Get a Dataset and Create a ML Model to Classify Yoga Poses (Using Azure Cognitive Services)

This is to use Azure Cognitive Services to get an image dataset and train a Custom Vision model. The repository has a simple nodejs console app that uses Bing Image Search to scrape images to a local directory based on search term(s). Then use the images scraped to create a Custom Vision model with Azure Cognitive Services.

Create an Azure Account

Click here to create your account

Create Bing Search Api Resource

Go to portal.azure.com
Select "Create Resource"
Select "AI + Machine Learning"
Select "Bing Search v7"
Complete the required fields
Select "Create"
When the deployment succeeds you will get a notification in the top right corner.
Select "Go to resource"
Select "Keys" from the left hand navigation for the resource.
Copy the Subscription Key and paste it into the script.

How to Run the Image Scraper Script

Image Scraper using Bing Search Cognitive Service

Clone the repo
https://github.com/coderganesh/yogaclassifier.git
cd YogaClassifier-master

Install packages
npm install

Open in VS Code
code .

Add the key into the app.js script
const subscriptionKey = 'addyourkeyhere'

Run the script
node app.js

Create Custom Vision Api Resource

Go to portal.azure.com
Select "Create Resource"
Search "Custom Vision"
Select "Create"
Complete the required fields
Select "Create"
When the deployment succeeds you will get a notification in the top right corner.
Select "Go to resource"

Create Project and Train Model

Select "Quick Start"
Select "Custom Vision Portal"
Select 'Sign in"
Select "New Project"
- Enter a Name and Resource Group and
- Project Types: Classification
- Classification Types: MultiClass General
- Domains: General
Create project
Add and tag images
- Select "Add Images" and navigate to the train folder for the scraped images
- Ctrl + A to select all the images in the folder
- Select "Open"
- Add tag for photos to indicate the yoga pose
- Select "Upload photos"
- Repeat these steps for each yoga pose (class)
Select "Train"
Test your model
- Select "Quick Test" from the upper right corner
- Browse to local files and select a image from the test folder
- The predication will appear on the right side of the window

Get the Prediction Url

Select "Performace" from the top navigation in the Custom Vision portal
Select "Prediction Url"
Copy the Url to either use an image url or image file
Additionally by selecting the settings icon in the right corner will bring you to the keys and urls to call the endpoint.

Consume the API in an App

Once the model is trained and at a satisfactory accuracy its ready to be used in your App

Go to the Resource Group that contains the Custom Vision Resource
There should be a resource named "YourCustomVisionResourceName_Prediction - Quick start"
The Select "Api Reference" from the list of helpful links
This will open a page with docs on how to call the api to get predictions from the model
If you scroll all the way to the bottom there are code samples in multiple languages to get you started

Test API in Postman

Get the predction url as described above
Select "Post" from the dropdown and paste in the url
Go to the settings and get the Prediction Key
In the headers add the Prediction-key as the key and the actual key as the value
Auth should be set to no auth
Select Body > Raw > Json
Paste in the following Json { "Url": "UrlForImageGoesHere" }
Send Request