From b65a84fac0728d5a57f7ff6789b67ec250eb8a08 Mon Sep 17 00:00:00 2001
From: Anna Petrovicheva <anna.petrovicheva@itseez.com>
Date: Tue, 19 Jul 2016 15:32:14 +0300
Subject: [PATCH] Modified the sample

---
 modules/dnn/samples/ssd_object_detection.cpp | 152 +++++++++++++++++++
 1 file changed, 152 insertions(+)
 create mode 100644 modules/dnn/samples/ssd_object_detection.cpp

diff --git a/modules/dnn/samples/ssd_object_detection.cpp b/modules/dnn/samples/ssd_object_detection.cpp
new file mode 100644
index 000000000..77567f155
--- /dev/null
+++ b/modules/dnn/samples/ssd_object_detection.cpp
@@ -0,0 +1,152 @@
+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+using namespace cv;
+using namespace cv::dnn;
+
+#include <fstream>
+#include <iostream>
+#include <cstdlib>
+using namespace std;
+
+static const string fcnType = "fcn8s";
+
+
+static void colorizeSegmentation(dnn::Blob& score,
+                                 const vector<cv::Vec3b>& colors,
+                                 cv::Mat& segm)
+{
+    const int rows = score.rows();
+    const int cols = score.cols();
+    const int chns = score.channels();
+
+    cv::Mat maxCl(rows, cols, CV_8UC1);
+    cv::Mat maxVal(rows, cols, CV_32FC1);
+    for (int ch = 0; ch < chns; ch++)
+    {
+        for (int row = 0; row < rows; row++)
+        {
+            const float* ptrScore = score.ptrf(0, ch, row);
+            uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
+            float* ptrMaxVal = maxVal.ptr<float>(row);
+            for (int col = 0; col < cols; col++)
+            {
+                if (ptrScore[col] > ptrMaxVal[col])
+                {
+                    ptrMaxVal[col] = ptrScore[col];
+                    ptrMaxCl[col] = ch;
+                }
+            }
+        }
+    }
+
+    segm.create(rows, cols, CV_8UC3);
+    for (int row = 0; row < rows; row++)
+    {
+        const uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
+        cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row);
+        for (int col = 0; col < cols; col++)
+        {
+            ptrSegm[col] = colors[ptrMaxCl[col]];
+        }
+    }
+}
+
+const char* about = "This sample uses Single-Shot Detector to detect objects "
+                    "from camera\n"; // TODO: link
+
+const char* params
+    = "{ help    | help                | false | print usage         }"
+      "{ proto   | model prototxt file |       | model configuration }"
+      "{ model   | caffemodel file     |       | model weights       }";
+
+int main(int argc, char** argv)
+{
+    cv::CommandLineParser parser(argc, argv, params);
+
+    if (parser.get<bool>("help"))
+    {
+        std::cout << about << std::endl;
+        parser.printMessage();
+        return 0;
+    }
+
+    String modelConfiguration = parser.get<string>("proto");
+    String modelBinary = parser.get<string>("model");
+
+    //! [Create the importer of Caffe model]
+    Ptr<dnn::Importer> importer;
+
+    // Import Caffe SSD model
+    try
+    {
+        importer = dnn::createCaffeImporter(modelConfiguration, modelBinary);
+    }
+    catch (const cv::Exception &err) //Importer can throw errors, we will catch them
+    {
+        cerr << err.msg << endl;
+    }
+    //! [Create the importer of Caffe model]
+
+    if (!importer)
+    {
+        cerr << "Can't load network by using the following files: " << endl;
+        cerr << "prototxt:   " << modelConfiguration << endl;
+        cerr << "caffemodel: " << modelBinary << endl;
+        cerr << "Models can be downloaded here:" << endl;
+        cerr << "https://github.com/weiliu89/caffe/tree/ssd#models" << endl;
+        exit(-1);
+    }
+
+    //! [Initialize network]
+    dnn::Net net;
+    importer->populateNet(net);
+    importer.release();          //We don't need importer anymore
+    //! [Initialize network]
+
+    VideoCapture camera;
+    if (!camera.open(0))
+    {
+        cout << "Unable to open camera stream" << endl;
+        return 0;
+    }
+
+    for (;; )
+    {
+        Mat frame;
+        camera >> frame;
+        if (frame.empty())
+            break;
+
+
+        //! [Prepare blob]
+        resize(frame, frame, Size(300, 300));       //SSD accepts 300x300 RGB-images
+        dnn::Blob inputBlob = dnn::Blob(frame);       //Convert Mat to dnn::Blob image
+        //! [Prepare blob]
+
+        //! [Set input blob]
+        net.setBlob(".data", inputBlob);            //set the network input
+        //! [Set input blob]
+
+        //! [Make forward pass]
+        net.forward();                              //compute output
+        //! [Make forward pass]
+
+        //! [Gather output]
+        dnn::Blob detection = net.getBlob("detection_out");
+
+        //    cv::Mat colorize;
+        //    colorizeSegmentation(score, colors, colorize);
+        //    cv::Mat show;
+        //    cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
+        //    cv::imshow("show", show);
+        //    cv::waitKey(0);
+        //    return 0;
+
+        imshow("frame", frame);
+        if (waitKey(1) == 27)
+            break;  // stop capturing by pressing ESC
+    }
+
+    camera.release();
+} // main