Object Detection Service

This service is responsible for handling object detection on image streams and the output is a vekg of each image.

It should be capable of running any Tensorflow 1.12-1.14 object detection trained on COCO dataset from the model zoo (The ones that output masks were not tested.)

Events Listened

• VEKG

Events Published

• SERVICE_WORKER_ANNOUNCED
• VEKG

Installation

Configure .env

Copy the example.env file to .env, and inside it replace the variables with the values you need.

Installing Dependencies

Using pipenv

Run $ pipenv shell to create a python virtualenv and load the .env into the environment variables in the shell.

Then run: $ pipenv install to install all packages, or $ pipenv install -d to also install the packages that help during development, eg: ipython. This runs the installation using pip under the hood, but also handle the cross dependency issues between packages and checks the packages MD5s for security mesure.

Using pip

To install from the requirements.txt file, run the following command:

$ pip install -r requirements.txt

Running

Enter project python environment (virtualenv or conda environment)

ps: It's required to have the .env variables loaded into the shell so that the project can run properly. An easy way of doing this is using pipenv shell to start the python environment with the .env file loaded or using the source load_env.sh command inside your preferable python environment (eg: conda).

Then, run the service with:

$ ./object_detection_service/run.py

Testing

Run the script run_tests.sh, it will run all tests defined in the tests directory.

Also, there's a python script at ./object_detection_service/send_msgs_test.py to do some simple manual testing, by sending msgs to the service stream key.

Docker

Build

Build the docker image using: docker-compose build

ps: It's required to have the .env variables loaded into the shell so that the container can build properly. An easy way of doing this is using pipenv shell to start the python environment with the .env file loaded or using the source load_env.sh command inside your preferable python environment (eg: conda).

Run

Use docker-compose run --rm service to run the docker image

Benchmark Tests

To run the benchmark tests one needs to manually start the Benchmark stage in the CI pipeline (Gitlab), it shoud be enabled after the tests stage is done. Only by passing the benchmark tests shoud the image be tagged with 'latest', to show that it is a stable docker image.