DeepStream-Yolo - DeepSORT

NVIDIA DeepStream SDK 6.0.1 YOLO models with Tracker Integration.

This repo focuses on the Tracking itself, for more information on the DeepStream YOLO plugin please refer to Marcos Luciano DeepStream Yolo Repo

There you can find benchmarks and extra tutorial and Info.

Getting started

Requirements
Tested models
Benchmarks
dGPU installation
Basic usage
YOLOv5 usage
YOLOR usage
INT8 calibration
Using your custom model
Implement Trackers (like DeepSort)

Requirements

x86 platform

Jetson platform

For YOLOv5 and YOLOR

x86 platform

PyTorch >= 1.7.0

Jetson platform

PyTorch >= 1.7.0

Tested models

Benchmarks

nms-iou-threshold = 0.6
pre-cluster-threshold = 0.001 (mAP eval) / 0.25 (FPS measurement)
batch-size = 1
valid = val2017 (COCO) - 1000 random images for INT8 calibration
sample = 1920x1080 video
NOTE: Used maintain-aspect-ratio=1 in config_infer file for YOLOv4 (with letter_box=1), YOLOv5 and YOLOR models.

dGPU installation

To install the DeepStream on dGPU (x86 platform), without docker, we need to do some steps to prepare the computer.

Open

1. Disable Secure Boot in BIOS

If you are using a laptop with newer Intel/AMD processors, please update the kernel to newer version.

wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100_5.11.0-051100.202102142330_all.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-headers-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-image-unsigned-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
wget https://kernel.ubuntu.com/~kernel-ppa/mainline/v5.11/amd64/linux-modules-5.11.0-051100-generic_5.11.0-051100.202102142330_amd64.deb
sudo dpkg -i  *.deb
sudo reboot

2. Install dependencies

sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
sudo apt-get install python3 python3-dev python3-pip
sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4
sudo apt-get install libglvnd-dev
sudo apt-get install linux-headers-$(uname -r)

NOTE: Install DKMS only if you are using the default Ubuntu kernel

sudo apt-get install dkms

NOTE: Purge all NVIDIA driver, CUDA, etc.

sudo apt-get remove --purge '*nvidia*'
sudo apt-get remove --purge '*cuda*'
sudo apt-get remove --purge '*cudnn*'
sudo apt-get remove --purge '*tensorrt*'
sudo apt autoremove --purge && sudo apt autoclean && sudo apt clean

3. Disable Nouveau

sudo nano /etc/modprobe.d/blacklist-nouveau.conf

blacklist nouveau
options nouveau modeset=0

sudo update-initramfs -u

4. Reboot the computer

sudo reboot

5. Download and install NVIDIA Driver without xconfig

TITAN, GeForce RTX / GTX series and RTX / Quadro series

wget https://us.download.nvidia.com/XFree86/Linux-x86_64/470.103.01/NVIDIA-Linux-x86_64-470.103.01.run
sudo sh NVIDIA-Linux-x86_64-470.103.01.run

Data center / Tesla series

wget https://us.download.nvidia.com/tesla/470.103.01/NVIDIA-Linux-x86_64-470.103.01.run
sudo sh NVIDIA-Linux-x86_64-470.103.01.run

NOTE: Only if you are using default Ubuntu kernel, enable the DKMS during the installation.

6. Download and install CUDA 11.4.3 without NVIDIA Driver

wget https://developer.download.nvidia.com/compute/cuda/11.4.3/local_installers/cuda_11.4.3_470.82.01_linux.run
sudo sh cuda_11.4.3_470.82.01_linux.run

Export environment variables

nano ~/.bashrc

export PATH=/usr/local/cuda-11.4/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

source ~/.bashrc
sudo ldconfig

NOTE: If you are using a laptop with NVIDIA Optimius, run

sudo apt-get install nvidia-prime
sudo prime-select nvidia

7. Download from NVIDIA website and install the TensorRT 8.0 GA (8.0.1)

echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda-repo.list
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt-key add 7fa2af80.pub
sudo apt-get update
sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub
sudo apt-get update
sudo apt-get install libnvinfer8=8.0.1-1+cuda11.3 libnvinfer-plugin8=8.0.1-1+cuda11.3 libnvparsers8=8.0.1-1+cuda11.3 libnvonnxparsers8=8.0.1-1+cuda11.3 libnvinfer-bin=8.0.1-1+cuda11.3 libnvinfer-dev=8.0.1-1+cuda11.3 libnvinfer-plugin-dev=8.0.1-1+cuda11.3 libnvparsers-dev=8.0.1-1+cuda11.3 libnvonnxparsers-dev=8.0.1-1+cuda11.3 libnvinfer-samples=8.0.1-1+cuda11.3 libnvinfer-doc=8.0.1-1+cuda11.3

