Unofficial python API for DeepStack. Provides classes for making requests to the object detection & face detection/recognition endpoints. Also includes some helper functions for processing the results. See the Jupyter notebooks for usage.
Run Deepstack with all three endpoints active (CPU mode):
docker run \
-e VISION-SCENE=True \
-e VISION-DETECTION=True \
-e VISION-FACE=True \
-v localstorage:/datastore \
-p 80:5000 \
-e API-KEY="" \
--name deepstack deepquestai/deepstack
Check deepstack is running using curl (from root of this repo):
curl -X POST -F image=@tests/images/test-image3.jpg 'http://localhost:5000/v1/vision/detection'
If all goes well you should see the following returned:
{"success":true,"predictions":[{"confidence":0.9998661,"label":"person","y_min":0,"x_min":258,"y_max":676,"x_max":485},{"confidence":0.9996547,"label":"person","y_min":0,"x_min":405,"y_max":652,"x_max":639},{"confidence":0.99745613,"label":"dog","y_min":311,"x_min":624,"y_max":591,"x_max":825}]}
- Create venv ->
python3.7 -m venv venv - Use venv ->
source venv/bin/activate pip3 install -r requirements.txtandpip3 install -r requirements-dev.txt- Run tests with
venv/bin/pytest tests/* - Black format with
venv/bin/black .
- Docs are created using Jupyter notebooks
- Install in venv with ->
pip3 install jupyterlab - Run ->
venv/bin/jupyter lab