Cloud Based Image Recognition System

A comprehensive GUI application built with Python and Tkinter that performs image recognition using Google Cloud Vision API, with Firebase authentication and Google Cloud Storage for persistent storage.

Features

🔐 User Authentication

• Firebase email/password authentication
• Secure login and signup functionality
• User-specific data storage

🖼️ Image Recognition

• Label Detection: Identifies objects and concepts in images
• Object Localization: Detects and locates specific objects
• Text Detection (OCR): Extracts text from images
• Face Detection: Identifies faces and emotions
• Landmark Detection: Recognizes famous landmarks
• Dominant Color Analysis: Analyzes color composition

☁️ Cloud Storage

• Automatic upload of analyzed images to Google Cloud Storage
• Results saved as JSON files
• User-specific storage paths: {UID}/images/ and {UID}/results/

📜 History Management

• View all previous analyses
• Detailed view of analysis results
• Delete unwanted history items
• Automatic cleanup of associated files

Requirements

• Python 3.7+
• Google Cloud Platform account with:
  • Vision API enabled
  • Cloud Storage bucket created
  • Service account credentials
• Firebase project with Authentication enabled

Installation

1. Clone or download the project

2. Install required dependencies:

pip install -r requirements.txt

Configuration

Google Cloud Setup

1. Create a GCP Project:

   • Go to Google Cloud Console
   • Create a new project or select existing one

2. Enable APIs:

   • Enable Cloud Vision API
   • Enable Cloud Storage API

3. Create Service Account:

   • Go to IAM & Admin > Service Accounts
   • Create a service account with appropriate permissions
   • Download the JSON key file
   • Rename it to match the one in the code or update the path in main.py

4. Create Storage Bucket:

   • Go to Cloud Storage
   • Create a bucket (e.g., image-recognition-storage-project)
   • Update the bucket name in main.py if different

Firebase Setup

1. Create Firebase Project:

   • Go to Firebase Console
   • Create a new project

2. Enable Authentication:

   • In Firebase Console, go to Authentication
   • Enable Email/Password sign-in method

3. Get Configuration:

   • Go to Project Settings
   • Copy your Web API Key and Project ID
   • Update the Firebase configuration in main.py:

   firebase_config = {
       "apiKey": "YOUR_API_KEY",
       "authDomain": "YOUR_PROJECT_ID.firebaseapp.com",
       "projectId": "YOUR_PROJECT_ID",
       "storageBucket": "YOUR_PROJECT_ID.appspot.com",
       "messagingSenderId": "YOUR_MESSAGING_ID",
       "appId": "YOUR_APP_ID",
       "databaseURL": ""
   }

Usage

1. Run the application:

python main.py

2. First Time Users:

   • Click "Sign Up" to create an account
   • Enter your email and password (minimum 6 characters)
   • Click "Sign Up" to create your account

3. Returning Users:

   • Enter your email and password
   • Click "Login" to access the application

4. Analyze Images:

   • Click "📁 Upload Image" to select an image
   • Click "🔍 Analyze & Save" to perform analysis
   • Results will be displayed and automatically saved to cloud

5. View History:

   • Click "📜 View History" to see all your previous analyses
   • Select an item and click "View Details" to see full results
   • Click "Delete" to remove unwanted items
   • Click "Refresh" to reload the history list

6. Logout:

   • Click "Logout" button in the top-right corner

Project Structure

cloud_image_recognition/
├── main.py                          # Main application file
├── requirements.txt                 # Python dependencies
├── README.md                        # This file
└── python-project-476206-*.json    # GCP service account credentials

Data Storage Format

Images

• Path: {UID}/images/{timestamp}_{filename}
• Example: abc123/images/20250125_143022_photo.jpg

Analysis Results

• Path: {UID}/results/{timestamp}_{filename}.json
• Example: abc123/results/20250125_143022_photo.json

JSON Structure

{
  "timestamp": "20250125_143022",
  "original_filename": "photo.jpg",
  "image_path": "abc123/images/20250125_143022_photo.jpg",
  "labels": [
    {"description": "Cat", "score": 0.95},
    {"description": "Pet", "score": 0.92}
  ],
  "objects": [
    {"name": "Cat", "score": 0.89}
  ],
  "text": "Detected text from image",
  "faces": 0
}

Features Overview

Authentication Flow

1. Login/Signup screen on startup
2. Credentials validated through Firebase
3. User session maintained throughout app usage
4. Secure logout functionality

Analysis Workflow

1. User uploads an image
2. Image is analyzed using Google Cloud Vision API
3. Results are displayed in real-time
4. Both image and results are saved to GCS
5. Files organized by user UID

History Management

1. List all user's previous analyses
2. View detailed results for any analysis
3. Delete analyses (removes both image and JSON)
4. Real-time list updates

Troubleshooting

API Errors

• "API not initialized": Check your GCP credentials file path
• "Permission denied": Ensure service account has proper permissions
• "Bucket not found": Verify bucket name and existence

Authentication Errors

• "Invalid email or password": Check credentials
• "Email already exists": Use login instead of signup
• "Weak password": Use at least 6 characters

Storage Errors

• "Failed to upload": Check internet connection and GCS permissions
• "Failed to load history": Verify bucket access permissions

Security Notes

• Never commit your service account JSON file to version control
• Keep your Firebase API key secure
• Use environment variables for sensitive configuration in production
• Implement proper IAM policies for GCS bucket access

Development

Adding New Features

1. Image filters and preprocessing
2. Batch image processing
3. Export reports as PDF
4. Image comparison features
5. Custom model integration

Customization

• Modify UI colors and layout in the respective setup functions
• Add new Vision API features (SafeSearch, Web Detection, etc.)
• Customize storage paths in the analysis function
• Extend JSON structure for additional metadata

License

This project is provided as-is for educational purposes.

Support

For issues related to:

• Google Cloud: GCP Documentation
• Firebase: Firebase Documentation
• Python/Tkinter: Python Documentation

Version History

• v2.0 (Current): Added Firebase authentication and GCS storage with history management
• v1.0: Basic image recognition with Google Cloud Vision API

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Note: Make sure to replace placeholder values in the configuration with your actual Firebase and GCP credentials before running the application.