This repository contains the OpenVisionCapsules SDK, a set of Python libraries for encapsulating machine learning and computer vision algorithms for intelligent video analytics.
Encapsulating an algorithm allows it to be deployed as a single, self-describing file that inputs and outputs data in a standard format. This makes deployment and integration significantly easier than starting with a model file or a snippet of source code. Capsules are descriptive of their input and output requirements, allowing OpenVisionCapsules to route data between capsules automatically.
This project is split into two packages, vcap and vcap-utils. vcap
contains the necessary facilities to create and encapsulate an algorithm.
vcap-utils contains a set of utilities that make encapsulating algorithms of
certain types easier.
OpenVisionCapsules is in a developer preview phase. We're looking for developer feedback before reaching a stable 1.0 release. If you find any bugs or have suggestions, please open an issue.
Take a look at the documentation here.
A couple example capsules are available under vcap/examples, demonstrating
how to create classifier and detector capsules from TensorFlow models.
To install OpenVisionCapsules locally, clone the repository and run the
following commands to install the vcap and vcap-utils packages in the
current environment.
pip3 install -e ./vcap
pip3 install -e ./vcap_utils
To make use of the example capsules in the vcap/examples/ directory, make
sure to run the tests with pytest (from the root of the repo). The tests
download all the necessary models and images, including the models for the
example capsules.
Make sure vcap & vcap-utils installation is done before tests,
pytest -v -x .
A repository of open source capsules can be found here.
capsule inference test,
python3 tools/openvisioncapsule_tools/capsule_infer/capsule_infer.py --capsule ../capsule-zoo/capsules/detector_person_vehicle_bike_openvino --images tests/test_images/two_people.jpg