GStreamer Python Analytics

This project provides python base classes and GStreamer elements supporting a broad range of analytics tasks. Supported functionality includes:

1. object detection
2. tracking
3. video captioning
4. translation
5. transcription
6. speech to text
7. text to speech
8. text to image
9. LLMs
10. serializing to Kafka server

Different ML toolkits are supported via the MLEngine abstraction - we have nominal support for TensorFlow, LiteRT and OpenVINO, but all testing thus far has been done with PyTorch.

These elements will work with your distribution's GStreamer packages. They have been tested on Ubuntu 24 with GStreamer 1.24.

Requirements

There are two installation options described below: installing on your host machine, or installing with a Docker container:

Host Install (Ubuntu 24)

Install packages

Note: nvidia-cuda-toolkit dependency assumes an Nvidia card on your system

sudo apt update && sudo apt -y upgrade
sudo apt install -y python3-pip  python3-venv \
    gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps \
    gstreamer1.0-plugins-good gstreamer1.0-plugins-bad \
    gir1.2-gst-plugins-bad-1.0 python3-gst-1.0 gstreamer1.0-python3-plugin-loader \
    libcairo2 libcairo2-dev git nvidia-cuda-toolkit

Install venv

python3 -m venv --system-site-packages ~/venv

Clone repo (host)

git clone https://github.com/GrokImageCompression/grok.git

Update .bashrc

export VIRTUAL_ENV=$HOME/venv
export PATH=$VIRTUAL_ENV/bin:$PATH
export GST_PLUGIN_PATH=$HOME/src/gst-python-analytics/plugins

and then

source ~/.bashrc

Activate venv and install basic pip packages

source $VIRTUAL_ENV/bin/activate
pip install --upgrade pip && \
pip install pygobject torch torchvision transformers numpy black ruff

Install pip requirements

cd $HOME/src/gst-python-analytics
pip install -r requirements.txt

Docker Install

Build Docker Container

Important Note:

This Dockerfile maps a local gst-python-analytics repository to the container, and expects this repository to be located in $HOME/src i.e. $HOME/src/gst-python-analytics.

Enable Docker GPU Support on Host

To use the host GPU in a docker container, you will need to install the nvidia container toolkit. If running on CPU, these steps can be skipped.

sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo systemctl restart docker

Build Ubuntu 24.04 Container

docker build -f ./Dockerfile -t ubuntu24:latest .

Run Docker Container

a) If running on CPU, just remove --gpus all from command below b) This command assumes you have set up a Kafka network as described below

docker run --network kafka-network -v ~/src/gst-python-analytics/:/root/gst-python-analytics -it --rm --gpus all --name ubuntu24 ubuntu24:latest /bin/bash

In the container shell, run the following

# cd gst-python-analytics && pip install -r requirements.txt

Now you should be able to inspect the objectdetector element:

gst-inspect-1.0 objectdetector

Docker Cleanup

If you want to purge existing docker containers and images:

docker container prune -f
docker image prune -a -f

Using Analytics Elements

kafkasink

Setting up kafka network

docker network create kafka-network

and list networks

docker network ls

Set up kafka and zookeeper

Note: setup below assumes you are running your pipeline in a docker container. If running pipeline from host, then the port changes from 9092 to 29092, and the broker changes from kafka to localhost.

docker stop kafka zookeeper
docker rm kafka zookeeper
docker run -d --name zookeeper --network kafka-network -e ZOOKEEPER_CLIENT_PORT=2181 confluentinc/cp-zookeeper:latest
docker run -d --name kafka --network kafka-network \
  -e KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181 \
  -e KAFKA_ADVERTISED_LISTENERS=INSIDE://kafka:9092,OUTSIDE://localhost:29092 \
  -e KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT \
  -e KAFKA_LISTENERS=INSIDE://0.0.0.0:9092,OUTSIDE://0.0.0.0:29092 \
  -e KAFKA_INTER_BROKER_LISTENER_NAME=INSIDE \
  -e KAFKA_BROKER_ID=1 \
  -e KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 \
  -p 9092:9092 \
  -p 29092:29092 \
  confluentinc/cp-kafka:latest

