kenilkb/DriverTrac

Fork 0

Kenil_KB 7aabf718b7 Basic_track

2025-11-24 18:38:24 +05:30

8.6 KiB

Raw Permalink Blame History

Raspberry Pi Deployment Guide

Testing Strategy: Ubuntu vs Raspberry Pi

✅ Recommendation: Test on Ubuntu First, Then Deploy to Raspberry Pi

Why test on Ubuntu first:

Faster Development Cycle: Ubuntu on x86_64 is much faster for debugging and iteration
Better Tooling: IDEs, debuggers, and development tools work better on Ubuntu
Easier Dependency Management: Most packages install smoothly on Ubuntu
Identify Logic Bugs: Fix algorithmic and code issues before dealing with hardware constraints
Protect SD Card: Avoid excessive writes during development (Raspberry Pi uses SD cards)

Then test on Raspberry Pi:

Architecture Validation: Ensure ARM compatibility
Performance Benchmarking: Real-world performance on target hardware
Memory Constraints: Test with actual 4-8GB RAM limits
Thermal Management: Check CPU throttling under load
Final Optimizations: Pi-specific tuning

Architecture Differences

Ubuntu (x86_64) vs Raspberry Pi (ARM)

Aspect	Ubuntu (x86_64)	Raspberry Pi (ARM)
CPU Architecture	x86_64 (Intel/AMD)	ARM (Broadcom)
Performance	High (multi-core, high clock)	Lower (4-8 cores, 1.5-2.4 GHz)
Memory	Typically 8GB+	4-8GB (Pi 4/5)
Python Packages	Pre-built wheels available	May need compilation
ONNX Runtime	`onnxruntime`	`onnxruntime` (ARM build)
PyTorch	CUDA support available	CPU-only (or limited GPU)
OpenCV	Full features	May need compilation for some features

Raspberry Pi Requirements

Hardware Recommendations

Minimum (for testing):

Raspberry Pi 4 (4GB RAM) or better
32GB+ Class 10 SD card (or better: USB 3.0 SSD)
Good power supply (5V 3A)
Active cooling (heatsink + fan recommended)

Recommended (for production):

Raspberry Pi 5 (8GB RAM) - Best choice
64GB+ high-speed SD card or USB 3.0 SSD
Official Raspberry Pi power supply
Active cooling system
Camera module v2 or v3

Software Requirements

OS:

Raspberry Pi OS (64-bit) - Recommended (better for Python packages)
Ubuntu Server 22.04 LTS (ARM64) - Alternative

Python:

Python 3.9+ (3.10 or 3.11 recommended)

Installation Steps for Raspberry Pi

1. Prepare Raspberry Pi OS

# Update system
sudo apt update && sudo apt upgrade -y

# Install essential build tools
sudo apt install -y python3-pip python3-venv build-essential cmake
sudo apt install -y libopencv-dev python3-opencv  # OpenCV system package (optional)

2. Create Virtual Environment

cd ~/work/tools/Driver_DSMS_ADAS
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies (Pi-Specific Considerations)

Important: Some packages may need ARM-specific builds or compilation.

# Upgrade pip first
pip install --upgrade pip setuptools wheel

# Install NumPy (may take time - compiles from source if no wheel)
pip install numpy

# Install PyTorch (CPU-only for ARM)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

# Install other dependencies
pip install -r requirements.txt

Note: Installation may take 30-60 minutes on Raspberry Pi due to compilation.

4. Install ONNX Runtime (ARM)

# For ARM64 (Raspberry Pi 4/5 64-bit)
pip install onnxruntime

# If above fails, try:
# pip install onnxruntime-arm64  # May not exist, check availability

5. Test Installation

python3 check_dependencies.py

Performance Optimizations for Raspberry Pi

1. Model Optimization

Already Implemented:

✅ ONNX format (faster than PyTorch)
✅ Frame skipping (inference_skip: 3)
✅ VideoMAE disabled (too heavy)

Additional Optimizations:

# In CONFIG, reduce further for Pi:
CONFIG = {
    'yolo_base': 'yolov8n.pt',  # Already nano (smallest)
    'conf_threshold': 0.7,
    'inference_skip': 5,  # Increase from 3 to 5 for Pi
    'frame_resize': (320, 240),  # Smaller resolution for face analysis
    'object_resize': (416, 416),  # Smaller for YOLO
}

2. System Optimizations

# Increase GPU memory split (if using GPU acceleration)
sudo raspi-config
# Advanced Options > Memory Split > 128 (or 256)

# Disable unnecessary services
sudo systemctl disable bluetooth
sudo systemctl disable avahi-daemon

# Set CPU governor to performance (temporary)
echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

3. Memory Management

# Add to track_drive.py for Pi:
import gc

# In run_inference, after processing:
if frame_idx % 10 == 0:
    gc.collect()  # Force garbage collection

4. Use USB 3.0 SSD Instead of SD Card

SD cards are slow and can wear out. For production:

Use USB 3.0 SSD for OS and application
Much faster I/O
Better reliability

Expected Performance on Raspberry Pi

Raspberry Pi 4 (4GB)

Current (After Fixes):

FPS: 3-5
Memory: 2-3GB
CPU: 80-100% (may throttle)
Temperature: 60-75°C (with cooling)

After Optimizations:

FPS: 5-8
Memory: 1.5-2.5GB
CPU: 70-85%
Temperature: 55-70°C

Raspberry Pi 5 (8GB) - Recommended

Current (After Fixes):

FPS: 5-8
Memory: 2-3GB
CPU: 60-80%
Temperature: 50-65°C

After Optimizations:

FPS: 8-12
Memory: 1.5-2.5GB
CPU: 50-70%
Temperature: 45-60°C

Common Issues and Solutions

Issue 1: Out of Memory

Symptoms: Process killed, "Killed" message

Solutions:

# Increase swap (temporary)
sudo dphys-swapfile swapoff
sudo nano /etc/dphys-swapfile  # Change CONF_SWAPSIZE=100 to 2048
sudo dphys-swapfile setup
sudo dphys-swapfile swapon

# Or reduce model sizes, increase frame skipping

Issue 2: Slow Model Loading

Solution: Pre-download models on Ubuntu, copy to Pi

# On Ubuntu, models download to ~/.cache/
# Copy to Pi:
scp -r ~/.cache/huggingface user@pi:~/.cache/
scp -r ~/.cache/ultralytics user@pi:~/.cache/

Issue 3: ONNX Runtime Not Found

Solution: Install ARM-compatible version

# Check architecture
uname -m  # Should show aarch64 for Pi 4/5 64-bit

# Install correct version
pip uninstall onnxruntime
pip install onnxruntime  # Should auto-detect ARM

Issue 4: Camera Not Detected

Solution:

# Check camera
vcgencmd get_camera  # Should show supported=1 detected=1

# For USB webcam:
lsusb  # Check if detected
v4l2-ctl --list-devices  # List video devices

Issue 5: High CPU Temperature