CS-Weekly-Item-Journal

A web application that analyzes your CS2 weekly drop screenshots, identifies items with AI, compares prices, and helps you track your drops over time in a personal journal. The system automatically recommends the highest-value item to select based on current Steam Market prices.

🚀 Features

• AI-Powered Item Detection : Custom-trained YOLO model detects weekly drop boxes in screenshots
• Automatic Text Recognition : Advanced OCR pipeline extracts item names from screenshots
• Intelligent Item Matching : Sophisticated fuzzy matching algorithms correctly identify items despite OCR imperfections
• Price Comparison : Automatically determines which item has the highest market value
• Real-time Price Tracking : Automatic Steam Market price monitoring and updates
• User Journal System : Track your drops over time and monitor your total collection value
• Web Interface : Easy-to-use Flask web application for uploading and analyzing screenshots

📋 Overview

This project combines computer vision, deep learning, and web technologies to solve the challenge of tracking weekly drops in Counter-Strike 2.

AI Model Training

The system uses a custom-trained YOLO (You Only Look Once) object detection model to identify the weekly drop boxes in CS2 screenshots. The model was trained on a dataset of annotated CS2 weekly drop screenshots to accurately detect and segment the relevant regions regardless of resolution, aspect ratio, or in-game visual settings.

Image Processing Pipeline

1. Box Detection : The YOLO model locates the region containing the weekly drops
2. Grid Segmentation : The detected region is divided into a 1x4 grid for individual item extraction
3. Text Region Targeting : The system targets the lower section of each item box where the name text appears
4. OCR Processing : Multiple OCR methods are applied for optimal text extraction

Item Matching System

The extracted text is processed through a sophisticated matching system that:

• Cleans and normalizes OCR output
• Uses multiple matching strategies (token-based, sequence, containment)
• Calculates similarity scores with confidence levels
• Provides appropriate wear variants for matched skins
• Specially handles case and graffiti item types

Database System

• Comprehensive SQLite database of CS2/CS items
• Includes skins, cases, and graffiti with price information
• Intelligent price update mechanisms (both individual and bulk)
• Support for different wear variations and quality types

Web Application

• User registration and authentication
• Screenshot upload via file selection or paste
• Visual results display with confidence indicators
• Personal journal system for tracking drops
• Collection value monitoring
• Recommendation of highest-value items

🛠️ Technologies Used

• Object Detection : Ultralytics YOLO
• Computer Vision : OpenCV, PIL
• OCR : EasyOCR, PyTesseract
• Backend : Python, Flask
• Database : SQLite
• Text Processing : Advanced fuzzy matching algorithms
• API Integration : Steam Market
• Task Scheduling : APScheduler
• Frontend : HTML, CSS, and JavaScript with Bootstrap for responsive design

🔧 Setup and Installation

Prerequisites

• Python 3.6 or higher
• CUDA-compatible GPU recommended for faster detection
• Node.js 12.0+ (for certain dataset scripts)

Installation Steps

bash
Copy
# Clone the repository
git clone https://github.com/yourusername/CS-Weekly-Item-Journal.git
cd CS-Weekly-Item-Journal

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create the database
python create_database.py

# Generate CS:GO skins data
node create_cs_skins.js

# Generate CS:GO cases data
python create_cases.py

# Populate the database
python populate_database.py

# Update item prices
python update_price.py

# Run the Flask application
python app.py

Visit http://127.0.0.1:5000 in your browser to access the application.

📊 Database Structure

The system uses a SQLite database with the following key tables:

• items : Stores information about CS2 items (skins, cases, graffiti)
• collections : Stores collection names for easier filtering
• users : User account information
• user_journals : Records of items in users' journals

🖼️ Usage

1. Register/Login : Create an account to track your drops over time
2. Upload Screenshot : Take a screenshot of your CS2 weekly drops screen and upload it
3. Review Results : The system will identify items and display matching candidates with price information
4. Select Item : The highest-priced item will be highlighted as the recommended choice
5. Add to Journal : Select the correct items to add to your personal journal
6. Track Value : Monitor the total value of your collected items in your journal

🔄 Updating Prices

The system automatically updates prices on a daily schedule, but you can also manually update them:

bash
Copy
# Update prices for all items
python update_price.py

# Update prices for specific collections
python update_price.py --collections "Clutch Case""Chroma Case"

# Use bulk updater (faster)
python bulk_scraper.py --collections "Clutch Case""Chroma Case"

Database Verification

bash
Copy
# Verify database structure and content
python verify_database.py

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Note: This project is not affiliated with Valve Corporation or the Counter-Strike franchise. All CS2/CS

• item names and related data are property of their respective owners.*