Create test topic

docker exec kafka kafka-topics --create --topic test-kafkasink-topic --bootstrap-server kafka:9092 --partitions 1 --replication-factor 1

list topics

docker exec -it kafka kafka-topics --list --bootstrap-server kafka:9092

delete topic

docker exec -it kafka kafka-topics --delete --topic test-topic --bootstrap-server kafka:9092

consume topic

docker exec -it kafka kafka-console-consumer --bootstrap-server kafka:9092 --topic test-kafkasink-topic --from-beginning

Object Detection

Possible model names: fasterrcnn_resnet50_fpn retinanet_resnet50_fpn

fasterrcnn/kafka

GST_DEBUG=4 gst-launch-1.0 multifilesrc location=data/000015.jpg ! jpegdec ! videoconvert ! videoscale ! objectdetector model-name=fasterrcnn_resnet50_fpn device=cuda batch-size=4 ! kafkasink schema-file=data/gst_analytics_object_detector.json broker=kafka:9092 topic=test-kafkasink-topic 2>&1 | grep kafkasink

maskrcnn

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/people.mp4 ! decodebin ! videoconvert ! videoscale ! maskrcnn device=cuda batch-size=4 model-name=maskrcnn_resnet50_fpn ! videoconvert ! objectdetectionoverlay labels-color=0xFFFF0000 object-detection-outline-color=0xFFFF0000 ! autovideosink

yolo with tracking

gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! yolo model-name=yolo11m device=cuda:0 track=True ! videoconvert ! objectdetectionoverlay labels-color=0xFFFF0000 object-detection-outline-color=0xFFFF0000 ! autovideosink

yolo with dectionoverlay

gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! yolo model-name=yolo11m device=cuda:0 track=True ! analyticsoverlay ! videoconvert ! autovideosink

analyticsstreammux pipeline

GST_DEBUG=4 gst-launch-1.0 analyticsstreammux name=mux ! videoconvert ! fakesink videotestsrc ! mux. videotestsrc pattern=ball ! mux. videotestsrc pattern=snow ! mux.

whispertranscribe

transcription with initial prompt set

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whispertranscribe device=cuda language=ko initial_prompt = "Air Traffic Control은, radar systems를, weather conditions에, flight paths를, communication은, unexpected weather conditions가, continuous training을, dedication과, professionalism" ! fakesink

translation to English

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whispertranscribe device=cuda language=ko translate=yes ! fakesink

coquitts

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whispertranscribe device=cuda language=ko translate=yes ! coquitts device=cuda ! audioconvert ! wavenc ! filesink location=output_audio.wav

whisperspeechtts

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whispertranscribe device=cuda language=ko translate=yes ! whisperspeechtts device=cuda ! audioconvert ! wavenc ! filesink location=output_audio.wav

mariantranslate

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whispertranscribe device=cuda language=ko translate=yes ! mariantranslate device=cuda src=en target=fr ! fakesink

Supported src/target languages:

https://huggingface.co/models?sort=trending&search=Helsinki

whisperlive

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/air_traffic_korean_with_english.wav ! decodebin ! audioconvert ! whisperlive device=cuda language=ko translate=yes llm-model-name="microsoft/phi-2" ! audioconvert ! wavenc ! filesink location=output_audio.wav

LLM

1. generate HuggingFace token

2. huggingface-cli login and pass in token

3. LLM pipeline (in this case, we use phi-2)

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/prompt_for_llm.txt ! llm device=cuda model-name="microsoft/phi-2" ! fakesink

stablediffusion pipeline

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/prompt_for_stable_diffusion.txt ! stablediffusion device=cuda ! pngenc ! filesink location=output_image.png

caption + yolo

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! yolo model-name=yolo11m device=cuda:0 track=True ! caption device=cuda:0 ! textoverlay ! analyticsoverlay ! videoconvert ! autovideosink

caption

GST_DEBUG=4 gst-launch-1.0 filesrc location=data/soccer_tracking.mp4 ! decodebin ! videoconvert ! caption device=cuda:0 downsampled_width=320 downsampled_height=240 prompt="What is the name of the game being played?" ! textoverlay ! fakesink

Building PyPI Package

1. pip install setuptools wheel twine
2. python setup.py sdist bdist_wheel
3. ls dist/