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Predictions via Redis

Redis is a fast, in-memory data store, which can be used as a database, cache, streaming engine, and message broker. It is a great way of incorporating deep learning frameworks that are run from within Docker containers, simplifying the dependencies due to decoupling.

wai.annotations has support for the following data domains:

  • image classification: sends image, receives JSON with label/probability pairs
  • object detection: sends image, receives JSON in OPEX format
  • image segmentation: sends image, receives segmented image (indexed PNG or blue-channel PNG)

Docker images#

The following Docker images are available for connecting to the prediction plugins:

Object detection example#

In this example we are using a prebuilt yolov5 model (using MS-COCO) to make predictions on the frames that come from a dashcam video, overlay the predictions on the images and display them. For the model and wai.annotations we will be using existing docker containers.

NB: No GPU required.



dashcam raw


dashcam annotated


NB: Place all the downloads in the current directory

  • Download the dashcam01.mp4 video from the BoofCV project
  • Download the model
  • Download the coco.yaml data description for the yolo5n model
  • The host machine must have a Redis server instance running. Two options:

    1. Install it natively via sudo apt-get install redis (and then restart it with sudo systemctl restart redis)
    2. Spin up a docker container with: docker run --net=host --name redis-server -d redis

Yolov5 model#

The following command launches a Yolov5 model via the container's yolov5_predict_redis command, running on the CPU:

docker run \
    --net=host -u $(id -u):$(id -g) \
    -v `pwd`:/workspace \
    -it waikatodatamining/pytorch-yolov5:2022-01-21_cpu \
  yolov5_predict_redis \
    --redis_in images \
    --redis_out predictions \
    --model /workspace/ \
    --data /workspace/coco.yaml


The following wai.annotations pipeline loads every 2nd frame from the dashcam01.mp4 video, obtains predictions from the Yolov5 model (using the Redis backend), overlays the predictions and then displays them:

docker run -u $(id -u):$(id -g) \
    --net=host \
    --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
    -v `pwd`:/workspace \
    -it waikatoufdl/wai.annotations:0.7.6 \
  wai-annotations convert \
    from-video-file-od \
      -i /workspace/dashcam01.mp4 \
      -n 2 \
    redis-predict-od \
      --channel-out images \
      --channel-in predictions \
      --timeout 1.0 \
    add-annotation-overlay-od \
      --outline-alpha 255 \
      --outline-thickness 1 \
      --fill \
      --fill-alpha 128 \
      --vary-colors \
      --font-size 10 \
      --text-format "{label}: {score}" \
      --text-placement T,L \
      --force-bbox \
    image-viewer-od \
      --size 800,224 \
      --delay 1


The above instructions also as a video: