light-distribution-analysis 0.2.0.0

Analyze the flux of images based on wavelength and frequency

Light-Distribution-Analysis

Overview

Light-Distribution-Analysis is a Python package designed for the analysis of light distribution in images. The package utilizes OpenCV, NumPy, and Matplotlib to enable in-depth yet simple analysis of the flux and power in images based on their wavelength and frequency representations.

Features

• Convert RGB images to wavelength and frequency representations.
• Calculate the flux and power of images.
• Save processed images in various forms, including original, wavelength, frequency, and power images.
• Calculate similarity scores based on flux and power.
• Plot power images.
• Export flux and power data to a CSV file for further analysis (feature planned).

Installation

To install Light-Distribution-Analysis, run the following command in your terminal:

pip install light-distribution-analysis

Usage

Comparing Flux of Two Images The package includes a function compare_flux that allows you to compare the flux of two images and obtain a similarity score.

Here's a simple example to demonstrate its usage:

1. Import the package:

import light_distribution_analysis as lda

2. Provide Paths to Images:

Provide the paths to the two images you want to compare.

image_path1 = "path/to/your/first/image.jpg"
image_path2 = "path/to/your/second/image.jpg"

3. Compare the Flux:

Use the compare_flux function to compare the flux of the two images.

similarity_score = lda.compare_flux(image_path1, image_path2)

4. Basic Image Processing

from light_distribution_analysis import process_single_image
process_single_image('path/to/image.jpg', 'output/directory/')

5. Flux Comparison

from light_distribution_analysis import compare_flux
similarity_score_flux = compare_flux('path/to/image1.jpg', 'path/to/image2.jpg')
print(f"Flux Similarity Score: {similarity_score_flux}")

6. Power Calculation and Comparison

from light_distribution_analysis import compare_power
similarity_score_power = compare_power('path/to/image1.jpg', 'path/to/image2.jpg')
print(f"Power Similarity Score: {similarity_score_power}")

Plotting Power Images The power images will be saved in the specified directory when you run compare_power.

Interpreting the Similarity Score:

A similarity score close to 1 indicates that the images are highly similar in terms of their flux, whereas a score close to 0 suggests they are dissimilar.