Accelerate In-Vehicle AI with TensorRT Edge-LLM and Jetson T4000

                      
                     
from typing import Dict, Any
import os
import tensorflow as tf
from jetson_inference import classifyNet
from jetson_utils import videoSource, videoOutput

# Configuration
class Config:
    MODEL_PATH: str = os.getenv('MODEL_PATH')  # Path to TensorRT model
    VIDEO_SOURCE: str = os.getenv('VIDEO_SOURCE', 'csi://0')
    VIDEO_OUTPUT: str = os.getenv('VIDEO_OUTPUT', 'display://0')

# Initialize AI Model
class InVehicleAI:
    def __init__(self, config: Config) -> None:
        self.model = classifyNet(config.MODEL_PATH)
        self.input = videoSource(config.VIDEO_SOURCE)
        self.output = videoOutput(config.VIDEO_OUTPUT)

    def process_frame(self) -> None:
        try:
            while True:
                img = self.input.Capture()
                if img is None:
                    break
                class_id, confidence = self.model.Classify(img)
                # Log classification results
                print(f'Class ID: {class_id}, Confidence: {confidence}')
                self.output.Render(img)
        except Exception as e:
            print(f'Error processing frame: {e}')

if __name__ == '__main__':
    config = Config()
    ai_service = InVehicleAI(config)
    ai_service.process_frame()