practice 3 Troynyakov Vladimir

include/detectedobject.h

@@ -3,10 +3,16 @@
 
 struct DetectedObject
 {
-    int Left;
-    int Right;
-    int Top;
-    int Bottom;
-    int uuid;
-    std::string classname;
+	int classid;
+	std::string className;
+	float score;
+	float left;
+	float bottom;
+	float right;
+	float top;
+
+
+	DetectedObject(int c, std::string cn, float s, float l, float b, float r, float t) : classid(c), className(cn), score(s), left(l), bottom(b), right(r), top(t)
+	{
+	}
 };

include/detector.h

@@ -1,6 +1,7 @@
 #pragma once
 #include <iostream>
 #include <string>
+#include <fstream>
 
 #include <opencv2/dnn.hpp>
 #include <opencv2/imgproc.hpp>
@@ -16,4 +17,22 @@ class Detector
 {
 public:
     virtual vector<DetectedObject> Detect(Mat image) = 0 {}
+	virtual string DecodeLabel(int n) = 0 {}
+};
+
+class DnnDetector : public Detector {
+private:
+	string path_to_model;
+	string path_to_config;
+	string path_to_labels;
+	int input_width = 300;
+	int input_height = 300;
+	double scale = 0.007843;
+	Scalar mean = Scalar(127.5, 127.5, 127.5);;
+	bool swapRB = false;
+	Net net;
+public:
+	DnnDetector(string path_to_model, string path_to_config, string path_to_labels);
+	vector<DetectedObject> Detect(Mat image);
+	string DecodeLabel(int n);
 };

samples/practice3_TroynyakovVladimir.cpp

@@ -0,0 +1,59 @@
+#include <string>
+#include <iostream>
+#include "detector.h"
+
+#include <opencv2/core.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/dnn.hpp>
+
+using namespace std;
+using namespace cv;
+using namespace cv::dnn;
+
+const char* cmdAbout =
+    "This is an empty application that can be treated as a template for your "
+    "own doing-something-cool applications.";
+
+const char* cmdOptions =
+"{ i  image                             | <none> | image to process                  }"
+"{ m  model_path                           |        | path to model                     }"
+"{ c  config_path                          |        | path to model configuration       }"
+"{ l  label_path                           |        | path to class labels              }"
+"{ h ? help usage                       |        | print help message                }";
+
+
+int main(int argc, const char** argv) {
+  // Parse command line arguments.
+  CommandLineParser parser(argc, argv, cmdOptions);
+  parser.about(cmdAbout);
+
+  // If help option is given, print help message and exit.
+  if (parser.get<bool>("help")) {
+    parser.printMessage();
+    return 0;
+  }
+
+  string imgName(parser.get<string>("image"));
+  string model_path(parser.get<string>("model_path"));
+  string config_path(parser.get<string>("config_path"));
+  string label_path(parser.get<string>("label_path"));
+  Mat image = imread(imgName);
+
+  Detector* detector = new DnnDetector(model_path, config_path, label_path);
+  vector<DetectedObject> vec = detector->Detect(image);
+  for (int i = 0; i < vec.size(); i++) {
+		  Point leftbottom(vec[i].left, vec[i].bottom);
+		  Point righttop(vec[i].right, vec[i].top);
+		  Rect rect(leftbottom, righttop);
+		  rectangle(image, rect, Scalar(0, 255, 0), 2);
+		  cout << "Class: " << vec[i].className << endl;
+		  cout << "Confidence: " << to_string(vec[i].score) << endl;
+		  cout << leftbottom << " " << righttop << endl;
+		  //cout << vec[i].left << " " << vec[i].right << " " << vec[i].bottom << " " << vec[i].top;
+
+  }
+  imshow("Detected", image);
+  waitKey(0);
+  delete detector;
+  return 0;
+}

src/detector.cpp

@@ -1 +1,48 @@
-#include "detector.h"
+#include "detector.h"
+DnnDetector::DnnDetector(string path_to_model,
+	string path_to_config,
+	string path_to_labels) {
+	this->path_to_model = path_to_model;
+	this->path_to_config = path_to_config;
+	this->path_to_labels = path_to_labels;
+
+	Net net = readNet(this->path_to_model, this->path_to_config);
+	int backendId = DNN_BACKEND_OPENCV;
+	int targetId = DNN_TARGET_CPU;
+	net.setPreferableBackend(backendId);
+	net.setPreferableTarget(targetId);
+	this->net = net;
+}
+
+
+vector<DetectedObject> DnnDetector::Detect(Mat image) {
+	Mat inputTensor;
+	blobFromImage(image, inputTensor, scale, Size(input_width, input_height), mean, swapRB, false);
+	net.setInput(inputTensor);
+	Mat output = net.forward().reshape(1, 1);
+	output = output.reshape(1, output.cols / 7);
+	vector<DetectedObject> vecobj;
+	for (int i = 0; i < output.rows; i++)
+	{
+		vecobj.push_back(DetectedObject((int)output.at<float>(i, 1),
+										this->DecodeLabel((int)output.at<float>(i, 1) - 1),
+										output.at<float>(i, 2), 
+										output.at<float>(i, 3)*image.cols,
+										output.at<float>(i, 4)*image.rows,
+										output.at<float>(i, 5)*image.cols,
+										output.at<float>(i, 6)*image.rows));
+	}
+	return vecobj;
+
+}
+
+
+string DnnDetector::DecodeLabel(int n) {
+	ifstream file_labels(path_to_labels);
+	vector<string> classesNames;
+	string line;
+	while (getline(file_labels, line)) {
+		classesNames.push_back(line);
+	}
+	return classesNames[n];
+}