refer to https://github.com/jkjung-avt/tensorrt_demos & https://jkjung-avt.github.io/jetpack-4.4/

HW: Neon-2000-JNX
Jetpack 4.5

1. Run the following commands to do basic set-up of the system.

$ sudo apt update
###
### Set proper environment variables
$ cd ~/Downloads
$ git clone https://github.com/jkjung-avt/jetson_nano.git
$ cd jetson_nano
$ ./install_basics.sh
$ source ${HOME}/.bashrc

2. Install dependencies for python3 "cv2"
$ sudo apt-get update
$ sudo apt-get install -y build-essential make cmake cmake-curses-gui \
                          git g++ pkg-config curl libfreetype6-dev \
                          libcanberra-gtk-module libcanberra-gtk3-module
$ sudo apt-get install -y python3-dev python3-testresources python3-pip
$ sudo pip3 install -U pip Cython
$ cd ~/Downloads/jetson_nano
$ ./install_protobuf-3.8.0.sh
$ sudo pip3 install numpy matplotlib==3.2.2


###
### Installing tensorflow-1.15.2

$ sudo apt-get install -y libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran
$ sudo pip3 install -U pip testresources setuptools
$ sudo pip3 install -U numpy==1.16.1 future mock h5py==2.10.0 keras_preprocessing keras_applications gast==0.2.2 futures pybind11
$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow==1.15.2

### Install pycuda==2019.1.2
$ cd ${HOME}/Downloads
$ git clone https://github.com/jkjung-avt/tensorrt_demos.git
$ cd ${HOME}/Downloads/tensorrt_demos/ssd
$ ./install_pycuda.sh

### Install ONNX == 1.4.1
### Install version "1.4.1" (not the latest version) of python3 "onnx" module. Note that the "onnx" module would depend on "protobuf" == 3.8.0

$ sudo pip3 install onnx==1.4.1

### Go to the "plugins/" subdirectory and build the "yolo_layer" plugin. 
### When done, a "libyolo_layer.so" would be generated.

$ cd ${HOME}/Downloads/tensorrt_demos/plugins
$ make

### Download yolo model
$ cd ${HOME}/Downloads/tensorrt_demos/yolo
$ ./download_yolo.sh

### convert ONNX model to TensorRT

$ python3 yolo_to_onnx.py -m yolov3-416

### Optimize onnx model by TensorRT (takes about 4 minutes)
### yolov3-416 FP32

$ /usr/src/tensorrt/bin/trtexec --onnx=yolov3-416.onnx --workspace=3000 --maxBatch=4 --verbose --saveEngine=yolov3-416-fp32.engine

### Optional
### Optimize onnx model by TensorRT (takes about 14 minutes)
### yolov3-416 FP16

$ /usr/src/tensorrt/bin/trtexec --onnx=yolov3-416.onnx --workspace=3000 --maxBatch=4 --fp16 --verbose --saveEngine=yolov3-416-fp16.engine

### Test by EVA
### yolov3-416 fp16

gst-launch-1.0 pylonsrc camera=0 fps=11 ! videoconvert ! adrt model=~/Downloads/tensorrt_demos/yolo/yolov3-416-fp16.engine scale=0.004 mean="0 0 0" device=0 batch=1 ! adtranslator label=~/Desktop/EVA_IDE/model/yolo_RT_labels.txt topology=yolov3 dims=1,255,13,13,1,255,26,26,1,255,52,52 input_width=416 engine-type=2 ! admetadrawer ! videoconvert ! fpsdisplaysink video-sink=xvimagesink text-overlay=true

### yolov3-416 fp32

gst-launch-1.0 pylonsrc camera=0 fps=6 ! videoconvert ! adrt model=~/Downloads/tensorrt_demos/yolo/yolov3-416-fp32.engine scale=0.004 mean="0 0 0" device=0 batch=1 ! adtranslator label=~/Desktop/EVA_IDE/model/yolo_RT_labels.txt topology=yolov3 dims=1,255,13,13,1,255,26,26,1,255,52,52 input_width=416 engine-type=2 ! admetadrawer ! videoconvert ! fpsdisplaysink video-sink=xvimagesink text-overlay=true

### Optional
### If class trained by 2, input size=416x416
### model name: yolov3-416_test.engine, label files: label.txt

$ python3 yolo_to_onnx.py -m yolov3-416 --category_num 2
$ /usr/src/tensorrt/bin/trtexec --onnx=yolov3-416.onnx --workspace=3000 --maxBatch=4 --verbose --saveEngine=yolov3-416_test.engine
$ gst-launch-1.0 pylonsrc camera=0 fps=6 ! videoconvert ! adrt model=yolov3-416_test.engine scale=0.004 mean="0 0 0" device=0 batch=1 ! adtranslator label=label.txt topology=yolov3 dims=1,21,13,13,1,21,26,26,1,21,52,52 input_width=416 engine-type=2 ! admetadrawer ! videoconvert ! ximagesink sync=false