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Command line tools utilising Azure Computer Vision Cognitive Services using the framework
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Azure Computer Vision

This MLHub package provides a quick introduction to the pre-built Computer Vision model provided through Azure's Cognitive Services. This service analyses images to extract descriptions and text found in the images.

In addition to the demonstration this package provides a collection of commands that turn the service into useful command line tools for word recognition from images, landmark identification, and thumbnail generation.

A free Azure subscription allowing up to 20,000 transactions per month is available from Once set up visit and Create a resource under AI and Machine Learning called Cognitive Services. Once created you can access the web API subscription key and endpoint from the portal. This will be prompted for in the demo.

This package is part of the Azure on MLHub repository. Please note that these Azure models, unlike the MLHub models in general, use closed source services which have no guarantee of ongoing availability and do not come with the freedom to modify and share.

Visit the github repository for more details:

The Python code is based on the Azure Cognitive Services Computer Vision SDK for Python Quick Start guide.


  • To install mlhub (Ubuntu 18.04 LTS)
$ pip3 install mlhub
  • To install and configure the demo:
$ ml install   azcv
$ ml configure azcv

Command Line Tools

In addition to the demo presented below, the azcv package provides a number of useful command line tools.

Landmarks and Tags

The landmark command takes an image (url or path to a local file) and identifies the main landmark contained within the image. The confidence of the identification is also returned.

$ ml landmark azcv img.jpg
0.95,Marina Bay Sands

The tag command takes an image (url or path to a local file) and generates a collection of tags that identify key elements of the image. Each tag has a confidence.

$ ml tag azcv img.jpg

See Landmarks and Tags for details.

Optical Character Recognition

The ocr command is useful for extracting text from a variety of images. See the specific examples:

$ ml ocr azcv img.jpg
325 305 1297 290 1302 594 329 609,ABBEY


Thumbnails require more than simply generating a small square section from an image. Ideally it is in some way representative of the image. The thumbnail command will choose a "good" region of the image to display as a thumbnail.

$ ml thumbnail azcv img.jpg


$ ml demo azcv

Azure Computer Vision API

Welcome to a demo of pre-built models for Computer Vision. This Azure 
Cognitive Service supports various operations related to Computer Vision.
This MLHub package demonstrates the various services. Other MLHub packages
exist for specific tasks like identifying the landmark in an image
(azlandmark), recoginising words in an image (azocr) and generating
thumbnails from images (azthumb).

An Azure resource is required to access this service (and to run this command).
See the README for details of a free subscription. If you have a subscription
then please paste the key and the endpoint here.

Please paste your Computer Vision subscription key: ********************************
Please paste your endpoint:

I've saved that information into the file:


Press Enter to continue: 

Analyze an image

We can analyze an image for certain features with analyze_image(). We use the
visual_features= property to set the types of analysis to perform on the image. 
Common values are VisualFeatureTypes.tags and VisualFeatureTypes.description. 

For our demonstration we will analyze the following image:

Path:     wikipedia/commons/thumb/1/12/Broadway_and_Times_Square_by_night.jpg/
Filename: 450px-Broadway_and_Times_Square_by_night.jpg

Press Enter to continue: 

Close the graphic window using Ctrl-w.

Press Enter to continue: 

Tag Analysis

We list the tags for the image together with a measure of confidence.

Confidence: 1.00 Tag: skyscraper
Confidence: 0.99 Tag: building
Confidence: 0.98 Tag: outdoor
Confidence: 0.92 Tag: light
Confidence: 0.91 Tag: street
Confidence: 0.87 Tag: downtown
Confidence: 0.86 Tag: cityscape
Confidence: 0.80 Tag: sky
Confidence: 0.79 Tag: city
Confidence: 0.70 Tag: street light
Confidence: 0.59 Tag: car
Confidence: 0.58 Tag: people
Confidence: 0.42 Tag: busy
Confidence: 0.28 Tag: night

Press Enter to continue: 

Subject Domain List

Various subject domains can be used to analyze images. The domains include
celebrities and landmarks.

celebrities: people_, 人_, pessoas_, gente_

landmarks: outdoor_, 户外_, 屋外_, aoarlivre_, alairelibre_, building_,
    建筑_, 建物_, edifício_

Press Enter to continue: 

Analyze an Image by Domain

We can specify a subject domain within which to analyze an image. For example,
below we use the landmarks domain to identify the landmark in an image. See the
separate azlandmark MLHub package for a standalone demonstration and tool.

For our demonstration we will analyze the following image:

Press Enter to continue: 

Close the graphic window using Ctrl-w.

Press Enter to continue: 

Identified "Eiffel Tower" with confidence 0.97.

Press Enter to continue: 

Text Description of an Image

We can obtain a language-based text description of an image and can request
several descriptions for our further text analysis for keywords associated
with the image. 

For our demonstration we will analyze the following image:

Path:     free-stock-photos-4/travel/san-francisco/
Filename: golden-gate-bridge-in-san-francisco.jpg

Press Enter to continue: 

Close the graphic window using Ctrl-w.

Press Enter to continue: 

With confidence of 0.76 found a train crossing Golden Gate Bridge over
a body of water

With confidence of 0.76 found a large bridge over a body of water with
Golden Gate Bridge in the background

With confidence of 0.74 found a train crossing Golden Gate Bridge over
a large body of water

Press Enter to continue: 

Text From Image

We can identify text from an image using Text Recognition Mode. This mode 
supports both handwritten and typed text. The results include the text as well
as the bounding box coordinates for the text so that the image itself can be
marked up with the identified text. See the standalone MLHub package azocr
which can be used as a tool for extracting text from any supplied image.

For our demonstration we will analyze the following image:

Hash:     cvt-1979217d3d0d31c5c87cbd991bccfee2d184b55eeb4081200012bdaf6a65601a/
Filename: images/shared/cognitive-services-demos/read-text/read-1-thumbnail.png

Press Enter to continue: 

Close the graphic window using Ctrl-w.

Press Enter to continue: 

Found "Sorry!" at [11, 35, 58, 32, 59, 48, 12, 51]

Found "Have a" at [84, 42, 135, 34, 138, 50, 87, 57]

Found "Oops!" at [23, 77, 60, 75, 62, 91, 23, 94]

Found "nice day!" at [82, 56, 148, 50, 150, 68, 84, 73]

Found "See you soon !" at [15, 115, 100, 109, 101, 126, 17, 132]

Found "Bye !" at [123, 96, 153, 95, 154, 112, 123, 113]

Press Enter to continue: 

Generate Thumbnail

A utility provided by the service can generate a thumbnail (JPG) of an image. 
The thumbnail does not need to be in the same proportions as the original
image. Here we create a square 50x50 thumbnail.

For our demonstration we will analyze the following image:

Path: travel/san-francisco/golden-gate-bridge-in-san-francisco.jpg

Press Enter to continue: 

Close the graphic window using Ctrl-w.

Press Enter to continue: 

Close the graphic window using Ctrl-w.


Thank you for exploring the 'azcv' package.


This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit

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