📷 Object Detection Using AWS Services

This project demonstrates how to perform Object Detection using Amazon Rekognition, a deep learning-based AWS service that can analyze images and return detailed label information.

🔍 Features

• Detects and displays all identifiable objects, scenes, and concepts.
• Provides confidence scores for each detected label.
• Shows bounding box coordinates for object positions.
• Identifies parent labels for hierarchical understanding.
• Interactive CLI-based Python script.

---

⚙️ Technologies Used

• Python 3
• AWS Rekognition
• Amazon S3
• Boto3 (AWS SDK for Python)

---

📁 How It Works

1. Upload your image to an AWS S3 bucket.
2. Run the script and enter the image name (including extension) when prompted.
3. The script:
   • Analyzes the image using Amazon Rekognition.
   • Returns:
     • Detected labels (objects).
     • Confidence scores.
     • Bounding box dimensions.
     • Parent categories (hierarchical).

---

🚀 Getting Started

Prerequisites

• An AWS account with Rekognition and S3 permissions.
• AWS credentials configured (via AWS CLI or environment variables).
• Python 3 installed.

Installation

pip install boto3

Update the Bucket Name

In the script, replace:

bucket = "your-s3-bucket-name"

with your actual S3 bucket name.

---

🧠 Example Output

Analyzing photo: car.jpg

Label: Car
Confidence: 98.67%
Bounding Box:
 - Top: 0.34
 - Left: 0.25
 - Width: 0.5
 - Height: 0.3
Parents:
 - Vehicle
-------------------------

Total Labels Detected: 8

---

💡 Use Cases

• Educational demos on AWS Rekognition.
• Building blocks for image classification apps.
• Quick prototypes for AI-powered projects.
• Real-time object detection systems.

---

📌 Notes

• Ensure the image is accessible in S3 with correct permissions.
• Avoid hardcoding sensitive information (use environment variables or config files).
• Recommended: Use .env for managing secrets securely in real-world projects.

---

👨‍💻 Author

Sanavulla Baig
🎓 Final Year CSE Student, KL University
🧠 AWS Certified Cloud Practitioner
💻 MERN Stack & Python Enthusiast
📫 LinkedIn | GitHub

---