本文通过修改classification.cpp实现用训练好的model文件实现多张图片的分类。

classification.cpp中main函数的源码为：：：

int main(int argc, char** argv) {if (argc != 6) {std::cerr << "Usage: " << argv[0]<< " deploy.prototxt network.caffemodel"<< " mean.binaryproto labels.txt img.jpg" << std::endl;return 1;}::google::InitGoogleLogging(argv[0]);string model_file   = argv[1];string trained_file = argv[2];string mean_file    = argv[3];string label_file   = argv[4];Classifier classifier(model_file, trained_file, mean_file, label_file);string file = argv[5];std::cout << "---------- Prediction for "<< file << " ----------" << std::endl;cv::Mat img = cv::imread(file, -1);CHECK(!img.empty()) << "Unable to decode image " << file;std::vector<Prediction> predictions = classifier.Classify(img);/* Print the top N predictions. */for (size_t i = 0; i < predictions.size(); ++i) {Prediction p = predictions[i];std::cout << std::fixed << std::setprecision(4) << p.second << " - \""<< p.first << "\"" << std::endl;}
}

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根据博客http://blog.csdn.net/lanxuecc/article/details/52795344 我们知道，该文件编译生成的.bin只能分类单张，那么如果我们训练出来了一个caffemodel,我们如何实现分类大量图片来统计分类准确率呢，我把代码做了一些修改，实现了这个功能

int main(int argc, char** argv) {string imgfilename;if (argc != 6) {std::cerr << "Usage: " << argv[0]<< " deploy.prototxt network.caffemodel"<< " mean.binaryproto labels.txt img.jpg" << std::endl;return 1;}::google::InitGoogleLogging(argv[0]);string model_file   = argv[1];string trained_file = argv[2];string mean_file    = argv[3];string label_file   = argv[4];Classifier classifier(model_file, trained_file, mean_file, label_file);string file = argv[5];  //传入的字符串是保存所有图片路径的文件fstream fin( file.c_str());  string fnd = "sglimg";string rep = "sglimgbak";string imgsave;string strinsert;while(getline(fin, imgfilename))  //依次读入文件中每个图片路径{std::cout << "---------- Prediction for "<< imgfilename << " ----------" << std::endl;     imgsave = imgfilename;cv::Mat img = cv::imread(imgfilename, -1);   //读取图片CHECK(!img.empty()) << "Unable to decode image " << imgfilename;std::vector<Prediction> predictions = classifier.Classify(img);  //对图片进行分类imgsave = imgsave.erase(61,2);imgsave = imgsave.replace(imgsave.find(fnd), fnd.length(), rep);strinsert = predictions[0].first + "/";imgsave.insert(64, strinsert);std::cout << "---------- Save2  for "<<predictions[0].first<<"in"<< imgsave << " ----------" << std::endl;  imwrite(imgsave, img);  //按图片围住度最高的分类来保存图片}}

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修改后重新编译caffe就可以分类图片了

make clean  //在caffe根目录下
make all    //在caffe根目录下

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只是传入的图片路径参数，改为记录所有待分类图片的路径的文件。

例如：：：：我的所有待分类的图片在目录/home/schao/sc_tmp/caffe/caffe-master/examples/numlet/sglimg/0~9,A~Z中，那么传入程序的图片目录为（见下截图，只保存一部分） 

现在我把这0～9，A~Z文件夹内的图片分类分别保存到/home/schao/sc_tmp/caffe/caffe-master/examples/numlet/sglimgbak这个目录下0～9，A~Z目录中 

则代码改成如上形式并且编译后执行下述命令：：：

./build/examples/cpp_classification/classification.bin \
./examples/numlet/lenet.prototxt \
./examples/numlet/caffenet_train_iter_20000.caffemodel \
./examples/numlet/mean.binaryproto \
./examples/numlet/synset_words.txt \
./examples/numlet/sglimg/imglist.txt \

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