SSD中ssd_detect调用caffe解读

SSD目录下，examples/ssd/下有一ssd_detect.cpp文件，编译连接后生成ssd_deetct.bin可以加以调用，该文件主要是对cpp_classification.cpp的改写，是一个利用C++调用Caffe的主体步骤，下面对其加以解读。

// This is a demo code for using a SSD model to do detection.
// The code is modified from examples/cpp_classification/classification.cpp.
// Usage:
//    ssd_detect [FLAGS] model_file weights_file list_file
//
// where model_file is the .prototxt file defining the network architecture, and
// weights_file is the .caffemodel file containing the network parameters, and
// list_file contains a list of image files with the format as follows:
//    folder/img1.JPEG
//    folder/img2.JPEG
// list_file can also contain a list of video files with the format as follows:
//    folder/video1.mp4
//    folder/video2.mp4
//
#include 
#ifdef USE_OPENCV
#include 
#include 
#include 
#endif  // USE_OPENCV
#include 
#include 
#include 
#include 
#include 
#include 
#include 

#ifdef USE_OPENCV
using namespace caffe;  // NOLINT(build/namespaces)
//定义检测器
class Detector {
 public:
  Detector(const string& model_file,
           const string& weights_file,
           const string& mean_file,
           const string& mean_value);

  std::vector > Detect(const cv::Mat& img);

 private:
  void SetMean(const string& mean_file, const string& mean_value);  //对mean_进行初始化

  void WrapInputLayer(std::vector* input_channels);        //将 input_channels与网络输入绑定

  void Preprocess(const cv::Mat& img,
                  std::vector* input_channels);           //一系列的预处理

 private:
  shared_ptr > net_;                                   //网络指针
  cv::Size input_geometry_;                                       //输入图片的size
  int num_channels_;                                             //输入图片的channel
  cv::Mat mean_;                                                 //均值图片
};
//构造函数
Detector::Detector(const string& model_file,
                   const string& weights_file,
                   const string& mean_file,
                   const string& mean_value) {
#ifdef CPU_ONLY                         //定义工作模式CPU or GPU
  Caffe::set_mode(Caffe::CPU);
#else
  Caffe::set_mode(Caffe::GPU);
#endif

  /* Load the network. */
  net_.reset(new Net(model_file, TEST));                        //从model_file中读取网络结构，初始化网络
  net_->CopyTrainedLayersFrom(weights_file);                                     //从权值文件中读取网络参数，初始化net_

  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* input_layer = net_->input_blobs()[0];
  num_channels_ = input_layer->channels();                                      //读取输入图片的channel
  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());      //读取输入图片的size

  /* Load the binaryproto mean file. */
  SetMean(mean_file, mean_value);                                               //初始化均值图片，至此所有成员变量都被初始化了
}

std::vector > Detector::Detect(const cv::Mat& img) {                       //检测器，输入图片，返回结果，每个vector代表一个结果，

                                                                                         //存有位置及信任程度信息
  Blob* 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();                                                            //对网络进行reshape

  std::vector input_channels;
  WrapInputLayer(&input_channels);                                         //这地方比较有意思，将网络输入与input_channels绑定 ，后面再提
  Preprocess(img, &input_channels);                                       //一些列预处理，后面详解

  net_->Forward();                                                       //网络的前向传播

  /* Copy the output layer to a std::vector */
  Blob* result_blob = net_->output_blobs()[0];                            
  const float* result = result_blob->cpu_data();                          //读取输出信息
  const int num_det = result_blob->height();                              //由此可见，只用了最后最后两维存储信息（只有一张图片，到现在没搞清前两维存什么）

                                                                          //height：检测到的数量。width：检测到的每个目标的信息
  vector > detections;
  for (int k = 0; k < num_det; ++k) {
    if (result[0] == -1) {                                            //-1代表是背景
      // Skip invalid detection.
      result += 7;                                                          //由此可见。每个目标占7个位置，后面解释每个位置的意思
      continue;
    }
    vector detection(result, result + 7);                            //vector的构造方法之一
    detections.push_back(detection);
    result += 7;
  }
  return detections;
}

/* Load the mean file in binaryproto format. */
void Detector::SetMean(const string& mean_file, const string& mean_value) {                //设置均值文件，Caffe中有两种设置均值文件的方式，

                                                                                       //mean_file or mean_value,mean_file类似于用caffe做图像分类时需要

                                                                                     //提供的lmdb文件，是关于每个像素点的均值，也就是对所有图片关于像素点

                                                                                   //均值， mean_value只有三个值，分别代表三个通道的均值，此函数初始化了

                                                                                   //均值图片mean_这一成员变量，此处不再详解
  cv::Scalar channel_mean;
  if (!mean_file.empty()) {
    CHECK(mean_value.empty()) <<
      "Cannot specify mean_file and mean_value at the same time";
    BlobProto blob_proto;
    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

