C++实现动物分类识别（caffe）

版权声明：学无止境，好好学习 https://blog.csdn.net/m0_38116269/article/details/88579622

前言：
C++代码部分借鉴的caffe中自带的classification.cpp稍微改动了一下，这一部分没有花费我太多时间。让我头疼的是CMakeLists.txt文件的编写，从昨天晚上一直搞到现在，终于现在是知道怎么才能正确的写了。期间犯了挺多错误，有的caffe目录还有libboost以及opencv的include目录你不放进去就会出现不识别的报错。没办法，对cmake不够了解，继续加油吧。

一、classify.cpp的编写

#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>

using namespace caffe;  // NOLINT(build/namespaces)
using std::string;
using namespace cv;
using namespace std;
/* 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.";
}

int main() {
    string model_file = "/your_path_to_caffe/caffe/models/animal_classifier/deploy.prototxt";
    string trained_file = "/your_path_to_caffe/caffe/models/animal_classifier/the_resnet_18_iter_20000.caffemodel";
    string label_file = "/your_path_to_caffe/caffe/data/animal_picture/labels.txt";
    string mean_file = "/your_path_to_caffe/caffe/data/animal_picture/animal_mean.binaryproto";
    Caffe::set_mode(Caffe::GPU);
    Classifier classifier(model_file, trained_file, mean_file, label_file);
    string filename;
    DIR *dir;
    struct dirent *entry;
    string filepath = "/your_path_to_data/data/";
    dir = opendir("/your_path_to_data/data/");
    while((entry = readdir(dir)) != NULL){
      if(strcmp(entry->d_name, ".")!=0 && strcmp(entry->d_name,"..")!=0){
        filename = filepath + string(entry->d_name);
        Mat img = imread(filename);
        if(img.empty()){
          cout<<"Unable to decode image " << filename<<endl;
          break;
        }
        //CHECK(!img.empty()) << "Unable to decode image " << filename;
        std::vector<Prediction> predictions = classifier.Classify(img, 1);

        for (size_t i = 0; i < predictions.size(); ++i) {
        Prediction p = predictions[i];
        std::cout <<entry->d_name<<": "<< std::fixed << std::setprecision(4) << p.second << " - \""
                << p.first << "\"" << std::endl;
        }

      }
      
    }
}
 

二、CMakeLists.txt的编写
（1）先学习一下cmake工程的建立方法 这是基础版的方法，以后得好好详细学习一下。还要搞清楚文件编译原理，头文件调用过程等等知识都需要恶补。
（2）参考了一下这篇博客，这篇博客写的很好，但是有点错误，缺了点东西，我自己在理解的基础上给自己的CMake调整一下成功编译通过。
这是我编写的：

cmake_minimum_required (VERSION 2.8)
 
project (classify_project)

#设定可执行二进制文件的目录
SET( EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/bin) 
add_executable(classify classify.cpp)

include_directories ( /your_path_to_caffe/caffe/include
    /your_path_to_caffe/caffe/build/include
    /usr/local/include
    /usr/local/cuda/include
    /usr/include)
 
target_link_libraries(classify
    /your_path_to_caffe/caffe/build/lib/libcaffe.so
    /usr/lib/x86_64-linux-gnu/libopencv_highgui.so 
    /usr/lib/x86_64-linux-gnu/libopencv_core.so 
    /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so
    /usr/lib/x86_64-linux-gnu/libglog.so
    /usr/lib/x86_64-linux-gnu/libboost_system.so
    )

输入如下命令

mkdir bin
mkdir build
cd build
cmake ..
make

命令执行过程

这是我建立的文件夹目录
这是最后生成的可执行文件

成功了，看来只要肯花时间，难题总会解决的，只要肯下功夫，小白也不会一直菜下去，现在看起来，也没什么难的，主要是自己对一些编译原理不懂，才会花了那么多功夫，不过慢慢的经验知识积累多了，以后就不会耗费那么多时间了。学无止境，怀着对知识的敬畏之心，好好学习鸭~

后续继续改进：
1.需要能够批量检测一堆图片，然后将预测结果保存到一个txt当中。
2.能够读取视频帧，每隔100ms往网络当中输送一张图片，并输出检测结果

刚才看到了个很好的博客：https://blog.csdn.net/haluoluo211/article/details/82958604 caffe c++示例（mnist 多层感知机c++训练，测试），待会可以试试学习一下。