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.
In order to use this library, you first need to go through the following steps:
- Select or create a Cloud Platform project.
- Enable billing for your project.
- Enable the Google Cloud Vision API.
- Setup Authentication.
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.
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-visionpip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-visionfrom 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}],
})- Read the Client Library Documentation for Google Cloud Vision API API to see other available methods on the client.
- Read the Product documentation to learn more about the product and see How-to Guides.