Added DNN Darknet Yolo v2 for object detection

samples/dnn/yolo_object_detection.cpp

@@ -0,0 +1,117 @@
+#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 <algorithm>
+#include <cstdlib>
+using namespace std;
+
+const size_t network_width = 416;
+const size_t network_height = 416;
+
+const char* about = "This sample uses You only look once (YOLO)-Detector "
+                    "(https://arxiv.org/abs/1612.08242)"
+                    "to detect objects on image\n"; // TODO: link
+
+const char* params
+    = "{ help           | false | print usage         }"
+      "{ cfg            |       | model configuration }"
+      "{ model          |       | model weights       }"
+      "{ image          |       | image for detection }"
+      "{ min_confidence | 0.24  | 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;
+    }
+
+    String modelConfiguration = parser.get<string>("cfg");
+    String modelBinary = parser.get<string>("model");
+
+    //! [Initialize network]
+    dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
+    //! [Initialize network]
+
+    if (net.empty())
+    {
+        cerr << "Can't load network by using the following files: " << endl;
+        cerr << "cfg-file:     " << modelConfiguration << endl;
+        cerr << "weights-file: " << modelBinary << endl;
+        cerr << "Models can be downloaded here:" << endl;
+        cerr << "https://pjreddie.com/darknet/yolo/" << endl;
+        exit(-1);
+    }
+
+    cv::Mat frame = cv::imread(parser.get<string>("image"));
+
+    //! [Resizing without keeping aspect ratio]
+    cv::Mat resized;
+    cv::resize(frame, resized, cv::Size(network_width, network_height));
+    //! [Resizing without keeping aspect ratio]
+
+    //! [Prepare blob]
+    Mat inputBlob = blobFromImage(resized, 1 / 255.F); //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]
+    cv::Mat detectionMat = net.forward("detection_out");	//compute output
+   //! [Make forward pass]
+
+
+    float confidenceThreshold = parser.get<float>("min_confidence");
+    for (int i = 0; i < detectionMat.rows; i++)
+    {
+        const int probability_index = 5;
+        const int probability_size = detectionMat.cols - probability_index;
+        float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
+
+        size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
+        float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
+
+        if (confidence > confidenceThreshold)
+        {
+            float x = detectionMat.at<float>(i, 0);
+            float y = detectionMat.at<float>(i, 1);
+            float width = detectionMat.at<float>(i, 2);
+            float height = detectionMat.at<float>(i, 3);
+            float xLeftBottom = (x - width / 2) * frame.cols;
+            float yLeftBottom = (y - height / 2) * frame.rows;
+            float xRightTop = (x + width / 2) * frame.cols;
+            float yRightTop = (y + height / 2) * frame.rows;
+
+            std::cout << "Class: " << objectClass << std::endl;
+            std::cout << "Confidence: " << confidence << std::endl;
+
+            std::cout << " " << xLeftBottom
+                << " " << yLeftBottom
+                << " " << xRightTop
+                << " " << yRightTop << std::endl;
+
+            Rect object((int)xLeftBottom, (int)yLeftBottom,
+                (int)(xRightTop - xLeftBottom),
+                (int)(yRightTop - yLeftBottom));
+
+            rectangle(frame, object, Scalar(0, 255, 0));
+        }
+    }
+
+    imshow("detections", frame);
+    waitKey();
+
+    return 0;
+} // main