Robin Cole
Robin Cole
As title, to allow object & face
As title * https://www.tensorflow.org/tfx/serving/api_rest * https://cloud.google.com/community/tutorials/standalone-tensorflow-raspberry-pi
Use `if flask.request.method != "POST" or not flask.request.files.get("image"):`, do the negative. This saves indentation. Same for `if not predictions` https://refactoring.com/catalog/replaceNestedConditionalWithGuardClauses.html
This seems to be the most popular request, but requires some preprocessing of the images **References** - https://thedatamage.com/face-recognition-tensorflow-tutorial/ - https://machinelearningmastery.com/how-to-develop-a-face-recognition-system-using-facenet-in-keras-and-an-svm-classifier/ - https://coral.withgoogle.com/examples/detect-image/#perform-face-detection
I tried posting a completely blank (black) image to see if this would crash the server, but it returned the predictions from a previous image (containing people).
Can we support coral on mac via docker? Seems we should be able to, I'm not sure how we mount the USB however
The Getting Started notebook is actually incompatible with the latest releases of [HASS-data-detective](https://github.com/robmarkcole/HASS-data-detective), currently at 2.4. This is causing issues for users of the jupyterlab addon since it is installing...
To enable reproducible runs of the getting started notebook it is ideal to have a small .db file which can be used as the data source for the notebook run.
Update boto3 to `boto3>=1.17.27` since earlier versions lack features and are causing issues on https://github.com/robmarkcole/HASS-amazon-rekognition-text/issues/4
https://docs.aws.amazon.com/sdk-for-javascript/v2/developer-guide/s3-example-photos-view.html