GPU-accelerated Docker container with OpenCV 4.5, Python 3.8 and GStreamer

• opencv + opencv_contrib
• Python 3.8.0
• Ubuntu 18.04 LTS
• GStreamer 1.14.5
• FFMPEG
• CUDA 10.2
• NVIDIA GPU arch: 30 35 37 50 52 60 61 70 75
• CUDA_ARCH_PTX = 75 ( The container does not work with NVIDIA Ampere GPUs sm_86 . For RTX 30 series, please see this new repo)
• cuDNN: 7.6.5
• OpenCL
• Qt5::OpenGL 5.9.5
• Intel IPP and TBB
• UNCOMPRESSED SIZE 6.02 GB

Pull the image from here :

• https://hub.docker.com/u/fizmath

  $ docker pull fizmath/gpu-opencv:latest

How to run :

• With GPU

  You need to install NVIDIA Container Toolkit on your machine. Run the container by this command :

  $ docker run --gpus all -it --rm  fizmath/gpu-opencv:latest
  root@22067ad0cc87:/myapp#  
  

• With CPU :

  If no GPU available on your machine, yet you can use the container with Docker

  $ docker run -it --rm fizmath/gpu-opencv:latest
  root@cc00562d816e:/myapp# 
  

  for running the example .py files in this repo with CPU you should comment these two lines :

  net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
  net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
  

Test with the examples in this repo :

1. Download this repo . The pretrained models are included, but you just need to download one of them yolov4.weights. And also no arg-parsing for the simplicity

2. Unzip and set its directory in your machine as your docker volume :

    $ docker run --gpus all -it --rm -v  <your volume dir>:/myapp fizmath/gpu-opencv:latest
    root@771c5bcb2895:/myapp# ls
    Dockerfile  YOLOv4_cam.py  face_SSD.py     model_SSD   model_YOLOv4
    README.md   cv2_info.py    golden_axe.png  model_SURE  super_resolution.py

3. Print out OpenCV build info into a textfile, check out the produced output in your volume :

   root@771c5bcb2895:/myapp# python3 cv2_info.py

4. Image Super-Resolution with OpenCV, Cuda and Docker : the included image, a SR image SURE_golden_axe.png will be produced in your volume by the following command :

root@771c5bcb2895:/myapp# python3 super_resolution.py 

compare both images visually. If you run out of GPU memory , make inference by your CPU cores ; see the .py file.

For the next two examples, you need to include these commands to docker run :

• -e DISPLAY=$DISPLAY : this sends the display id from your machine to the container.

• --device="/dev/video0:/dev/video0" : this lets the container find the camera.

• -v /tmp/.X11-unix:/tmp/.X11-unix:rw : this lets the container find the display via X server. In order to display the GUI with Docker, the X client in the Docker container needs to communicate with the host X server.

Note that I tested the above commands in UBUNTU. These may differ in other systems.

5. Real-time face detection with OpenCV DNN, GStreamer, CUDA and Docker : . Before running the container type in your CMD :

   $ xhost +
   access control disabled, clients can connect from any host

   which allows the user to access the running X server. After being done with the examples type xhost - for the sake of security

    $ docker run --gpus all --rm -it -e DISPLAY=$DISPLAY -v  <volume dir>:/myapp -v /tmp/.X11-unix:/tmp/.X11-unix:rw --device="/dev/video0:/dev/video0"  fizmath/gpu-opencv:latest
    root@1be1f7efabf9:/myapp# python3 face_SSD.py

   

   I drew encircling ellipses instead of the common face bounding boxes . Click q to terminate the session.

6. Real-time object detection with YOLO v4, GStreamer, CUDA and Docker. First download yolov4.weights and put it in it's folder. Following from the above:

   root@1be1f7efabf9:/myapp# python3 YOLOv4_cam.py 

   bring some objects from this list in front of your camera.

Inspect the GStreamer API out of the OpenCV wrapper

Type into the container's shell :

root@1be1f7efabf9:/myapp# gst-launch-1.0 videotestsrc ! videoconvert ! autovideosink

then a GStreamer hello world window pops up . See the documentation.

OpenCV and GStreamer debugging

Export OpenCV Log Levels or GStreamer Debug Levels into our container's shell . For example:

root@1be1f7efabf9:/myapp# export OPENCV_LOG_LEVEL=INFO 

or

root@1be1f7efabf9:/myapp# export GST_DEBUG=2

then run one of the examples and browse the output.

Build your own image

The Dockerfile culprit for the image may not have a perfect structure . It is just my own assembly. You may modify, upgrade and build a proper one for your requirements :

$ docker build -f Dockerfile -t <name>:<tag> .

It won't be that straight-forward, you will get some deprecation warnings and compatibility issues.
To keep the image light-weight and compatible with old GPU architectures SM_30 , SM_35 , SM_37 I implanted 10.2-cudnn7-devel-ubuntu18.04 as base image .