ConsistentlyInconsistentYT--Pixeltovoxelprojector/DEPENDENCIES.md

Dependencies Documentation

Overview

This document provides a comprehensive list of all dependencies required for the Pixel to Voxel Projector system, including system libraries, Python packages, and optional components.

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Table of Contents

1. System Requirements
2. System Libraries
3. Python Packages
4. GPU Dependencies
5. Optional Dependencies
6. Hardware-Specific
7. Dependency Graphs

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System Requirements

Operating System

OS	Version	Status
Ubuntu	20.04+	Recommended
Debian	11+	Supported
CentOS/RHEL	8+	Supported
Windows	10/11 with WSL2	Supported
macOS	11+	Limited (CPU only)

Hardware

Component	Minimum	Recommended	Notes
CPU	Intel i5 / Ryzen 5	Intel i9 / Ryzen 9	8+ cores preferred
RAM	16 GB	32+ GB	64 GB for 8K processing
GPU	GTX 1660	RTX 3090/4090	NVIDIA only
VRAM	6 GB	24 GB	For GPU acceleration
Storage	50 GB	500 GB+	SSD recommended

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System Libraries

Build Tools

# Ubuntu/Debian
sudo apt-get install -y \
    build-essential \
    cmake \
    ninja-build \
    git \
    pkg-config

Package	Version	Purpose
gcc/g++	9.0+	C++ compiler
cmake	3.18+	Build system
ninja	1.10+	Fast build tool
git	2.25+	Version control
pkg-config	0.29+	Library configuration

Core Libraries

# Ubuntu/Debian
sudo apt-get install -y \
    libomp-dev \
    libgomp1 \
    libpthread-stubs0-dev

Library	Version	Purpose
OpenMP	4.5+	Parallel computing
pthread	2.31+	Threading

Video Processing

# Ubuntu/Debian
sudo apt-get install -y \
    ffmpeg \
    libavcodec-dev \
    libavformat-dev \
    libavutil-dev \
    libswscale-dev \
    libavresample-dev \
    libx264-dev \
    libx265-dev \
    libvpx-dev

Library	Version	Purpose
FFmpeg	4.3+	Video encoding/decoding
x264	0.160+	H.264 encoding
x265	3.4+	H.265/HEVC encoding
libvpx	1.9+	VP8/VP9 encoding

Computer Vision

# Ubuntu/Debian
sudo apt-get install -y \
    libopencv-dev \
    libopencv-contrib-dev

Library	Version	Purpose
OpenCV	4.5+	Computer vision algorithms

Graphics & Visualization

# Ubuntu/Debian
sudo apt-get install -y \
    libgl1-mesa-dev \
    libglu1-mesa-dev \
    freeglut3-dev \
    libglfw3-dev \
    libglew-dev

Library	Version	Purpose
OpenGL	4.5+	3D graphics
GLFW	3.3+	Window/context management
GLEW	2.1+	OpenGL extension loading

Networking & Communication

# Ubuntu/Debian
sudo apt-get install -y \
    libzmq3-dev \
    libzmqpp-dev

Library	Version	Purpose
ZeroMQ	4.3+	High-performance messaging

Compression

# Ubuntu/Debian
sudo apt-get install -y \
    liblz4-dev \
    libzstd-dev \
    libsnappy-dev

Library	Version	Purpose
LZ4	1.9+	Fast compression
Zstandard	1.4+	High-ratio compression
Snappy	1.1+	Fast compression

Protocol Buffers

# Ubuntu/Debian
sudo apt-get install -y \
    protobuf-compiler \
    libprotobuf-dev

Library	Version	Purpose
Protocol Buffers	3.19+	Data serialization

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Python Packages

Core Dependencies

Package	Version	Purpose	Required
numpy	≥1.21.0, <2.0.0	Numerical computing	✓
pybind11	≥2.9.0, <3.0.0	C++ bindings	✓
opencv-python	≥4.5.5, <5.0.0	Computer vision	✓
ffmpeg-python	≥0.2.0	Video processing	✓
scipy	≥1.7.0, <2.0.0	Scientific computing	✓

GPU Acceleration

Package	Version	CUDA Version	Notes
cupy-cuda11x	≥10.0.0	11.x	For CUDA 11
cupy-cuda12x	≥12.0.0	12.x	For CUDA 12
pycuda	≥2021.1	All	GPU detection

Networking

Package	Version	Purpose
pyzmq	≥22.3.0, <26.0.0	ZeroMQ bindings
websockets	≥10.1, <12.0	WebSocket support
msgpack	≥1.0.3, <2.0.0	Message packing

Protocol Buffers & gRPC

Package	Version	Purpose
protobuf	≥3.19.0, <5.0.0	Protocol buffers
grpcio	≥1.43.0, <2.0.0	gRPC framework
grpcio-tools	≥1.43.0, <2.0.0	gRPC code generation

Compression

Package	Version	Purpose
lz4	≥4.0.0, <5.0.0	LZ4 compression
zstandard	≥0.17.0, <1.0.0	Zstandard compression
python-snappy	≥0.6.1, <1.0.0	Snappy compression

Visualization

Package	Version	Purpose
open3d	≥0.15.0, <1.0.0	3D visualization
vtk	≥9.1.0, <10.0.0	Visualization Toolkit
PyOpenGL	≥3.1.5, <4.0.0	OpenGL bindings
plotly	≥5.5.0, <6.0.0	Interactive plots

System Monitoring

Package	Version	Purpose
psutil	≥5.9.0, <6.0.0	System metrics
py-cpuinfo	≥8.0.0, <10.0.0	CPU information
pynvml	≥11.4.1	NVIDIA GPU monitoring

Development Tools

Package	Version	Purpose
pytest	≥7.0.0, <8.0.0	Testing framework
pytest-benchmark	≥3.4.1	Performance testing
black	≥22.3.0, <24.0.0	Code formatting
mypy	≥0.942, <2.0.0	Type checking

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GPU Dependencies

NVIDIA CUDA Toolkit

Component	Version	Purpose
CUDA Runtime	11.0+ / 12.0+	GPU computing
cuBLAS	Included	Linear algebra
cuFFT	Included	Fast Fourier Transform
cuSPARSE	Included	Sparse matrix operations
NVRTC	Included	Runtime compilation

cuDNN

Version	CUDA Compatibility	Purpose
8.x	CUDA 11.x / 12.x	Deep learning primitives

NVIDIA Driver

Driver Version	CUDA Support	GPU Support
470+	CUDA 11.x	GTX 10xx+
525+	CUDA 12.x	RTX 30xx/40xx

Installation

# Check CUDA installation
nvcc --version
nvidia-smi

# Verify compute capability
python3 << EOF
import pycuda.driver as cuda
import pycuda.autoinit
for i in range(cuda.Device.count()):
    dev = cuda.Device(i)
    print(f"GPU {i}: {dev.name()}")
    print(f"  Compute Capability: {dev.compute_capability()}")
EOF

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Optional Dependencies

Camera Hardware

Package	Hardware	Installation
pypylon	Basler GigE cameras	pip install pypylon
simple-pyspin	FLIR cameras	pip install simple-pyspin
picamera	Raspberry Pi cameras	pip install picamera

Note: These packages require hardware-specific drivers:

# Basler Pylon SDK
wget https://www.baslerweb.com/media/downloads/software/pylon_software/pylon_7.2.0_linux-x86_64_setup.tar.gz
tar -xzf pylon_*.tar.gz
cd pylon_*/
sudo ./setup-usb.sh

# FLIR Spinnaker SDK
# Download from: https://www.flir.com/products/spinnaker-sdk/

Machine Learning

Package	Purpose	Installation
torch	PyTorch deep learning	pip install torch torchvision
onnxruntime	ONNX inference	pip install onnxruntime-gpu
tensorflow	TensorFlow (optional)	pip install tensorflow-gpu

Distributed Computing

Package	Purpose	Installation
dask	Parallel computing	pip install dask[complete]
ray	Distributed computing	pip install ray[default]

Interactive Development

Package	Purpose	Installation
jupyter	Jupyter notebooks	pip install jupyter
jupyterlab	JupyterLab IDE	pip install jupyterlab
matplotlib	Plotting	pip install matplotlib

