JetsonCV

Installation script for computer vision libraries using in NVIDIA Jetson.

Framework	Version
OS	Ubuntu 16.04 64bit
Jetpack	3.2
Jetson	TX2
OpenCV	3.4
Caffe	1.0
Tensorflow	1.6

Install OpenCV

Before installation, please check you have correctly installed jetpack3.2. Simply run Install_OpenCV_JetsonTX2.sh.

$ chmod +x Install_OpenCV_JetsonTX2.sh
$ ./Install_OpenCV_JetsonTX2.sh

To verify the installation:

$ ls /usr/local/lib/python3.5/dist-packages/cv2.*
<result cv2.so here>
$ ls /use/local/lib/python2.7/dist-packages/cv2.*
<result cv2.so here>
$ python3 -c 'import cv2; print(cv2.__version__)'
<result 3.4.0 here>
$ python2 -c 'import cv2; print(cv2.__version__)'
<result 3.4.0 here>

Install Caffe

Before installation, please check you have correctly installed jetpack3.2 and have installed git. Please add cuda path to your $PATH in .bashrc

echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc

After setting environment variables, just run Install_Caffe_JetsonTX2.sh.

$ chmod +x Install_Caffe_JetsonTX2.sh
$ ./Install_Caffe_JetsonTX2.sh

The modified Caffe Makefile.config and Makefile has been copied to caffe root directory.

Install Tensorflow

cd ./tensorflow/Jetpack-3.2/1.6

sudo pip2 install tensorflow-1.6.0rc1-cp27-cp27mu-linux_aarch64.whl
sudo pip3 install tensorflow-1.6.0rc1-cp35-cp35m-linux_aarch64.whl

To verify the installation:

python3 -c 'import tensorflow as tf; print(tf.__version__)'
<result 1.6.0 here>
$ python2 -c 'import tensorflow as tf; print(tf.__version__)'
<result 1.6.0 here>

$$ \sum_{k} \frac{1}{N_{k}^{c}} \sum_{i \in A_{k}} l_{c} (p_i, g_i) + \lambda \sum_{k} \frac{1}{N_{k}^{r}} \sum_{i \in A_{k}} \mathbf{1}(g_i=1)l_{r}(b_i,t_i) $$ 这里:

$k = index({\mathit{M}}_1^k)$

$M\in {M_1,M_2,M_3}$

$A_{k}$ is the set of anchors in $M_k$.

$\mathbf{1}()$ is Indicator function

$l_c$ is face classification loss

$p_i$ predicted category

$g_i$ ground truth

$l_r$ represents the bounding box regression loss

$b_i$ predicted bounding box

$t_i$ target box

$N_{k}^{c}$ is the number of anchors in module $M_k$

$N_{k}^{r}=\sum_{i \in A_{k}}\mathbf{1}(g_i=1)$ is the number of anchors in module $M_k$, limits the regression loss only to the positively assigned anchors