Caffe 源码的修改（用于车辆的定位）

主页 分类标签链接关于

修改后的caffe源码 https://gitcafe.com/lxiongh/Caffe_for_Multi-label

Caffe 的源码仅适用于模式分类问题，其标签是一维的。为了能够将 Caffe 代码应用于车辆的检测问题上，首先要解决的问题就是将 Caffe 源码中与标签相关的代码由一维改为多维。本项目由于只需预测车辆的上下两角的坐标（x1,y1, x2,y2），即将标签修改为4维。

这次能够很顺利的修改源码，很大一原因是实验室的师弟太给力了，他们对 Caffe 源码的研究开始地很早，并在以 Caffe 作为其深度学习的工具，并且在也发表了一些很不错的文章。Thanks to TianShui, LiangLi, JinZhu. 『车辆定位项目链接http://lxiongh.github.io/2015/02/04/car_localization/』

首先，利用 Understanding 软件，可以方便的查看到 caffe 源码的目录结构，如下图所示。

可以注意到，在 Caffe 源码里有一个『Tools』的目录，里面有一些相当有用的工具，如『compute_image_mean.cpp』、『convert_imageset.cpp』等，其中『convert_imageset.cpp』直接操作到了文本文件，如下列代码所示。那么函数『ReadImageToDatum』将就是突破口。

1 // convert_imageset.cpp, line 129-2 for (int line_id = 0; line_id < lines.size(); ++line_id) {3     if (!ReadImageToDatum(root_folder + lines[line_id].first,4         lines[line_id].second, resize_height, resize_width, is_color, &datum)) {5       continue;6     }7     // ...8 }

定义在io.cpp里的函数ReadImageToDatum完成了将图片数据转换成caffe能够处理的Datum类型，主要修改的文件大都集中在数据层，将其单标签改成多标签支持。针对车辆检测，对caffe所作的修改有如下的部分：

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• caffe.proto

将optional改为repeated，使得标签变量label为数组，即支持多标签。否则无此属性Datum.label_size()。

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• data_layer.hpp

将lines_由protected修改为public，使得后续能够利用指针直接访问lines_数据，其中保存了图片名及其对应的标签信息。详见test_det_net.cpp

• data_layer.cpp

修改top_label，使得其保存图片的多标签信息。

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• image_data_layer.cpp

从文本文件里读取图片的路径及标签信息，将原来int label修改成std::<vector> vec_label。同时需要特别注意的就是不要忘记申请相应的存储空间(*top)[1]->Reshape(this->...)，否则在初始化网络时就会出现错误。

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• memory_data_layer.cpp

虽然这个在实际应用中没有用到，但因其涉及到最底层的数据层，所以也修改了。

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• convert_imageset.cpp

  这个程序是将图片数据打包与数据库的形式，默认为leveldb

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• io.hpp

• io.cpp

这个文件涉及到最底层的数据读写工作。

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• test_det_net.cpp，由extract_feature.cpp修改而来的对输入的图片进行车辆预测，并画框输出

