gmontamat/python-darknet repository overview

⁠Darknet & Python Docker Images

Python Docker image with the Darknet⁠ package included. These images eliminate the burden of compiling Darknet's library (libdarknet.so) and import it into Python using its wrapper⁠ to create YOLOv4, v3, v2 sample apps. Based on daisukekobayashi's darknet-docker images⁠.

⁠Base Image Tags

CPU images are based on Ubuntu Docker Official Images⁠ (ubuntu:20.04) and GPU images are based on nvidia/cuda⁠ (nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04). Images include python3.8, an updated version of pip, and the following libraries:

• Darknet⁠
• Numpy⁠
• OpenCV⁠

Tags indicate whether the image supports GPU or not and which base image to use. They also refer to different flags in the Makefile⁠ for building the library. You can check the meaning of each flag here⁠.

• cpu tag indicates images are built with AVX=1 and OPENMP=1
  • The noopt tag means the AVX option is disabled (AVX=0). If you use a cpu image and get errors, try this tag.
• cv tag means the library is built with the OPENCV=1 flag
• gpu tag means images are built with GPU=1 and CUDNN=1
  • The cc** tag indicates compute compatibility of GPU. Images with this tag are optimized for a certain GPU architecture. You can check compute compatibility of your GPU here⁠. If compute compatibility is greater than or equal to 7.0, images are built with CUDNN_HALF=1.
• u1804 tag means the images are based on ubuntu:18.04 (when CPU-based) or nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 (when GPU-based), and include python3.6 instead of python3.8. Note that cc80 and cc86 tags are not compatible with ubuntu:18.04 base images.

⁠Usage

⁠Basic

The python interpreter runs by default when you start the container:

$ docker run -it --rm gmontamat/python-darknet:cpu
Python 3.8.10 (default, Jun  2 2021, 10:49:15)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import darknet
>>> import numpy as np
>>> import cv2
>>> 

⁠Run custom code

Use volumes to share Python code in /usr/src/app, define it as the working directory, and specify the command to be run. The following example shows how to do this using the code included⁠ which is taken from the Darknet repository⁠:

$ git clone https://github.com/gmontamat/python-darknet-docker.git
$ cd python-darknet-docker
$ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights \
       -O test/yolov4.weights
$ xhost +
$ docker run --gpus all -it --rm -v $(realpath ./test):/usr/src/app \
             -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY \
             -w /usr/src/app gmontamat/python-darknet:gpu python3 darknet_images.py \
             --input sample.jpg --weights yolov4.weights \
             --config_file cfg/yolov4.cfg --data_file cfg/coco.data

Check the troubleshooting docker GUI apps section⁠ for more information about the xhost command.

⁠Video inference

Python is not the most suitable language to handle video streams. Compared to other compiled languages like C++ or SDKs such as NVIDIA DeepStream⁠, video inference won't run as smoothly. However, it is possible to perform real-time tracking on Python using its multiprocessing module and a powerful GPU. YOLOv4 & SORT run at 15fps on a system with an Intel Core i7 and a GeForce GTX 1080. Mobile GPUs such as the Quadro M1000M run YOLOv4 at 3fps but can run YOLOv4-Tiny at 60fps.

You can test video inference using the sample code in this repo⁠ taken from the Darknet repository⁠:

$ git clone https://github.com/gmontamat/python-darknet-docker.git
$ cd python-darknet-docker
$ wget https://github.com/intel-iot-devkit/sample-videos/raw/master/face-demographics-walking.mp4 \
       -O test/face-demographics-walking.mp4
$ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights \
       -O test/yolov4-tiny.weights
$ xhost +
$ docker run --gpus all -it --rm -v $(realpath ./test):/usr/src/app \
             -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY \
             -w /usr/src/app gmontamat/python-darknet:gpu python3 darknet_video.py \
             --input face-demographics-walking.mp4 --weights yolov4-tiny.weights \
             --config_file cfg/yolov4-tiny.cfg --data_file cfg/coco.data

Or, if you prefer using your webcam (/dev/video0 on Linux):

$ git clone https://github.com/gmontamat/python-darknet-docker.git
$ cd python-darknet-docker
$ wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights \
       -O test/yolov4-tiny.weights
$ xhost +
$ docker run --gpus all -it --rm -v $(realpath ./test):/usr/src/app \
             -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY \
             --device=/dev/video0 -w /usr/src/app gmontamat/python-darknet:gpu \
             python3 darknet_video.py --weights yolov4-tiny.weights \
             --config_file cfg/yolov4-tiny.cfg --data_file cfg/coco.data

⁠Troubleshooting docker GUI apps

Running xhost + grants any local user access to your X screen. That might be OK for a single-user machine, but usually not for a multi-user system. To get things back to normal, with controlled access to the X screen, run xhost -.

If you skip this command, you may encounter an error message like the following:

No protocol specified
Error: Can't open display X:X

It can also be fixed by running xhost local:root before you start the container, if you use Docker with sudo. Or, if you use the docker group to run containers without sudo, run xhost local:docker.

⁠Advanced usage

These images can serve as base images for more complex Docker applications where additional packages and dependencies are required. In such case, begin the Dockerfile with:

FROM gmontamat/python-darknet:gpu

⁠TODO

• Use GitHub Actions instead of DockerHub hooks
• Support other python versions (3.7/3.9)
• Use different base images (ubuntu:18.04/ubuntu:20.04)
• Compile OpenCV python library for CUDA support instead of just using pre-built binaries from opencv-python⁠

These images are still a work in progress. Feel free to submit feature requests in the issues page⁠.