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main.cpp
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main.cpp
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#define CPU_ONLY
#define USE_OPENCV
#include "caffe/net.hpp"
#include "caffe/layers/memory_data_layer.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "vector"
#include "fstream"
int main(int argc, char** argv) {
// initialize logging
google::InitGoogleLogging(argv[0]);
google::SetCommandLineOption("GLOG_minloglevel", "2");
// load network
caffe::Net<float> net("res/deploy.prototxt", caffe::TEST);
net.CopyTrainedLayersFrom("res/squeezenet_v1.1.caffemodel");
// input layer
boost::shared_ptr<caffe::MemoryDataLayer<float> > inputLayer;
inputLayer = boost::static_pointer_cast<caffe::MemoryDataLayer<float> >(net.layer_by_name("data"));
// load image
cv::Mat img = cv::imread("res/cat.jpg");
cv::resize(img, img, cv::Size(inputLayer->height(), inputLayer->width()));
// classify
std::vector<cv::Mat> inputData(1, img);
inputLayer->AddMatVector(inputData, std::vector<int>(1));
const float* probs = net.Forward()[1]->cpu_data();
// print top-1 prediction
std::string className;
std::ifstream ilsvrcClasses("res/imagenet-classes.txt");
int class_id = 0;
while (std::getline(ilsvrcClasses, className)) {
if(class_id++ == probs[0]) {
printf("Class: '%s'\tScore: %.2f\n", className.c_str(), probs[1]);
break;
}
}
ilsvrcClasses.close();
return 0;
}