From d6cb8889b474ced09ae9622763e3c967fa917ef4 Mon Sep 17 00:00:00 2001 From: Wangyida Date: Wed, 22 Jul 2015 08:21:04 +0800 Subject: [PATCH] add feature extraction demo in samples folder --- modules/cnn_3dobj/CMakeLists.txt | 2 +- modules/cnn_3dobj/README.md | 75 ++- .../cnn_3dobj/include/opencv2/cnn_3dobj.hpp | 5 +- modules/cnn_3dobj/samples/CMakeLists.txt | 4 + modules/cnn_3dobj/samples/build/binary_image | Bin 282640 -> 0 bytes modules/cnn_3dobj/samples/build/binary_label | Bin 77 -> 0 bytes .../cnn_3dobj/samples/build/header_for_image | Bin 16 -> 0 bytes .../cnn_3dobj/samples/build/header_for_label | Bin 8 -> 0 bytes .../build/linemod_triplet_solver.prototxt | 25 - .../build/linemod_triplet_train_test.prototxt | 500 ------------------ .../samples/build/train_linemod_triplet.sh | 37 -- .../data/3d_triplet_iter_10000.caffemodel | Bin 0 -> 6163737 bytes .../3d_triplet_train_test.prototxt} | 96 +++- .../samples/feature_extract_demo.cpp | 99 ++++ modules/cnn_3dobj/samples/images2db_demo.cpp | 12 +- modules/cnn_3dobj/src/cnn_image2db.cpp | 21 +- modules/cnn_3dobj/src/cnn_sphereview.cpp | 4 +- modules/cnn_3dobj/src/precomp.hpp | 6 +- 18 files changed, 265 insertions(+), 621 deletions(-) delete mode 100644 modules/cnn_3dobj/samples/build/binary_image delete mode 100644 modules/cnn_3dobj/samples/build/binary_label delete mode 100644 modules/cnn_3dobj/samples/build/header_for_image delete mode 100644 modules/cnn_3dobj/samples/build/header_for_label delete mode 100644 modules/cnn_3dobj/samples/build/linemod_triplet_solver.prototxt delete mode 100644 modules/cnn_3dobj/samples/build/linemod_triplet_train_test.prototxt delete mode 100755 modules/cnn_3dobj/samples/build/train_linemod_triplet.sh create mode 100644 modules/cnn_3dobj/samples/data/3d_triplet_iter_10000.caffemodel rename modules/cnn_3dobj/samples/{build/linemod_triplet.prototxt => data/3d_triplet_train_test.prototxt} (51%) create mode 100644 modules/cnn_3dobj/samples/feature_extract_demo.cpp diff --git a/modules/cnn_3dobj/CMakeLists.txt b/modules/cnn_3dobj/CMakeLists.txt index a86727b5f..358f7fcba 100644 --- a/modules/cnn_3dobj/CMakeLists.txt +++ b/modules/cnn_3dobj/CMakeLists.txt @@ -1,3 +1,3 @@ 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 caffe protobuf leveldb glog OPTIONAL WRAP python) -target_link_libraries(cnn_3dobj caffe protobuf leveldb glog) +target_link_libraries(opencv_cnn_3dobj caffe protobuf leveldb glog) diff --git a/modules/cnn_3dobj/README.md b/modules/cnn_3dobj/README.md index f625633ae..4e578d954 100644 --- a/modules/cnn_3dobj/README.md +++ b/modules/cnn_3dobj/README.md @@ -1,21 +1,88 @@ -CNN for 3D object recognition and pose estimation including a completed Sphere View of 3D objects from .ply files, when the windows shows the coordinate, press 'q' to go on image generation. +##CNN for 3D object recognition and pose estimation including a completed Sphere View of 3D objects from .ply files, when the windows shows the coordinate, press 'q' to go on image generation. ============================================ -Building Process: + +#Building Process: +###Prerequisite for this module: protobuf, leveldb, glog, gflags and caffe, for the libcaffe installation, you can install it on standard system path for being able to be linked by this OpenCV module when compiling. Just using: -D CMAKE_INSTALL_PREFIX=/usr/local, so the building process on Caffe on system could be like this: +``` +$ cd +$ mkdir biuld +$ cd build +$ cmake -D CMAKE_INSTALL_PREFIX=/usr/local .. +$ make all +$ make install +``` +###After all these steps, the headers and libs of caffe will be set on /usr/local/ path, and when you compiling opencv with opencv_contrib modules as below, the protobif, leveldb, glog, gflags and caffe will be recognized as already installed while building. + +#Compiling OpenCV +``` $ cd $ 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=OFF -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D WITH_VTK=ON -D OPENCV_EXTRA_MODULES_PATH=/modules .. $ make -j4 $ sudo make install +``` + +================================================ +#Building samples +``` $ cd /modules/cnn_3dobj/samples $ mkdir build $ cd build $ cmake .. $ make +``` + +============= +#Demo1: +###Imagas generation from different pose, 4 models are used, there will be 276 images in all which each class contains 69 iamges +``` $ ./sphereview_test -ite_depth=2 -plymodel=../3Dmodel/ape.ply -imagedir=../data/images_ape/ -labeldir=../data/label_ape.txt -num_class=4 -label_class=0 +``` +###press q to start +``` $ ./sphereview_test -ite_depth=2 -plymodel=../3Dmodel/ant.ply -imagedir=../data/images_ant/ -labeldir=../data/label_ant.txt -num_class=4 -label_class=1 +``` +###press q to start +``` $ ./sphereview_test -ite_depth=2 -plymodel=../3Dmodel/cow.ply -imagedir=../data/images_cow/ -labeldir=../data/label_cow.txt -num_class=4 -label_class=2 +``` +###press q to start +``` $ ./sphereview_test -ite_depth=2 -plymodel=../3Dmodel/plane.ply -imagedir=../data/images_plane/ -labeldir=../data/label_plane.txt -num_class=4 -label_class=3 -============================================== -Then press 'q' to run the demo for images generation when you see the gray background and a coordinate. +``` +###press q to start, when all images are created in each class folder, you should copy all images from ../data/images_ape, ../data/images_ant, ../data/images_cow and ../data/images_plane into ../data/images_all folder as a collection of images for network tranining and feature extraction, when all images are copyed correctlly, proceed on. +###After this demo, the binary files of images and labels will be stored as 'binary_image' and 'binary_label' in current path, you should copy them into the leveldb folder in Caffe triplet training, for example: copy these 2 files in /data/linemod and rename them as 'binary_image_train', 'binary_image_test' and 'binary_label_train', 'binary_label_train'. +###We could start triplet tranining using Caffe +``` +$ cd +$ cd +$ ./examples/triplet/create_3d_triplet.sh +$ ./examples/triplet/train_3d_triplet.sh +``` +###After doing this, you will get .caffemodel files as the trained net work. I have already provide the net definition .prototxt files and the trained .caffemodel in /modules/cnn_3dobj/samples/build folder, you could just use them without training in caffe. If you are not interested on feature analysis with the help of binary files provided in Demo2, just skip to Demo3 for feature extraction or Demo4 for classifier. + +============== +``` +$ cd +$ cd /modules/cnn_3dobj/samples/build +``` +#Demo2: +###Convert data into leveldb format from folder ../data/images_all for feature extraction afterwards. The leveldb files including all data will be stored in ../data/dbfile. If you will use the OpenCV defined feature extraction process, you could also skip Demo2 for data converting, just run Demo3 after Demo1 for feature extraction because Demo3 also includes the db file converting process before feature extraction. +``` +$ ./images2db_test -images2db_demo=../data/images_all -src_dst=../data/dbfile -attach_dir=../data/dbfile -channel=1 -width=64 -height=64 +``` + +============== +#Demo3: +###feature extraction, this demo will convert a set of images in a particular path into leveldb database for feature extraction using Caffe. +``` +$ ./feature_extract_test +``` +###This will extract feature from a set of images in a folder as vector for further classification and a binary file with containing all feature vectors of each sample. Pay attention: if it's warning you that 'Check failed: leveldb::DB::Open(options, outputdb, &db).ok()', the reason is that there is alreay leveldb files in ../data/dbfile as previous running of Demo2 or Demo3, just delete all files in ../data/dbfile and run Demo3 again. +###After running this, you will get a binary file storing features in ../data/feature folder, I can provide a Matlab script reading this file if someone need it. If you don't need the binary file, the feature could also be stored in vector for directly classification using the softmax layer as shown in Demo4. + +============== +#Demo4: +###Classifier ============================================== diff --git a/modules/cnn_3dobj/include/opencv2/cnn_3dobj.hpp b/modules/cnn_3dobj/include/opencv2/cnn_3dobj.hpp index 3c55ae104..4e9c1e649 100644 --- a/modules/cnn_3dobj/include/opencv2/cnn_3dobj.hpp +++ b/modules/cnn_3dobj/include/opencv2/cnn_3dobj.hpp @@ -46,8 +46,6 @@ the use of this software, even if advised of the possibility of such damage. #define __OPENCV_CNN_3DOBJ_HPP__ #ifdef __cplusplus -#include -#include #include #include #include @@ -62,7 +60,7 @@ the use of this software, even if advised of the possibility of such damage. #include #include #include -#include +//#include #include #include #include @@ -159,7 +157,6 @@ class CV_EXPORTS_W DataTrans 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 CV_WRAP std::vector 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 for classification, the model proto and network proto are needed, All images in the file root will be used for feature extraction. */ diff --git a/modules/cnn_3dobj/samples/CMakeLists.txt b/modules/cnn_3dobj/samples/CMakeLists.txt index d957c698e..50a4a71ca 100644 --- a/modules/cnn_3dobj/samples/CMakeLists.txt +++ b/modules/cnn_3dobj/samples/CMakeLists.txt @@ -11,3 +11,7 @@ target_link_libraries(sphereview_test ${OpenCV_LIBS}) set(SOURCES2 images2db_demo.cpp) add_executable(images2db_test ${SOURCES2}) target_link_libraries(images2db_test ${OpenCV_LIBS}) + +set(SOURCES3 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