Practice 2 Sedova

include/filter_SedovaSasha.h

@@ -0,0 +1,35 @@
+#pragma once
+#include <iostream>
+#include <string>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+using namespace cv;
+using namespace std;
+
+class Filter
+{
+    public:
+    virtual Mat ProcessImage(Mat image) = 0 {}
+};
+class GrayFilter : Filter
+{
+private:
+
+public:
+    Mat ProcessImage(Mat image);
+
+};
+
+class ResizeFilter : Filter
+{
+private:
+	int width;
+	int height;
+public:
+    ResizeFilter(int newWidth, int newHeight);
+
+    Mat ProcessImage(Mat image);
+
+};

samples/practice1_Sedova.cpp

@@ -0,0 +1,53 @@
+#include <iostream>
+#include <string>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+#include "filter_SedovaSasha.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"));
+	String path_image = parser.get<String>("i");
+	Mat image = cv::imread(path_image);
+	GrayFilter g1;
+	ResizeFilter r1(200,200);
+    // Filter image
+	Mat GImage = g1.ProcessImage(image);
+	Mat RImage = r1.ProcessImage(image);
+    // Show image
+	imshow("Gray", GImage);
+	imshow("Image", image);
+	imshow("Resize", RImage);
+	waitKey();
+
+    return 0;
+}

samples/practice1.cpp → samples/practice1_SedovaSasha.cpp

@@ -36,16 +36,14 @@ int main(int argc, char** argv)
 
     // Load image
     String imgName(parser.get<String>("image"));
-
-    
+	Mat image = imread("unn_neuromobile.jpg");
+	GrayFilter g1;
     // Filter image
-
+	Mat GImage = g1.ProcessImage(image);
 
     // Show image
-
-
-
-
+	imshow("Gray", GImage);
+	waitKey();
 
     return 0;
 }

samples/practice1_SedovaSasha1.cpp

@@ -0,0 +1,52 @@
+#include <iostream>
+#include <string>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+#include "filter_SedovaSasha.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 = cv::imread("C:\\Users\\temp2019\\Desktop\\CV-SUMMER-CAMP\\data\\unn_neuromobile.jpg");
+	GrayFilter g1;
+	ResizeFilter r1(200,200);
+    // Filter image
+	Mat GImage = g1.ProcessImage(image);
+	Mat RImage = r1.ProcessImage(image);
+    // Show image
+	imshow("Gray", GImage);
+	imshow("Image", image);
+	imshow("Resize", RImage);
+	waitKey();
+
+    return 0;
+}

samples/practice2_Sedova.cpp

@@ -0,0 +1,54 @@
+#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"));
+
+
+	//Image classification
+
+
+	//Show result
+
+
+	return 0;
+}

samples/practice2_Sedova1.cpp

@@ -0,0 +1,66 @@
+#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 path_image = parser.get<String>("i");
+	string path_to_model = parser.get<String>("model_path");
+	string path_to_config=parser.get<String>("config_path");
+	string path_to_labesparser=parser.get<String>("label_path");
+	int width = 10;
+		//=parser.get<int>("w");
+	int height = 10;
+		//= parser.get<int>("h");
+	Scalar mean= Scalar(0, 0, 0, 0);
+	int swapRB=0;
+	Mat image = cv::imread(path_image);
+	DnnClassificator dnn_class(path_to_model, path_to_config, path_to_labesparser, width, height, mean, swapRB);
+
+	//Image classification
+
+	dnn_class.Classify(image);
+	//minMaxLoc(image,100,100,100,100);
+	//Show result
+	imshow("image", image);
+	waitKey();
+	return 0;
+}

samples/practice2_SedovaS.cpp

@@ -0,0 +1,70 @@
+#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 path_image = parser.get<String>("i");
+	string path_to_model = parser.get<String>("model_path");
+	string path_to_config=parser.get<String>("config_path");
+	string path_to_labesparser=parser.get<String>("label_path");
+	int width = 10;
+		//=parser.get<int>("w");
+	int height = 10;
+		//= parser.get<int>("h");
+	Scalar mean= Scalar(0, 0, 0, 0);
+	int swapRB=0;
+	Mat image = cv::imread(path_image);
+	DnnClassificator dnn_class(path_to_model, path_to_config, path_to_labesparser, width, height, mean, swapRB);
+	Mat dnnImage = dnn_class.Classify(image);
+	//Image classification
+	Point classIdPoint;
+	double confidence;
+	dnn_class.Classify(image);
+	minMaxLoc(dnnImage.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
+	int classId = classIdPoint.x;
+	//Show result
+	imshow("image", image);
+	cout << "class:" << classId << "\n";
+	cout << "confidence:" << confidence<<"";
+	waitKey();
+	return 0;
+}

src/classificator_Sedova.cpp

@@ -0,0 +1,23 @@
+#include "classificator.h"
+#include <opencv2/opencv.hpp>
+DnnClassificator::DnnClassificator(string path_to_model, string path_to_config, string path_to_labes, int inputWidth, int inputHeight, vector<int> mean1, int flag_swapRB)
+{
+	width = inputWidth;
+	height = inputHeight;
+	path_to_config = this->path_to_config;
+	path_to_labes = this->path_to_labes;
+	path_to_model = this->path_to_model;
+	Net net = readNet(path_to_model, path_to_config);
+	net.setPreferableBackend(0);
+	net.setPreferableTarget(0);
+
+};
+Mat DnnClassificator::Classify(Mat image)
+{
+	Mat inputTensor;
+	Net net = readNet(path_to_model, path_to_config);
+	blobFromImage(image, inputTensor, 1.0, Size(width, height), mean, swapRB, false);
+	net.setInput(inputTensor);
+	inputTensor=net.forward();
+	return inputTensor;
+}