dnn: move samples

samples/dnn/ssd_object_detection.cpp

@@ -0,0 +1,154 @@
+#include <opencv2/dnn.hpp>
+#include <opencv2/dnn/shape_utils.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;
+
+const size_t width = 300;
+const size_t height = 300;
+
+Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
+{
+    Mat mean;
+
+    const int meanValues[3] = {104, 117, 123};
+    vector<Mat> meanChannels;
+    for(size_t i = 0; i < 3; i++)
+    {
+        Mat channel(imageHeight, imageWidth, CV_32F, Scalar(meanValues[i]));
+        meanChannels.push_back(channel);
+    }
+    cv::merge(meanChannels, mean);
+    return mean;
+}
+
+Mat preprocess(const Mat& frame)
+{
+    Mat preprocessed;
+    frame.convertTo(preprocessed, CV_32F);
+    resize(preprocessed, preprocessed, Size(width, height)); //SSD accepts 300x300 RGB-images
+
+    Mat mean = getMean(width, height);
+    cv::subtract(preprocessed, mean, preprocessed);
+
+    return preprocessed;
+}
+
+const char* about = "This sample uses Single-Shot Detector "
+                    "(https://arxiv.org/abs/1512.02325)"
+                    "to detect objects on image\n"; // TODO: link
+
+const char* params
+    = "{ help           | false | print usage         }"
+      "{ proto          |       | model configuration }"
+      "{ model          |       | model weights       }"
+      "{ image          |       | image for detection }"
+      "{ min_confidence | 0.5   | min confidence      }";
+
+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;
+    }
+
+    cv::dnn::initModule();          //Required if OpenCV is built as static libs
+
+    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]
+
+    cv::Mat frame = cv::imread(parser.get<string>("image"), -1);
+
+    if (frame.channels() == 4)
+        cvtColor(frame, frame, COLOR_BGRA2BGR);
+    //! [Prepare blob]
+    Mat preprocessedFrame = preprocess(frame);
+
+    Mat inputBlob = blobFromImage(preprocessedFrame); //Convert Mat to batch of images
+    //! [Prepare blob]
+
+    //! [Set input blob]
+    net.setInput(inputBlob, "data");                //set the network input
+    //! [Set input blob]
+
+    //! [Make forward pass]
+    Mat detection = net.forward("detection_out");                                  //compute output
+    //! [Make forward pass]
+
+    Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
+
+    float confidenceThreshold = parser.get<float>("min_confidence");
+    for(int i = 0; i < detectionMat.rows; i++)
+    {
+        float confidence = detectionMat.at<float>(i, 2);
+
+        if(confidence > confidenceThreshold)
+        {
+            size_t objectClass = detectionMat.at<float>(i, 1);
+
+            float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
+            float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
+            float xRightTop = detectionMat.at<float>(i, 5) * frame.cols;
+            float yRightTop = detectionMat.at<float>(i, 6) * frame.rows;
+
+            std::cout << "Class: " << objectClass << std::endl;
+            std::cout << "Confidence: " << confidence << std::endl;
+
+            std::cout << " " << xLeftBottom
+                      << " " << yLeftBottom
+                      << " " << xRightTop
+                      << " " << yRightTop << std::endl;
+
+            Rect object(xLeftBottom, yLeftBottom,
+                        xRightTop - xLeftBottom,
+                        yRightTop - yLeftBottom);
+
+            rectangle(frame, object, Scalar(0, 255, 0));
+        }
+    }
+
+    imshow("detections", frame);
+    waitKey();
+
+    return 0;
+} // main