If the target platform is a dGPU, download and install DeepStream 3.0. For Tegra platforms, flash your device with Jetpack 3.3 and install Deepstream 1.5.
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. However CUDA 10.0 and TensorRT 5 should be installed. See Note for additional installation caveats.
Update all the parameters in Makefile.config
file present in the root directory
-
Go to the
data
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
-
Set the right macro in the
network_config.h
file to choose a model architecture -
[OPTIONAL] Update the paths of the .cfg and .weights file and other network params in
network_config.cpp
file if required. -
Add absolute paths of images to be used for calibration in the
calibration_images.txt
file within thedata
directory. -
Run the following command from
sources/plugins/gst-yoloplugin-tesla
for dGPU's or fromsources/plugins/gst-yoloplugin-tegra
for tegra devices to build and install the pluginmake && sudo make install
There are multiple apps that can be used to perform object detection in deepstream.
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
Following steps describe how to run the YOLO plugin in the deepstream-app
-
The section below in the config file corresponds to ds-example(yolo) plugin in deepstream. The config file is located at
config/deepstream-app_yolo_config.txt
. Make any changes to this section if required.[ds-example] enable=1 processing-width=1280 processing-height=720 full-frame=1 unique-id=15 gpu-id=0
-
Update path to the video file source in the URI field under
[source0]
group of the config fileuri=file://relative/path/to/source/video
-
Go to the root folder of this repo and run
$ deepstream-app -c config/deepstream-app_yolo_config.txt
-
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 fileuri=file://path/to/source/video
-
Go to the root folder of this repo and run
$ nvgstiva-app -c config/nvgstiva-app_yolo_config.txt
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. Add a list of absolute paths of images to be used for inference in the test_images.txt
file located at data
and run trt-yolo-app
from the root directory of this repo. Additionally, the detections on test images can be saved by setting kSAVE_DETECTIONS
config param to true
in network_config.cpp
file. The images overlayed with detections will be saved in the data/detections/
directory.
This app has three command line arguments(optional).
1. batch_size - Integer value to be used for batch size of TRT inference. Default value is 1.
2. decode - Boolean value representing if the detections have to be decoded. Default value is true.
3. seed - Integer value to set the seed of random number generators. Default value is `time(0)`.
$ cd sources/apps/trt-yolo
$ make && sudo make install
$ cd ../../../
To run the app with default arguments
$ trt-yolo-app
To change the batch_size of the TRT engine
$ trt-yolo-app --batch_size=4
-
If you are using the plugin with deepstream-app (located at
/usr/bin/deepstream-app
), register the yolo plugin as dsexample. To do so, replace line 671 ingstyoloplugin.cpp
withreturn gst_element_register(plugin, "dsexample", GST_RANK_PRIMARY, GST_TYPE_YOLOPLUGIN);
This registers the plugin with the namedsexample
so that the deepstream-app can pick it up and add to it's pipeline. Now go tosources/gst-yoloplugin/
and run$ make && sudo make install
to build and install the plugin. -
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. -
Tesla users working with Deepstream 2.0/TensorRT 4.x/CUDA 9.2, checkout the DS2 version of this repo to avoid any build conflicts.