windows下使用训练好的caffemodel做分类（2016-11-01）（车型分类）

转自：http://blog.csdn.net/shakevincent/article/details/52995253

随着深度学习的发展，越来越多的人开始进入这个行业，希望可以有所进展，但是各行业的大牛们，速度超神，deep哈希、deep稀疏、deep做分类、识别、跟踪、等等。很多人也开始训练自己的数据集或者下载别人训练好的model。训练好的caffemodel怎么使用对于刚入门的深度学习人来说是比较困难的，或者怎么用模型去验证自己的数据集等等
下面就从以下方向来说明怎么使用训练好model来预测自己的图片
1：编译caffe for windows
微软提供Windows工具包(caffe-master)：https://github.com/Microsoft/caffe
此版严格只支持VS2013。此版本无需配置任何第三方环境哦，因为在项目属性里面都已经包含进去了，非常方便。如果出现了一些文件打不开或者其它问题，第一可能是VS自身问题，第二可能你配置过其它caffe，修改过环境变量，导致找不到头文件之类的。
caffe for windows的编译：
可以参考我的博客：
http://blog.csdn.net/shakevincent/article/details/51694686
http://m.blog.csdn.net/article/details?id=51355143
http://m.blog.csdn.net/article/details?id=51549105
编译好caffe-windows后删除其他不需要的工程，仅保留caffelib 和classfication工程。
打开classfication工程里面的classfication.cpp函数修改：


#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>#include <iostream>
#include <string>
#include <sstream>
#include "io.h"#include "stdio.h"
#include "stdlib.h"
#include "time.h" #ifdef USE_OPENCV
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_ONLYCaffe::set_mode(Caffe::CPU);
#elseCaffe::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], static_cast<int>(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);elsesample = img;cv::Mat sample_resized;if (sample.size() != input_geometry_)cv::resize(sample, sample_resized, input_geometry_);elsesample_resized = sample;cv::Mat sample_float;if (num_channels_ == 3)sample_resized.convertTo(sample_float, CV_32FC3);elsesample_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.";
}
//获取路径path下的文件，并保存在files容器中
void getFiles(string path, vector<string>& files)
{//文件句柄  long   hFile = 0;//文件信息  struct _finddata_t fileinfo;string p;if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1){do{if ((fileinfo.attrib &  _A_SUBDIR)){if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)getFiles(p.assign(path).append("\\").append(fileinfo.name), files);}else{files.push_back(p.assign(path).append("\\").append(fileinfo.name));}} while (_findnext(hFile, &fileinfo) == 0);_findclose(hFile);}
}int main(int argc, char** argv) {string model_file("../model/deploy.prototxt");string trained_file("../model/type.caffemodel");string mean_file("../model/type_mean.binaryproto");string label_file("../model/labels.txt");string picture_path("../model/type");Classifier classifier(model_file, trained_file, mean_file, label_file);vector<string> files;getFiles(picture_path, files);for (int i = 0; i < files.size(); i++){clock_t start, finish;double   duration;start = clock();cv::Mat img = cv::imread(files[i], -1);cv::Mat img2;std::vector<Prediction> predictions = classifier.Classify(img);//Prediction p = predictions[i];IplImage* show;CvSize sz;sz.width = img.cols;sz.height = img.rows;float scal = 0;scal = sz.width > sz.height ? (300.0 / (float)sz.height) : (300.0 / (float)sz.width);sz.width *= scal;sz.height *= scal;resize(img, img2, sz, 0, 0, CV_INTER_LINEAR);show = cvCreateImage(sz, IPL_DEPTH_8U, 3);cvCopy(&(IplImage)img2, show);CvFont font;cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX, 0.5, 0.5, 0, 1, 8);  //初始化字体  //cvPutText(show, text.c_str(), cvPoint(10, 30), &font, cvScalar(0, 0, 255, NULL));string name_text;name_text = files[i].substr(files[i].find_last_of("\\") + 1);name_text = "Test picture ID::"+ name_text;cvPutText(show, name_text.c_str(), cvPoint(10, 130), &font, cvScalar(0, 0, 255, NULL));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;string text = p.first;char buff[20];_gcvt(p.second, 4, buff);text = text + ":" + buff;/***************************输出英文标签*****************************************///CvFont font;//cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX, 0.5, 0.5, 0, 1, 8);  //初始化字体  //cvPutText(show, text.c_str(), cvPoint(10, 30), &font, cvScalar(0, 0, 255, NULL));//string name_text;cvPutText(show, text.c_str(), cvPoint(10, 30 + i * 20), &font, cvScalar(0, 0, 255, NULL));/**********************************************************************************/cvNamedWindow("结果");cvShowImage("结果", show);cvWaitKey(1);}finish = clock();duration = (double)(finish - start) / CLOCKS_PER_SEC;printf("Time to do is ::");printf("%f seconds\n", duration);int c = cvWaitKey();cvDestroyWindow("结果");cvReleaseImage(&show);std::cout << "///" << std::endl;if (c == 27){return 0;}}return 0;
}
#else
int main(int argc, char** argv) {LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif  // USE_OPENCV

1：下载model：这里使用训练好的车辆识别的model
2：修改一下文件：

    string model_file("../model/deploy.prototxt");string trained_file("../model/type.caffemodel");string mean_file("../model/type_mean.binaryproto");string label_file("../model/labels.txt");string picture_path("../model/type");//测试图片的路径

简单修改后就可以使用了。

model下载地址：链接：http://pan.baidu.com/s/1hs3CF9y 密码：j7m4

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windows下使用训练好的caffemodel做分类（2016-11-01）（车型分类）

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