This package contains a set of intelligent APIs understanding images: It can detect and analyze people's faces, their age, gender, and similarity. It can identify people based on a set of images. It can understand what is displayed in a picture and crop it according to where the important features are. It can tell you whether an image contains adult content, what the main colors are, and which of your images belong in a group. If your image features text, it will tell you the language and return the text as a string. It's basically magic. For more details on the Project Oxford API, please visit projectoxford.ai.
This python module implements all APIs available in the Face and Vision APIs of Project Oxford.
To install the latest release you can use pip.
$ pip install projectoxford
Note: before you can send data to you will need an API key. There are separate API keys for face and vision.
Initialize a client
from projectoxford import Client
client = Client('<api_key>')Face detection
result = client.face.detect({'url': 'https://upload.wikimedia.org/wikipedia/commons/1/19/Bill_Gates_June_2015.jpg'})
print(result['faceId'])
print(result['attributes']['age'])Face identification
personGroup = 'example-person-group'
bill = 'https://upload.wikimedia.org/wikipedia/commons/1/19/Bill_Gates_June_2015.jpg'
billAndMelinda = 'https://upload.wikimedia.org/wikipedia/commons/2/28/Bill_og_Melinda_Gates_2009-06-03_%28bilde_01%29.JPG'
# get a face ID and create and train a person group with a person
faceId = client.face.detect({'url': bill})[0]['faceId']
client.face.personGroup.createOrUpdate(personGroup, 'my person group')
client.face.person.createOrUpdate(personGroup, [faceId], 'bill gates')
client.face.personGroup.trainAndPollForCompletion(personGroup)
# detect faces in a second photo
detectResults = client.face.detect({'url': billAndMelinda})
faceIds = []
for result in detectResults:
faceIds.append(result['faceId'])
# identify any known faces from the second photo
identifyResults = client.face.identify(personGroup, faceIds)
for result in identifyResults:
for candidate in result['candidates']:
confidence = candidate['confidence']
personData = client.face.person.get(personGroup, candidate['personId'])
name = personData['name']
print('identified {0} with {1}% confidence'.format(name, str(float(confidence) * 100)))
# remove the example person group from your subscription
client.face.personGroup.delete(personGroup)Development environment
-
- Optionally Install Visual Studio, python tools for VS
-
Get a Project Oxford API key
-
Install dev dependencies
pip install -r requirements.txt -
Set environment variable API key
set OXFORD_FACE_API_KEY=<insert_your_key_here> set OXFORD_VISION_API_KEY=<insert_your_key_here> -
Run tests
python setup.py test -
Publishing
- update version number in setup.py
git tag <version number> -m "update tag version" git push --tags origin master python setup.py register -r pypi # first time only python setup.py sdist upload
Licensed as MIT - please see LICENSE for details.
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