8. Download from NVIDIA website and install the DeepStream SDK 6.0.1 (6.0)

sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb
rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin
sudo ln -snf /usr/local/cuda-11.4 /usr/local/cuda

9. Reboot the computer

sudo reboot

Basic usage

1. Download the repo

git clone https://github.com/marcoslucianops/DeepStream-Yolo.git
cd DeepStream-Yolo

2. Download cfg and weights files from your model and move to DeepStream-Yolo folder

3. Compile lib

x86 platform

CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

Jetson platform

CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo

4. Edit config_infer_primary.txt for your model (example for YOLOv4)

[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# YOLO cfg
custom-network-config=yolov4.cfg
# YOLO weights
model-file=yolov4.weights
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
[class-attrs-all]
# IOU threshold
nms-iou-threshold=0.45
# Score threshold
pre-cluster-threshold=0.25

5. Run

deepstream-app -c deepstream_app_config.txt

NOTE: If you want to use YOLOv2 or YOLOv2-Tiny models, change the deepstream_app_config.txt file before run it

...
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV2.txt

YOLOv5 usage

NOTE: Make sure to change the YOLOv5 repo version to your model version before conversion.

1. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils to ultralytics/yolov5 folder

2. Open the ultralytics/yolov5 folder

3. Download pt file from ultralytics/yolov5 website (example for YOLOv5n 6.1)

wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt

4. Generate cfg and wts files (example for YOLOv5n)

python3 gen_wts_yoloV5.py -w yolov5n.pt

5. Copy generated cfg and wts files to DeepStream-Yolo folder

6. Open DeepStream-Yolo folder

7. Compile lib

x86 platform

CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

Jetson platform

CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo

8. Edit config_infer_primary_yoloV5.txt for your model (example for YOLOv5n)

[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# CFG
custom-network-config=yolov5n.cfg
# WTS
model-file=yolov5n.wts
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
[class-attrs-all]
# IOU threshold
nms-iou-threshold=0.45
# Score threshold
pre-cluster-threshold=0.25

8. Change the deepstream_app_config.txt file

...
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV5.txt

9. Run

deepstream-app -c deepstream_app_config.txt

NOTE: For YOLOv5 P6 or custom models, check the gen_wts_yoloV5.py args and use them according to your model

Input weights (.pt) file path (required)

-w or --weights

Input cfg (.yaml) file path

-c or --yaml

Model width (default = 640 / 1280 [P6])

-mw or --width

Model height (default = 640 / 1280 [P6])

-mh or --height

Model channels (default = 3)

-mc or --channels

P6 model

--p6

YOLOR usage

1. Copy gen_wts_yolor.py from DeepStream-Yolo/utils to yolor folder

2. Open the yolor folder

3. Download pt file from yolor website

4. Generate wts file (example for YOLOR-CSP)

python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg

5. Copy cfg and generated wts files to DeepStream-Yolo folder

6. Open DeepStream-Yolo folder

7. Compile lib

x86 platform

CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

Jetson platform

CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo

8. Edit config_infer_primary_yolor.txt for your model (example for YOLOR-CSP)

[property]
...
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
# CFG
custom-network-config=yolor_csp.cfg
# WTS
model-file=yolor_csp.wts
# Generated TensorRT model (will be created if it doesn't exist)
model-engine-file=model_b1_gpu0_fp32.engine
# Model labels file
labelfile-path=labels.txt
# Batch size
batch-size=1
# 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# Number of classes in label file
num-detected-classes=80
...
[class-attrs-all]
# IOU threshold
nms-iou-threshold=0.5
# Score threshold
pre-cluster-threshold=0.25

8. Change the deepstream_app_config.txt file

...
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yolor.txt

9. Run

deepstream-app -c deepstream_app_config.txt

INT8 calibration

1. Install OpenCV

sudo apt-get install libopencv-dev

2. Compile/recompile the nvdsinfer_custom_impl_Yolo lib with OpenCV support

x86 platform

cd DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo

Jetson platform

cd DeepStream-Yolo
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo

3. For COCO dataset, download the val2017, extract, and move to DeepStream-Yolo folder

Select 1000 random images from COCO dataset to run calibration

mkdir calibration

for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
    cp ${jpg} calibration/; \
done

Create the calibration.txt file with all selected images

realpath calibration/*jpg > calibration.txt

Set environment variables

export INT8_CALIB_IMG_PATH=calibration.txt
export INT8_CALIB_BATCH_SIZE=1

Change config_infer_primary.txt file

...
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
...
network-mode=0
...

