[30/100] #100DaysOfCloud Today, I learned how to analyze and compare images using Amazon Rekognition.
Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Amazon Rekognition API, and the service can identify objects, people, text, scenes, and activities. It can detect any inappropriate content as well. Amazon Rekognition also provides highly accurate facial analysis, face comparison, and face search capabilities. You can detect, analyze, and compare faces for a wide variety of use cases, including user verification, cataloging, people counting, and public safety.
Features :
- Content moderation
- Detect potentially unsafe, inappropriate, or unwanted content across images and videos.
- Face compare and search
- Determine the similarity of a face against another picture or from your private image repository.
- Face detection and analysis
- Detect faces appearing in images and videos and recognize attributes such as open eyes, glasses, and facial hair for each.
- Labels
- Detect objects, scenes, activities, landmarks, dominant colors, and image quality.
- Custom labels
- Detect custom objects such as brand logos using automated machine learning (AutoML) to train your models with as few as 10 images.
- Text detection
- Extract skewed and distorted text from images and videos of street signs, social media posts, and product packaging.
- Celebrity recognition
- Identify well-known people to catalog photos and footage for media, marketing, and advertising.
- Video segment detection
- Detect key segments in videos, such as black frames, start or end credits, slates, color bars, and shots.
- Streaming Video Events detection
- Detect objects such as packages, pets, or people in real-time from live video streams.
- Send connected home smart alerts
- Deliver timely and actionable alerts when a desired object is detected in your live video streams. Create home automation experiences such as automatically turning on the light when a person is detected.
You can try do it by yourself by following the steps from the link below: GitHub
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