Open Source Computer Vision Library https://opencv.org/
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
// Sample of using Halide backend in OpenCV deep learning module.
// Based on dnn/samples/caffe_googlenet.cpp.
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
/* Find best class for the blob (i. e. class with maximal probability) */
void 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<std::string> readClassNames(const char *filename = "synset_words.txt")
{
std::vector<std::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;
}
int main(int argc, char **argv)
{
initModule(); // Required if OpenCV is built as static libs.
std::string modelTxt = "train_val.prototxt";
std::string modelBin = "squeezenet_v1.1.caffemodel";
std::string imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
//! [Read and initialize network]
Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
//! [Read and initialize network]
//! [Check that network was read successfully]
if (net.empty())
{
std::cerr << "Can't load network by using the following files: " << std::endl;
std::cerr << "prototxt: " << modelTxt << std::endl;
std::cerr << "caffemodel: " << modelBin << std::endl;
std::cerr << "SqueezeNet v1.1 can be downloaded from:" << std::endl;
std::cerr << "https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1" << std::endl;
exit(-1);
}
//! [Check that network was read successfully]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
if (img.channels() != 3)
{
std::cerr << "Image " << imageFile << " isn't 3-channel" << std::endl;
exit(-1);
}
resize(img, img, Size(227, 227)); // SqueezeNet v1.1 predict class by 3x227x227 input image.
Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(), false); // Convert Mat to 4-dimensional batch.
//! [Prepare blob]
//! [Set input blob]
net.setInput(inputBlob); // Set the network input.
//! [Set input blob]
//! [Enable Halide backend]
net.setPreferableBackend(DNN_BACKEND_HALIDE); // Tell engine to use Halide where it possible.
//! [Enable Halide backend]
//! [Make forward pass]
Mat prob = net.forward("prob"); // Compute output.
//! [Make forward pass]
//! [Determine the best class]
int classId;
double classProb;
getMaxClass(prob, &classId, &classProb); // Find the best class.
//! [Determine the best class]
//! [Print results]
std::vector<std::string> classNames = readClassNames();
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
return 0;
} //main