Merge pull request #661 from cuda-geek:merge-cpu-gpu-detections

modules/softcascade/include/opencv2/softcascade.hpp

@@ -49,18 +49,21 @@
 namespace cv { namespace softcascade {
 
 // Representation of detectors result.
+// We assume that image is less then 2^16x2^16.
 struct CV_EXPORTS Detection
 {
-    // Default object type.
-    enum {PEDESTRIAN = 1};
-
     // Creates Detection from an object bounding box and confidence.
     // Param b is a bounding box
     // Param c is a confidence that object belongs to class k
     // Param k is an object class
-    Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {}
+    Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN);
+    cv::Rect bb() const;
+    enum {PEDESTRIAN = 1};
 
-    cv::Rect bb;
+    ushort x;
+    ushort y;
+    ushort w;
+    ushort h;
     float confidence;
     int kind;
 };
@@ -247,19 +250,6 @@ class CV_EXPORTS SCascade : public cv::Algorithm
 {
 public:
 
-    // Representation of detectors result.
-    struct CV_EXPORTS Detection
-    {
-        ushort x;
-        ushort y;
-        ushort w;
-        ushort h;
-        float confidence;
-        int kind;
-
-        enum {PEDESTRIAN = 0};
-    };
-
     enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT, NMS_MASK = 0xF};
 
     // An empty cascade will be created.

modules/softcascade/perf/perf_cuda_softcascade.cpp

@@ -27,8 +27,8 @@ void fixture##_##name::__cpu() { FAIL() << "No such CPU implementation analogy";
 namespace {
     struct DetectionLess
     {
-        bool operator()(const cv::softcascade::SCascade::Detection& a,
-            const cv::softcascade::SCascade::Detection& b) const
+        bool operator()(const cv::softcascade::Detection& a,
+            const cv::softcascade::Detection& b) const
         {
             if (a.x != b.x)      return a.x < b.x;
             else if (a.y != b.y) return a.y < b.y;
@@ -41,7 +41,7 @@ namespace {
     {
         cv::Mat detections(objects);
 
-        typedef cv::softcascade::SCascade::Detection Detection;
+        typedef cv::softcascade::Detection Detection;
         Detection* begin = (Detection*)(detections.ptr<char>(0));
         Detection* end = (Detection*)(detections.ptr<char>(0) + detections.cols);
         std::sort(begin, end, DetectionLess());
@@ -73,7 +73,7 @@ RUN_GPU(SCascadeTest, detect)
 
     ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
 
-    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::SCascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
+    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
     rois.setTo(1);
 
     cascade.detect(colored, rois, objectBoxes);
@@ -215,7 +215,7 @@ RUN_GPU(SCascadeTest, detectStream)
 
     ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
 
-    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::SCascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
+    cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
     rois.setTo(1);
 
     cv::gpu::Stream s;

modules/softcascade/perf/perf_softcascade.cpp

@@ -17,7 +17,7 @@ void extractRacts(std::vector<Detection> objectBoxes, std::vector<Rect>& rects)
 {
     rects.clear();
     for (int i = 0; i < (int)objectBoxes.size(); ++i)
-        rects.push_back(objectBoxes[i].bb);
+        rects.push_back(objectBoxes[i].bb());
 }
 
 }

modules/softcascade/src/detector.cpp

@@ -42,11 +42,14 @@
 
 #include "precomp.hpp"
 
-using cv::softcascade::Detection;
-using cv::softcascade::Detector;
-using cv::softcascade::ChannelFeatureBuilder;
+cv::softcascade::Detection::Detection(const cv::Rect& b, const float c, int k)
+: x(static_cast<ushort>(b.x)), y(static_cast<ushort>(b.y)),
+  w(static_cast<ushort>(b.width)), h(static_cast<ushort>(b.height)), confidence(c), kind(k) {}
 
-using namespace cv;
+cv::Rect cv::softcascade::Detection::bb() const
+{
+    return cv::Rect(x, y, w, h);
+}
 
 namespace {
 
@@ -151,13 +154,13 @@ struct Level
         scaleshift = static_cast<int>(relScale * (1 << 16));
     }
 
-    void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const
+    void addDetection(const int x, const int y, float confidence, std::vector<cv::softcascade::Detection>& detections) const
     {
         // fix me
         int shrinkage = 4;//(*octave).shrinkage;
         cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
 
-        detections.push_back(Detection(rect, confidence));
+        detections.push_back(cv::softcascade::Detection(rect, confidence));
     }
 
     float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
@@ -183,7 +186,7 @@ struct ChannelStorage
     size_t step;
     int model_height;
 
-    cv::Ptr<ChannelFeatureBuilder> builder;
+    cv::Ptr<cv::softcascade::ChannelFeatureBuilder> builder;
 
     enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
 
@@ -192,7 +195,7 @@ struct ChannelStorage
         model_height = cvRound(colored.rows / (float)shrinkage);
         if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
 
-        builder = ChannelFeatureBuilder::create(featureTypeStr);
+        builder = cv::softcascade::ChannelFeatureBuilder::create(featureTypeStr);
         (*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
 
         step = hog.step1();
@@ -213,8 +216,7 @@ struct ChannelStorage
 
 }
 
-
-struct Detector::Fields
+struct cv::softcascade::Detector::Fields
 {
     float minScale;
     float maxScale;
@@ -421,17 +423,17 @@ struct Detector::Fields
     }
 };
 
-Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
+cv::softcascade::Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
 : fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
 
-Detector::~Detector() { delete fields;}
+cv::softcascade::Detector::~Detector() { delete fields;}
 