...
model-engine-file=model_b1_gpu0_int8.engine
int8-calib-file=calib.table
...
network-mode=1
...

Run

deepstream-app -c deepstream_app_config.txt

NOTE: NVIDIA recommends at least 500 images to get a good accuracy. In this example I used 1000 images to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will increase the accuracy and calibration speed. Set it according to you GPU memory. This process can take a long time.

Implement Tracking

The Deepstream allows you easy acess to different trackers with different settings. Among them are:

IoU Tracker (Very simple one, should only be used as a Base case to compare)
NvDCF (Nvidia's Tracker)
DeepSORT (Alpha)

You can find more information about each specific tracker and it's settings on the NVIDIA website.

For a quickstart you can run the following configs (edit first to choose which yolo you want).

deepstream-app -c deepstream_app_config-tracker.txt

deepstream-app -c deepstream_app_config-tracker-webcam.txt

If you want to customize which tracker you are using you have to edit the following lines in the deepstream config file.

You can also change the interval of the detector for faster inference. We don't need to detect every single frame if we have a tracker on, this will boost the FPS. We might loose some detections so consider the interval gap according to the use of your application.

[primary-gie]
enable=1
gpu-id=0
interval=2
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yolor.txt

More of these config instructions can be found on Using your custom model

[tracker]
enable=1
tracker-width=640
tracker-height=384
gpu-id=0
ll-lib-file=/opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
#ll-config-file=/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_DeepSORT.yml
#enable-past-frame=1
enable-batch-process=1

Choosing different Trackers

The NVIDIA tracker plugin unites all trackers under the same common library called nvmultiobjecttracker

Each different tracker can be choosen with a config-file

For example to use the IoU Tracker you would use:

ll-config-file=/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_IoU.yml

To use NvDCF with max performance

ll-config-file=/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_NvDCF_max_perf.yml

To use DeepSORT:

ll-config-file=/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_tracker_DeepSORT.yml

Note to make DeepStream work you first need to compile the engine according to the instructions in: DeepSORT DeepStream Tutorial

You can try different trackers outisde of DeepStream default library such as Byte. Instructions can be found here. ByteTrack DeepStream

Using Webcam with DeepStream

To use the USB-Cam (or any type of cam) in DeepStream you must eddit the [source] on the deepstream-config.

More of these config instructions on different type of sources can be found on Using your custom model

It's important that the camera fps / width / height match the settings of your camera.

#Camera Source
[source0]
enable=1
# Type – 1=CameraV4L2 2=URI 3=MultiURI
type=1
camera-width=640
camera-height=480
camera-fps-n=30
camera-fps-d=1
camera-v4l2-dev-node=1
num-sources=1

Extract metadata

You can get metadata from deepstream in Python and C++. For C++, you need edit deepstream-app or deepstream-test code. For Python your need install and edit deepstream_python_apps.

You need manipulate NvDsObjectMeta (Python/C++), NvDsFrameMeta (Python/C++) and NvOSD_RectParams (Python/C++) to get label, position, etc. of bboxes.

In C++ deepstream-app application, your code need be in analytics_done_buf_prob function. In C++/Python deepstream-test application, your code need be in osd_sink_pad_buffer_probe/tiler_src_pad_buffer_probe function.

To extract meta-data on the kitti format from the trackers and detectors you can add the following likes to your deepstream-app config under the [application] part.

Make sure to choose the directory you wish to save the metadata accordingly (create the directory previously).

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
kitti-track-output-dir=/home/Tracker_Info
gie-kitti-output-dir=/home/Detector_Info

Saving Video Output of DeepStream app

To save the video output of your DeepStream app you must add another [sink] to your config file as such.

[sink1]
enable=1
type=3
container=1
sync=0
codec=1
bitrate=2000000
output-file=out.mp4

Acknowledgements

Most of this code is from MarcosLuciano's Repo. Please refer to it if for further information . As for my part of the config files you can do whatever you want with them. Please check Marcos other projects and inference videos :) : https://www.youtube.com/MarcosLucianoTV

DeepStream-Yolo

naosi / deepstream-yolo-deepsort Goto Github PK