微软libcaffe封装成dll和lib！！！

Windows下利用VS使用Caffe可以为开发者提供很好的体验，但是每次编译的时候的总是十分钟的时间在改代码，剩下50分钟在编译的过程中，另外在实际图像分类开发中，很多情况下我们可能只需要一两个函数，所以怎么把caffe的classfy封装成我们需要的dll和lib，可以不依赖caffe的框架，在新建的解决方案中，可以直接调用。
本文主要封装了两个版本的caffe
1：happynear版本：https://github.com/happynear/caffe-windows
http://blog.csdn.net/sinat_30071459/article/details/51823390
以上版本主要参考了小咸鱼的博客，给我提供了很大的帮助，大家可以按照他的方法
2：微软caffe版本：
1：编译微软caffe http://blog.csdn.net/shakevincent/article/details/51694686
2：添加需要的文件：
添加classification.h

#ifndef CLASSIFICATION_H_
#define CLASSIFICATION_H_

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

using namespace caffe;
using std::string;
typedef std::pair<int, float> Prediction;

class  ClassifierImpl {
public:
    ClassifierImpl(const string& model_file,
        const string& trained_file,
        const string& mean_file
        );

    std::vector<Prediction>  Classify(const cv::Mat& img, int N = 2);
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_;
};
#endif

添加classification.cpp

#include "classification.h"

ClassifierImpl::ClassifierImpl(const string& model_file,
    const string& trained_file,
    const string& mean_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);

    //Blob<float>* output_layer = net_->output_blobs()[0];

}

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> ClassifierImpl::Classify(const cv::Mat& img, int N) {
    std::vector<float> output = Predict(img);

    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(idx, output[idx]));
    }

    return predictions;
}
/* Load the mean file in binaryproto format. */
void ClassifierImpl::SetMean(const string& mean_file) {
    BlobProto blob_proto;
    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
    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.";
    std::vector<cv::Mat> channels;
    float* data = mean_blob.mutable_cpu_data();
    for (int i = 0; i < num_channels_; ++i) {
        cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
        channels.push_back(channel);
        data += mean_blob.height() * mean_blob.width();
    }

    cv::Mat mean;
    cv::merge(channels, mean);
    cv::Scalar channel_mean = cv::mean(mean);
    mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
}

std::vector<float> ClassifierImpl::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_->ForwardPrefilled();

    /* 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);
}
void ClassifierImpl::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 ClassifierImpl::Preprocess(const cv::Mat& img,
    std::vector<cv::Mat>* input_channels) {
    cv::Mat sample;
    if (img.channels() == 3 && num_channels_ == 1)
        cv::cvtColor(img, sample, CV_BGR2GRAY);
    else if (img.channels() == 4 && num_channels_ == 1)
        cv::cvtColor(img, sample, CV_BGRA2GRAY);
    else if (img.channels() == 4 && num_channels_ == 3)
        cv::cvtColor(img, sample, CV_BGRA2BGR);
    else if (img.channels() == 1 && num_channels_ == 3)
        cv::cvtColor(img, sample, CV_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);
    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.";
}

添加multi_recognition_gpu.h

#ifndef MULTI_RECOGNITION_GPU_H_
#define MULTI_RECOGNITION_GPU_H_

#ifdef MULTI_RECOGNITION_API_EXPORTS
#define MULTI_RECOGNITION_API __declspec(dllexport)
#else
#define MULTI_RECOGNITION_API __declspec(dllimport)
#endif
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <string>
#include <vector>
#include <iostream>
#include <io.h>
class ClassifierImpl;
using std::string;
using std::vector;
typedef std::pair<int, float> Prediction;

class MULTI_RECOGNITION_API MultiClassifier
{
public:
    MultiClassifier(const string& model_file,
        const string& trained_file,
        const string& mean_file);

    ~MultiClassifier();
    vector<Prediction> Classify(const cv::Mat& img, int N = 2);
    void getFiles(std::string path, std::vector<std::string>& files);
private:
    ClassifierImpl *Impl;
};

#endif

添加multi_recognition_gpu.cpp

#include "multi_recognition_gpu.h"
#include "classification.h"


