Migrating to Python Client Library v0.25.1

The Client Library for Python v0.25.1 includes some significant changes to how previous client libraries were designed. These changes can be summarized as follows:

Consolidation of modules into fewer types
Replacing untyped parameters with strongly-typed classes and enumerations

This topic provides details on the changes that you will need to make to your Python code for the Cloud Vision API client libraries in order to use the v0.25.1 Python client library.

Running previous versions of the client library

You are not required to upgrade your Python client library to v0.25.1. If you want to continue using a previous version of the Python client library and do not want to migrate your code, then you should specify the version of the Python client library used by your app. To specify a specific library version, edit the requirements.txt file as shown:

  google 
 - 
 cloud 
 - 
 vision 
 == 
 0.25

Removed Modules

The following modules were removed in the Python Client Library v0.25.1 package.

google.cloud.vision.annotations
google.cloud.vision.batch
google.cloud.vision.client
google.cloud.vision.color
google.cloud.vision.crop_hint
google.cloud.vision.entity
google.cloud.vision.face
google.cloud.vision.feature
google.cloud.vision.geometry
google.cloud.vision.image
google.cloud.vision.likelihood
google.cloud.vision.safe_search
google.cloud.vision.text
google.cloud.vision.web

Required Code Changes

Imports

Include the new google.cloud.vision.types module in order to access the new types in the Python Client Library v0.25.1.

The types module contains the new classes that are required for creating requests, such as types.Image .

  from 
  
 google.cloud 
  
 import 
 vision

Additionally, the new google.cloud.vision.enums module contains the enumerations useful for parsing and understanding API responses, such as enums.Likelihood.UNLIKELY and enums.FaceAnnotation.Landmark.Type.LEFT_EYE .

Create a client

The Client class has been replaced with the ImageAnnotatorClient class. Replace references to the Client class with ImageAnnotatorClient .

Previous versions of the client libraries:

  old_client 
 = 
 vision 
 . 
 Client 
 ()

Python Client Library v0.25.1:

  client 
 = 
 vision 
 . 
 ImageAnnotatorClient 
 ()

Constructing objects that represent image content

To identify image content from a local file, from a Google Cloud Storage URI, or from a web URI, use the new Image class.

Constructing objects that represent image content from a local file

The following example shows the new way to represent image content from a local file.

Previous versions of the client libraries:

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 )

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 )

Constructing objects that represent image content from a URI

The following example shows the new way to represent image content from a Google Cloud Storage URI or a web URI. uri is the URI to an image file on Google Cloud Storage or on the web.

Previous versions of the client libraries:

  image 
 = 
 old_client 
 . 
 image 
 ( 
 source_uri 
 = 
 uri 
 )

Python Client Library v0.25.1:

  image 
 = 
 vision 
 . 
 Image 
 () 
 image 
 . 
 source 
 . 
 image_uri 
 = 
 uri

Making requests and processing responses

With the Python Client Library v.0.25.1 the API methods such as face_detection belong to the ImageAnnotatorClient object as opposed to the Image objects.

The returned values are different for several methods as explained below.

Particularly, bounding box vertices are now stored in bounding_poly.vertices as opposed to bounds.vertices . The coordinates of each vertex are stored in vertex.x and vertex.y as opposed to vertex.x_coordinate and vertex.y_coordinate .

The bounding box change affects face_detection , logo_detection , text_detection , document_text_detection , and crop_hints .

Making a face detection request and processing the response

Emotion likelihoods are now returned as enumerations stored in face.surprise_likelihood as opposed to face.emotions.surprise . The names of likelihood labels can be recovered by importing google.cloud.vision.enums.Likelihood .

