My Machine Vision Development Setup

After years of building machine vision systems for manufacturing, I’ve settled on a set of tools and workflows that maximize productivity. Here’s what’s in my toolkit and why.

🎯 Vision Libraries & Frameworks

Halcon (Primary)

When I use it: Production vision systems requiring robustness and speed

Why I love it:

• Extremely fast and reliable operators
• Excellent calibration and 3D tools
• Great documentation with example code
• HDevelop IDE is perfect for rapid prototyping

Trade-offs:

• Expensive licensing (but worth it for production)
• Proprietary ecosystem
• Learning curve for beginners

Typical use case: Vision-guided robotics, high-precision measurement, geometric analysis

Halcon

When I use it: Deep learning integration and complex inspection tasks

Why it’s useful:

• HDevelop makes DL deployment straightforward
• Good integration with cameras and hardware
• Powerful for defect detection
• Industry-standard reliability

Trade-offs:

• Also expensive
• Less flexible than open-source alternatives
• Vendor lock-in

Typical use case: Defect detection, anomaly detection, complex pattern matching

OpenCV (Python/C++)

When I use it: Prototyping, research, custom algorithms

Why I still use it:

• Free and open-source
• Massive community and resources
• Complete control over implementation
• Great for learning and experimentation

Trade-offs:

• More coding required vs commercial tools
• Performance optimization needed for production
• Less out-of-the-box features

Typical use case: Algorithm development, proof-of-concepts, custom processing

💻 Development Environment

IDEs

• Visual Studio 2022 - Primary C++ development for production systems
• VS Code - Python, configuration files, quick edits
• HDevelop - Halcon algorithm development and prototyping

Version Control

• Git + GitHub - All code versioned, even small scripts
• GitHub Actions - CI/CD pipelines for automated testing and deployment

Testing Frameworks

• C# NetFramework Unit Test - Unit testing for vision algorithms
• pytest (Python) - Testing data processing and utilities
• Manual test sequences - Hardware-in-the-loop validation

🔧 Supporting Tools

Image Annotation & Dataset Management

• LabelImg - Quick bounding box annotations
• CVAT - More complex annotation tasks
• Custom Python scripts - Data augmentation and preprocessing

Performance Profiling

• Visual Studio Profiler - Finding bottlenecks in C++ code
• CUDA Profiler - GPU performance analysis
• Python cProfile - Python optimization

Communication

• Microsoft Teams - Daily team communication
• Email - Formal communications and documentation
• In-person reviews - Critical for hardware-integrated systems

🎨 My Typical Workflow

1. Prototype in HDevelop/Python

Start with quick experiments to validate approach:

# Quick prototype in Python
import cv2
import numpy as np

# Test algorithm with sample images
img = cv2.imread('test_image.png')
# ... rapid iteration ...

2. Validate with Real Data

Test on actual production images:

• Edge cases
• Lighting variations
• Part variations
• Different camera positions

3. Implement in Production Language (C##)

Once algorithm is proven

4. Create Automated Tests

Cover critical functionality

5. Deploy via CI/CD

• Automated builds on commit
• Unit tests must pass
• Integration tests on test station
• Staged rollout to production

📊 Data Management Strategy

Image Storage

• Local SSD - Active development images
• Network share - Shared datasets and archives
• Cloud backup - Critical datasets

Dataset Organization

datasets/
├── raw/              # Original unprocessed images
├── annotated/        # Labeled data
├── train/            # Training sets
├── validation/       # Validation sets
└── test/             # Test sets

Version Control for Data

• DVC (Data Version Control) - For large datasets
• Git LFS - For smaller image sets
• Metadata in Git - Dataset descriptions and split info

🎓 Learning Resources I Actually Use

Documentation

• Official Halcon reference manual (seriously, read it)
• Stack Overflow (of course)

Books

• Machine Vision Handbook - Industry perspective

Communities

• Company internal channels - Domain knowledge

💡 Lessons Learned

1. Start simple - Get basic version working first, then optimize
2. Test on real data early - Synthetic data lies
3. Version everything - Code, configs, even test images
4. Automate testing - Manual testing doesn’t scale
5. Document assumptions - Future you will forget why
6. Profile before optimizing - Measure, don’t guess
7. Backup critical data - Lost datasets = lost time

🔮 Tools I’m Exploring

• ONNX Runtime - Model deployment standardization
• DVC - Better data version control

📫 What’s Your Setup?

I’m always curious about what tools other vision engineers use. If you have suggestions or use different tools, I’d love to hear about them!

What tools do you rely on for machine vision development? Feel free to reach out via email or LinkedIn!

This post reflects my personal experiences and opinions. Tool choices should be based on your specific requirements, budget, and constraints.