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Integrating Microsoft Computer Vision API with Python.png

Dockerfile.tar.gz

Azure

Django

In this course, you will learn to integrate Microsoft Computer Vision API. This API provides multiple functionalities such as extracting text from images, describing the image in human-understandable language, and moderating the content of an image.

More specifically, you will dive into Optical Character Recognition (OCR) and image analysis, effectively giving you a toolset to analyze any image you want.

Integrating Microsoft Computer Vision API with Python

## Introduction
In this lesson, we'll use a sample application to test Microsoft Computer Vision's APIs. First, we made a new project on Django by the name `mscv`. Then we made an application by the name `apis` in the project. The application has the following three models:

* **`Key_Endpoint`** to store the key and endpoint to make the API call.
* **`OCR`** to extract texts from images and PDFs
* **`ImageAnalysis2`** to perform all the operations explained in the Image Analysis section.

## Workflow

The application has the following functionalities:
* **"Home" tab:** It describes the functionalities of the application.
* **"OCR" tab:** In this tab, we simply have to paste the URL of the image in the "Image URL" field and press the "Run" button to get our results.
* **"Analysis" tab:** In this tab, we have to paste the URL of the image in the "Image URL" field and select all the functionalities we want to perform on the image. Then we’ll press the "Run" button to get our results.

Press the "Run" button below to see how it works.



#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys


def main():
    """Run administrative tasks."""
    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mscv.settings')
    try:
        from django.core.management import execute_from_command_line
    except ImportError as exc:
        raise ImportError(
            "Couldn't import Django. Are you sure it's installed and "
            "available on your PYTHONPATH environment variable? Did you "
            "forget to activate a virtual environment?"
        ) from exc
    execute_from_command_line(sys.argv)


if __name__ == '__main__':
    main()


import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


React

Dart

GoJS React

Typescript React

Vue.js

# Introduction
In this lesson, we'll use a sample application to test Microsoft Computer Vision's APIs. First, we made a new project on Django by the name `mscv`. Then we made an application by the name `apis` in the project. The application has the following three models:

* **`Key_Endpoint`** to store the key and endpoint to make the API call.
* **`OCR`** to extract texts from images and PDFs
* **`ImageAnalysis2`** to perform all the operations explained in the Image Analysis section.

# Workflow

The application has the following functionalities:
* **"Home" tab:** It describes the functionalities of the application.
* **"OCR" tab:** In this tab, we simply have to paste the URL of the image in the "Image URL" field and press the "Run" button to get our results.
* **"Analysis" tab:** In this tab, we have to paste the URL of the image in the "Image URL" field and select all the functionalities we want to perform on the image. Then we’ll press the "Run" button to get our results.

Press the "Run" button below to see how it works.



Let’s explore the uses of Microsoft Computer Vision with the help of a Django application.

Get Started

Optical Character Recognition

Image Analysis

Sample Application

Conclusion

Analyze Images with Cognitive Services

Introduction

Workflow