實現目標：

1、載入一張圖片，應用深度學習框架Caffe訓練好的模型分類圖片，顯示圖片的類別，輸出到控制檯；
2、載入一個資料夾，分類所有資料夾內的圖片，非圖片檔案選擇無視，生成同名txt儲存所屬類別。

3、工程project實現

一、/home/name/caffe-master/examples/cpp_classification/classification.cpp這個檔案是呼叫Caffe框架介面的事例，它對應的程式是這個：/home/name/caffe-master/build/examples/cpp_classification/classification.bin

執行這個程式並填上網路、模型、均值檔案、標籤引數和圖片即可對單張圖片進行分類。C++分類的呼叫介面也參照這個CPP。

二、現在仿照這個CPP對本地某個檔案內的所有jpg圖片分類。假設需要對某些輸入的照片進行分類，修改圖片來源介面即可。

txt按行為程式提供引數：資料夾名、模型網路、模型、均值檔案、標籤
修改如下：

//#define USE_OPENCV
#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>

#include <sys/types.h>
#include<dirent.h>
#include<stdio.h>
#include<sys/stat.h>
#include<iostream>
#include<fstream>

//下面這行不可用！
//using namespace std;
#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);

  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_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.";
}

//遍歷資料夾類檔名函式
char filename[256][256];
int len = 0;
int trave_dir(char* path, int depth)
{
    DIR *d; //宣告一個控制代碼
    struct dirent *file; //readdir函式的返回值就存放在這個結構體中
    struct stat sb;

    if(!(d = opendir(path)))
    {
        printf("error opendir %s!!!\n",path);
        return -1;
    }
    while((file = readdir(d)) != NULL)
    {
        //把當前目錄.，上一級目錄..及隱藏檔案都去掉，避免死迴圈遍歷目錄
        if(strncmp(file->d_name, ".", 1) == 0)
            continue;
        strcpy(filename[len++], file->d_name); //儲存遍歷到的檔名
        //判斷該檔案是否是目錄，及是否已搜尋了三層，這裡我定義只搜尋了三層目錄，太深就不搜了，省得搜出太多檔案
        //stat
        if(stat(file->d_name, &sb) >= 0 && S_ISDIR(sb.st_mode) && depth <= 3)
        {
            trave_dir(file->d_name, depth + 1);
        }
    }
    closedir(d);
    return 0;
}

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 ar[10];
    if(argc!=2){
        std::cerr<<"程式需要一個包含5行文字的引數："<<std::endl<<"第1行資料夾名（含路徑）,然後各行依次分別是模型網路、模型、均值檔案、標籤"<<std::endl;
        return 1;
    }
    fstream arg_file;
    arg_file.open(argv[1]);
    if(!arg_file){
        std::cerr<<"打不開引數檔案"<<std::endl;
        return 1;
    }
    else{
        for(int i=0;!arg_file.eof();i++){
            getline(arg_file,ar[i],'\n');
        }
    }
//  string model_file   = argv[1];
//  string trained_file = argv[2];
//  string mean_file    = argv[3];
//  string label_file   = argv[4];
    string dir=ar[0];
    char* p=(char*)dir.data();
    string model_file   = ar[1];
    string trained_file = ar[2];
    string mean_file    = ar[3];
    string label_file   = ar[4];
  Classifier classifier(model_file, trained_file, mean_file, label_file);

//  string file = argv[5];
  int depth=1;
  //遍歷獲取檔名、總數了
  trave_dir(p,depth);
  for(int j=0;j<len;j++){

      //char轉化為string
      string filename_str=filename[j];
      //判斷是不是jpg圖片
      int len1=filename_str.length();
      string argv2;
      for(int i=3;i>=0;i--){
         argv2.push_back(filename_str[len1-i-1]);
      }
      if(argv2!=".jpg"){
          std::cout<<filename[j]<<"不是jpg圖片～～～～～"<<std::endl;
          continue;
      }

      string file=dir+"//"+filename[j];

      std::cout << "---------- Prediction for "
                << filename[j] << " ----------" << 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