在c++程序中调用caffe训练完毕的模型进行分类

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

#ifdef USE_OPENCV
using namespace caffe;  // NOLINT(build/namespaces)
using std::string;

/* Pair (label, confidence) representing a prediction. */
typedef std::pair<string, float> Prediction;//记录每一个类的名称以及概率

//Classifier为构造函数，主要进行模型初始化，读入训练完毕的模型参数，均值文件和标签文件
class Classifier {
public:
Classifier(const string& model_file,//model_file为测试模型时记录网络结构的prototxt文件路径
const string& trained_file,//trained_file为训练完毕的caffemodel文件路径
const string& mean_file,//mean_file为记录数据集均值的文件路径，数据集均值的文件的格式通常为binaryproto
const string& label_file);//label_file为记录类别标签的文件路径，标签通常记录在一个txt文件中，一行一个

std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);//Classify函数去进行网络前传，得到img属于各个类的概率

private:
void SetMean(const string& mean_file);//SetMean函数主要进行均值设定，每张检测图输入后会进行减去均值的操作，这个均值可以是模型使用的数据集图像的均值

std::vector<float> Predict(const cv::Mat& img);//Predict函数是Classify函数的主要组成部分，将img送入网络进行前向传播，得到最后的类别

void WrapInputLayer(std::vector<cv::Mat>* input_channels);//WrapInputLayer函数将img各通道(input_channels)放入网络的输入blob中

void Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels);//Preprocess函数将输入图像img按通道分开(input_channels)

private:
shared_ptr<Net<float> > net_;//net_表示caffe中的网络
cv::Size input_geometry_;//input_geometry_表示了输入图像的高宽，同时也是网络数据层中单通道图像的高宽
int num_channels_;//num_channels_表示了输入图像的通道数
cv::Mat mean_;//mean_表示了数据集的均值，格式为Mat
std::vector<string> labels_;//字符串向量labels_表示了各个标签
};

//构造函数Classifier进行了各种各样的初始化工作，并对网络的安全进行了检验
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);//如果caffe是只在cpu上运行的，将运行模式设置为CPU
#else
Caffe::set_mode(Caffe::GPU);//一般我们都是用的GPU模式
#endif

/* Load the network. */
net_.reset(new Net<float>(model_file, TEST));//从model_file路径下的prototxt初始化网络结构
net_->CopyTrainedLayersFrom(trained_file);//从trained_file路径下的caffemodel文件读入训练完毕的网络参数

CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";//核验是不是只输入了一张图像，输入的blob结构为(N,C,H,W)，在这里，N只能为1
CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";//核验输出的blob结构，输出的blob结构同样为(N,C,W,H)，在这里，N同样只能为1

Blob<float>* input_layer = net_->input_blobs()[0];//获取网络输入的blob，表示网络的数据层
num_channels_ = input_layer->channels();//获取输入的通道数
CHECK(num_channels_ == 3 || num_channels_ == 1)//核验输入图像的通道数是否为3或者1，网络只接收3通道或1通道的图片
<< "Input layer should have 1 or 3 channels.";
input_geometry_ = cv::Size(input_layer->width(), input_layer->height());//获取输入图像的尺寸(宽与高)

/* Load the binaryproto mean file. */
SetMean(mean_file);//进行均值的设置

/* Load labels. */
std::ifstream labels(label_file.c_str());//从标签文件路径读入定义的标签文件
CHECK(labels) << "Unable to open labels file " << label_file;
string line;//line获取标签文件中的每一行(每一个标签)
while (std::getline(labels, line))
labels_.push_back(string(line));//将所有的标签放入labels_

/*output_layer指向网络最后的输出，举个例子，最后的分类器采用softmax分类，且类别有10类，那么，输出的blob就会有10个通道，每个通道的长
宽都为1(因为是10个数，这10个数表征输入属于10类中每一类的概率，这10个数之和应该为1)，输出blob的结构为(1,10,1,1)*/
Blob<float>* output_layer = net_->output_blobs()[0];
CHECK_EQ(labels_.size(), output_layer->channels())//在这里核验最后网络输出的通道数是否等于定义的标签的通道数
<< "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;
}//PairCompare函数比较分类得到的物体属于某两个类别的概率的大小，若属于lhs的概率大于属于rhs的概率，返回真，否则返回假

