Add feature extraction codes using Caffe in Class DataTrans

modules/cnn_3dobj/CMakeLists.txt

 set(the_description "CNN for 3D object recognition and pose estimation including a completed Sphere View on 3D objects")
-ocv_define_module(cnn_3dobj opencv_core opencv_imgproc opencv_viz opencv_highgui OPTIONAL WRAP python)
+ocv_define_module(cnn_3dobj opencv_core opencv_imgproc opencv_viz opencv_highgui caffe protobuf leveldb glog OPTIONAL WRAP python)
+target_link_libraries(cnn_3dobj caffe protobuf leveldb glog)

modules/cnn_3dobj/README.md

@@ -4,7 +4,7 @@ Building Process:
 $ cd <opencv_source_directory>
 $ mkdir build
 $ cd build
-$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D WITH_VTK=ON -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules ..
+$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=OFF -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D WITH_VTK=ON -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules ..
 $ make -j4
 $ sudo make install
 $ cd <opencv_contrib>/modules/cnn_3dobj/samples

modules/cnn_3dobj/include/opencv2/cnn_3dobj.hpp

@@ -45,25 +45,39 @@ the use of this software, even if advised of the possibility of such damage.
 #ifndef __OPENCV_CNN_3DOBJ_HPP__
 #define __OPENCV_CNN_3DOBJ_HPP__
 #ifdef __cplusplus
-#include <glog/logging.h>
-#include <leveldb/db.h>
-#include <caffe/proto/caffe.pb.h>
-#include <opencv2/calib3d.hpp>
-#include <opencv2/viz/vizcore.hpp>
-#include <opencv2/highgui.hpp>
-#include <opencv2/highgui/highgui_c.h>
-//#include <opencv2/imgproc/imgproc.hpp>
+
+#include <opencv/cv.h>
+#include <opencv/cxcore.h>
 #include <string>
 #include <fstream>
 #include <vector>
 #include <stdio.h>
+#include <math.h>
 #include <iostream>
-#include <string>
 #include <set>
 #include <string.h>
 #include <stdlib.h>
+#include <tr1/memory>
 #include <dirent.h>
+#include <glog/logging.h>
+#include <google/protobuf/text_format.h>
+#include <leveldb/db.h>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/viz/vizcore.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/highgui/highgui_c.h>
+#define CPU_ONLY
+#include <caffe/blob.hpp>
+#include <caffe/common.hpp>
+#include <caffe/net.hpp>
+#include <caffe/proto/caffe.pb.h>
+#include <caffe/util/io.hpp>
+#include <caffe/vision_layers.hpp>
 using std::string;
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Datum;
+using caffe::Net;
 /** @defgroup cnn_3dobj CNN based on Caffe aimming at 3D object recognition and pose estimation
 */
 namespace cv
@@ -131,10 +145,24 @@ class CV_EXPORTS_W DataTrans
 	public:
 		DataTrans();
 		CV_WRAP void list_dir(const char *path,std::vector<string>& files,bool r);
+		/** @brief Use directory of the file including images starting with an int label as the name of each image.
+		*/
 		CV_WRAP string get_classname(string path);
+		/** @brief
+		*/
 		CV_WRAP int get_labelid(string fileName);
+		/** @brief Get the label of each image.
+		*/
 		CV_WRAP void loadimg(string path,char* buffer,bool is_color);
-		CV_WRAP void convert(string imgdir,string outputdb,string attachdir,int channel,int width,int height);	
+		/** @brief Load images.
+		*/
+		CV_WRAP void convert(string imgdir,string outputdb,string attachdir,int channel,int width,int height);
+		/** @brief Convert a set of images as a leveldb database for CNN training.
+		*/
+		template<typename Dtype>
+		CV_WRAP std::vector<cv::Mat> feature_extraction_pipeline(std::string pretrained_binary_proto, std::string feature_extraction_proto, std::string save_feature_dataset_names, std::string extract_feature_blob_names, int num_mini_batches, std::string device, int dev_id);
+		/** @brief Extract feature into a binary file and vector<cv::Mat> for classification, the model proto and network proto are needed, All images in the file root will be used for feature extraction.
+		*/
 };
 //! @}
 }}

modules/cnn_3dobj/src/cnn_image2db.cpp

@@ -147,4 +147,92 @@ namespace cnn_3dobj
 	  writefile.close();
 