  1 #include <stdio.h>  // for snprintf  2 #include <string>  3 #include <vector>  4   5 #include "boost/algorithm/string.hpp"  6 #include "google/protobuf/text_format.h"  7 #include "leveldb/db.h"  8 #include "leveldb/write_batch.h"  9  10 #include "caffe/blob.hpp" 11 #include "caffe/common.hpp" 12 #include "caffe/net.hpp" 13 #include "caffe/proto/caffe.pb.h" 14 #include "caffe/util/io.hpp" 15 #include "caffe/vision_layers.hpp" 16  17 // liu  18 #include <opencv2/core/core.hpp> 19 #include <opencv2/highgui/highgui.hpp> 20 #include <opencv2/highgui/highgui_c.h> 21 #include <opencv2/imgproc/imgproc.hpp> 22 #include <opencv2/opencv.hpp> 23 #include <sys/stat.h>  // for mkdir 24  25 using namespace caffe;  // NOLINT(build/namespaces) 26  27 template<typename Dtype> 28 int feature_extraction_pipeline(int argc, char** argv); 29  30 int main(int argc, char** argv) { 31   return feature_extraction_pipeline<float>(argc, argv); 32 //  return feature_extraction_pipeline<double>(argc, argv); 33 } 34  35 template<typename Dtype> 36 int feature_extraction_pipeline(int argc, char** argv) { 37   ::google::InitGoogleLogging(argv[0]); 38   const int num_required_args = 5; 39   if (argc < num_required_args) { 40     LOG(ERROR)<< 41     "This program takes in a trained network and an input data layer, and then" 42     " extract features of the input data produced by the net.\n" 43     "Usage: test_det_net  pretrained_net_param" 44     "  feature_extraction_proto_file num_mini_batches" 45         "  output_dir" 46     "  [CPU/GPU] [DEVICE_ID=0]\n" 47     "Note: the feature blob names is fixed as 'fc_8_det' in code\n"; 48     return 1; 49   } 50   int arg_pos = num_required_args; 51  52   arg_pos = num_required_args; 53   if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) { 54     LOG(ERROR)<< "Using GPU"; 55     uint device_id = 0; 56     if (argc > arg_pos + 1) { 57       device_id = atoi(argv[arg_pos + 1]); 58       CHECK_GE(device_id, 0); 59     } 60     LOG(ERROR) << "Using Device_id=" << device_id; 61     Caffe::SetDevice(device_id); 62     Caffe::set_mode(Caffe::GPU); 63   } else { 64     LOG(ERROR) << "Using CPU"; 65     Caffe::set_mode(Caffe::CPU); 66   } 67   Caffe::set_phase(Caffe::TEST); 68  69   arg_pos = 0;  // the name of the executable 70   string pretrained_binary_proto(argv[++arg_pos]); 71  72   string feature_extraction_proto(argv[++arg_pos]); 73   shared_ptr<Net<Dtype> > feature_extraction_net( 74       new Net<Dtype>(feature_extraction_proto)); 75   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto); 76     // to get image_paths 77     const vector<shared_ptr<Layer<float> > > layers = feature_extraction_net->layers(); 78     const caffe::ImageDataLayer<float> *image_layer = dynamic_cast<caffe::ImageDataLayer<float>* >(layers[0].get()); 79     CHECK(image_layer); 80              81   const string blob_name = "fc_8_det"; 82    83   CHECK(feature_extraction_net->has_blob(blob_name))   \ 84         << "Unknown feature blob name " << blob_name      \ 85         << " in the network " << feature_extraction_proto; 86  87  88   int num_mini_batches = atoi(argv[++arg_pos]); 89     string output_dir = argv[++arg_pos]; 90     CHECK_EQ(mkdir(output_dir.c_str(),0744), 0) << "mkdir " << output_dir << " failed"; 91  92   LOG(ERROR)<< "Extracting Features"; 93  94   vector<Blob<float>*> input_vec; 95   int image_index=0; 96   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) { 97     feature_extraction_net->Forward(input_vec); 98          99         const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net->blob_by_name(blob_name);100     101         int batch_size = feature_blob->num();102         103         int dim_features = feature_blob->count() / batch_size;104         CHECK_EQ(dim_features, 4) << "the dim of feature is not equal to 4";105         106         Dtype* feature_blob_data;107         int x1, y1, x2, y2;108         for (int n = 0; n < batch_size; ++n) {109             feature_blob_data = feature_blob->mutable_cpu_data() + feature_blob->offset(n);110 111             x1 = feature_blob_data[0];112             y1 = feature_blob_data[1];113             x2 = feature_blob_data[2];114             y2 = feature_blob_data[3];115             116             string image_path = image_layer->lines_[image_index].first;117             //LOG(ERROR) << "image_index " << image_index << " " <<  image_path   \118                                 << " x1 " << feature_blob_data[0] << " y1 " << feature_blob_data[1] \119                                 << " x2 " << feature_blob_data[2] << " y2 " << feature_blob_data[3];120             121             cv::Mat img_origin = cv::imread(image_path);122             123             std::vector<string> part_names;124             boost::split(part_names, image_path, boost::is_any_of("/"));125             string subname = part_names[part_names.size()-1];             // the last element is the image name.126             string out_path(output_dir + "/" + subname);127             128             //LOG(ERROR) << subname;129             line(img_origin, cv::Point(x1, y1), cv::Point(x2, y1), cv::Scalar(0, 0, 255), 3);130             line(img_origin, cv::Point(x2, y1), cv::Point(x2, y2), cv::Scalar(0, 0, 255), 3);131             line(img_origin, cv::Point(x2, y2), cv::Point(x1, y2), cv::Scalar(0, 0, 255), 3);132             line(img_origin, cv::Point(x1, y2), cv::Point(x1, y1), cv::Scalar(0, 0, 255), 3);133             CHECK_EQ(imwrite(output_dir + "/" + subname, img_origin), true) << "write image " + out_path + " failed";134             135             image_index ++ ;136             if (image_index>=image_layer->lines_.size()){137                 LOG(ERROR) << "Restarting data prefetching from start.";138                 image_index = 0;139             }140             // (image_index>image_layer->lines_.size()-1)?(image_index=0):(image_index++);141         }142         143   }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)144   // write the last batch145   146   LOG(ERROR)<< "Successfully extracted the features!";147   return 0;  148 }