/* Return the indices of the top N values of vector v. */
/*Argmax函数返回前N个得分概率的类标*/
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));//按照分类结果存储输入属于每一个类的概率以及类标
std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);/*partial_sort函数按照概率大
小筛选出pairs中概率最大的N个组合，并将它们按照概率从大到小放在pairs的前N个位置*/

std::vector<int> result;
for (int i = 0; i < N; ++i)
result.push_back(pairs[i].second);//将前N个较大的概率对应的类标放在result中
return result;
}

/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
std::vector<float> output = Predict(img);//进行网络的前向传输，得到输入属于每一类的概率，存储在output中

N = std::min<int>(labels_.size(), N);//找到想要得到的概率较大的前N类，这个N应该小于等于总的类别数目
std::vector<int> maxN = Argmax(output, N);//找到概率最大的前N类，将他们按概率由大到小将类标存储在maxN中
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]));//在labels_找到分类得到的概率最大的N类对应的实际的名称
}

return predictions;
}

/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {//设置数据集的平均值
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);//用定义的均值文件路径将均值文件读入proto中

/* Convert from BlobProto to Blob<float> */
Blob<float> mean_blob;
mean_blob.FromProto(blob_proto);//将proto中存储的均值文件转移到blob中
CHECK_EQ(mean_blob.channels(), num_channels_)//核验均值的通道数是否等于输入图像的通道数，如果不相等的话则为异常
<< "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;//将mean_blob中的数据转化为Mat时的存储向量
float* data = mean_blob.mutable_cpu_data();//指向均值blob的指针
for (int i = 0; i < num_channels_; ++i) {
/* Extract an individual channel. */
cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);//存储均值文件的每一个通道转化得到的Mat
channels.push_back(channel);//将均值文件的所有通道转化成的Mat一个一个地存储到channels中
data += mean_blob.height() * mean_blob.width();//在均值文件上移动一个通道
}

/* Merge the separate channels into a single image. */
cv::Mat mean;
cv::merge(channels, mean);//将得到的所有通道合成为一张图

/* Compute the global mean pixel value and create a mean image
* filled with this value. */
cv::Scalar channel_mean = cv::mean(mean);//求得均值文件的每个通道的平均值，记录在channel_mean中
mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);//用上面求得的各个通道的平均值初始化mean_，作为数据集图像的均值
}

std::vector<float> Classifier::Predict(const cv::Mat& img) {
Blob<float>* input_layer = net_->input_blobs()[0];//input_layer是网络的输入blob
input_layer->Reshape(1, num_channels_,
input_geometry_.height, input_geometry_.width);//表示网络只输入一张图像，图像的通道数是num_channels_，高为input_geometry_.height，宽为input_geometry_.width
/* Forward dimension change to all layers. */
net_->Reshape();//初始化网络的各层

std::vector<cv::Mat> input_channels;//存储输入图像的各个通道
WrapInputLayer(&input_channels);//将存储输入图像的各个通道的input_channels放入网络的输入blob中
Preprocess(img, &input_channels);//将img的各通道分开并存储在input_channels中

net_->Forward();//进行网络的前向传输

/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];//output_layer指向网络输出的数据，存储网络输出数据的blob的规格是(1,c,1,1)
const float* begin = output_layer->cpu_data();//begin指向输入数据对应的第一类的概率
const float* end = begin + output_layer->channels();//end指向输入数据对应的最后一类的概率
return std::vector<float>(begin, end);//返回输入数据经过网络前向计算后输出的对应于各个类的分数
}

/* 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];//input_layer指向网络输入的blob

int width = input_layer->width();//得到网络指定的输入图像的宽
int height = input_layer->height();//得到网络指定的输入图像的高
float* input_data = input_layer->mutable_cpu_data();//input_data指向网络的输入blob
for (int i = 0; i < input_layer->channels(); ++i) {
cv::Mat channel(height, width, CV_32FC1, input_data);//将网络输入blob的数据同Mat关联起来
input_channels->push_back(channel);//将上面的Mat同input_channels关联起来
input_data += width * height;//一个一个通道地操作
}
}

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;//if-else嵌套表示了要将输入的img转化为num_channels_通道的

cv::Mat sample_resized;
if (sample.size() != input_geometry_)
cv::resize(sample, sample_resized, input_geometry_);//将输入图像的尺寸强制转化为网络规定的输入尺寸
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);//将输入图像转化成为网络前传合法的数据规格

cv::Mat sample_normalized;
cv::subtract(sample_float, mean_, sample_normalized);//将图像减去均值