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 faces 
 = 
 image 
 . 
 detect_faces 
 () 
 for 
 face 
 in 
 faces 
 : 
 print 
 ( 
 'anger: 
 {} 
 ' 
 . 
 format 
 ( 
 face 
 . 
 emotions 
 . 
 anger 
 )) 
 print 
 ( 
 'joy: 
 {} 
 ' 
 . 
 format 
 ( 
 face 
 . 
 emotions 
 . 
 joy 
 )) 
 print 
 ( 
 'surprise: 
 {} 
 ' 
 . 
 format 
 ( 
 face 
 . 
 emotions 
 . 
 surprise 
 )) 
 vertices 
 = 
 ([ 
 '( 
 {} 
 , 
 {} 
 )' 
 . 
 format 
 ( 
 bound 
 . 
 x_coordinate 
 , 
 bound 
 . 
 y_coordinate 
 ) 
 for 
 bound 
 in 
 face 
 . 
 bounds 
 . 
 vertices 
 ]) 
 print 
 ( 
 'face bounds: 
 {} 
 ' 
 . 
 format 
 ( 
 ',' 
 . 
 join 
 ( 
 vertices 
 )))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 face_detection 
 ( 
 image 
 = 
 image 
 ) 
 faces 
 = 
 response 
 . 
 face_annotations 
 # Names of likelihood from google.cloud.vision.enums 
 likelihood_name 
 = 
 ( 
 "UNKNOWN" 
 , 
 "VERY_UNLIKELY" 
 , 
 "UNLIKELY" 
 , 
 "POSSIBLE" 
 , 
 "LIKELY" 
 , 
 "VERY_LIKELY" 
 , 
 ) 
 print 
 ( 
 "Faces:" 
 ) 
 for 
 face 
 in 
 faces 
 : 
 print 
 ( 
 f 
 "anger: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 anger_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "joy: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 joy_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "surprise: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 surprise_likelihood 
 ] 
 } 
 " 
 ) 
 vertices 
 = 
 [ 
 f 
 "( 
 { 
 vertex 
 . 
 x 
 } 
 , 
 { 
 vertex 
 . 
 y 
 } 
 )" 
 for 
 vertex 
 in 
 face 
 . 
 bounding_poly 
 . 
 vertices 
 ] 
 print 
 ( 
 "face bounds: 
 {} 
 " 
 . 
 format 
 ( 
 "," 
 . 
 join 
 ( 
 vertices 
 ))) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a label detection request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 labels 
 = 
 image 
 . 
 detect_labels 
 () 
 for 
 label 
 in 
 labels 
 : 
 print 
 ( 
 label 
 . 
 description 
 )

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 label_detection 
 ( 
 image 
 = 
 image 
 ) 
 labels 
 = 
 response 
 . 
 label_annotations 
 print 
 ( 
 "Labels:" 
 ) 
 for 
 label 
 in 
 labels 
 : 
 print 
 ( 
 label 
 . 
 description 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a landmark detection request and processing the response

Previous versions of the client libraries::

Landmark locations' latitude and longitude are now stored in location.lat_lng.latitude and location.lat_lng.longitude , as opposed to location.latitude and location.longitude .

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 landmarks 
 = 
 image 
 . 
 detect_landmarks 
 () 
 for 
 landmark 
 in 
 landmarks 
 : 
 print 
 ( 
 landmark 
 . 
 description 
 , 
 landmark 
 . 
 score 
 ) 
 for 
 location 
 in 
 landmark 
 . 
 locations 
 : 
 print 
 ( 
 'Latitude' 
 . 
 format 
 ( 
 location 
 . 
 latitude 
 )) 
 print 
 ( 
 'Longitude' 
 . 
 format 
 ( 
 location 
 . 
 longitude 
 ))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 landmark_detection 
 ( 
 image 
 = 
 image 
 ) 
 landmarks 
 = 
 response 
 . 
 landmark_annotations 
 print 
 ( 
 "Landmarks:" 
 ) 
 for 
 landmark 
 in 
 landmarks 
 : 
 print 
 ( 
 landmark 
 . 
 description 
 ) 
 for 
 location 
 in 
 landmark 
 . 
 locations 
 : 
 lat_lng 
 = 
 location 
 . 
 lat_lng 
 print 
 ( 
 f 
 "Latitude 
 { 
 lat_lng 
 . 
 latitude 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "Longitude 
 { 
 lat_lng 
 . 
 longitude 
 } 
 " 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a logo detection request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 logos 
 = 
 image 
 . 
 detect_logos 
 () 
 for 
 logo 
 in 
 logos 
 : 
 print 
 ( 
 logo 
 . 
 description 
 , 
 logo 
 . 
 score 
 )

