electrochromic_characterization

Electrochromic Characterization using Video Analysis

Repository for extraction of reflectance curves of electrochromic materials reducing the effect of gamma correction using computer vision techniques and LEGO color calibration patches.

🎯 Overview

This project provides tools for analyzing electrochromic materials using video recordings and standardized LEGO color patches. The system includes:

Interactive LEGO patch selector for precise region selection
Gamma correction optimization for improved color accuracy
Automated video analysis with ROI mask generation
Reflectance curve extraction with temporal analysis

🛠️ Installation

Quick Start with uv (Recommended)

# Clone the repository
git clone git@github.com:pgalantec/electrochromic_characterization.git
cd electrochromic_characterization

# Setup environment (installs uv if needed)
make init

# Activate environment
source .venv/bin/activate

Alternative Installation (Standard Python)

# Clone the repository
git clone git@github.com:pgalantec/electrochromic_characterization.git
cd electrochromic_characterization

# Create virtual environment
make create_env

# Activate environment
source env/bin/activate

Available Commands

make help           # Show all available commands
make init           # Setup environment with uv (fast)
make create_env     # Setup environment with standard venv
make clean          # Remove all virtual environments

📁 Project Structure

electrochromic_characterization/
├── assets/                     # Logo images
├── dataset/                    # Video files (.mp4, .avi)
├── output/                     # Experiment results
├── src/                        # Source code
│   ├── lego_app_v4.py         # Interactive configuration app
│   ├── experiment.py          # Analysis pipeline
│   ├── opt_gamma.py           # Gamma optimization
│   └── utils/                 # Utility functions
├── params.yaml                # Experiment configuration
├── Makefile                   # Development commands
└── README.md

🚀 Usage

Step 1: Prepare Your Data

Place your video files in the dataset/ directory
Supported formats: .mp4, .avi, .mov

Step 2: Configure Experiments (Interactive Method - Recommended)

Launch the interactive LEGO patch selector:

# Using uv environment
make run_config_app_uv

# Or using standard environment
make run_config_app

The application will open in your browser at http://localhost:8050 and provides:

📹 Video Selection: Choose from available videos in dataset
🧱 LEGO Patch Selection: Interactive selection of 4 color patches (black, dark grey, clear grey, white)
🎯 ROI Definition: Draw region of interest for analysis
⚡ Gamma Calibration: Enable/disable gamma correction
💾 Configuration Export: Automatic generation of params.yaml

Step 3: Configure Experiments (Manual Method)

Alternatively, you can manually edit params.yaml:

videos:
  - video_path: "dataset/your_video.mp4"
    exp_name: "your_experiment_name"
    calibration: false                    # Enable gamma correction
    frame_dtype: Y                       # Color space: Y/gray/L
    patches: [                           # LEGO patch coordinates [x1,y1], [x2,y2]
        [[20, 1200], [100, 1380]],      # Black patch
        [[20, 950], [100, 1150]],       # Dark grey patch  
        [[20, 660], [100, 880]],        # Clear grey patch
        [[20, 400], [100, 600]]         # White patch
    ]

lego_srgb:                              # Nominal LEGO sRGB values
  black: [18, 18, 21]
  dark_grey: [98, 101, 102]  
  clear_grey: [160, 163, 164]
  white: [244, 238, 228]

Step 4: Run Analysis

Execute the experiment analysis:

# Using uv environment
make run_experiment_uv

# Or using standard environment  
make run_experiment

The analysis pipeline will:

🎨 Extract color patches from the first frame
⚡ Optimize gamma correction (if enabled)
🎭 Generate ROI mask automatically or load existing
📊 Analyze temporal changes throughout the video
📈 Generate reflectance curves and statistics

📊 Output Results

Results are stored in output/{video_name}/{experiment_name}/:

output/ExpCalY-OptGamma/
├── config.json                 # Experiment configuration
├── patches.png                 # Visualization of selected patches
├── Mask.png                   # ROI binary mask
├── gamma_linearization.jpg    # Gamma correction plot
├── r2_optimization.jpg        # R² optimization curve
├── reflectance_curves.png     # Main results visualization
├── statistics.txt             # Numerical analysis
└── temporal_analysis/         # Frame-by-frame data

Key Output Files:

reflectance_curves.png: Main visualization showing temporal evolution
statistics.txt: Numerical summary of the analysis
config.json: Complete experiment configuration for reproducibility
gamma_linearization.jpg: Gamma correction validation plots
Mask.png: ROI mask used for analysis

🔧 Development Tools

# Code formatting
make format

# Code linting  
make lint

# Clean all environments
make clean

# Show help
make help

📝 LEGO Color Patches

The system uses 4 standardized LEGO color patches for calibration:

Patch	Color	Nominal sRGB	Purpose
🖤	Black	[18, 18, 21]	Dark reference
⚫	Dark Grey	[98, 101, 102]	Low-mid reference
⚪	Clear Grey	[160, 163, 164]	Mid-high reference
⬜	White	[244, 238, 228]	Bright reference

Selection Order: Always select patches in this order for consistent results.

🎨 Interactive Configuration Features

The LEGO patch selector application provides:

🖱️ Click-and-drag selection: Draw rectangles around each LEGO patch
🎯 ROI drawing tools: Rectangle and freehand selection for analysis region
📊 Real-time preview: See your selections as you make them
⚡ Configuration validation: Automatic verification of patch order and completeness
💾 One-click export: Generate params.yaml automatically
🔄 Reset functionality: Start over easily if needed

🚀 Advanced Features

Gamma Optimization

Automatically finds optimal gamma value for color linearization
Generates R² optimization curves
Validates correction effectiveness

Automated ROI Detection

Intelligent region of interest detection
Fallback to manual mask loading
Temporal stability validation

Multi-format Support

Various video formats (.mp4, .avi, .mov)
Different color spaces (Y, gray, L*)
Flexible resolution handling

🆘 Troubleshooting

Common Issues:

Video not found: Ensure video files are in dataset/ directory
Permission errors: Check file permissions and virtual environment activation
Memory issues: Use smaller video files or reduce resolution
LEGO patch detection fails: Ensure good lighting and clear patch visibility

Getting Help:

# Check environment
make help

# Verify installation
python -c "import dash; print('Dash installed successfully')"

# Check video files
ls dataset/

👨‍💻 Contributors

Pablo Galán - Tecnalia Research & Innovation
Artzai Picón - Tecnalia Research & Innovation
Jon Velasco - BCMaterials

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Citation

If you use this work in your research, please cite:

@software{electrochromic_characterization,
  title={Electrochromic Characterization using Video Analysis},
  author={Galán, Pablo and Picón, Artzai and Velasco, Jon},
  year={2024},
  institution={Tecnalia Research \& Innovation, BCMaterials}
}

🎯 Quick Reference

Task	Command
Setup	`make init`
Configure	`make run_config_app_uv`
Analyze	`make run_experiment_uv`
Help	`make help`
Clean	`make clean`

Happy analyzing! 🎬✨

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