/* 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);/*将减去均值的图像分散在input_channels中，由于在WrapInputLayer函数中，
input_channels已经和网络的输入blob关联起来了，因此在这里实际上是把图像送入了网络的输入blob*/

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.";//核验图像是否被送入了网络作为输入
}

int main(int argc, char** argv) {//主函数
if (argc != 6) {/*核验命令行参数是否为6，这6个参数分别为classification编译生成的可执行文件，测试模型时记录网络结构的prototxt文件路径，
训练完毕的caffemodel文件路径，记录数据集均值的文件路径，记录类别标签的文件路径，需要送入网络进行分类的图片文件路径*/
std::cerr << "Usage: " << argv[0]
<< " deploy.prototxt network.caffemodel"
<< " mean.binaryproto labels.txt img.jpg" << std::endl;
return 1;
}

::google::InitGoogleLogging(argv[0]);//InitGoogleLogging做了一些初始化glog的工作

//取四个参数
string model_file   = argv[1];
string trained_file = argv[2];
string mean_file    = argv[3];
string label_file   = argv[4];
Classifier classifier(model_file, trained_file, mean_file, label_file);//进行检测网络的初始化

string file = argv[5];//取得需要进行检测的图片的路径

std::cout << "---------- Prediction for "
<< file << " ----------" << std::endl;

cv::Mat img = cv::imread(file, -1);//读入图片
CHECK(!img.empty()) << "Unable to decode image " << file;
std::vector<Prediction> predictions = classifier.Classify(img);//进行网络的前向计算，并且取到概率最大的前N类对应的类别名称

/* Print the top N predictions. */
for (size_t i = 0; i < predictions.size(); ++i) {//打印出概率最大的前N类并给出概率
Prediction p = predictions[i];
std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
<< p.first << "\"" << std::endl;
}
}
#else
int main(int argc, char** argv) {
LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif  // USE_OPENCV

Classifier::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)

std::vector<float> Predict(const cv::Mat& img)

name: "AlexNet"
layer {
name: "forkdata"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "forkfc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 2
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc8"
top: "prob"
}

#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;

/* 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(int argc, char** argv) {

clock_t start_time1,end_time1,start_time2,end_time2;

::google::InitGoogleLogging(argv[0]);

string model_file   = "/home/ubuntu/classification_test/fork_net.prototxt";
string trained_file = "/home/ubuntu/classification_test/fork.caffemodel";
string mean_file    = "/home/ubuntu/classification_test/mean.binaryproto";
string label_file   = "/home/ubuntu/classification_test/label.txt";
start_time1 = clock();
Classifier classifier(model_file, trained_file, mean_file, label_file);
end_time1 = clock();
double seconds1 = (double)(end_time1-start_time1)/CLOCKS_PER_SEC;
std::cout<<"init time="<<seconds1<<"s"<<std::endl;

string file = "/home/ubuntu/classification_test/test_images/dCut2.jpg";

std::cout << "---------- Prediction for "
<< file << " ----------" << std::endl;

cv::Mat img = cv::imread(file, -1);
CHECK(!img.empty()) << "Unable to decode image " << file;
start_time2 = clock();
std::vector<Prediction> predictions = classifier.Classify(img);
end_time2 = clock();
double seconds2 = (double)(end_time2-start_time2)/CLOCKS_PER_SEC;
std::cout<<"classify time="<<seconds2<<"s"<<std::endl;

/* 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;
}
}

cmake_minimum_required (VERSION 2.8)

project (classification_test)

add_executable(classification_test classification.cpp)

include_directories ( /home/ubuntu/caffe/include
/usr/local/include
/usr/local/cuda/include
/usr/include )

target_link_libraries(classification_test
/home/ubuntu/caffe/build/lib/libcaffe.so
/usr/lib/arm-linux-gnueabihf/libopencv_highgui.so
/usr/lib/arm-linux-gnueabihf/libopencv_core.so
/usr/lib/arm-linux-gnueabihf/libopencv_imgproc.so
/usr/lib/arm-linux-gnueabihf/libglog.so
/usr/lib/arm-linux-gnueabihf/libboost_system.so
)

开始时捱一些苦 栽种绝处的花