-void Detector::read(const cv::FileNode& fn)
+void cv::softcascade::Detector::read(const cv::FileNode& fn)
 {
     Algorithm::read(fn);
 }
 
-bool Detector::load(const cv::FileNode& fn)
+bool cv::softcascade::Detector::load(const cv::FileNode& fn)
 {
     if (fields) delete fields;
 
@@ -473,7 +475,7 @@ void DollarNMS(dvector& objects)
         {
             const Detection &b = *next;
 
-            const float ovl =  overlap(a.bb, b.bb) / std::min(a.bb.area(), b.bb.area());
+            const float ovl =  overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
 
             if (ovl > DollarThreshold)
                 next = objects.erase(next);
@@ -485,13 +487,13 @@ void DollarNMS(dvector& objects)
 
 static void suppress(int type, std::vector<Detection>& objects)
 {
-    CV_Assert(type == Detector::DOLLAR);
+    CV_Assert(type == cv::softcascade::Detector::DOLLAR);
     DollarNMS(objects);
 }
 
 }
 
-void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
+void cv::softcascade::Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
 {
     Fields& fld = *fields;
     // create integrals
@@ -518,7 +520,7 @@ void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects
     if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
 }
 
-void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
+void cv::softcascade::Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
 {
     // only color images are suppered
     cv::Mat image = _image.getMat();
@@ -570,7 +572,7 @@ void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<D
     if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
 }
 
-void Detector::detect(InputArray _image, InputArray _rois,  OutputArray _rects, OutputArray _confs) const
+void cv::softcascade::Detector::detect(InputArray _image, InputArray _rois,  OutputArray _rects, OutputArray _confs) const
 {
     std::vector<Detection> objects;
     detect( _image, _rois, objects);
@@ -588,7 +590,7 @@ void Detector::detect(InputArray _image, InputArray _rois,  OutputArray _rects,
     int i = 0;
     for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
     {
-        rectPtr[i] = (*it).bb;
+        rectPtr[i] = (*it).bb();
         confPtr[i] = (*it).confidence;
     }
 }

modules/softcascade/test/test_cuda_softcascade.cpp

@@ -76,7 +76,7 @@ TEST(SCascadeTest, readCascade)
 
 namespace
 {
-    typedef cv::softcascade::SCascade::Detection Detection;
+    typedef cv::softcascade::Detection Detection;
 
     cv::Rect getFromTable(int idx)
     {
@@ -194,7 +194,7 @@ TEST_P(SCascadeTestRoi, Detect)
     cascade.detect(colored, rois, objectBoxes);
 
     cv::Mat dt(objectBoxes);
-    typedef cv::softcascade::SCascade::Detection Detection;
+    typedef cv::softcascade::Detection Detection;
 
     Detection* dts = ((Detection*)dt.data) + 1;
     int* count = dt.ptr<int>(0);
@@ -262,7 +262,7 @@ TEST_P(SCascadeTestAll, detect)
 
     cascade.detect(colored, rois, objectBoxes);
 
-    typedef cv::softcascade::SCascade::Detection Detection;
+    typedef cv::softcascade::Detection Detection;
     cv::Mat dt(objectBoxes);
 
 
@@ -303,7 +303,7 @@ TEST_P(SCascadeTestAll, detectStream)
     cascade.detect(colored, rois, objectBoxes, s);
     s.waitForCompletion();
 
-    typedef cv::softcascade::SCascade::Detection Detection;
+    typedef cv::softcascade::Detection Detection;
     cv::Mat detections(objectBoxes);
     int a = *(detections.ptr<int>(0));
     ASSERT_EQ(a, expected);

samples/cpp/peopledetect.cpp

@@ -139,12 +139,11 @@ int main(int argc, char** argv)
                     std::stringstream conf(std::stringstream::in | std::stringstream::out);
                     conf << d.confidence;
 
-                    cv::rectangle(frame, cv::Rect(d.bb.x, d.bb.y, d.bb.width, d.bb.height), cv::Scalar(b, 0, 255 - b, 255), 2);
-                    cv::putText(frame, conf.str() , cv::Point(d.bb.x + 10, d.bb.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA);
+                    cv::rectangle(frame, cv::Rect((int)d.x, (int)d.y, (int)d.w, (int)d.h), cv::Scalar(b, 0, 255 - b, 255), 2);
+                    cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA);
 
                     if (wf)
-                        myfile << d.bb.x     << "," <<  d.bb.y      << ","
-                               << d.bb.width << "," <<  d.bb.height << "," << d.confidence << "\n";
+                        myfile << d.x << "," <<  d.y << "," << d.w << "," <<  d.h << "," << d.confidence << "\n";
                 }
             }
         }

samples/gpu/softcascade.cpp

@@ -3,6 +3,8 @@
 #include <opencv2/highgui.hpp>
 #include <iostream>
 
+typedef cv::softcascade::Detection Detection;
+
 int main(int argc, char** argv)
 {
     const std::string keys =
@@ -64,7 +66,7 @@ int main(int argc, char** argv)
         return 1;
     }
 
-    cv::gpu::GpuMat objects(1, sizeof(SCascade::Detection) * 10000, CV_8UC1);
+    cv::gpu::GpuMat objects(1, sizeof(Detection) * 10000, CV_8UC1);
     cv::gpu::printShortCudaDeviceInfo(parser.get<int>("device"));
     for (;;)
     {
@@ -80,7 +82,6 @@ int main(int argc, char** argv)
         cascade.detect(dframe, roi, objects);
 
         cv::Mat dt(objects);
-        typedef cv::softcascade::SCascade::Detection Detection;
 
         Detection* dts = ((Detection*)dt.data) + 1;
         int* count = dt.ptr<int>(0);