G21-2025

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---

> h1. Code

*config_reader.py*

<pre><code class="python">

#!/usr/bin/env python

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

# ˅

import json

import os

# ˄

class ConfigReader(object):

    # ˅

    CONFIG_FILENAME = "config.json"

    # ˄

    def __init__(self):

        self.__config = {}

        # ˅

        self._load_config()

        # ˄

    # ˅

    def _load_config(self):

        """Loads the configuration from the JSON file."""

        if not os.path.exists(self.CONFIG_FILENAME):

            print(f"Warning: {self.CONFIG_FILENAME} not found. Using defaults.")

            return

        with open(self.CONFIG_FILENAME, 'r') as f:

            self.__config = json.load(f)

    # ˄

    def getProjectedImageWidth(self):

        # ˅

        # The physical width of the image projected by ONE projector

        return float(self.__config.get("projected_image_width", 0))

        # ˄

    def getDistanceBetweenProjectors(self):

        # ˅

        # The physical distance between the lenses of the two projectors

        return float(self.__config.get("distance_between_projectors", 0))

        # ˄

    def getImageWidth(self):

        # ˅

        # The pixel width of the source image file

        return int(self.__config.get("image_width", 1920))

        # ˄

    def getImageHeight(self):

        # ˅

        # The pixel height of the source image file

        return int(self.__config.get("image_height", 1080))

        # ˄

    def getImageName(self):

        # ˅

        return self.__config.get("image_name", "")

        # ˄

    def getSide(self):

        # ˅

        # 'left' or 'right'

        return self.__config.get("side", "left").lower()

        # ˄

    def getGamma(self):

        # ˅

        # Usually 2.2 for standard displays

        return float(self.__config.get("gamma", 2.2))

        # ˄

</code></pre>

*config.json*

<pre><code class="python">

{

    "image_name": "right.jpg",

    "image_width": 1920,

    "image_height": 1080,

    "projected_image_width": 200,

    "distance_between_projectors": 150,

    "gamma": 3,

    "side": "right"

}

</code></pre>

*graph_gen _IND.py*

<pre><code class="python">

import numpy as np

import matplotlib.pyplot as plt

def generate_individual_graphs():

    # Input gradient (0 to 1)

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

    # The three specified Gamma values

    gamma_values = [0.5, 3, 50]

    for gamma in gamma_values:

        plt.figure(figsize=(8, 6))

        # 1. Reference Line (Linear - No Correction)

        # This is the 'Reference Curve'. It shows the output without blending/correction.

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

        # 2. Gamma Correction Curve

        # Logic: y = x^(1/gamma)

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

        # Plotting the curve

        plt.plot(x, y, color='blue', linewidth=3, label=f'Gamma Correction (γ={gamma})')

        # Decoration

        plt.title(f'Gamma Correction Curve: γ = {gamma}', fontsize=14)

        plt.xlabel('Input Pixel Intensity (0 to 1)', fontsize=12)

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

        plt.legend(loc='best')

        plt.grid(True, alpha=0.3)

        plt.xlim(0, 1.0)

        plt.ylim(0, 1.05)

        # Saving separate files for each gamma

        filename = f"gamma_curve_{gamma}.png"

        plt.savefig(filename)

        print(f"Generated graph: {filename}")

        # Close plot to start fresh for next gamma

        plt.close()

if __name__ == "__main__":

    generate_individual_graphs()

</code></pre>

*graph_gen.py*

<pre><code class="python">

import numpy as np

import matplotlib.pyplot as plt

def plot_gamma_curves():

    # Generate input gradient values from 0 to 1

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

    # Defined gamma values for comparison

    gamma_values = [0.5, 3, 50]

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

    # Reference Line (Linear - No Correction)

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

    # Blending logic: y = x^(1/gamma)

    for gamma in gamma_values:

        # Formula derived from the provided code

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

        # Plotting

        plt.plot(x, y, linewidth=2.5, label=f'Gamma = {gamma} (Exp: 1/{gamma} ≈ {1.0 / gamma:.2f})')

    # Graph Formatting

    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)

    # Save chart

    output_filename = 'gamma_comparison_graph.png'

    plt.savefig(output_filename)

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

    plt.show()

if __name__ == "__main__":

    plot_gamma_curves()