    /* Convert from BlobProto to Blob */
    Blob 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 channels;
    float* data = mean_blob.mutable_cpu_data(); //data is a pointer that point the mean_blob
    for (int i = 0; i < num_channels_; ++i) {
      /* Extract an individual channel. */
      cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); // 注意这种构造方式，将Mat的头指针与data同指向，而不进行拷贝，可去

                                                                              //docs.opencv.org见详细解释
      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. */
    channel_mean = cv::mean(mean);
    mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
  }
  if (!mean_value.empty()) {
    CHECK(mean_file.empty()) <<
      "Cannot specify mean_file and mean_value at the same time";
    stringstream ss(mean_value);
    vector values;
    string item;
    while (getline(ss, item, ',')) {
      float value = std::atof(item.c_str());
      values.push_back(value);
    }
    CHECK(values.size() == 1 || values.size() == num_channels_) <<
      "Specify either 1 mean_value or as many as channels: " << num_channels_;

    std::vector channels;
    for (int i = 0; i < num_channels_; ++i) {
      /* Extract an individual channel. */
      cv::Mat channel(input_geometry_.height, input_geometry_.width, CV_32FC1,
          cv::Scalar(values[i]));                                            //利用scalar对Mat进行初始化的方式很好，

                                                                            //例如Mat（height,width,CV_32F2,Scalar（1，2）），2个channel的图片，一层为1，

                                                                            //一层为2
      channels.push_back(channel);
    }
    cv::merge(channels, mean_);
  }
}

/* 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 //bang ding
 * layer. */
void Detector::WrapInputLayer(std::vector* input_channels) {    //这里将输入input_channels与网络的输入绑定（wrap）

  Blob* 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);               //就是这一步完成了绑定，将Mat的头指针与input_data指向相同，也就意味着，

                                                                     //向Mat里写东西，就等同于向网络的输入写数据
    input_channels->push_back(channel);
    input_data += width * height;
  }
}

void Detector::Preprocess(const cv::Mat& img,
                            std::vector* input_channels) {
  /* Convert the input image to the input image format of the network. */              //各种初始化
  cv::Mat sample;                                                                       //输入图片的channel与网络规定的channel不同，怎么办？
  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;                                                               //输入图片的size，与网络规定不同，怎么办
  if (sample.size() != input_geometry_)
    cv::resize(sample, sample_resized, input_geometry_);
  else
    sample_resized = sample;

  cv::Mat sample_float;                                                                //将像素值转化为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);                          //减去均值图像，0均值化

  /* 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);                             //这一步完成数据输入，感觉放在外面更容易理解，结合Detect方法看，上面提到

                                                                              //input_channel已经和网络输入绑定，即指向相同，所以将数据写入input_channel的

                                                                             //同时，就写入了网络输入

  CHECK(reinterpret_cast(input_channels->at(0).data)
        == net_->input_blobs()[0]->cpu_data())
    << "Input channels are not wrapping the input layer of the network.";
}
//一堆定义命令行输入的指令，配置Caffe时用到的一个包，好像是gflags实现的，第一个参数是名称，第二个是默认值，第三个是解释 

//例如在命令行通过 -mean_value=" "就可以对mean_value进行赋值
DEFINE_string(mean_file, "",
    "The mean file used to subtract from the input image.");
DEFINE_string(mean_value, "104,117,123",
    "If specified, can be one value or can be same as image channels"
    " - would subtract from the corresponding channel). Separated by ','."
    "Either mean_file or mean_value should be provided, not both.");
DEFINE_string(file_type, "image",
    "The file type in the list_file. Currently support image and video.");
DEFINE_string(out_file, "",
    "If provided, store the detection results in the out_file.");
DEFINE_double(confidence_threshold, 0.01,
    "Only store detections with score higher than the threshold.");
DEFINE_string(detect_type, "trace",
    "Do detection:detect Do tracing :trace");

int main(int argc, char** argv) {
  ::google::InitGoogleLogging(argv[0]);
  // Print output to stderr (while still logging)
  FLAGS_alsologtostderr = 1;

#ifndef GFLAGS_GFLAGS_H_
  namespace gflags = google;
#endif

  gflags::SetUsageMessage("Do detection using SSD mode.\n"
        "Usage:\n"
        "    ssd_detect [FLAGS] model_file weights_file list_file\n");
  gflags::ParseCommandLineFlags(&argc, &argv, true);

  if (argc < 4) {
    gflags::ShowUsageWithFlagsRestrict(argv[0], "examples/ssd/ssd_detect");
    return 1;
  }
//这里不再细讲，都是常规的操作
  const string& model_file = argv[1];
  const string& weights_file = argv[2];
  const string& mean_file = FLAGS_mean_file;        //通过这种方式，读入上一步注释的赋值
  const string& mean_value = FLAGS_mean_value;
  const string& file_type = FLAGS_file_type;
  const string& out_file = FLAGS_out_file;
  const float confidence_threshold = FLAGS_confidence_threshold;
  const string& detect_type=FLAGS_detect_type;

