Build Image Alert with Rekognition

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Build an Image-Based Automatic Alert System
with Amazon Rekognition
Sireesha Muppala, Solutions Architect

What to expect?
• Goal: Learn how to build computer vision-based smart applications
• Overview of Amazon Rekognition
• Outline of the Workshop Scenario
• Preview of the Lab/Instructions

The Amazon Machine Learning Stack
FRAMEWORKS & INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES

PLATFORM SERVICES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Chainer Keras Gluon
AWS Deep Learning AMIs

Caffe2 CNTK
Apache
MXNet
Amazon SageMaker
AWS
DeepLens
EDUCATION
PLATFORM SERVICES
Amazon Mechanical Turk

Caffe2 CNTK
Apache
MXNet
Amazon SageMaker
Rekognition Transcribe Translate Polly Comprehend Lex
AWS
DeepLens
EDUCATION
PLATFORM SERVICES
Amazon Mechanical Turk

Amazon Rekognition Image
Facial Analysis Face Recognition
Text in Image
Deep learning-based image analysis service
Unsafe Image Detection
Object & Scene Detection
Celebrity Recognition

Amazon Rekognition Video
Object & Activity Detection Face Detection &
Recognition
Real-time Live Stream
Deep learning-based video analysis service
Unsafe Video DetectionCelebrity Recognition
Pathing

Boat 99.3%
Plant 95.1%
Harbor 94.8%
Yacht 78.1%
Dock 75.7%
City 72.4%
Architecture 71.8%
Urban 63.9%
Building 62.3%
Marina 60.3%
Plaza 51.1%
Spire 50.8%
Neighborhood 50.7%
Flower 50.6%
Waterfront 94.8%
Object and Scene Detection

Analyze facial characteristics in multiple dimensions
Image Quality
Facial Landmarks
Demographic Data Emotion Expressed
General Attributes
Facial Pose
Brightness 23.6%
Sharpness 99.9%
EyeLeft,EyeRight,Nose
RightPupil,LeftPupil
MouthRight,LeftEyeBrowUp
Bounding Box...
Age Range 29-45
Gender:Male 96.5%
Happy 83.8%
Surprised 0.65%
Smile:True 23.6%
EyesOpen:True 99.8%
Beard:True 99.5%
Mustache:True 99.9%...
Pitch 1.446
Roll 5.725
Yaw 4.383
Facial Analysis

Measure the likelihood that faces are of the same person
Similarity 93% Similarity 0%
Face Comparison

Detect explicit and suggestive contentRecognize thousands of famous individuals
DetectModerationLabelsRecognizeCelebrities
Celebrity Recognition & Image Moderation

FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690
Similarity: 97
FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d
Similarity: 92
FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d
Similarity: 85
Index Faces Collection Search Faces
MaxFaces
FaceMatchThreshold
Face Search
Real-time search against tens of millions of faces from your private face collection

Celebrity Guests at the Royal Wedding
• “Who’s Who Live” function
• Celebrity guests identified in live
stream
• On Screen captions of relation to the
royal couple

Sports Media: Player Tracking and
Recognition

Image API – Request and Response
DetectLabels
{
"Image": {
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
},
"MaxLabels": number,
"MinConfidence": number
}
{
"Labels": [
{
"Confidence": number,
"Name": "string”
}
],
"OrientationCorrection": "string"
}

Video API – Request and Response
GetLabelDetection
StartLabelDetection
{
“ClientRequestToken": "string",
"JobTag": "string",
"MinConfidence": number,
"NotificationChannel": {
"RoleArn": "string",
"SNSTopicArn": "string”
},
"Video": {
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string”
}
}
}
{
"JobStatus": string,
"StatusMessage": string,
"VideoMetadata": {
"Format": string,
"Codec": string,
"DurationMillis": number,
"FrameRate": float,
"FrameWidth": number,
"FrameHeight": number
},
"NextToken": string,
"Labels": [
{
"Timestamp": number,
"Label":
{
"Name": string,
"Confidence": float
}
}
],
...
JobId

Other Services You Will Use Today

Amazon Elastic Compute Cloud (EC2)
Match Capacity and
Demand
Global Footprint Elasticity
Provision Servers in
Minutes
Infrastructure as
Code
Programmatic
Networking

Amazon Kinesis Data Firehose

AWS Lambda
No Servers to Manage Continuous Scaling Don’t Pay for Idle Capacity
Lambda allows you to run
application logic without
provisioning servers or worrying
about the health or security of
underlying resources
Lambda scales infrastructure
beneath your application logic;
just send requests and events
and Lambda will automatically
scale to accommodate it
With Lambda, you’re billed in
100ms increments of execution
time and number of requests
and you’re never charged for
anything when your code isn’t
running

Amazon DynamoDB
• Fast and flexible NoSQL database service
for any scale
Dead Simple
• GetItem(primaryKey)
• PutItem(item)
Robust Depth
• Fine-Grained Access Control
• Streams
• Triggers
• Global Tables
• Encryption
• DynamoDB local
• Free-text search
• Strong consistency option
• Atomic counters

Amazon Simple Storage Service (S3)
• Durable, massively scalable object storage
• Designed for 99.999999999% durability and
99.99% availability
• Stores trillions of objects and regularly handles millions of requests per
second
• Effectively infinite storage without provisioning capacity

Workshop Overview

Build an Image-Based Automatic Alert System
• Identify images that match a reference image.
– Images : From Twitter Feed
• Need to have Twitter Account & Twitter Developer Account ( Prerequisite)
• Need to create a Twitter App and gather Consumer/API keys (Prerequisite)
• Listen to twitter feed and send to S3 via Kinesis Streams
• Trigger Lambda to start a Rekognition Celebrity Detection API
• Trigger SNS Notification when match is found.
– Reference Image : Celebrity Image uploaded to S3
• Alert on matched image.

Architecture

Extension

Workshop Step by Step Guide
• https://github.com/aws-samples/aws-developer-
workshop/blob/master/episode2/Instructions.md
https://bit.ly/2tdSmH8

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Build Image Alert with Rekognition

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