G21-2025-Final Project

#!/usr/bin/env python

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

##

# @file blending_mechanics_visualization.py

# @brief Visualizes spatial alpha blending mechanics with and without gamma correction.

#

# @details

# This program generates a detailed visualization of projector edge blending

# in an overlap region. It compares:

# - Linear alpha blending (baseline / incorrect approach)

# - Gamma-corrected alpha blending (final solution)

#

# The graph highlights how gamma correction compensates for nonlinear

# projector luminance response, preventing visible dark seams

# in the overlap region.

#

# The output image is designed for inclusion in technical documentation

# or a project wiki to explain blending mechanics visually.

import numpy as np

import matplotlib.pyplot as plt

##

# @brief Plots spatial alpha blending behavior in an overlap region.

#

# @details

# This function simulates the intensity distribution of two overlapping

# projectors across a horizontal overlap zone.

#

# Two blending strategies are visualized:

# 1. Linear alpha blending (y = x)

# 2. Gamma-corrected alpha blending (y = x^(1/gamma))

#

# The resulting plot illustrates how gamma correction increases effective

# light energy in the overlap region, correcting the dark band artifact

# caused by naive linear blending.

#

# The shaded regions emphasize the difference in projected light energy

# between linear and gamma-corrected approaches.

#

# @note

# This visualization is analytical and explanatory in nature.

# It does not represent raw pixel blending, but projected luminance behavior.

#

# @return None

def plot_blending_mechanics_unique():

    ##

    # @brief Define overlap region configuration.

    #

    # @details

    # overlap_pixels represents the horizontal width (in pixels)

    # where two projectors overlap.

    overlap_pixels = 256  # Example: Change this to your actual overlap width

    ##

    # @brief Generate pixel coordinate ranges.

    #

    # @details

    # x_pixels is used for spatial positioning,

    # while x_normalized maps the same region into [0, 1]

    # for alpha calculations.

    x_pixels = np.linspace(0, overlap_pixels, 500)

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

    ##

    # @brief Compute linear alpha blending weights (baseline).

    #

    # @details

    # These represent a naive linear fade-out / fade-in

    # without accounting for projector gamma characteristics.

    alpha_left_lin = 1.0 - x_normalized

    alpha_right_lin = x_normalized

    ##

    # @brief Compute gamma-corrected alpha blending weights.

    #

    # @details

    # Gamma correction compensates for nonlinear luminance response

    # of projectors.

    #

    # Formula:

    # @f[

    #   \alpha_{corrected} = \alpha^{(1/\gamma)}

    # @f]

    gamma = 2.2  # Projector gamma value

    alpha_left_corr = np.power(alpha_left_lin, 1.0 / gamma)

    alpha_right_corr = np.power(alpha_right_lin, 1.0 / gamma)

    ##

    # @brief Initialize plotting canvas.

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

    ##

    # @brief Plot gamma-corrected projector outputs.

    #

    # @details

    # These curves represent the final projected light intensities

    # after gamma compensation.

    plt.plot(x_pixels, alpha_left_corr, 'b-', linewidth=3, label='Left Projector Output')

    plt.plot(x_pixels, alpha_right_corr, 'r-', linewidth=3, label='Right Projector Output')

    ##

    # @brief Plot linear blending reference curves.

    #

    # @details

    # Dashed lines illustrate the baseline case where no gamma

    # correction is applied.

    plt.plot(x_pixels, alpha_left_lin, 'k:', linewidth=1.5, alpha=0.5,

             label='Linear Reference (No Correction)')

    plt.plot(x_pixels, alpha_right_lin, 'k:', linewidth=1.5, alpha=0.5)

    ##

    # @brief Visualize additional light energy introduced by gamma correction.

    #

    # @details

    # The shaded regions represent the difference between

    # linear and gamma-corrected intensity distributions,

    # emphasizing how gamma correction boosts luminance

    # in the overlap region.

    plt.fill_between(x_pixels, alpha_left_corr, alpha_left_lin,

                     color='blue', alpha=0.1)

    plt.fill_between(x_pixels, alpha_right_corr, alpha_right_lin,

                     color='red', alpha=0.1)

    ##

    # @brief Annotate gamma compensation effect.

    #

    # @details

    # Highlights the central region where gamma correction

    # mitigates the dark band artifact.

    plt.text(overlap_pixels / 2, 0.8,

             'Gamma Boost Region\n(Corrects Dark Band)',

             horizontalalignment='center',

             fontsize=10,

             bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))

    ##

    # @brief Configure graph labels and appearance.

    plt.title(f'Spatial Intensity Distribution (Overlap: {overlap_pixels}px)',

              fontsize=14, fontweight='bold')

    plt.xlabel('Pixel Position in Overlap Zone', fontsize=12)

    plt.ylabel('Projected Light Intensity (0.0 - 1.0)', fontsize=12)

    plt.legend()

    plt.grid(True, alpha=0.3)

    ##

    # @brief Save visualization to file.

    #

    # @details

    # The generated image is saved as a PNG file for

    # easy embedding into documentation or wiki pages.

    plt.savefig('custom_blending_curve.png', dpi=100)

    print("Saved: custom_blending_curve.png")

##

# @brief Program entry point.

#

# @details

# Executes the blending mechanics visualization when the script

# is run directly.

if __name__ == "__main__":

    plot_blending_mechanics_unique()