Quickstart: Use the Bing Visual Search Python client library

Use this quickstart to begin getting image insights from the Bing Visual Search service, using the Python client library. While Bing Visual Search has a REST API compatible with most programming languages, the client library provides an easy way to integrate the service into your applications. The source code for this sample can be found on GitHub.

Reference documentation | Library source code | Package (PyPi) | Samples

Prerequisites

• Python 2.x or 3.x.
• It is recommended to use a virtual environment. Install and initialize the virtual environment with the venv module.
• The Bing Visual Search client library for Python. You can install it with the following commands:
  1. cd mytestenv
  2. python -m pip install azure-cognitiveservices-search-visualsearch

Create and initialize the application

1. Create a new Python file in your favorite IDE or editor, and add the following import statements:

   import http.client, urllib.parse
   import json
   import os.path
   from azure.cognitiveservices.search.visualsearch import VisualSearchClient
   from azure.cognitiveservices.search.visualsearch.models import (
       VisualSearchRequest,
       CropArea,
       ImageInfo,
       Filters,
       KnowledgeRequest,
   )
   from msrest.authentication import CognitiveServicesCredentials
   

2. Create variables for your subscription key, Custom Configuration ID, and the image you want to upload.

   subscription_key = 'YOUR-VISUAL-SEARCH-ACCESS-KEY'
   PATH = 'C:\\Users\\USER\\azure-cognitive-samples\\mytestenv\\TestImages\\'
   image_path = os.path.join(PATH, "image.jpg")
   

3. Instantiate the client.

   client = VisualSearchClient(endpoint="https://api.bing.microsoft.com", credentials=CognitiveServicesCredentials(subscription_key))
   

Send the search request

1. With the image file open, serialize VisualSearchRequest(), and pass it as the knowledge_request parameter for the visual_search().

   with open(image_path, "rb") as image_fd:
       # You need to pass the serialized form of the model
       knowledge_request = json.dumps(VisualSearchRequest().serialize())
   
       print("\r\nSearch visual search request with binary of dog image")
       result = client.images.visual_search(image=image_fd, knowledge_request=knowledge_request)
   

2. If any results were returned, print them, the tags, and the actions in the first tag.

   if not result:
           print("No visual search result data.")
   
           # Visual Search results
       if result.image.image_insights_token:
           print("Uploaded image insights token: {}".format(result.image.image_insights_token))
       else:
           print("Couldn't find image insights token!")
   
       # List of tags
       if result.tags:
           first_tag = result.tags[0]
           print("Visual search tag count: {}".format(len(result.tags)))
   
           # List of actions in first tag
           if first_tag.actions:
               first_tag_action = first_tag.actions[0]
               print("First tag action count: {}".format(len(first_tag.actions)))
               print("First tag action type: {}".format(first_tag_action.action_type))
           else:
               print("Couldn't find tag actions!")
       else:
           print("Couldn't find image tags!")
   

Next steps