使用Caffe C++ API调用生成模型进行分类

一时失言乱红尘 2022-05-19 11:39 157阅读 0赞

# 1. 前言 #

对于在Caffe环境之间设计和训练模型，一般是采用python接口，这样比较方便。但是到实际部署的时候，为了速度等因素的考虑，都会使用Caffe下的C++ API接口实现的。在这里使用Caffe自带的classification.cpp进行改造，希望能给大家理解带来帮助。

# 2. 实现 #

## 2.1 Classifier类 ##

首先来看类的声明文件classification.h：

#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;
    
    /* Pair (label, confidence) representing a prediction. */
    typedef std::pair<string, float> Prediction; //用作结果保存的数据机构，label名称和概率
    
    //类的声明
    class Classifier 
    { 
    public:
        Classifier(const string& model_file, //Caffe model的部署文件也就是deploy文件
                 const string& trained_file, //model文件
                 const string& mean_file, //训练集均值文件
                 const string& label_file); //标签文本
    	//调用进行分类的接口，默认返回前五个最有可能的类别
        std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
    
    private:
    	//设置均值文件
        void SetMean(const string& mean_file);
    	//前向传播，实现分类
        std::vector<float> Predict(const cv::Mat& img);
    	//数据输入
        void WrapInputLayer(std::vector<cv::Mat>* input_channels);
    	//图像的预处理
        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_;
    };

接下来再来看类的定义classification.cpp：

#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>
    
    #include "classification.h"
    
    using namespace caffe;  // NOLINT(build/namespaces)
    using std::string;
    
    
    Classifier::Classifier(const string& model_file,
                           const string& trained_file,
                           const string& mean_file,
                           const string& label_file) 
    { 
        //Caffe::set_mode(Caffe::CPU);
        Caffe::set_mode(Caffe::GPU);  //GPU mode
    
        /* 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.";
    }
    
    //vec sort method
    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.";
    }

上面只是在classification.cpp文件进行了简单的拆分，对于这个类的拆分只是简单的处理。在实际使用中可以考虑采用单例模式的思路进行改造，因为模型的加载也是很花时间的，不必每次去跑图片的时候都加载一次。  
PS：对于Caffe中deploy文件的具体是干啥的，与train val 文件有什么区别，可以看看这篇文章[Caffe中deploy.prototxt 和 train\_val.prototxt 区别][Caffe_deploy.prototxt _ train_val.prototxt]

## 2.2 main函数 ##

#include <stdio.h>
    #include <iostream>
    #include <stdlib.h>
    #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>
    
    #include "caffe_classification/classification.h"
    
    using namespace std;
    
    //主函数
    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];
      double time_start = cv::getTickCount();
      Classifier classifier(model_file, trained_file, mean_file, label_file);
      double time_end = cv::getTickCount();
      std::cout << "load model time spend: " << (time_end-time_start)*1000/cv::getTickFrequency() << std::endl;
      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;
      }
      time_end = cv::getTickCount();
      std::cout << "time spend:" << (time_end-time_start)*1000/cv::getTickFrequency() << std::endl;
    
      return 0;
    }

## 2.3 相关Makefile文件编写 ##

这是针对上述内容写的Makefile，写得丑，勿喷-\_-||…

LIBDIR =  -lpthread -lm -lstdc++ \
    		  -Wl,--start-group \
    		  -Wl,--end-group -L. -lcurl\
    		  -lopencv_imgcodecs -lopencv_imgproc  -lopencv_core -lopencv_highgui -lopencv_videoio\
    		  -lcaffe -lglog\
    
    
    CAFFE_LIB =  -L ~/Desktop/caffe/build/lib/ 
    
    
    CPPFLAGS = -std=c++11 
    
    INCLUDEDIR = -I ~/Desktop/caffe/include/ \
    			 -I ~/local_install/bin/include/ \  # opencv之类库的安装路径
    			 -I ~/caffe/.build_release/src/ \
    			 -I /usr/local/cuda/include/ \
    
    # 当前目录的caffe_classification目录下装前面说到的classifiction.cpp和classifiction.h
    CAFFE_CLASSIFICATION = ./caffe_classification/
    
    # run file
    my_claasify: main.o classification.o 
    	g++ $(CPPFLAGS) -o my_classify $(INCLUDEDIR) $(CAFFE_LIB) $(LIBDIR) main.o classification.o ThreadPool.o
    
    # main.o
    main.o: main.cpp $(CAFFE_CLASSIFICATION)classification.h
    	g++ $(CPPFLAGS) -c main.cpp $(INCLUDEDIR) $(CAFFE_LIB) $(LIBDIR)
    
    # classification.o
    classification.o: $(CAFFE_CLASSIFICATION)classification.cpp  $(CAFFE_CLASSIFICATION)classification.h
    	g++ $(CPPFLAGS) -c $(CAFFE_CLASSIFICATION)classification.cpp $(INCLUDEDIR) $(CAFFE_LIB) $(LIBDIR)

之后就是make生成my\_claasify可执行文件，按照如下方式执行(路径可能与大家不太一样，参照着改一下)

./my_classify ../deploy.prototxt ../snapshot/solver_iter_55000.caffemodel ../handwrite_mean.binaryproto class_labels.txt test.jpg

[Caffe_deploy.prototxt _ train_val.prototxt]: https://www.cnblogs.com/zhonghuasong/p/7353221.html