这个是在之前的博客点击打开链接去掉均值部分的classification.cpp的基础上进行改动的，如果不去均值用caffe自带的classification.cpp，改动的部分都是一样的。
改动是添加了一个ReadImageFromFile函数，用来将txt中的每一行内容作为一个单独的元素存到vector容器中。在主函数中用范围for语句遍历vector容器即可。更多的注释都在代码里标明了。
改动后的classification.cpp是在VS2013下新建的工程项目里添加进源文件中的，并没有用bat文件运行。如何在VS2013下新建一个caffe相关的工程项目请参考之前的博客点击打开链接。
#include <caffe/caffe.hpp> #ifdef USE_OPENCV #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include<head.h> #endif // USE_OPENCV #include <algorithm> #include <iosfwd> #include <memory> #include <string> #include <utility> #include <vector> #include<fstream> #ifdef USE_OPENCV using namespace caffe; // NOLINT(build/namespaces) using std::string; /* Pair (label, confidence) representing a prediction. */ typedef std::pair<string, float> Prediction; class Classifier { public: Classifier(const string& model_file, const string& trained_file, // const string& mean_file, const string& label_file); std::vector<Prediction> Classify(const cv::Mat& img, int N = 1); private: //void SetMean(const string& mean_file); std::vector<float> Predict(const cv::Mat& img); void WrapInputLayer(std::vector<cv::Mat>* input_channels); void Preprocess(const cv::Mat& img, std::vector<cv::Mat>* input_channels); private: shared_ptr<Net<float> > net_; cv::Size input_geometry_; int num_channels_; //cv::Mat mean_; std::vector<string> labels_; }; Classifier::Classifier(const string& model_file, const string& trained_file, //const string& mean_file, const string& label_file) { #ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU); #else Caffe::set_mode(Caffe::GPU); #endif /* Load the network. */ net_.reset(new Net<float>(model_file, TEST)); net_->CopyTrainedLayersFrom(trained_file); CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; Blob<float>* input_layer = net_->input_blobs()[0]; num_channels_ = input_layer->channels(); CHECK(num_channels_ == 3 || num_channels_ == 1) << "Input layer should have 1 or 3 channels."; input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); /* Load the binaryproto mean file. */ //SetMean(mean_file); /* Load labels. */ std::ifstream labels(label_file.c_str()); CHECK(labels) << "Unable to open labels file " << label_file; string line; while (std::getline(labels, line)) labels_.push_back(string(line)); Blob<float>* output_layer = net_->output_blobs()[0]; CHECK_EQ(labels_.size(), output_layer->channels()) << "Number of labels is different from the output layer dimension."; }//至此Classifier类的构造函数的定义结束 static bool PairCompare(const std::pair<float, int>& lhs, const std::pair<float, int>& rhs) { return lhs.first > rhs.first; } /* Return the indices of the top N values of vector v. */ static std::vector<int> Argmax(const std::vector<float>& v, int N) { std::vector<std::pair<float, int> > pairs; for (size_t i = 0; i < v.size(); ++i) pairs.push_back(std::make_pair(v[i], static_cast<int>(i))); std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare); std::vector<int> result; for (int i = 0; i < N; ++i) result.push_back(pairs[i].second); return result; } /* Return the top N predictions. */ std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) { std::vector<float> output = Predict(img); N = std::min<int>(labels_.size(), N); std::vector<int> maxN = Argmax(output, N); std::vector<Prediction> predictions; for (int i = 0; i < N; ++i) { int idx = maxN[i]; predictions.push_back(std::make_pair(labels_[idx], output[idx])); } return predictions; } /* Load the mean file in binaryproto format. */ //void Classifier::SetMean(const string& mean_file) { //BlobProto blob_proto; // ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); /* Convert from BlobProto to Blob<float> */ // Blob<float> mean_blob; // mean_blob.FromProto(blob_proto); // CHECK_EQ(mean_blob.channels(), num_channels_) // << "Number of channels of mean file doesn't match input layer."; /* The format of the mean file is planar 32-bit float BGR or grayscale. */ // std::vector<cv::Mat> channels; // float* data = mean_blob.mutable_cpu_data(); //for (int i = 0; i < num_channels_; ++i) { /* Extract an individual channel. */ //cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); //channels.push_back(channel); // data += mean_blob.height() * mean_blob.width(); // } /* Merge the separate channels into a single image. */ //cv::Mat mean; //cv::merge(channels, mean); /* Compute