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Hardware-Specific

Camera Requirements

Basler GigE Cameras

• Driver: Pylon SDK 7.2+
• Network: Gigabit Ethernet adapter
• Bandwidth: 1000 Mbps minimum
• MTU: 9000 (Jumbo frames recommended)

FLIR Cameras

• Driver: Spinnaker SDK 3.0+
• Interface: USB 3.0 / GigE
• Power: External power for high-speed operation

Network Requirements

Feature	Requirement	Purpose
Bandwidth	10 Gbps+	Multi-camera streaming
Latency	<1ms	Real-time processing
Protocol	UDP multicast	Efficient distribution

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Dependency Graphs

Build Dependencies

Build System
├── cmake (3.18+)
├── ninja-build
├── gcc/g++ (9.0+)
└── pkg-config

Python Build
├── setuptools
├── wheel
├── pybind11
└── grpcio-tools (for protobuf)

CUDA Build (Optional)
├── CUDA Toolkit (11.x/12.x)
├── cuDNN (8.x)
└── NVIDIA Driver (470+/525+)

Runtime Dependencies

Core Runtime
├── numpy
├── opencv-python
├── scipy
└── ffmpeg-python

GPU Runtime (Optional)
├── cupy-cuda11x/12x
├── pycuda
└── CUDA Runtime

Networking
├── pyzmq
├── websockets
└── msgpack

Visualization
├── open3d
├── vtk
└── PyOpenGL

Development Dependencies

Development
├── Testing
│   ├── pytest
│   ├── pytest-benchmark
│   └── pytest-cov
├── Code Quality
│   ├── black
│   ├── mypy
│   └── flake8
└── Documentation
    ├── sphinx
    └── sphinx-rtd-theme

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Installation Order

Recommended Installation Sequence

1. System Libraries (requires sudo)

   sudo apt-get install build-essential cmake ninja-build
   sudo apt-get install ffmpeg libopencv-dev libgl1-mesa-dev
   

2. CUDA Toolkit (if using GPU)

   # Download from NVIDIA website
   wget https://developer.download.nvidia.com/compute/cuda/...
   sudo sh cuda_*.run
   

3. Python Environment

   python3 -m venv venv
   source venv/bin/activate
   pip install --upgrade pip setuptools wheel
   

4. Python Dependencies

   pip install -r requirements.txt
   

5. GPU Packages (optional)

   pip install cupy-cuda12x pycuda
   

6. Build Project

   pip install -e ".[full,dev]"
   

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Version Compatibility Matrix

CUDA Version Compatibility

CUDA	Python	GCC	CuPy	PyTorch
11.8	3.8-3.11	9-11	11.x	2.0+
12.0	3.8-3.11	9-12	12.x	2.1+
12.2	3.8-3.12	9-12	12.x	2.2+

Operating System Compatibility

OS	Python	CUDA	Notes
Ubuntu 20.04	3.8+	11.x/12.x	Recommended
Ubuntu 22.04	3.10+	12.x	Latest
Debian 11	3.9+	11.x/12.x	Stable
CentOS 8	3.8+	11.x/12.x	Supported
Windows WSL2	3.8+	11.x/12.x	GPU passthrough required

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Troubleshooting

Common Issues

Missing System Libraries

# Error: cannot find -lGL
sudo apt-get install libgl1-mesa-dev

# Error: cannot find -lX11
sudo apt-get install libx11-dev

Python Package Conflicts

# Clear pip cache
pip cache purge

# Reinstall with no cache
pip install --no-cache-dir -r requirements.txt

CUDA Version Mismatch

# Check CUDA version
nvcc --version
python -c "import torch; print(torch.version.cuda)"

# Install matching cupy
pip install cupy-cuda11x  # or cupy-cuda12x

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Dependency Updates

Keeping Dependencies Current

# List outdated packages
pip list --outdated

# Update specific package
pip install --upgrade numpy

# Update all packages (careful!)
pip install --upgrade -r requirements.txt

Security Updates

# Check for security vulnerabilities
pip-audit

# Update to secure versions
pip install --upgrade package-name

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Resources

• CUDA Toolkit Downloads
• cuDNN Downloads
• OpenCV Documentation
• FFmpeg Documentation
• Protocol Buffers Guide

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Last Updated: 2025-01-13 Version: 1.0.0