opencv_contrib/modules/cnn_3dobj

##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: ###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 <caffe_source_directory>
$ 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 <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=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

================================================ #Building samples

$ cd <opencv_contrib>/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 -plymodel=../3Dmodel/ape.ply -label_class=0

###press q to start

$ ./sphereview_test -plymodel=../3Dmodel/ant.ply -label_class=1

###press q to start

$ ./sphereview_test -plymodel=../3Dmodel/cow.ply -label_class=2

###press q to start

$ ./sphereview_test -plymodel=../3Dmodel/plane.ply -label_class=3

###press q to start, when all images are created in images_all folder as a collection of images for network tranining and feature extraction, then 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 <caffe_source_directory>/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 <caffe_source_directory>
$ ./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 <opencv_contrib>/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.

============== #Demo4:

$ cd
$ cd <opencv_contrib>/modules/cnn_3dobj/samples/build

###Classifier, this will extracting the feature of a single image and compare it with features of gallery samples for prediction. Demo2 should be used in advance to generate a file name list for the prediction list. This demo uses a set of images for feature extraction in a given path, these features will be a reference for prediction on target image. Just run:

$ ./classify_test

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