Reference Apps for Video Analytics using TensorRT 5 and DeepStream SDK 3.0

Installing Pre-requisites:

Install GStreamer pre-requisites using:
$ sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev

Install Google flags using:
$ sudo apt-get install libgflags-dev

To use just the stand alone trt-yolo-app, Deepstream Installation can be skipped. Refer to the table below for information regarding installation dependencies. See Note for additional installation caveats.

Platform	Applications	Deepstream Version	Dependancies	Repo Release Tag
dGPU	All apps and plugins	DS 3.0	DS 3.0, Cuda 10, TensorRT 5	TOT
dGPU	All apps and plugins	DS 2.0	DS 2.0, Cuda 9.2, TensorRT 4	DS2 Release
dGPU	trt-yolo-app	Not required	Cuda 10, TensorRT 5	TOT
tegra	nvgstiva-app, yoloplugin and trt-yolo-app	DS 1.5	DS 1.5, Jetpack 3.3 (Cuda 9.0, TensorRT 4)	DS2 Release
tegra	trt-yolo-app	Not required	Jetpack 3.3 (Cuda 9.0, TensorRT 4)	DS2 Release

Tegra users working with Deepstream 1.5 and Jetpack 3.3 will have to regenerate the .cache files to use the standard caffe models available in the SDK. This can be done by deleting all the .cache files in /home/nvidia/Model directory and all its subdirectories and then running the nvgstiva-app using the default config file.

Setup

Update all the parameters in Makefile.config file present in the root directory

Building NvYolo Plugin

Go to the data/yolo directory and add your yolo .cfg and .weights file.

For yolo v2,
$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg
$ wget https://pjreddie.com/media/files/yolov2.weights

For yolo v2 tiny,
$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg
$ wget https://pjreddie.com/media/files/yolov2-tiny.weights

For yolo v3,
$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg
$ wget https://pjreddie.com/media/files/yolov3.weights

For yolo v3 tiny,
$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg
$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
Add absolute paths of images to be used for calibration in the calibration_images.txt file within the data/yolo directory.
For dGPU's,
$cd sources/plugins/gst-yoloplugin-tesla
make && sudo make install
For tegra,
$cd sources/plugins/gst-yoloplugin-tegra
make && sudo make install

Object Detection using DeepStream

There are multiple apps that can be used to perform object detection in deepstream.

deepstream-yolo-app [TESLA platform only]

The deepStream-yolo-app located at sources/apps/deepstream_yolo is a sample app similar to the Test-1 & Test-2 apps available in the DeepStream SDK. Using the yolo app we build a sample gstreamer pipeline using various components like H264 parser, Decoder, Video Converter, OSD and Yolo plugin to run inference on an elementary h264 video stream.

$ cd sources/apps/deepstream-yolo
$ make && sudo make install
$ cd ../../../
$ deepstream-yolo-app /path/to/sample_video.h264 /path/to/yolo-plugin-config-file.txt

Refer to sample config files yolov2.txt, yolov2-tiny.txt, yolov3.txt and yolov3-tiny.txt in config/ directory.

nvgstiva-app [TEGRA platform only]

The section below in the config file corresponds to ds-example(yolo) plugin in deepstream. The config file is located at config/nvgstiva-app_yolo_config.txt. Make any changes to this section if required.
```
[ds-example]
enable=1
processing-width=640
processing-height=480
full-frame=1
unique-id=15
```
Update path to the video file source in the URI field under [source0] group of the config file uri=file://path/to/source/video
Go to the root folder of this repo and run $ nvgstiva-app -c config/nvgstiva-app_yolo_config.txt

Object Detection using trt-yolo-app

The trt-yolo-app located at sources/apps/trt-yolo is a sample standalone app, which can be used to run inference on test images. This app does not have any deepstream dependencies and can be built independently.

$ cd sources/apps/trt-yolo
$ make && sudo make install
$ cd ../../../
$ trt-yolo-app --flagfile=/path/to/config-file.txt