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 logo_detection 
 ( 
 image 
 = 
 image 
 ) 
 logos 
 = 
 response 
 . 
 logo_annotations 
 print 
 ( 
 "Logos:" 
 ) 
 for 
 logo 
 in 
 logos 
 : 
 print 
 ( 
 logo 
 . 
 description 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a SafeSearch detection request and processing the response

SafeSearch likelihoods are now returned as enumerations. The names of likelihood labels can be recovered by importing google.cloud.vision.enums.Likelihood .

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 safe 
 = 
 image 
 . 
 detect_safe_search 
 () 
 print 
 ( 
 'Safe search:' 
 ) 
 print 
 ( 
 'adult: 
 {} 
 ' 
 . 
 format 
 ( 
 safe 
 . 
 adult 
 )) 
 print 
 ( 
 'medical: 
 {} 
 ' 
 . 
 format 
 ( 
 safe 
 . 
 medical 
 )) 
 print 
 ( 
 'spoofed: 
 {} 
 ' 
 . 
 format 
 ( 
 safe 
 . 
 spoof 
 )) 
 print 
 ( 
 'violence: 
 {} 
 ' 
 . 
 format 
 ( 
 safe 
 . 
 violence 
 ))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 safe_search_detection 
 ( 
 image 
 = 
 image 
 ) 
 safe 
 = 
 response 
 . 
 safe_search_annotation 
 # Names of likelihood from google.cloud.vision.enums 
 likelihood_name 
 = 
 ( 
 "UNKNOWN" 
 , 
 "VERY_UNLIKELY" 
 , 
 "UNLIKELY" 
 , 
 "POSSIBLE" 
 , 
 "LIKELY" 
 , 
 "VERY_LIKELY" 
 , 
 ) 
 print 
 ( 
 "Safe search:" 
 ) 
 print 
 ( 
 f 
 "adult: 
 { 
 likelihood_name 
 [ 
 safe 
 . 
 adult 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "medical: 
 { 
 likelihood_name 
 [ 
 safe 
 . 
 medical 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "spoofed: 
 { 
 likelihood_name 
 [ 
 safe 
 . 
 spoof 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "violence: 
 { 
 likelihood_name 
 [ 
 safe 
 . 
 violence 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "racy: 
 { 
 likelihood_name 
 [ 
 safe 
 . 
 racy 
 ] 
 } 
 " 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a text detection request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 texts 
 = 
 image 
 . 
 detect_text 
 () 
 for 
 text 
 in 
 texts 
 : 
 print 
 ( 
 ' 
 \n 
 " 
 {} 
 "' 
 . 
 format 
 ( 
 text 
 . 
 description 
 )) 
 vertices 
 = 
 ([ 
 '( 
 {} 
 , 
 {} 
 )' 
 . 
 format 
 ( 
 bound 
 . 
 x_coordinate 
 , 
 bound 
 . 
 y_coordinate 
 ) 
 for 
 bound 
 in 
 text 
 . 
 bounds 
 . 
 vertices 
 ]) 
 print 
 ( 
 'bounds: 
 {} 
 ' 
 . 
 format 
 ( 
 ',' 
 . 
 join 
 ( 
 vertices 
 )))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 text_detection 
 ( 
 image 
 = 
 image 
 ) 
 texts 
 = 
 response 
 . 
 text_annotations 
 print 
 ( 
 "Texts:" 
 ) 
 for 
 text 
 in 
 texts 
 : 
 print 
 ( 
 f 
 ' 
 \n 
 " 
 { 
 text 
 . 
 description 
 } 
 "' 
 ) 
 vertices 
 = 
 [ 
 f 
 "( 
 { 
 vertex 
 . 
 x 
 } 
 , 
 { 
 vertex 
 . 
 y 
 } 
 )" 
 for 
 vertex 
 in 
 text 
 . 
 bounding_poly 
 . 
 vertices 
 ] 
 print 
 ( 
 "bounds: 
 {} 
 " 
 . 
 format 
 ( 
 "," 
 . 
 join 
 ( 
 vertices 
 ))) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a document text detection request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 document 
 = 
 image 
 . 
 detect_full_text 
 () 
 for 
 page 
 in 
 document 
 . 
 pages 
 : 
 for 
 block 
 in 
 page 
 . 
 blocks 
 : 
 block_words 
 = 
 [] 
 for 
 paragraph 
 in 
 block 
 . 
 paragraphs 
 : 
 block_words 
 . 
 extend 
 ( 
 paragraph 
 . 
 words 
 ) 
 block_symbols 
 = 
 [] 
 for 
 word 
 in 
 block_words 
 : 
 block_symbols 
 . 
 extend 
 ( 
 word 
 . 
 symbols 
 ) 
 block_text 
 = 
 '' 
 for 
 symbol 
 in 
 block_symbols 
 : 
 block_text 
 = 
 block_text 
 + 
 symbol 
 . 
 text 
 print 
 ( 
 'Block Content: 
 {} 
 ' 
 . 
 format 
 ( 
 block_text 
 )) 
 print 
 ( 
 'Block Bounds: 
 \n 
  
