AWS Rekognition Image Moderation

A Python implementation for detecting inappropriate content in images using AWS Rekognition's image moderation feature. This implementation analyzes images stored in S3 buckets for potentially inappropriate or offensive content.

Features

• Detects inappropriate content in images
• Provides confidence scores for detected content
• Works with images stored in S3 buckets

Prerequisites

• Python 3.8.8
• boto3 (AWS SDK for Python)
• AWS Account with Rekognition and S3 access
• AWS Access Key and Secret Access Key
• S3 bucket containing images to analyze

Installation

Install the required AWS SDK for Python:

pip install boto3

Configuration

You'll need to configure the following AWS credentials:

aws_accesskey = <Your Access Key>
aws_secretaccess = <Your secret access key>
myregion = <your region>

Required Libraries

import boto3

Usage

The main functionality is provided through the Moderate_Image function:

Moderate_Image(aws_access, aws_secret, aws_region, photo_name, bucket_name)

Parameters

• aws_access: Your AWS access key ID
• aws_secret: Your AWS secret access key
• aws_region: AWS region for Rekognition service
• photo_name: Name of the image file in the S3 bucket
• bucket_name: Name of the S3 bucket containing the image

Return Value

Returns the number of moderation labels detected in the image (integer).

Output Format

For each detected moderation label, the function prints:

• Label name
• Confidence score
• Parent category name

Example output:

Detected labels for Smoking.jpg
Smoking : 96.02770233154297
Drug & Tobacco Paraphernalia & Use
Drug & Tobacco Paraphernalia & Use : 96.02770233154297
Drugs & Tobacco
Drugs & Tobacco : 96.02770233154297

Example Usage

# Analyze an image for inappropriate content
label_count = Moderate_Image(
    aws_accesskey,
    aws_secretaccess,
    myregion,
    'image.jpg',
    'your-bucket-name'
)

# The function returns 0 if no inappropriate content is detected
# Returns positive integer if moderation labels are found