MultiClassifier::MultiClassifier(const string& model_file, const string& trained_file, const string& mean_file)
{
    Impl = new ClassifierImpl(model_file, trained_file, mean_file);
}
MultiClassifier::~MultiClassifier()
{
    delete Impl;
}
std::vector<Prediction>  MultiClassifier::Classify(const cv::Mat& img, int N /* = 2 */)
{
    return Impl->Classify(img, N);
}

很不要脸的基本上全是抄的小咸鱼的代码：http://blog.csdn.net/sinat_30071459/article/details/53786732
。
代码添加完成就要开始编译了：！！！！！！！！！！！！！！
但是会出现一些错误：link Error 等！正常理解在编译caffe的已经把需要的lib都包含了，为什么还是有很多的错误：
怎么办呢？
重新添加一下呗：include和lib

libboost_chrono-vc120-mt-1_59.lib
libboost_date_time-vc120-mt-1_59.lib
libboost_filesystem-vc120-mt-1_59.lib
libboost_python-vc120-mt-1_59.lib
libboost_system-vc120-mt-1_59.lib
libboost_thread-vc120-mt-1_59.lib
gflags.lib
gflags_nothreads.lib
gflags_nothreadsd.lib
gflagsd.lib
libglog.lib
hdf5.lib
hdf5_cpp.lib
hdf5_f90cstub.lib
hdf5_fortran.lib
hdf5_hl_cpp.lib
hdf5_hl.lib
hdf5_hl_f90cstub.lib
hdf5_hl_fortran.lib
hdf5_tools.lib
szip.lib
zlib.lib
LevelDb.lib
lmdb.lib
lmdbD.lib
libprotobuf.lib
opencv_calib3d2410.lib
opencv_contrib2410.lib
opencv_core2410.lib
opencv_features2d2410.lib
opencv_flann2410.lib
opencv_gpu2410.lib
opencv_highgui2410.lib
opencv_imgproc2410.lib
opencv_legacy2410.lib
opencv_ml2410.lib
opencv_nonfree2410.lib
opencv_objdetect2410.lib
opencv_ocl2410.lib
opencv_photo2410.lib
opencv_stitching2410.lib
opencv_superres2410.lib
opencv_ts2410.lib
opencv_video2410.lib
opencv_videostab2410.lib
cublas.lib
cuda.lib
cublas_device.lib
cudadevrt.lib
cudart_static.lib
cudart.lib
cudnn.lib
cufftw.lib
cufft.lib
cusolver.lib
curand.lib
cusparse.lib
nppc.lib
npps.lib
nppi.lib
nvcuvid.lib
nvblas.lib
nvrtc.lib
OpenCL.lib

千万注意不要把NugetPackages中所有的lib全部添加到链接器-输入中！可能是我对-s -mt -sgd的理解不透彻才会出现这个错误，大牛可能就一眼就知道的怎么回事。
添加完成后就可以成功生成需要的lib和dll，剩下的就是测试一下生成的文件能不能用了，

#include <iostream>
#include <string>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "multi_recognition_gpu.h"
#pragma comment(lib,"type_recognition_ver2_api_gpu.lib")
using namespace cv;
int main(int argc, char** argv)
{
    std::string model_file("./model/deploy.prototxt");
    std::string trained_file("./model/net.caffemodel");
    std::string mean_file("./model/type_mean.binaryproto");
    std::string label_file("./model/typelabels.txt");

    //const Scalar bgr_mean(0, 0, 0);
    MultiClassifier myclassifier(model_file, trained_file, mean_file);//, label_file);//, label_file);
    cv::Mat img = cv::imread("./model/1.jpg", -1);

    std::vector<Prediction> result = myclassifier.Classify(img);
    Prediction p = result[0];
    std::cout << "类别:" << p.first << "确信度：" << p.second << "\n";

    return 0;
}

另外如果大家需要自己的分类函数，可以在classfication中修改，也可以修改成多输出的，等等！
至于dll和lib的下载大家请移步小咸鱼的博客。只是现在很多人在用微软的caffe，所以就借用了一些资源！