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.liblibboost_date_time-vc120-mt-1_59.liblibboost_filesystem-vc120-mt-1_59.liblibboost_python-vc120-mt-1_59.liblibboost_system-vc120-mt-1_59.liblibboost_thread-vc120-mt-1_59.libgflags.libgflags_nothreads.libgflags_nothreadsd.libgflagsd.liblibglog.libhdf5.libhdf5_cpp.libhdf5_f90cstub.libhdf5_fortran.libhdf5_hl_cpp.libhdf5_hl.libhdf5_hl_f90cstub.libhdf5_hl_fortran.libhdf5_tools.libszip.libzlib.libLevelDb.liblmdb.liblmdbD.liblibprotobuf.libopencv_calib3d2410.libopencv_contrib2410.libopencv_core2410.libopencv_features2d2410.libopencv_flann2410.libopencv_gpu2410.libopencv_highgui2410.libopencv_imgproc2410.libopencv_legacy2410.libopencv_ml2410.libopencv_nonfree2410.libopencv_objdetect2410.libopencv_ocl2410.libopencv_photo2410.libopencv_stitching2410.libopencv_superres2410.libopencv_ts2410.libopencv_video2410.libopencv_videostab2410.libcublas.libcuda.libcublas_device.libcudadevrt.libcudart_static.libcudart.libcudnn.libcufftw.libcufft.libcusolver.libcurand.libcusparse.libnppc.libnpps.libnppi.libnvcuvid.libnvblas.libnvrtc.libOpenCL.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！