##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.
#Convolutional Neural Network for 3D object classification and pose estimation.
============================================
#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:
#Module Description on cnn_3dobj:
This learning structure construction and feature extraction concept is based on Convolutional Neural Network, the main reference paper could be found at:
I implemented the training and feature extraction codes mainly based on CAFFE project which will be compiled as libcaffe for the cnn_3dobj OpenCV module, codes are mainly concentrating on triplet and pair-wise jointed loss layer, the training data arrangement is also important which basic training information.
Codes about my triplet version of caffe are released on GIthub, you can git it through:
###Prerequisite for this module: protobuf and caffe, for the libcaffe installation, you can install it on standard system path for the aim of being able to be linked by this OpenCV module when compiling and function using. Using: -D CMAKE_INSTALL_PREFIX=/usr/local as an building option when you cmake, 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
$ make all -j4
$ sudo 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.
###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 protobuf and caffe will be recognized as already installed while building. Protobuf is
###If you encouter the no declaration errors when you 'make', it might becaused that you have installed the older version of cnn_3dobj module and the header file changed in a newly released version of codes. This problem is the cmake and make can't detect the header should be updated and it keeps the older header remains in /usr/local/include/opencv2 whithout updating. This error could be solved by remove the installed older version of cnn_3dobj module by:
```
$ cd /
$ cd usr/local/include/opencv2/
$ sudo rm -rf cnn_3dobj.hpp
```
###And redo the compiling steps above again.
================================================
#Building samples
```
...
...
@@ -34,41 +51,43 @@ $ make
```
=============
#Demo1:
###Imagas generation from different pose, 4 models are used, there will be 276 images in all which each class contains 69 iamges
#Demos
##Demo1: training data generation
###Imagas generation from different pose, by default there are 4 models used, there will be 276 images in all which each class contains 69 iamges, if you want to use additional .ply models, it is necessary to change the class number parameter to the new class number and also give it a new class label.
###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
###press 'Q' to start
###When all images are created in images_all folder as a collection of training images for network tranining and as a gallery of reference images for the classification part, 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'. Here I use the same as trianing and testing data, you can use different data for training and testing the performance in the CAFFE training process. It's important to observe the loss of testing data to check whether training data is suitable for the your aim. Loss should be obseved as keep decreasing and remain on a much smaller number than the initial loss.
###You could start triplet tranining using Caffe like this:
```
$ 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.
###After doing this, you will get .caffemodel files as the trained parameter of net work. I have already provide the net definition .prototxt files and the pretrained .caffemodel in <opencv_contrib>/modules/cnn_3dobj/samples/build/data folder, you could just use them without training in caffe.
==============
#Demo4:
##Demo2: feature extraction and classification
```
$ 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:
###Classifier, this will extracting the feature of a single image and compare it with features of gallery samples for prediction. 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:
/** @brief 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 layer.
/** @brief 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 layer.
