G21-2025-Final Project

#!/usr/bin/env python

# -*- coding: utf-8 -*-

##

# @file gamma_curve_plot.py

# @brief Generates and visualizes gamma correction curves for comparison.

#

# @details

# This script creates a graph comparing multiple gamma correction curves

# against a linear reference (no gamma correction).

# The generated plot is intended for documentation and wiki usage

# to visually explain how different gamma values affect intensity mapping.

#

# Gamma correction is calculated using the formula:

# @f[

#   y = x^{(1/\gamma)}

# @f]

#

# The resulting graph is saved as a PNG image file.

import numpy as np

import matplotlib.pyplot as plt

##

# @brief Plots gamma correction curves and saves the graph as an image.

#

# @details

# This function generates a normalized input gradient from 0 to 1

# and applies multiple gamma correction transformations to it.

# The corrected curves are plotted alongside a linear reference curve

# to highlight the differences in brightness response.

#

# The output graph is saved locally and displayed on screen.

#

# @note

# This visualization is intended for explanatory purposes,

#

# @return None

def plot_gamma_curves():

    ##

    # @brief Generate input gradient values.

    #

    # @details

    # Creates a smooth range of normalized intensity values

    # from 0.0 to 1.0 for curve plotting.

    x = np.linspace(0, 1, 500)

    ##

    # @brief List of gamma values to compare.

    #

    # @details

    # These gamma values demonstrate different correction strengths,

    # from low (brightening) to very high (strong compression).

    gamma_values = [0.5, 3, 50]

    ##

    # @brief Initialize matplotlib figure.

    plt.figure(figsize=(10, 6))

    ##

    # @brief Plot linear reference curve (no gamma correction).

    #

    # @details

    # This dashed line represents a direct linear mapping (y = x)

    # and serves as a baseline for comparison.

    plt.plot(x, x, 'k--', label='Linear (No Correction)', alpha=0.5)

    ##

    # @brief Plot gamma-corrected curves.

    #

    # @details

    # Applies the gamma correction formula y = x^(1/gamma)

    # and plots each resulting curve.

    for gamma in gamma_values:

        y = np.power(x, 1.0 / gamma)

        plt.plot(

            x,

            y,

            linewidth=2.5,

            label=f'Gamma = {gamma} (Exp: 1/{gamma} ≈ {1.0 / gamma:.2f})'

        )

    ##

    # @brief Configure graph appearance and labels.

    plt.title('Gamma Correction Curves Comparison', fontsize=14)

    plt.xlabel('Input Value (Gradient 0 to 1)', fontsize=12)

    plt.ylabel('Corrected Output Value', fontsize=12)

    plt.legend()

    plt.grid(True, alpha=0.3)

    plt.ylim(0, 1.05)

    plt.xlim(0, 1.0)

    ##

    # @brief Save the generated graph to a file.

    #

    # @details

    # The image is saved in PNG format for easy embedding in documentation.

    output_filename = 'gamma_comparison_graph.png'

    plt.savefig(output_filename)

    print(f"Graph successfully saved as: {output_filename}")

    ##

    # @brief Display the graph window.

    plt.show()

##

# @brief Program entry point.

#

# @details

# Executes the gamma curve plotting function when the script

# is run directly.

if __name__ == "__main__":

    plot_gamma_curves()