the global mean pixel value and create a mean image * filled with this value. */ //cv::Scalar channel_mean = cv::mean(mean); //mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); //} std::vector<float> Classifier::Predict(const cv::Mat& img) { Blob<float>* input_layer = net_->input_blobs()[0]; input_layer->Reshape(1, num_channels_, input_geometry_.height, input_geometry_.width); /* Forward dimension change to all layers. */ net_->Reshape(); std::vector<cv::Mat> input_channels; WrapInputLayer(&input_channels); Preprocess(img, &input_channels); net_->Forward(); /* Copy the output layer to a std::vector */ Blob<float>* output_layer = net_->output_blobs()[0]; const float* begin = output_layer->cpu_data(); const float* end = begin + output_layer->channels(); return std::vector<float>(begin, end); } /* Wrap the input layer of the network in separate cv::Mat objects * (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing * operation will write the separate channels directly to the input * layer. */ void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) { Blob<float>* input_layer = net_->input_blobs()[0]; int width = input_layer->width(); int height = input_layer->height(); float* input_data = input_layer->mutable_cpu_data(); for (int i = 0; i < input_layer->channels(); ++i) { cv::Mat channel(height, width, CV_32FC1, input_data); input_channels->push_back(channel); input_data += width * height; } } void Classifier::Preprocess(const cv::Mat& img, std::vector<cv::Mat>* input_channels) { /* Convert the input image to the input image format of the network. */ cv::Mat sample; if (img.channels() == 3 && num_channels_ == 1) cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY); else if (img.channels() == 4 && num_channels_ == 1) cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY); else if (img.channels() == 4 && num_channels_ == 3) cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR); else if (img.channels() == 1 && num_channels_ == 3) cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR); else sample = img; cv::Mat sample_resized; if (sample.size() != input_geometry_) cv::resize(sample, sample_resized, input_geometry_); else sample_resized = sample; cv::Mat sample_float; if (num_channels_ == 3) sample_resized.convertTo(sample_float, CV_32FC3); else sample_resized.convertTo(sample_float, CV_32FC1); //cv::Mat sample_normalized; //cv::subtract(sample_float, mean_, sample_normalized); /* This operation will write the separate BGR planes directly to the * input layer of the network because it is wrapped by the cv::Mat * objects in input_channels. */ cv::split(sample_float, *input_channels); CHECK(reinterpret_cast<float*>(input_channels->at(0).data) == net_->input_blobs()[0]->cpu_data()) << "Input channels are not wrapping the input layer of the network."; } /*读取txt文本中的内容，并将每一行作为单独的元素给vec*/ void ReadImageFromFile(const string&filename, vector<string>&vec) { std::ifstream in(filename); if (in) { string buf; while (getline(in, buf)) vec.push_back(buf); } else std::cout << "File could not be found!" << std::endl; } /*主函数开始*/ /*把你的每一张待分类图片的绝对路径写入txt文本的一行，按这样的格式把批量的图片路径通过若干行写入txt文本中*/ int main(int argc, char** argv) { vector<string>vec; /*给txtname的路径就是写好图片绝对路径的那个txt文件的路径*/ const string& txtname = "E:\\platform\\caffe-master\\examples\\Median_Filtering_Forensics\\classification_image.txt"; /*调用ReadImageFromFile函数把txt文本的每一行内容按单独元素给vec*/ ReadImageFromFile(txtname, vec); ::google::InitGoogleLogging(argv[0]); argv[1] = "E:\\platform\\caffe-master\\examples\\Median_Filtering_Forensics\\deploy.prototxt"; argv[2] = "E:\\platform\\caffe-master\\examples\\Median_Filtering_Forensics\\mfr_imagedatalayer_iter_20000.caffemodel"; argv[3] = "E:\\platform\\caffe-master\\examples\\Median_Filtering_Forensics\\synset_words.txt"; string model_file = argv[1]; string trained_file = argv[2]; string label_file = argv[3]; Classifier classifier(model_file, trained_file, label_file); /*通过范围for语句遍历vec中的所有元素，即每张图片的绝对路径，file就是每张图片的绝对路径的字符串*/ for ( auto&file : vec) { 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; std::cout << std::endl; } } system("pause"); } #else int main(int argc, char** argv) { LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV."; } #endif // USE_OPENCVtxt文本内容如图所示：
程序运行后的结果如图所示：
