Traffic Vision

bydeepakdudeja mmu

Create a complete Traffic Sign Recognition System using Deep Learning and Convolutional Neural Networks (CNN). Project Title: Traffic Sign Recognition using CNN and Deep Learning Objective: Develop a web application that can recognize and classify traffic signs from uploaded images using a trained CNN model. Frontend Requirements: - React.js frontend - Modern and responsive UI - Attractive dashboard design - Drag and Drop image upload - Browse image upload option - Image preview before prediction - Loading animation during prediction - Display predicted traffic sign name - Display confidence percentage - Error handling for invalid image uploads - Mobile and desktop responsive design Backend Requirements: - Python FastAPI backend - REST API architecture - Image upload endpoint - Prediction endpoint - Health check endpoint - Proper error handling - CORS support for React frontend Deep Learning Requirements: - TensorFlow/Keras framework - Convolutional Neural Network (CNN) built from scratch - Use: - Convolution Layers - ReLU Activation - MaxPooling Layers - Dropout Layers - Dense Layers - Softmax Output Layer - Multi-class traffic sign classification - Model saved as traffic_sign_model.h5 Dataset Requirements: - Use GTSRB (German Traffic Sign Recognition Benchmark) dataset - Image resizing to 32x32 - Image normalization - Data preprocessing pipeline - Data augmentation using ImageDataGenerator - Train/Test split Model Training Features: - CNN training script - Model checkpoint saving - Early stopping - Accuracy and loss tracking - Training history visualization Evaluation Features: - Accuracy graph - Loss graph - Confusion Matrix - Classification Report - Final test accuracy display Prediction Features: - Upload traffic sign image - Real-time prediction - Display sign class name - Display confidence score - Show uploaded image with result Project Structure: TrafficSignRecognition/ │ ├── frontend/ │ ├── src/ │ │ ├── components/ │ │ ├── pages/ │ │ ├── App.js │ │ ├── index.js │ │ └── api.js │ │ │ ├── public/ │ ├── package.json │ └── README.md │ ├── backend/ │ ├── server.py │ ├── classifier.py │ ├── requirements.txt │ ├── uploads/ │ └── models/ │ └── traffic_sign_model.h5 │ ├── training/ │ ├── train_model.py │ ├── evaluate_model.py │ ├── plots/ │ └── saved_models/ │ ├── dataset/ │ └── README.md API Endpoints: - GET /health - POST /predict Required Libraries: Backend: - TensorFlow - Keras - OpenCV - NumPy - Pandas - Matplotlib - FastAPI - Uvicorn - Pillow Frontend: - React.js - Axios - React Icons Additional Requirements: - Clean code with comments - Professional folder structure - Production-ready project - Fully functional frontend-backend integration - Step-by-step setup instructions - Complete source code for every file - No placeholder code - All files should be fully implemented and ready to run Generate the complete code for frontend, backend, CNN model training, dataset preprocessing, API integration, and project documentation. run ho jaye and give me the code for the vs code as try to give the best frontend and backend