Cloud Vision API API client library


License
Apache-2.0
Install
pip install google-cloud-vision==2.2.0

Documentation

Python Client for Google Cloud Vision

GA pypi versions

The Google Cloud Vision 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.

Quick Start

In order to use this library, you first need to go through the following steps:

  1. Select or create a Cloud Platform project.
  2. Enable billing for your project.
  3. Enable the Google Cloud Vision API.
  4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it's possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.6

Deprecated Python Versions

Python == 2.7.

The last version of this library compatible with Python 2.7 is google-cloud-vision==1.0.0.

RaspberryPi ARM devices

Note: Raspberry Pi ARMv6 is not supported because there is an internal binary google that does not comply with armv6 processors.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-vision

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-vision

Example Usage

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.Feature.Type.FACE_DETECTION}]
})

Next Steps