  // Initialize the network.
  Detector detector(model_file, weights_file, mean_file, mean_value);

  // Set the output mode.
  std::streambuf* buf = std::cout.rdbuf();
  std::ofstream outfile;
  if (!out_file.empty()) {
    outfile.open(out_file.c_str());
    if (outfile.good()) {
      buf = outfile.rdbuf();
    }
  }
  std::ostream out(buf);

  // Process image one by one.
  std::ifstream infile(argv[3]);
  std::string file;
  while (infile >> file) {
    if (file_type == "image") {
      cv::Mat img = cv::imread(file, -1);
      CHECK(!img.empty()) << "Unable to decode image " << file;
      std::vector > detections = detector.Detect(img);

      /* Print the detection results. */
      for (int i = 0; i < detections.size(); ++i) {
        const vector& d = detections[i];
        // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].这里指明了那七个值
        CHECK_EQ(d.size(), 7);
        const float score = d[2];   //第三个是confidence
        if (score >= confidence_threshold) {
          out << file << " ";
          out << static_cast(d[1]) << " ";
          out << score << " ";
          out << static_cast(d[3] * img.cols) << " ";
          out << static_cast(d[4] * img.rows) << " ";
          out << static_cast(d[5] * img.cols) << " ";
          out << static_cast(d[6] * img.rows) << std::endl;
        }
      }
    } else if (file_type == "video" && detect_type=="trace") {
      cv::VideoCapture cap(file);
      if (!cap.isOpened()) {
        LOG(FATAL) << "Failed to open video: " << file;
      }
      cv::Mat img;
      int frame_count = 0;
      while (true) {
        bool success = cap.read(img);
        if (!success) {
          LOG(INFO) << "Process " << frame_count << " frames from " << file;
          break;
        }
        CHECK(!img.empty()) << "Error when read frame";
        std::vector > detections = detector.Detect(img);

        /* Print the detection results. */
        for (int i = 0; i < detections.size(); ++i) {
          const vector& d = detections[i];
          // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].
          CHECK_EQ(d.size(), 7);
          const float score = d[2];
          if (score >= confidence_threshold) {
            out << file << "_";
            out << std::setfill('0') << std::setw(6) << frame_count << " ";
            out << static_cast(d[1]) << " ";
            out << score << " ";
            out << static_cast(d[3] * img.cols) << " ";
            out << static_cast(d[4] * img.rows) << " ";
            out << static_cast(d[5] * img.cols) << " ";
            out << static_cast(d[6] * img.rows) << std::endl;
          }
        }
        ++frame_count;
      }
      if (cap.isOpened()) {
        cap.release();
      }
    } else if(file_type=="video" && detect_type=="detect"){
		cv::VideoCapture cap(file);
		bool detected=false;
        if (!cap.isOpened()) {
           LOG(FATAL) << "Failed to open video: " << file;
        }
        cv::Mat img;
        int frame_count = 0;
        while (true) {
           bool success = cap.read(img);
           if (!success) {
              LOG(INFO) << "Process " << frame_count << " frames from " << file;
              break;
            }
           CHECK(!img.empty()) << "Error when read frame";
           std::vector > detections = detector.Detect(img);

           /* Print the detection results. */
           for (int i = 0; i < detections.size(); ++i) {
           const vector& d = detections[i];
          // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].
           CHECK_EQ(d.size(), 7);
           const float score = d[2];
           if (score >= confidence_threshold) {
              out << file << "_";
              out << std::setfill('0') << std::setw(6) << frame_count << " ";
              out << static_cast(d[1]) << " ";
              out << score << " ";
              out << static_cast(d[3] * img.cols) << " ";
              out << static_cast(d[4] * img.rows) << " ";
              out << static_cast(d[5] * img.cols) << " ";
              out << static_cast(d[6] * img.rows) << std::endl;
			  detected=true;
			  break;
           }
         }
         ++frame_count;
		 if(detected==true) break;
      }
      if (cap.isOpened()) {
        cap.release();
      }
	}else {
      LOG(FATAL) << "Unknown file_type: " << file_type;
    }
  }
  return 0;
}
#else
int main(int argc, char** argv) {
  LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif  // USE_OPENCV

至此，解释完了ssd_detect.cpp的内容，类似的可以看与之大体相同的cpp_classification的内容，也在examples下面，通过这个函数，就可以实现对于训练好的网络的调用，后期将会对如何利用C++对网络进行训练详解