 {} 
 ' 
 . 
 format 
 ( 
 block 
 . 
 bounding_box 
 ))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 document_text_detection 
 ( 
 image 
 = 
 image 
 ) 
 for 
 page 
 in 
 response 
 . 
 full_text_annotation 
 . 
 pages 
 : 
 for 
 block 
 in 
 page 
 . 
 blocks 
 : 
 print 
 ( 
 f 
 " 
 \n 
 Block confidence: 
 { 
 block 
 . 
 confidence 
 } 
 \n 
 " 
 ) 
 for 
 paragraph 
 in 
 block 
 . 
 paragraphs 
 : 
 print 
 ( 
 "Paragraph confidence: 
 {} 
 " 
 . 
 format 
 ( 
 paragraph 
 . 
 confidence 
 )) 
 for 
 word 
 in 
 paragraph 
 . 
 words 
 : 
 word_text 
 = 
 "" 
 . 
 join 
 ([ 
 symbol 
 . 
 text 
 for 
 symbol 
 in 
 word 
 . 
 symbols 
 ]) 
 print 
 ( 
 "Word text: 
 {} 
 (confidence: 
 {} 
 )" 
 . 
 format 
 ( 
 word_text 
 , 
 word 
 . 
 confidence 
 ) 
 ) 
 for 
 symbol 
 in 
 word 
 . 
 symbols 
 : 
 print 
 ( 
 " 
 \t 
 Symbol: 
 {} 
 (confidence: 
 {} 
 )" 
 . 
 format 
 ( 
 symbol 
 . 
 text 
 , 
 symbol 
 . 
 confidence 
 ) 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making an image properties request and processing the response

Dominant color information is now stored in props.dominant_colors.colors as opposed to props.colors .

