This directory contains samples for Google Cloud Vision API. Google Cloud Vision API allows developers to easily integrate vision detection features within applications, including image labeling, face and landmark detection, optical character recognition (OCR), and tagging of explicit content

This sample demonstrates how to use the Cloud Vision API to do face detection.

Authentication is typically done through Application Default Credentials, which means you do not have to change the code to authenticate as long as your environment has credentials. You have a few options for setting up authentication:

1. When running locally, use the Google Cloud SDK

   gcloud beta auth application-default login

2. When running on App Engine or Compute Engine, credentials are already set-up. However, you may need to configure your Compute Engine instance with additional scopes.

3. You can create a Service Account key file. This file can be used to authenticate to Google Cloud Platform services from any environment. To use the file, set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the key file, for example:

   export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json

1. Install pip and virtualenv if you do not already have them.

2. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.

   $ virtualenv env
   $ source env/bin/activate

3. Install the dependencies needed to run the samples.

   $ pip install -r requirements.txt

To run this sample:

$ python faces.py

usage: faces.py [-h] [--out OUTPUT] [--max-results MAX_RESULTS] input_image

Detects faces in the given image.

positional arguments:
  input_image           the image you'd like to detect faces in.

optional arguments:
  -h, --help            show this help message and exit
  --out OUTPUT          the name of the output file.
  --max-results MAX_RESULTS
                        the max results of face detection.