Merge branch 'main' of https://github.com/aws-samples/serverless-computer-vision-label-detection into main

Author: hi4rahul

README.md

@@ -1,8 +1,12 @@
-# serverless-computer-vision-label-detection 
+# Serverless computer vision label detection 
 
-This project contains source code and supporting files for a serverless application that you can deploy with the SAM CLI. The application can be used for Computer Vision inferencing using Amazon Rekognition or Amazon Lookout for Vision. It includes the following files and folders:
+The ability to apply custom inferencing in images is a use case that is of advantage in many domains. This ability can help us use computer vision to identify the objects and scenes that are specific to a business need. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants etc. The computer vision capability is often built with deep learning models. It automates extraction, analysis, classification and understanding of useful information from a single image or a sequence of images. The image data can take many forms, however for this example solution we will focus on png and jpg/ jpeg images only.
 
-- functions - Code for the application's Lambda functions to check the presence of messages in a Queue, start or stop a Amazon Rekognition Custom Label Model, Analyse Images using a Custom Label Model.
+In this project, we will reference Amazon Rekognition or Amazon Lookout for Vision to use for a computer vision inferencing capability.  To use it with either service, customers need to create a project, upload a few images and train a model to recognise their custom labels in the provided images. To use custom inferencing, instead of thousands of images, you can upload a small set of training images (typically a few hundred images or less) that are specific to your use case. You can do this by using the easy-to-use console. If your images are already labeled, the Amazon computer vision service (Amazon Rekognition or Amazon Lookout for Vision) can begin training a model in a short time. Once trained, the model can be started to use it for inferencing. It it recommended to stop the model after use to optimise on cost incurred while the model is running.
+
+This project contains source code and supporting files for a serverless application that you can deploy with the SAM CLI or using the Cloudformation links below. The application can be used for Computer Vision inferencing using Amazon Rekognition or Amazon Lookout for Vision. It includes the following files and folders:
+
+- \functions - Code for the application's Lambda functions to check the presence of messages in a Queue, start or stop a Amazon Rekognition Custom Label Model, Analyse Images using a Custom Label Model.
 - template.yaml - A template that defines the application's AWS resources.
 
 This application creates a serverless Amazon Rekognition Custom Label Detection workflow which runs on a pre-defined schedule (note that the schedule is enabled by default at deployment). It demonstrates the power of Step Functions to orchestrate Lambda functions and other AWS resources to form complex and robust workflows, coupled with event-driven development using Amazon EventBridge.
@@ -16,15 +20,16 @@ The application uses several AWS resources, including Amazon Simple Storage Serv
 
 Please note that this code is provided as a working sample. However, if you intend to use it in production, it is recommended that you implement production best practices including but not limited to error handling, message visibility settings, timeouts, storage lifecycle rules etc. 
 
-When deployoing this application you will need to provide the following two parameters for your Custom Label Project.
-1. Amazon Rekognition Model Project ARN
-2. Amazon Rekognition Model Project Version ARN
-
 ### Usage
 
 #### Prerequisites
 
-To deploy the sample application, you will require an AWS account. If you don’t already have an AWS account, create one at <https://aws.amazon.com> by following the on-screen instructions. Your access to the AWS account must have IAM permissions to launch AWS CloudFormation templates that create IAM roles.
+1. Please refer [here](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/gs-introduction.html) for instructions on getting started with Amazon Rekognition Custom Labels. When deploying this application you will need to provide the following two parameters for your Custom Label Project.
+   - Amazon Rekognition Model Project ARN: The Amazon Resource Name (ARN) of the Amazon Rekognition Custom Labels project that contains the models you want to use. 
+   - Amazon Rekognition Model Project Version ARN: The Amazon Resource Name(ARN) of the model version that you want to use.
+
+2. To deploy the sample application, you will require an AWS account. If you don’t already have an AWS account, create one at <https://aws.amazon.com> by following the on-screen instructions. Your access to the AWS account must have IAM permissions to launch AWS CloudFormation templates that create IAM roles.
+
 
 #### Deployment
 
@@ -34,6 +39,7 @@ The demo application is deployed as an [AWS CloudFormation](https://aws.amazon.c
 > You are responsible for the cost of the AWS services used while running this sample deployment. There is no additional cost for using this sample. For full details, see the following pricing pages for each AWS service you will be using in this sample. Prices are subject to change.
 >
 > - [Amazon Rekognition Pricing](https://aws.amazon.com/rekognition/pricing/)
+> - [Amazon Lookout for Vision Pricing](https://aws.amazon.com/lookout-for-vision/pricing/)
 > - [Amazon S3 Pricing](https://aws.amazon.com/s3/pricing/)
 > - [Amazon SQS Pricing](https://aws.amazon.com/sqs/pricing/)
 > - [AWS Lambda Pricing](https://aws.amazon.com/lambda/pricing/)
@@ -82,9 +88,11 @@ After deployment, the application can be used by configuring the following servi
 3. The Final S3 bucket holds the images that have been processed along with the inferenced custom label jsons.
 
 
-### Remove the application
+### Removing the application
+
+To remove the application open the AWS CloudFormation Console, click on the name of the project, right-click and select "_Delete Stack_". Your stack will take some time to be deleted. You can track its progress in the "Events" tab. When it is done, the status will change from "_DELETE_IN_PROGRESS_" to "_DELETE_COMPLETE_". It will then disappear from the list. 
 
-To remove the application open the AWS CloudFormation Console, click on the name of the project, right-click and select "_Delete Stack_". Your stack will take some time to be deleted. You can track its progress in the "Events" tab. When it is done, the status will change from "_DELETE_IN_PROGRESS_" to "_DELETE_COMPLETE_". It will then disappear from the list. Please note that the Amazon S3 buckets and their content will be retained.
+**Note:** Please note that the provided configuration will ensure that the Amazon S3 buckets and their contents are retained when removing the application via the AWS Cloudformation console. This is to ensure that no data is accidently lost while removing the application. The buckets can be deleted from the S3 console.
 
 
 ## License