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 props 
 = 
 image 
 . 
 detect_properties 
 () 
 for 
 color 
 in 
 props 
 . 
 colors 
 : 
 print 
 ( 
 'fraction: 
 {} 
 ' 
 . 
 format 
 ( 
 color 
 . 
 pixel_fraction 
 )) 
 print 
 ( 
 ' 
 \t 
 r: 
 {} 
 ' 
 . 
 format 
 ( 
 color 
 . 
 color 
 . 
 red 
 )) 
 print 
 ( 
 ' 
 \t 
 g: 
 {} 
 ' 
 . 
 format 
 ( 
 color 
 . 
 color 
 . 
 green 
 )) 
 print 
 ( 
 ' 
 \t 
 b: 
 {} 
 ' 
 . 
 format 
 ( 
 color 
 . 
 color 
 . 
 blue 
 )) 
 print 
 ( 
 ' 
 \t 
 a: 
 {} 
 ' 
 . 
 format 
 ( 
 color 
 . 
 color 
 . 
 alpha 
 ))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 image_properties 
 ( 
 image 
 = 
 image 
 ) 
 props 
 = 
 response 
 . 
 image_properties_annotation 
 print 
 ( 
 "Properties:" 
 ) 
 for 
 color 
 in 
 props 
 . 
 dominant_colors 
 . 
 colors 
 : 
 print 
 ( 
 f 
 "fraction: 
 { 
 color 
 . 
 pixel_fraction 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 r: 
 { 
 color 
 . 
 color 
 . 
 red 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 g: 
 { 
 color 
 . 
 color 
 . 
 green 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 b: 
 { 
 color 
 . 
 color 
 . 
 blue 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 a: 
 { 
 color 
 . 
 color 
 . 
 alpha 
 } 
 " 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a web detection request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 notes 
 = 
 image 
 . 
 detect_web 
 () 
 if 
 notes 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 ' 
 \n 
 {} 
 Pages with matching images retrieved' 
 ) 
 for 
 page 
 in 
 notes 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 'Score : 
 {} 
 ' 
 . 
 format 
 ( 
 page 
 . 
 score 
 )) 
 print 
 ( 
 'Url   : 
 {} 
 ' 
 . 
 format 
 ( 
 page 
 . 
 url 
 )) 
 if 
 notes 
 . 
 full_matching_images 
 : 
 print 
 ( 
 ' 
 \n 
 {} 
 Full Matches found: ' 
 . 
 format 
 ( 
 len 
 ( 
 notes 
 . 
 full_matching_images 
 ))) 
 for 
 image 
 in 
 notes 
 . 
 full_matching_images 
 : 
 print 
 ( 
 'Score: 
 {} 
 ' 
 . 
 format 
 ( 
 image 
 . 
 score 
 )) 
 print 
 ( 
 'Url  : 
 {} 
 ' 
 . 
 format 
 ( 
 image 
 . 
 url 
 )) 
 if 
 notes 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 ' 
 \n 
 {} 
 Partial Matches found: ' 
 . 
 format 
 ( 
 len 
 ( 
 notes 
 . 
 partial_matching_images 
 ))) 
 for 
 image 
 in 
 notes 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 'Score: 
 {} 
 ' 
 . 
 format 
 ( 
 image 
 . 
 score 
 )) 
 print 
 ( 
 'Url  : 
 {} 
 ' 
 . 
 format 
 ( 
 image 
 . 
 url 
 )) 
 if 
 notes 
 . 
 web_entities 
 : 
 print 
 ( 
 ' 
 \n 
 {} 
 Web entities found: ' 
 . 
 format 
 ( 
 len 
 ( 
 notes 
 . 
 web_entities 
 ))) 
 for 
 entity 
 in 
 notes 
 . 
 web_entities 
 : 
 print 
 ( 
 'Score      : 
 {} 
 ' 
 . 
 format 
 ( 
 entity 
 . 
 score 
 )) 
 print 
 ( 
 'Description: 
 {} 
 ' 
 . 
 format 
 ( 
 entity 
 . 
 description 
 ))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 web_detection 
 ( 
 image 
 = 
 image 
 ) 
 annotations 
 = 
 response 
 . 
 web_detection 
 if 
 annotations 
 . 
 best_guess_labels 
 : 
 for 
 label 
 in 
 annotations 
 . 
 best_guess_labels 
 : 
 print 
 ( 
 f 
 " 
 \n 
 Best guess label: 
 { 
 label 
 . 
 label 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Pages with matching images found:" 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 pages_with_matching_images 
 ) 
 ) 
 ) 
 for 
 page 
 in 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Page url   : 
 { 
 page 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Full Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 full_matching_images 
 )) 
 ) 
 for 
 image 
 in 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Partial Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 partial_matching_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Web entities found: " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 web_entities 
 ))) 
 for 
 entity 
 in 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Score      : 
 { 
 entity 
 . 
 score 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 Description: 
 { 
 entity 
 . 
 description 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 visually similar images found: 
 \n 
 " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 visually_similar_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 f 
 " 
 \t 
 Image url    : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Making a crop hints request and processing the response

