Google Cloud Vision API Python Samples

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

.. _Google Cloud Vision API: https://cloud.google.com/vision/docs

Setup

Authentication

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:

#. When running locally, use the `Google Cloud SDK`_

.. code-block:: bash

#. 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`_.

#. 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:

.. code-block:: bash

.. _Application Default Credentials: https://cloud.google.com/docs/authentication#getting_credentials_for_server-centric_flow

.. _additional scopes: https://cloud.google.com/compute/docs/authentication#using

.. _Service Account key file: https://developers.google.com/identity/protocols/OAuth2ServiceAccount#creatinganaccount

Install Dependencies

#. Install `pip`_ and `virtualenv`_ if you do not already have them.

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

.. code-block:: bash

#. Install the dependencies needed to run the samples.

.. code-block:: bash

.. _pip: https://pip.pypa.io/

.. _virtualenv: https://virtualenv.pypa.io/

Samples

Document Text tutorial

To run this sample:

.. code-block:: bash

The client library

This sample uses the `Google Cloud Client Library for Python`_.

You can read the documentation for more details on API usage and use GitHub

to `browse the source`_ and `report issues`_.

.. _Google Cloud SDK: https://cloud.google.com/sdk/

# This file is used to generate README.rst

product:

name: Google Cloud Vision API

short_name: Cloud Vision API

url: https://cloud.google.com/vision/docs

description: >

`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.

setup:

- auth

- install_deps

samples:

- name: Document Text tutorial

file: doctext.py

show_help: True

cloud_client_library: true

import argparse

from enum import Enum

import io

from google.cloud import vision

from PIL import Image, ImageDraw

class FeatureType(Enum):

PAGE = 1

BLOCK = 2

PARA = 3

WORD = 4

SYMBOL = 5

def draw_boxes(image, blocks, color):

"""Draw a border around the image using the hints in the vector list."""

# [START draw_blocks]

draw = ImageDraw.Draw(image)

for block in blocks:

draw.polygon([

block.vertices[0].x, block.vertices[0].y,

block.vertices[1].x, block.vertices[1].y,

block.vertices[2].x, block.vertices[2].y,

block.vertices[3].x, block.vertices[3].y], None, color)

return image

# [END draw_blocks]

def get_document_bounds(image_file, feature):

# [START detect_bounds]

"""Returns document bounds given an image."""

vision_client = vision.Client()

bounds = []

with io.open(image_file, 'rb') as image_file:

content = image_file.read()

image = vision_client.image(content=content)

document = image.detect_full_text()

# Collect specified feature bounds by enumerating all document features

for page in document.pages:

for block in page.blocks:

for paragraph in block.paragraphs:

for word in paragraph.words:

for symbol in word.symbols:

if (feature == FeatureType.SYMBOL):

bounds.append(symbol.bounding_box)

if (feature == FeatureType.WORD):

bounds.append(word.bounding_box)

if (feature == FeatureType.PARA):

bounds.append(paragraph.bounding_box)

if (feature == FeatureType.BLOCK):

bounds.append(block.bounding_box)

if (feature == FeatureType.PAGE):

bounds.append(block.bounding_box)

return bounds

# [END detect_bounds]

def render_doc_text(filein, fileout):

# [START render_doc_text]

image = Image.open(filein)

bounds = get_document_bounds(filein, FeatureType.PAGE)

draw_boxes(image, bounds, 'blue')

bounds = get_document_bounds(filein, FeatureType.PARA)

draw_boxes(image, bounds, 'red')

bounds = get_document_bounds(filein, FeatureType.WORD)

draw_boxes(image, bounds, 'yellow')

if fileout is not 0:

image.save(fileout)

else:

image.show()

# [END render_doc_text]

if __name__ == '__main__':

# [START run_crop]

parser = argparse.ArgumentParser()

parser.add_argument('detect_file', help='The image for text detection.')

parser.add_argument('-out_file', help='Optional output file', default=0)

args = parser.parse_args()

parser = argparse.ArgumentParser()

render_doc_text(args.detect_file, args.out_file)

# [END run_crop]

import os

import doctext

def test_text(cloud_config, capsys):

"""Checks the output image for drawing the crop hint is created."""

doctext.render_doc_text('resources/text_menu.jpg', 'output-text.jpg')

out, _ = capsys.readouterr()

assert os.path.isfile('output-text.jpg')