The Google Cloud Vision (Vision API docs) API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. It quickly classifies images into thousands of categories (e.g., "sailboat", "lion", "Eiffel Tower"), detects individual objects and faces within images, and finds and reads printed words contained within images. You can build metadata on your image catalog, moderate offensive content, or enable new marketing scenarios through image sentiment analysis. Analyze images uploaded in the request or integrate with your image storage on Google Cloud Storage.

• For an overview of authentication in google-cloud-python, see :doc:`/core/auth`.

• In addition to any authentication configuration, you should also set the :envvar:`GOOGLE_CLOUD_PROJECT` environment variable for the project you'd like to interact with. If the :envvar:`GOOGLE_CLOUD_PROJECT` environment variable is not present, the project ID from JSON file credentials is used.

  If you are using Google App Engine or Google Compute Engine this will be detected automatically.

• After configuring your environment, create a :class:`~google.cloud.vision_v1.ImageAnnotatorClient`.

>>> from google.cloud import vision
>>> client = vision.ImageAnnotatorClient()

or pass in credentials explicitly.

>>> from google.cloud import vision
>>> client = vision.ImageAnnotatorClient(
...     credentials=creds,
... )

You can call the :meth:`annotate_image` method directly:

>>> from google.cloud import vision
>>> client = vision.ImageAnnotatorClient()
>>> response = client.annotate_image({
...   'image': {'source': {'image_uri': 'gs://my-test-bucket/image.jpg'}},
...   'features': [{'type': vision.enums.Feature.Type.FACE_DETECTION}],
... })
>>> len(response.annotations)
2
>>> for face in response.annotations[0].faces:
...     print(face.joy)
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY
>>> for logo in response.annotations[0].logos:
...     print(logo.description)
'google'
'github'

If you are only requesting a single feature, you may find it easier to ask for it using our direct methods:

>>> from google.cloud import vision
>>> client = vision.ImageAnnotatorClient()
>>> response = client.face_detection({
...   'source': {'image_uri': 'gs://my-test-bucket/image.jpg'},
... })
>>> len(response.annotations)
1
>>> for face in response.annotations[0].faces:
...     print(face.joy)
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY
Likelihood.VERY_LIKELY

If no results for the detection performed can be extracted from the image, then an empty list is returned. This behavior is similar with all detection types.

Example with :meth:`~google.cloud.vision.ImageAnnotatorClient.logo_detection`:

>>> from google.cloud import vision
>>> client = vision.ImageAnnotatorClient()
>>> with open('./image.jpg', 'rb') as image_file:
...     content = image_file.read()
>>> response = client.logo_detection({
...     'content': content,
... })
>>> len(response.annotations)
0

.. toctree::
  :maxdepth: 2

  gapic/v1/api
  gapic/v1/types

For a list of all google-cloud-vision releases:

.. toctree::

  releases