0;
+    for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
+    {
+        rectPtr[i] = (*it).bb;
+        confPtr[i] = (*it).confidence;
+    }
 }
\ No newline at end of file

modules/objdetect/test/test_softcascade.cpp

@@ -66,34 +66,26 @@ TEST(SCascade, detect)
     std::vector<Detection> objects;
 
     cascade.detect(colored, cv::noArray(), objects);
-
-    // cv::Mat out = colored.clone();
-    // int level = 0, total = 0;
-    // int levelWidth = objects[0].bb.width;
-
-    // for(int i = 0 ; i < (int)objects.size(); ++i)
-    // {
-    //     if (objects[i].bb.width != levelWidth)
-    //     {
-    //         std::cout << "Level: " << level << " total " << total << std::endl;
-    //         cv::imshow("out", out);
-    //         cv::waitKey(0);
-    //         out = colored.clone();
-    //         levelWidth = objects[i].bb.width;
-    //         total = 0;
-    //         level++;
-    //     }
-    //     cv::rectangle(out, objects[i].bb, cv::Scalar(255, 0, 0, 255), 1);
-    //     std::cout << "detection: " << objects[i].bb.x
-    //                                << " " << objects[i].bb.y
-    //                                << " " << objects[i].bb.width
-    //                                << " " << objects[i].bb.height << std::endl;
-    //     total++;
-    // }
-    // std::cout << "detected: " << (int)objects.size() << std::endl;
     ASSERT_EQ((int)objects.size(), 3498);
 }
 
+TEST(SCascade, detectSeparate)
+{
+    typedef cv::SCascade::Detection Detection;
+    std::string xml =  cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
+    cv::SCascade cascade;
+    cv::FileStorage fs(xml, cv::FileStorage::READ);
+    ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
+
+    cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
+    ASSERT_FALSE(colored.empty());
+
+    cv::Mat rects, confs;
+
+    cascade.detect(colored, cv::noArray(), rects, confs);
+    ASSERT_EQ(confs.cols, 3498);
+}
+
 TEST(SCascade, detectRoi)
 {
     typedef cv::SCascade::Detection Detection;
@@ -110,31 +102,6 @@ TEST(SCascade, detectRoi)
     rois.push_back(cv::Rect(0, 0, 640, 480));
 
     cascade.detect(colored, rois, objects);
-
-    // cv::Mat out = colored.clone();
-    // int level = 0, total = 0;
-    // int levelWidth = objects[0].bb.width;
-
-    // for(int i = 0 ; i < (int)objects.size(); ++i)
-    // {
-    //     if (objects[i].bb.width != levelWidth)
-    //     {
-    //         std::cout << "Level: " << level << " total " << total << std::endl;
-    //         cv::imshow("out", out);
-    //         cv::waitKey(0);
-    //         out = colored.clone();
-    //         levelWidth = objects[i].bb.width;
-    //         total = 0;
-    //         level++;
-    //     }
-    //     cv::rectangle(out, objects[i].bb, cv::Scalar(255, 0, 0, 255), 1);
-    //     std::cout << "detection: " << objects[i].bb.x
-    //                                << " " << objects[i].bb.y
-    //                                << " " << objects[i].bb.width
-    //                                << " " << objects[i].bb.height << std::endl;
-    //     total++;
-    // }
-    // std::cout << "detected: " << (int)objects.size() << std::endl;
     ASSERT_EQ((int)objects.size(), 3498);
 }