 	};
+
+	template<typename Dtype>
+	std::vector<cv::Mat> feature_extraction_pipeline(std::string pretrained_binary_proto, std::string feature_extraction_proto, std::string save_feature_dataset_names, std::string extract_feature_blob_names, int num_mini_batches, std::string device, int dev_id) {
+	  if (strcmp(device.c_str(), "GPU") == 0) {
+	    LOG(ERROR)<< "Using GPU";
+	    int device_id = 0;
+	    if (strcmp(device.c_str(), "GPU") == 0) {
+	      device_id = dev_id;
+	      CHECK_GE(device_id, 0);
+	    }
+	    LOG(ERROR) << "Using Device_id=" << device_id;
+	    Caffe::SetDevice(device_id);
+	    Caffe::set_mode(Caffe::GPU);
+	  } else {
+	    LOG(ERROR) << "Using CPU";
+	    Caffe::set_mode(Caffe::CPU);
+	  }
+	  boost::shared_ptr<Net<Dtype> > feature_extraction_net(
+	      new Net<Dtype>(feature_extraction_proto, caffe::TEST));
+	  feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
+	  std::vector<std::string> blob_names;
+	  blob_names.push_back(extract_feature_blob_names);
+	  std::vector<std::string> dataset_names;
+	  dataset_names.push_back(save_feature_dataset_names);
+	  CHECK_EQ(blob_names.size(), dataset_names.size()) <<
+	      " the number of blob names and dataset names must be equal";
+	  size_t num_features = blob_names.size();
+
+	  for (size_t i = 0; i < num_features; i++) {
+	    CHECK(feature_extraction_net->has_blob(blob_names[i]))
+		<< "Unknown feature blob name " << blob_names[i]
+		<< " in the network " << feature_extraction_proto;
+	  }
+	  std::vector<FILE*> files;
+	  for (size_t i = 0; i < num_features; ++i)
+	  {
+		  LOG(INFO) << "Opening file " << dataset_names[i];
+		  FILE * temp = fopen(dataset_names[i].c_str(), "wb");
+		  files.push_back(temp);
+	  }
+
+
+	  LOG(ERROR)<< "Extacting Features";
+
+	  Datum datum;
+	  std::vector<cv::Mat> featureVec;
+	  std::vector<Blob<float>*> input_vec;
+	  std::vector<int> image_indices(num_features, 0);
+	  for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
+	    feature_extraction_net->Forward(input_vec);
+	    for (int i = 0; i < num_features; ++i) {
+			const boost::shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
+		  ->blob_by_name(blob_names[i]);
+	      int batch_size = feature_blob->num();
+	      int dim_features = feature_blob->count() / batch_size;
+		  if (batch_index == 0)
+		  {
+			  int fea_num = batch_size*num_mini_batches;
+			  fwrite(&dim_features, sizeof(int), 1, files[i]);
+			  fwrite(&fea_num, sizeof(int), 1, files[i]);
+		  }
+	      const Dtype* feature_blob_data;
+	      for (int n = 0; n < batch_size; ++n) {
+
+		feature_blob_data = feature_blob->cpu_data() +
+		    feature_blob->offset(n);
+			fwrite(feature_blob_data, sizeof(Dtype), dim_features, files[i]);
+			for (int dim = 0; dim < dim_features; dim++) {
+				cv::Mat tempfeat = cv::Mat(1, dim_features, CV_32FC1);
+				tempfeat.at<Dtype>(0,dim) = *(feature_blob_data++);
+				featureVec.push_back(tempfeat);
+			}
+		++image_indices[i];
+		if (image_indices[i] % 1000 == 0) {
+		  LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
+		      " query images for feature blob " << blob_names[i];
+		}
+	      }  // for (int n = 0; n < batch_size; ++n)
+	    }  // for (int i = 0; i < num_features; ++i)
+	  }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
+	  // write the last batch
+	  for (int i = 0; i < num_features; ++i) {
+		  fclose(files[i]);
+	  }
+
+	  LOG(ERROR)<< "Successfully extracted the features!";
+	  return featureVec;
+	};
 }}

modules/cnn_3dobj/src/precomp.hpp

@@ -43,14 +43,37 @@ the use of this software, even if advised of the possibility of such damage.
 #define __OPENCV_CNN_3DOBJ_PRECOMP_HPP__
 
 #include <opencv2/cnn_3dobj.hpp>
-#include <opencv2/calib3d.hpp>
-#include <opencv2/viz/vizcore.hpp>
-#include <opencv2/highgui.hpp>
-#include <iostream>
-#include <vector>
+#include <opencv/cv.h>
+#include <opencv/cxcore.h>
 #include <string>
+#include <fstream>
+#include <vector>
 #include <stdio.h>
-#include <iostream>
 #include <math.h>
+#include <iostream>
+#include <set>
+#include <string.h>
+#include <stdlib.h>
+#include <tr1/memory>
+#include <dirent.h>
+#include <glog/logging.h>
+#include <google/protobuf/text_format.h>
+#include <leveldb/db.h>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/viz/vizcore.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/highgui/highgui_c.h>
+#define CPU_ONLY
+#include <caffe/blob.hpp>
+#include <caffe/common.hpp>
+#include <caffe/net.hpp>
+#include <caffe/proto/caffe.pb.h>
+#include <caffe/util/io.hpp>
+#include <caffe/vision_layers.hpp>
+using std::string;
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Datum;
+using caffe::Net;
 
 #endif