Choke Coverage Analysis

Overview

This project is designed to analyze the percentage of white pixels within a specific circular region of an image. It is useful for image analysis tasks that require detecting and quantifying white areas within a defined part of an image, such as evaluating coverage in various types of samples or analyzing light reflection.

The project is split into three main stages:

1. Image Processing Setup: Load and preprocess images to grayscale format and define the circular region of interest.
2. Image Analysis: Analyze individual images by applying a threshold to detect white pixels and calculate the percentage of white pixels in the defined region.
3. Batch Processing and Analysis: Process multiple images, calculate individual percentages of white pixels, and generate an average percentage as well as a visualization of results.

Project Structure

• image_processing_setup.py: Handles loading and preprocessing of images, including converting them to grayscale and defining the circular region of interest.
• image_analysis.py: Analyzes individual images by identifying white pixels within a circular region and calculating the percentage.
• batch_processing_analysis.py: Processes multiple images, calculates percentages, and visualizes the results.

How to Use

1. Clone the repository:

   git clone <repository_url>
   cd choke_coverage_analysis

2. Install the required Python libraries using pip:

   pip install -r requirements.txt

   Alternatively, you can install them manually:

   pip install numpy pillow matplotlib

3. Place your images in the root directory and update the image_paths list in batch_processing_analysis.py with the correct file paths.

4. Run the batch processing script to analyze all images:

   python batch_processing_analysis.py

Example Output

After running the batch analysis, you will see:

• The percentage of white pixels for each image.
• The average percentage of white pixels across all images.
• A linear plot visualizing the rate of white pixels across the images.
• A visual representation of the measurement

Analysis Results

Here are some example outputs of the image processing:

Original Image, Thresholded Image, and Visual Representation

Requirements

• Python 3.x
• Libraries: numpy, pillow, matplotlib

Contact

Author: Kimia K

Feel free to reach out if you have any questions or suggestions for improvements!