LeafAreaCalc

Table of Contents

• Introduction
• Features
• Installation
• Configuration
• Usage
  • Graphical User Interface (GUI)
  • Command-Line Interface (CLI)
• Logging
• Contributing

Introduction

LeafAreaCalc is a comprehensive tool designed for accurate leaf area classification from digital images. LeafAreaCalc provides an intuitive interface and powerful processing capabilities to streamline your leaf analysis workflow, whether you're a botanist, researcher, or hobbyist.

Features

• Adaptive Thresholding in RGB Space: Enhance leaf detection by excluding near-white non-leaf areas immediately after cropping.
• Configurable Cropping: Adjust cropping percentages to focus on specific regions of interest within your images.
• Contrast Adjustment: Optionally apply CLAHE (Contrast Limited Adaptive Histogram Equalization) to improve image contrast.
• Morphological Operations: Refine binary images using morphological closing with adjustable kernel sizes and iterations.
• Contour Detection: Accurately identify and count leaf contours, calculating their respective areas.
• Mean RGB Calculation: Compute average RGB values within detected leaf areas for color analysis.
• Graphical User Interface (GUI): User-friendly interface for configuring settings, selecting directories, and monitoring processing.
• Logging: Comprehensive logging with adjustable verbosity levels to track processing steps and debug issues.
• Image Preview: Preview images directly within the GUI for easy verification.
• Configuration Management: Save and reset configurations with ease, ensuring reproducible results.

Installation

Prerequisites

• Python 3.6+: Ensure you have Python installed. You can download it from python.org.
• Virtual Environment (Recommended): It's advisable to use a virtual environment to manage dependencies.

Steps

1. Clone the Repository

git clone https://github.com/yourusername/LeafAreaCalc.git
cd LeafAreaCalc

3. Create and Activate Virtual Environment

python -m venv venv
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate

3. Install Dependencies

Once your virtual environment is activated, install the necessary Python packages by running the following command:

pip install -r requirements.txt

4. Verify Installation

Ensure that all dependencies are installed correctly by running:

python src/gui_leaf_area_classification.py

Configuration

LeafAreaCalc uses a configuration file (config/config.ini) to manage processing parameters. This file allows you to customize various aspects of the image processing pipeline.

Configuration File Structure

[DEFAULT]
# Directory containing leaf images
image_directory = C:/Path/To/Your/Images

# Processing parameters
# Minimum leaf area in mm² to consider
area_threshold = 10

# Skip contrast adjustment (True or False)
skip_contrast_adjustment = False

# Save intermediate images (True or False)
img_debug = False

# Cropping percentages
# Percentage to crop from the left
crop_left = 20

# Percentage to crop from the right
crop_right = 3

# Percentage to crop from the top
crop_top = 3

# Percentage to crop from the bottom
crop_bottom = 3

# Specific filename to process (leave empty to process all images)
filename =

# Adaptive Thresholding Parameters
# Enable or disable adaptive thresholding (True or False)
adaptive_threshold = True

# Window size for adaptive thresholding (Must be an odd integer)
adaptive_window_size = 15

# Constant subtracted from mean in adaptive thresholding
adaptive_C = 2

# RGB value above which pixels are considered near-white
color_threshold = 240

# Morphological Closing Parameters
# Kernel size for morphological closing (width,height)
kernel_size = (5, 5)

# Logging
# Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
log_level = DEBUG

Editing Configuration

Manual Editing: You can manually edit config/config.ini using any text editor to adjust parameters. GUI Configuration: The GUI provides options to modify these settings with explanations and default values.

Parameter Explanations

• image_directory: Path to the folder containing your leaf images.
• area_threshold: Minimum area (in mm²) for a contour to be considered a valid leaf.
• skip_contrast_adjustment: If set to True, skips the CLAHE contrast adjustment step.
• img_debug: If True, saves intermediate images for debugging purposes.
• crop_left, crop_right, crop_top, crop_bottom: Percentages to crop from each side of the image to focus on the region of interest.
• filename: Specify a particular image file to process. Leave empty to process all images in the directory.
• adaptive_threshold: Enables adaptive thresholding in RGB space to exclude near-white areas.
• adaptive_window_size: Window size for calculating the local mean in adaptive thresholding. Must be an odd integer.
• adaptive_C: Constant subtracted from the mean in adaptive thresholding to fine-tune thresholding.
• color_threshold: RGB value above which pixels are considered near-white and excluded from leaf detection.
• kernel_size: Size of the structuring element for morphological closing. Must be a tuple of two integers, e.g., (5,5).
• log_level: Sets the verbosity of logging output. Options include DEBUG, INFO, WARNING, ERROR, CRITICAL.

Usage

LeafAreaCalc offers both a Graphical User Interface (GUI) and a Command-Line Interface (CLI) for flexibility.

Graphical User Interface (GUI)

1. Launch the GUI

python src/gui_leaf_area_classification.py

2. Select Image Directory

• Click on the "Select Image Directory" button.
• Choose the folder containing your leaf images.

3. Configure Settings

• Adjust processing parameters as needed.
• Each parameter includes an explanation and default value.

4. Execute Processing

• Click on the "Execute Processing" button to start analyzing the images.
• Monitor the progress and logs in the enlarged output panel at the bottom.

5. View Results

• Processed images with annotations will be saved in the result_img/ folder within your image directory.
• Intermediate images (if enabled) will be saved in the intermediate_img/ folder.

6. Stop Processing

• Click on the "Stop Processing" button to terminate an ongoing analysis.

Command-Line Interface (CLI)

For users who prefer command-line operations, LeafAreaCalc can be executed directly:

python src/leaf_area_classification.py

Note: Ensure that all necessary parameters are set in the config/config.ini file before running the CLI.

Logging

LeafAreaCalc provides detailed logging to help monitor processing and debug issues.

Adjusting Logging Level

• Via GUI: Use the "Logging Level" dropdown menu to select the desired verbosity.
• Via Configuration File: Edit the log_level parameter in config/config.ini to one of the following:
  • DEBUG
  • INFO
  • WARNING
  • ERROR
  • CRITICAL

Log Output

Logs are displayed in the GUI's log output panel and saved to leaf_analysis.log in the image directory.

Contributing

Contributions are welcome! If you'd like to contribute to LeafAreaCalc, please follow these steps:

1. Fork the Repository Click the "Fork" button at the top-right corner of the repository page.

2. Create a New Branch

git checkout -b feature/YourFeatureName

2. Make Your Changes Implement your feature or fix.

3. Commit Your Changes

git commit -m "Add your descriptive commit message here"

4. Push to Your Fork

git push origin feature/YourFeatureName

5. Create a Pull Request Navigate to your forked repository and click "New Pull Request".

Please ensure that your code follows the project's coding standards and includes appropriate documentation.