Практика 5. Перов Дима.

include/classificator.h

@@ -16,4 +16,28 @@ class Classificator
 public:
     vector<string> classesNames;
     virtual Mat Classify(Mat image) = 0 {}
+};
+
+
+
+class DnnClassificator :Classificator
+{
+private:
+    string model, config, labels;
+    int width, height, swapRB;
+    Scalar mean;
+    Net net;
+    int backendId;
+    int targetId;
+    Mat blob;
+    double scale;
+    int ddepth;
+    bool crop;
+
+
+public:
+    DnnClassificator(string pthModel, string pthConfig, string pthLabels, int inputWidth, int inputHeight, 
+        Scalar myMean = (0, 0, 0, 0), int mySwapRB = 0);
+   virtual Mat Classify(Mat image);
+
 };

samples/practice1_PEROV_DIMA.cpp

@@ -0,0 +1,58 @@
+#include <iostream>
+#include <string>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+#include "filter.h"
+
+using namespace cv;
+using namespace std;
+
+const char* cmdAbout = "Sample of OpenCV usage. ";
+
+const char* cmdOptions =
+"{ i  image         | <none> | image to process        }"
+"{ w  width         | <none> | width for image resize  }"
+"{ h  height        | <none> | height for image resize }"
+"{ q ? help usage   | <none> | print help message      }";
+
+int main(int argc, char** argv)
+{
+    // Process input arguments
+    CommandLineParser parser(argc, argv, cmdOptions);
+    parser.about(cmdAbout);
+
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+    if (!parser.check())
+    {
+        parser.printErrors();
+        return 0;
+    }
+
+    // Load image
+    String imgName(parser.get<String>("image"));
+    Mat image = imread(imgName);
+    Mat res(image.size(), CV_8UC3);
+    Mat res1(image.size(), CV_8UC1);
+
+    // Filter image
+
+    GrayFilter myFilter;
+    ResizeFilter myResizeFilter(parser.get<int>("width"), parser.get<int>("height"));
+
+    res = myResizeFilter.ProcessImage(image);
+    res1 = myFilter.ProcessImage(res);
+
+    // Show image
+
+    imwrite("res.jpg", res1);
+    imshow("image", res1);
+    waitKey();
+
+    return 0;
+}

samples/practice2_PEROV_DIMA.cpp

@@ -0,0 +1,101 @@
+#include <iostream>
+#include <fstream>
+#include <string>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+#include "classificator.h"
+
+using namespace cv;
+using namespace std;
+
+const char* cmdAbout = "Sample of OpenCV usage. ";
+
+const char* cmdOptions =
+"{ i  image                             | <none> | image to process                  }"
+"{ w  width                             |        | image width for classification    }"
+"{ h  heigth                            |        | image heigth fro classification   }"
+"{ model_path                           |        | path to model                     }"
+"{ config_path                          |        | path to model configuration       }"
+"{ label_path                           |        | path to class labels              }"
+"{ mean                                 |        | vector of mean model values       }"
+"{ swap                                 |        | swap R and B channels. TRUE|FALSE }"
+"{ q ? help usage                       |        | print help message                }";
+
+int main(int argc, char** argv)
+{
+	// Process input arguments
+	CommandLineParser parser(argc, argv, cmdOptions);
+	parser.about(cmdAbout);
+
+	if (parser.has("help"))
+	{
+		parser.printMessage();
+		return 0;
+	}
+	if (!parser.check())
+	{
+		parser.printErrors();
+		return 0;
+	}
+
+	// Load image and init parameters
+	String imgName(parser.get<String>("image"));
+    String model_path(parser.get<String>("model_path"));
+    String config_path(parser.get<String>("config_path"));
+    String path_label(parser.get<String>("label_path"));
+    int width(parser.get<int>("width"));
+    int height(parser.get<int>("heigth"));
+    Scalar mean(parser.get<Scalar>("mean"));
+    int swapRB(parser.get<int>("swap"));
+
+    DnnClassificator dnn(model_path, config_path, 
+      path_label, width , height, mean, swapRB);
+
+    Mat image = imread(imgName);
+    Mat prob;
+	//Image classification
+
+    prob = dnn.Classify(image);
+
+	//Show result
+    Point classIdPoint;
+    double confidence;
+    minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
+
+    int classId = classIdPoint.x;
+
+    std::cout << "Class: " << classId << '\n';
+    std::cout << "Confidence: " << confidence << '\n';
+
+    std::string name;
+
+    std::ifstream in("../../CV-SUMMER-CAMP/data/squeezenet1.1.labels"); 
+    int count = 0;
+    if (in.is_open())
+    {
+        while (getline(in, name))
+        {
+            if (count == classId)
+            {
+                break;
+            }
+            count++;
+
+        }
+    }
+    in.close();
+
+
+
+    Mat res = image;
+    putText(res, "Class: "+to_string(classId), Point(30, 30), FONT_HERSHEY_COMPLEX_SMALL, 0.8, Scalar(200, 200, 250));
+    putText(res, "Confidence: " + to_string(confidence), Point(30, 60), FONT_HERSHEY_COMPLEX_SMALL, 0.8, Scalar(200, 200, 250));
+    putText(res, name, Point(30, 90), FONT_HERSHEY_COMPLEX_SMALL, 0.8, Scalar(200, 200, 250));
+
+
+    imshow("image", res);
+    waitKey();
+	return 0;
+}