Google Vision AI

Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.

Industry-leading accuracy for image understanding

Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy.

Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.

Google Vision A.I. on local media file

  from ML_APIs.google import vision_ai_local

  path = <your-image-path>
  model = 'logos'
  credentials = <your-credentials.json>

  image_classification = vision_ai_local(path, model, credentials)

  print('Labels:')
  image_classification.logos

Google Vision A.I. on uri media file

  from ML_APIs.google import vision_ai_cloud

  uri = <your-image-uri>
  model = 'logos'
  credentials = <your-credentials.json>

  image_classification = vision_ai_cloud(path, model, credentials)

  print('Labels:')
  image_classification.logos

Sample Image

Sample Output

Google Video AI

Enable powerful content discovery and engaging video experiences.

Two ways to make your media more discoverable

Google offers two fully-featured video AI products to make your video library more searchable and valuable.

Video Intelligence API has pre-trained machine learning models that automatically recognize a vast number of objects, places, and actions in stored and streaming video. It’s highly efficient for common use cases and improves over time as new concepts are introduced.

Google Vision A.I. on local path file

  from ML_APIs.google import video_ai_local

  path = <your-path>
  model = 'labels'
  credentials = <your-credentials.json>

  video_classification = video_ai_local(path, model, credentials)

  print("Process video/segment level label annotations")
  video_classification.analyze_segment_labels

  print("Process shot level label annotations")
  video_classification.analyze_shot_labels

Google Vision A.I. on uri media

  from ML_APIs.google import video_ai_cloud

  uri = <your-uri>
  model = 'entity'
  credentials = <your-credentials.json>

  video_classification = video_ai_cloud(uri, model, credentials)

  print("Process video/segment level label annotations")
  video_classification.analyze_segment_labels

  print("Process shot level label annotations")
  video_classification.analyze_shot_labels

Google Natural Language

Derive insights from unstructured text using Google machine learning.

Insightful text analysis

Natural Language uses machine learning to reveal the structure and meaning of text. You can extract information about people, places, and events, and better understand social media sentiment and customer conversations. Natural Language enables you to analyze text and also integrate it with your document storage on Cloud Storage.

The powerful pre-trained models of the Natural Language API let developers work with natural language understanding features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.

Google Vision A.I. on local text_content file

  from ML_APIs.google import nlp_ai_local

  text_content = <your-text_content>
  model = 'entity'
  credentials = <your-credentials.json>

  text_classification = nlp_ai_local(text_content, model, credentials)
  print("Analyzing Entities in a String")
  text_classification.analyze_entities

Google Vision A.I. on uri text_content file

  from ML_APIs.google import nlp_ai_cloud

  text_content_uri = <your-text_content_uri>
  model = 'entity'
  credentials = <your-credentials.json>

  text_classification = nlp_ai_cloud(text_content_uri, model, credentials)
  print("Analyzing Entities in a String")
  text_classification.analyze_entities