Easily integrate state-of-the-art machine learning models in your app
from picterra import APIClient
# Replace this with the id of one of your detectors
detector_id = 'd552605b-6972-4a68-8d51-91e6cb531c24'
# Set the PICTERRA_API_KEY environment variable to define your API key
client = APIClient()
print('Uploading raster...')
raster_id = client.upload_raster('data/raster1.tif', name='a nice raster')
print('Upload finished, starting detector...')
result_id = client.run_detector(detector_id, raster_id)
client.download_result_to_feature_collection(result_id, 'result.geojson')
print('Detection finished, results are in result.geojson')
pip install picterra
See the examples
folder for examples.
API Reference and User Guide available on Read the Docs
Running
pip install --editable "[lint,test]"
would allow to run test and linting locally, and also avoid re-installing the library every time you change the code.
In order to test locally, run:
python setup.py test
- Bump the version number in
setup.py
- Manually run the publish to testpypi workflow
- Check the publication result on testpypi
- Create a release through github
- The 'publish to pypi' workflow should automatically run
- Updated package should be available on pypi