__2017-12-16 如一模式识别研究

如一模式识别研究

准日记>>caffe_分类器代码详解

#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <iostream>
//#define CPU_ONLY  // 这里也可以在 项目属性/ C/C++ / 预定义选项中定义,没有装GPU电脑上,需要加上这个宏
using namespace caffe;  // NOLINT(build/namespaces)
using std::string;
//using namespace System;

/* 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);
	 ~Classifier();


  std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);

 private:
  void SetMean(const string& mean_file);// 从二进制的bin文件中读取均值,并设置到blob_中

  std::vector<float> Predict(const cv::Mat& img); // 对图片进行预测

  void WrapInputLayer(std::vector<cv::Mat>* input_channels);// 将net_的数据接口与input_channels 对接

  void Preprocess(const cv::Mat& img,
                  std::vector<cv::Mat>* input_channels); // 以img为输入,用net_来forword计算输出层值。

 private:
  shared_ptr<Net<float> > net_;   //网络对象
  cv::Size input_geometry_;       //输入数据的几何维度,宽和高
  int num_channels_;//通道数
  cv::Mat mean_;// 均值
  std::vector<string> labels_; //各类的标记
};
Classifier::~Classifier()   // 自己添加的函数,给的例程中是没有的
{
	mean_.release();
	labels_.clear();
}
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
  std::cout << "set cpu" << std::endl;
  /* Load the network. */
  net_.reset(new Net<float>(model_file, TEST)); // 加载网络拓扑结构
  net_->CopyTrainedLayersFrom(trained_file); // 加载网络权重
  std::cout << "0" << std::endl;
  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; // 调用glog的检查
  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";// 检查
  std::cout << "1" << std::endl;

  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());// 
  std::cout << "2" << std::endl;

  /* Load the binaryproto mean file. */
  SetMean(mean_file); 
  std::cout << "3" << std::endl;

  /* 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));
  std::cout << "4" << std::endl;

  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.";//检查labels_的长度与输出层的维数是否一致
  std::cout << "5" << std::endl;

}
// 下面两个函数是排序函数代码
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);

  std::vector<int> maxN = Argmax(output, N);// 取前N个预测结果
  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]));//保存在predictions中
  }

  return predictions;
}

/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {
  BlobProto blob_proto; // 调用google/protobuf?? ,用于加速运算的数据接口,有时间再详细了解其应用方法
  //这个函数是实现了从二进制文件中读取数据到blob_proto中,猜测函数来自第3方库的google/protobuf模块
  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

  /* Convert from BlobProto to Blob<float> */
  Blob<float> mean_blob;
  mean_blob.FromProto(blob_proto); // 调用Blob类的成员函数FromRroto从BlobProto中加载数据
  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();// 用可读写的方式取得指针
  // 把均值上的各个通道的复制到 vector<Mat>  channels,即channels[0]中对应均值中的通道0,
  // 这样做的原因是 Blob类的数据存储方式是一维的。
  // 我们这里是把一维度的数组  转化为Mat数组了
  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);//合并分开的通道为一个图像,即把channels的所有Mat合并为一个Mat.

  /* Compute the global mean pixel value and create a mean image
   * filled with this value. */
  cv::Scalar channel_mean = cv::mean(mean); //计算每个像素在所有通道上的平均值,保存在channel_mean中
  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); //赋值给 本类的成员变量mean_
}

std::vector<float> Classifier::Predict(const cv::Mat& img) {
  Blob<float>* input_layer = net_->input_blobs()[0]; // 得到net的输入层数据指针
  input_layer->Reshape(1, num_channels_,
                       input_geometry_.height, input_geometry_.width);//分配内存???
  /* Forward dimension change to all layers. */
  net_->Reshape();

  std::vector<cv::Mat> input_channels;
  WrapInputLayer(&input_channels);// 将net_->input_blobs()[0]的地址给input_channels

  Preprocess(img, &input_channels);//将图片地址给input_channels
    
  net_->ForwardPrefilled(); //猜测是所有层前向计算

  /* Copy the output layer to a std::vector */
  // 将net的输出层数据复制到vector<float>类型的变量中,并返回
  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();// 取出Blob类的数据,并在后续部分对齐进行修改(即在Preprocess中,将图片的值放入input_layer中。
  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. */
  // 保证输入图片的channels与 网络channels一致
  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;

  // 保证大小一致
  cv::Mat sample_resized;
  if (sample.size() != input_geometry_)
    cv::resize(sample, sample_resized, input_geometry_);
  else
    sample_resized = sample;


  // 保证数据类型一致为 float 
  cv::Mat sample_float;
  if (num_channels_ == 3)
    sample_resized.convertTo(sample_float, CV_32FC3);
  else
    sample_resized.convertTo(sample_float, CV_32FC1);

  // 减去均值得到sample_normalized
  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. */
  //将 sample_normalized 放入 input_channels中,即放入net_->input_blob中。
  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) {
	try{
		
		if (argc != 6) {
			std::cerr << "Usage: " << argv[0]
				<< " deploy.prototxt network.caffemodel"
				<< " mean.binaryproto labels.txt img.jpg" << std::endl;
			return 1;
		}

		::google::InitGoogleLogging(argv[0]);//glog 库内函数,glog 库是一个做日志的库

		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);

		string file = argv[5];



		/* Load imglists. */
		std::ifstream imglists(file.c_str());
		CHECK(imglists) << "Unable to open labels file " << label_file;
		string line;
		while (std::getline(imglists, line))
		{

			//labels_.push_back(string(line));
			string filename(line);
			std::cout << "---------- Prediction for "
				<< filename << " ----------" << std::endl;

			cv::Mat img = cv::imread(filename, -1);

			// 这里开始用图片列表,并且显示出来
			CHECK(!img.empty()) << "Unable to decode image " << filename;
			std::vector<Prediction> predictions = classifier.Classify(img, 2);

			/* Print the top N predictions. */
			//for (size_t i = 0; i < predictions.size(); ++i) {
			for (size_t i = 0; i < 1; ++i) {

				Prediction p = predictions[i];
				//	std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
				//		<< p.first << "\"" << std::endl;
				std::cout << std::fixed << std::setprecision(4) << p.second << "," << p.first << std::endl;

			}

			CvFont font;
			cvInitFont(&font, CV_FONT_HERSHEY_DUPLEX, 1.0f, 1.0f, 0, 1, CV_AA);
			//cv::addText(img, predictions[1].first, cv::Point(10, 10), &font);
			for (int i = 6; i < 7; i++)
			{
				cv::Mat imgt = img.clone();
				cv::putText(imgt, predictions[0].first, cv::Point(80, 40), i, 2.0f, CV_RGB(255, 0, 0));
				cv::imshow("img", imgt);
				cv::waitKey(1);

			}
		}
		std::cout << "done" << std::endl;
		classifier.~Classifier();
		return 0;
		exit(0);
	}
	//catch (ArithmeticException^ e)
	//{
	//	Console::WriteLine("ArithmeticException Handler: {0}", e);
	//}
	//catch (Exception^ e)
	//{
	//	Console::WriteLine("Generic Exception Handler: {0}", e);
	//}
	catch (std::exception e)
	{
		std::cout << e.what() << std::endl;
	}

}

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