text: #include <caffe/caffe.hpp>
  #ifdef USE_OPENCV
  #include <opencv2/core/core.hpp>
  #include <opencv2/highgui/highgui.hpp>
  #include <opencv2/imgproc/imgproc.hpp>
  #endif    // USE_OPENCV
  #include <algorithm>
  #include <iosfwd>
  #include <memory>
  #include <string>
  #include <utility>
  #include <vector>
  
  #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 = 5);
  
  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.";
  }
  
  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], 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);
  
  #ifdef MOBILE_NET
      cv::Mat sample_normalized, sample_normalized1;
      cv::subtract(sample_float, mean_, sample_normalized1);
      cv::multiply(sample_normalized1, 0.017, sample_normalized);
  #else
      cv::Mat sample_normalized;
      cv::subtract(sample_float, mean_, sample_normalized);
  #endif
  
      /* 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_normalized, *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.";
  }
  
  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;
      }
  }
  #else
  int main(int argc, char** argv)
  {
      LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
  }
  #endif    // USE_OPENCV