自己写程序利用lenet模型识别手写数字

自己编写程序，将手写图片送入训练得到的lenet模型，评估识别结果。
code:https://github.com/lhnows/caffeProjects
如果自己的caffe是用CMakeLists编译安装的，这样的话，可以运行如下的CMakeLists来编译自己的调用了caffe库的程序

CMakeLists.txt

cmake_minimum_required (VERSION 2.8)PROJECT (mnistTest)# Requires OpenCV v2.4.1 or later  FIND_PACKAGE( OpenCV REQUIRED )  IF (${OpenCV_VERSION} VERSION_LESS 2.4.1)      MESSAGE(FATAL_ERROR "OpenCV version is not compatible : ${OpenCV_VERSION}. requires atleast OpenCV v2.4.1")  ENDIF() find_package(Caffe)  include_directories(${Caffe_INCLUDE_DIRS})  add_definitions(${Caffe_DEFINITIONS})   add_executable(${PROJECT_NAME} mnistTest.cpp)include_directories ( /Users/liuhao/devlibs/deeplearning/caffe/install/include    /usr/local/include    /usr/local/cuda/include )target_link_libraries(${PROJECT_NAME} ${Caffe_LIBRARIES}                        ${OpenCV_LIBS}  )

mnistTest.cpp

#define USE_OPENCV 1#define CPU_ONLY 1//貌似caffe有3种矩阵计算加速方式 mkl  accelerate blas，本人Mac编译的可能是下面这种（其他会报错找不到头文件）//#define USE_ACCELERATE#include <iostream>#include <string>#include <caffe/caffe.hpp>#include <vector>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include "head.h"#include <algorithm>#include <iosfwd>#include <memory>#include <utility>#include <vector>using namespace caffe;using namespace cv;cv::Point previousPoint(-1, -1), nowPoint(-1, -1);Mat srcimage=Mat::zeros(280,280,CV_8UC1);Mat srcimageori = Mat::zeros(280, 280, CV_8UC1);class Classifier {public:    Classifier(const string& model_file,        const string& trained_file);    int Classify(const cv::Mat& img);private:    std::vector<int> 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_;};Classifier::Classifier(const string& model_file,    const string& trained_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());}/* Return the top N predictions. */int Classifier::Classify(const cv::Mat& img) {    std::vector<int> output = Predict(img);    std::vector<int>::iterator iter=find(output.begin(), output.end(), 1);    int prediction = distance(output.begin(), iter);    return prediction<10 ? prediction:0;}std::vector<int> 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<int>(begin, end);}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::split(sample_float, *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.";}static void on_Mouse(int event, int x, int y, int flags, void*){    if (event == EVENT_LBUTTONUP || !(flags&EVENT_FLAG_LBUTTON))    {        previousPoint = cv::Point(-1,-1);    }    else        if (event == EVENT_LBUTTONDOWN)        {            previousPoint = cv::Point(x, y);        }        else if (event == EVENT_MOUSEMOVE || (flags&EVENT_FLAG_LBUTTON))        {            cv::Point pt(x, y);            if (previousPoint.x<0)            {                previousPoint = pt;            }            line(srcimage, previousPoint, pt, Scalar(255), 16, 8, 0);            previousPoint = pt;            imshow("result", srcimage);        }}int main(int argc, char** argv){    ::google::InitGoogleLogging(argv[0]);#ifdef CPU_ONLY    Caffe::set_mode(Caffe::CPU);#else    Caffe::set_mode(Caffe::GPU);#endif    string model_file = "lenet.prototxt";    string trained_file = "lenet_iter_10000.caffemodel";    Classifier classifier(model_file, trained_file);    std::cout << "------directed by watersink------" << std::endl;    std::cout << "------------enter:退出-----------" << std::endl;    std::cout << "--------------1:还原-------------" << std::endl;    std::cout << "-------------2:写数字------------" << std::endl;    std::cout << "[e-mail]-----" << std::endl;    imshow("result", srcimage);    setMouseCallback("result", on_Mouse, 0);    while (1)    {        char c = (char)waitKey();        if (c == 27)            break;        if (c=='1')        {            srcimageori.copyTo(srcimage);            imshow("result", srcimage);        }        if (c == '2')        {            cv::Mat img;            cv::resize(srcimage, img, cv::Size(28, 28));            CHECK(!img.empty()) << "Unable to decode image " << std::endl;            int  prediction = classifier.Classify(img);            std::cout << "prediction:" << prediction << std::endl;            imshow("result", srcimage);        }    }    waitKey();    return 0;}

head.h

#include <caffe/common.hpp>#include <caffe/layer.hpp>#include <caffe/layer_factory.hpp>#include <caffe/layers/input_layer.hpp>#include <caffe/layers/inner_product_layer.hpp>#include <caffe/layers/dropout_layer.hpp>#include <caffe/layers/conv_layer.hpp>#include <caffe/layers/relu_layer.hpp>#include <caffe/layers/pooling_layer.hpp>#include <caffe/layers/softmax_layer.hpp> namespace caffe{    extern INSTANTIATE_CLASS(InputLayer);    extern INSTANTIATE_CLASS(InnerProductLayer);    extern INSTANTIATE_CLASS(DropoutLayer);    extern INSTANTIATE_CLASS(ConvolutionLayer);    //REGISTER_LAYER_CLASS(Convolution);    extern INSTANTIATE_CLASS(ReLULayer);    //REGISTER_LAYER_CLASS(ReLU);    extern INSTANTIATE_CLASS(PoolingLayer);    //REGISTER_LAYER_CLASS(Pooling);    extern INSTANTIATE_CLASS(SoftmaxLayer);    //REGISTER_LAYER_CLASS(Softmax);}

cmake.. & make

 $ cmake ..-- The C compiler identification is AppleClang 8.1.0.8020042-- The CXX compiler identification is AppleClang 8.1.0.8020042-- Check for working C compiler: /Library/Developer/CommandLineTools/usr/bin/cc-- Check for working C compiler: /Library/Developer/CommandLineTools/usr/bin/cc -- works-- Detecting C compiler ABI info-- Detecting C compiler ABI info - done-- Detecting C compile features-- Detecting C compile features - done-- Check for working CXX compiler: /Library/Developer/CommandLineTools/usr/bin/c++-- Check for working CXX compiler: /Library/Developer/CommandLineTools/usr/bin/c++ -- works-- Detecting CXX compiler ABI info-- Detecting CXX compiler ABI info - done-- Detecting CXX compile features-- Detecting CXX compile features - done-- Found OpenCV: /usr/local (found version "3.2.0") -- Configuring done-- Generating done-- Build files have been written to: /Users/liuhao/projects/caffeProjects/mnistTest/build $ makeScanning dependencies of target mnistTest[ 50%] Building CXX object CMakeFiles/mnistTest.dir/mnistTest.cpp.o[100%] Linking CXX executable mnistTest[100%] Built target mnistTest

输入./mnistTests 执行