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Image classification #9

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72b9bc8
init commit
Jun 4, 2017
71622b6
Windows commit.
SamoylovMihail Jun 23, 2017
ddcdcc5
dnn and tensorflow
ShlyapkinIN Jun 25, 2017
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28 changes: 28 additions & 0 deletions 3. Image Classification/HSEOpenCVProject/HSEOpenCVProject.sln
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158 changes: 158 additions & 0 deletions 3. Image Classification/HSEOpenCVProject/HSEOpenCVProject/HSEOpenCVProject.vcxproj
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30 changes: 30 additions & 0 deletions 3. Image Classification/HSEOpenCVProject/HSEOpenCVProject/HSEOpenCVProject.vcxproj.filters
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81 changes: 81 additions & 0 deletions 3. Image Classification/HSEOpenCVProject/HSEOpenCVProject/ImageClassification.cpp
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#include "ImageClassification.h"

//////////////////////////////////////////////////////////////////////////////////////////////////////

ImageClassification::ImageClassification()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////////////

ImageClassification::~ImageClassification()
{
}

//////////////////////////////////////////////////////////////////////////////////////////////////////

void ImageClassification::getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}

//////////////////////////////////////////////////////////////////////////////////////////////////////

std::vector<String> ImageClassification::readClassNames(const char *filename)
{
std::vector<String> classNames;
std::ifstream fp(filename);
if (!fp.is_open())
{
std::cerr << "File with classes labels not found: " << filename << std::endl;
exit(-1);
}
std::string name;
while (!fp.eof())
{
std::getline(fp, name);
if (name.length())
classNames.push_back(name.substr(name.find(' ') + 1));
}
fp.close();
return classNames;
}

//////////////////////////////////////////////////////////////////////////////////////////////////////

void ImageClassification::recognize(const string & modelTxtPath, const string & modelBinPath,
const string & classesFilePath, Mat & image)
{
cv::dnn::initModule(); //Required if OpenCV is built as static libs

Net net = dnn::readNetFromCaffe(modelTxtPath, modelBinPath);
if (net.empty())
{
std::cerr << "Can't load network by using the following files: " << std::endl;
std::cerr << "prototxt: " << modelTxtPath << std::endl;
std::cerr << "caffemodel: " << modelBinPath << std::endl;
std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
exit(-1);
}
Mat img = image;

resize(img, img, Size(224, 224)); //GoogLeNet accepts only 224x224 RGB-images
Mat inputBlob = blobFromImage(img); //Convert Mat to batch of images
net.setInput(inputBlob, "data"); //set the network input
Mat prob = net.forward("prob"); //compute output
int classId;
double classProb;
getMaxClass(prob, &classId, &classProb);//find the best class
if (classProb > 0.1)
{
std::vector<String> classNames = readClassNames(classesFilePath.c_str());
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
}
}

//////////////////////////////////////////////////////////////////////////////////////////////////////
34 changes: 34 additions & 0 deletions 3. Image Classification/HSEOpenCVProject/HSEOpenCVProject/ImageClassification.h
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#pragma once

#include <opencv2/core/utility.hpp>
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/dnn.hpp"
#include "opencv2/opencv.hpp"

#include <iostream>
#include <ctype.h>
#include <fstream>
#include <cstdlib>
#include <map>
#include <iomanip>
#include <time.h>
#include <string>

using namespace cv;
using namespace cv::dnn;
using namespace std;

class ImageClassification
{
public:
ImageClassification();
~ImageClassification();
std::vector<String> readClassNames(const char *filename = "synset_words.txt");
void getMaxClass(const Mat &probBlob, int *classId, double *classProb);
void recognize(const string & modelTxtPath, const string & modelBinPath,
const string & classesFilePath, Mat & image);
};

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