Previous versions of the client libraries::

  with 
 io 
 . 
 open 
 ( 
 file_name 
 , 
 'rb' 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 old_client 
 . 
 image 
 ( 
 content 
 = 
 content 
 ) 
 hints 
 = 
 image 
 . 
 detect_crop_hints 
 ( 
 aspect_ratios 
 = 
 [ 
 1.77 
 ]) 
 for 
 n 
 , 
 hint 
 in 
 enumerate 
 ( 
 hints 
 ): 
 print 
 ( 
 ' 
 \n 
 Crop Hint: 
 {} 
 ' 
 . 
 format 
 ( 
 n 
 )) 
 vertices 
 = 
 ([ 
 '( 
 {} 
 , 
 {} 
 )' 
 . 
 format 
 ( 
 bound 
 . 
 x_coordinate 
 , 
 bound 
 . 
 y_coordinate 
 ) 
 for 
 bound 
 in 
 hint 
 . 
 bounds 
 . 
 vertices 
 ]) 
 print 
 ( 
 'bounds: 
 {} 
 ' 
 . 
 format 
 ( 
 ',' 
 . 
 join 
 ( 
 vertices 
 )))

Python Client Library v0.25.1:

  with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
 Image 
 ( 
 content 
 = 
 content 
 ) 
 crop_hints_params 
 = 
 vision 
 . 
 CropHintsParams 
 ( 
 aspect_ratios 
 = 
 [ 
 1.77 
 ]) 
 image_context 
 = 
 vision 
 . 
 ImageContext 
 ( 
 crop_hints_params 
 = 
 crop_hints_params 
 ) 
 response 
 = 
 client 
 . 
 crop_hints 
 ( 
 image 
 = 
 image 
 , 
 image_context 
 = 
 image_context 
 ) 
 hints 
 = 
 response 
 . 
 crop_hints_annotation 
 . 
 crop_hints 
 for 
 n 
 , 
 hint 
 in 
 enumerate 
 ( 
 hints 
 ): 
 print 
 ( 
 f 
 " 
 \n 
 Crop Hint: 
 { 
 n 
 } 
 " 
 ) 
 vertices 
 = 
 [ 
 f 
 "( 
 { 
 vertex 
 . 
 x 
 } 
 , 
 { 
 vertex 
 . 
 y 
 } 
 )" 
 for 
 vertex 
 in 
 hint 
 . 
 bounding_poly 
 . 
 vertices 
 ] 
 print 
 ( 
 "bounds: 
 {} 
 " 
 . 
 format 
 ( 
 "," 
 . 
 join 
 ( 
 vertices 
 ))) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

Note that the aspect ratios need to be passed in through a CropHintsParams and an ImageContext .

Migrating to Python Client Library v0.25.1 Stay organized with collections Save and categorize content based on your preferences.

Running previous versions of the client library

Removed Modules

Required Code Changes

Imports

Create a client

Constructing objects that represent image content

Constructing objects that represent image content from a local file

Constructing objects that represent image content from a URI

Making requests and processing responses

Making a face detection request and processing the response

Making a label detection request and processing the response

Making a landmark detection request and processing the response

Making a logo detection request and processing the response

Making a SafeSearch detection request and processing the response

Making a text detection request and processing the response

Making a document text detection request and processing the response

Making an image properties request and processing the response

Making a web detection request and processing the response

Making a crop hints request and processing the response

Migrating to Python Client Library v0.25.1