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LeafAreaCalc

LeafAreaCalc Logo

Table of Contents

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.

Sample Leaf Image

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.

GUI Screenshot

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
  1. Create and Activate Virtual Environment
python -m venv venv
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate
  1. Install Dependencies

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

pip install -r requirements.txt
  1. 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
  1. Select Image Directory
  • Click on the "Select Image Directory" button.
  • Choose the folder containing your leaf images.
  1. Configure Settings
  • Adjust processing parameters as needed.
  • Each parameter includes an explanation and default value.
  1. 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.
  1. 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.
  1. 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

  1. Make Your Changes Implement your feature or fix.

  2. Commit Your Changes

git commit -m "Add your descriptive commit message here"
  1. Push to Your Fork
git push origin feature/YourFeatureName
  1. 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.