</code></pre>

*main_alpha_blender.py*

<pre><code class="python">

#!/usr/bin/env python

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

# ˅

import cv2

import numpy as np

from config_reader import ConfigReader

# ˄

class MainAlphaBlender(object):

    # ˅

    # ˄

    def __init__(self):

        self.__configReader = None

        # ˅

        self.__configReader = ConfigReader()

        # ˄

    # ˅

    def run(self):

        print("--- Starting Alpha Blend Process (Gamma Corrected) ---")

        # 1. Get Configuration

        image_name = self.__configReader.getImageName()

        side = self.__configReader.getSide()

        proj_width_phys = self.__configReader.getProjectedImageWidth()

        dist_phys = self.__configReader.getDistanceBetweenProjectors()

        gamma = self.__configReader.getGamma()

        # 2. Load Image

        img = cv2.imread(image_name, cv2.IMREAD_UNCHANGED)

        if img is None:

            print(f"Error: Could not load image '{image_name}'")

            return

        # Ensure image is BGRA

        if img.shape[2] == 3:

            img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)

        height, width = img.shape[:2]

        # 3. Calculate Overlap

        if proj_width_phys > 0:

            overlap_ratio = 1.0 - (dist_phys / proj_width_phys)

        else:

            overlap_ratio = 0.0

        if overlap_ratio <= 0:

            print("Warning: No overlap detected.")

            return

        overlap_pixels = int(width * overlap_ratio)

        print(f"Processing '{side}' | Overlap: {overlap_pixels} pixels | Gamma: {gamma}")

        # 4. Generate Mask

        # Create a linear ramp from 0 to 1

        linear_ramp = np.linspace(0, 1, overlap_pixels)

        # Determine the base curve direction BEFORE gamma

        if side == "left":

            # Left image fades OUT (1 -> 0) on the right side

            base_curve = 1.0 - linear_ramp

        elif side == "right":

            # Right image fades IN (0 -> 1) on the left side

            base_curve = linear_ramp

        else:

            print("Error: Side must be 'left' or 'right'")

            return

        # 5. Apply Gamma Correction

        # This "boosts" the midtones. 50% gray becomes ~73% gray.

        # This is crucial because 0.73^2.2 (projector gamma) + 0.73^2.2 = ~1.0 Light

        mask_curve = np.power(base_curve, 1 / gamma)

        # 6. Apply to Image

        alpha_channel = img[:, :, 3].astype(float) / 255.0

        mask_block = np.tile(mask_curve, (height, 1))

        if side == "left":

            # Apply to the right-most columns

            alpha_channel[:, width - overlap_pixels:] *= mask_block

        elif side == "right":

            # Apply to the left-most columns

            alpha_channel[:, :overlap_pixels] *= mask_block

        # 7. Save Output

        img[:, :, 3] = (alpha_channel * 255).astype(np.uint8)

        # Optional: Burn transparency into RGB for easier debugging/viewing

        # (This makes the transparent parts black, ensuring no 'ghosting' if alpha is ignored)

        for c in range(3):

            if side == "left":

                roi = img[:, :, c][:, width - overlap_pixels:]

                img[:, :, c][:, width - overlap_pixels:] = (roi * mask_block).astype(np.uint8)

            else:

                roi = img[:, :, c][:, :overlap_pixels]

                img[:, :, c][:, :overlap_pixels] = (roi * mask_block).astype(np.uint8)

        output_name = f"output_{side}.png"

        cv2.imwrite(output_name, img)

        print(f"Saved: {output_name}")

    # ˄

# ˅

if __name__ == "__main__":

    blender = MainAlphaBlender()

    blender.run()

# ˄

</code></pre>

*x_curve.py*

<pre><code class="python">

import numpy as np

import matplotlib.pyplot as plt

def plot_blending_mechanics_unique():

    # Setup - Use your specific project values here if you have them

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

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

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

    # 1. Linear Alpha (The "Wrong" Way)

    alpha_left_lin = 1.0 - x_normalized

    alpha_right_lin = x_normalized

    # 2. Gamma Corrected Alpha (Your Solution)

    gamma = 2.2  # Your specific gamma

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

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

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

    # Plot Gamma Corrected (Solid Colors) - The "Hero" of the graph

    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')

    # Plot Linear (Dashed) - The "Baseline"

    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)

    # Unique Touch: Shade the area to show "Light Energy"

    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)

    # Annotations specific to your analysis

    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'))

    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)

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

    print("Saved: custom_blending_curve.png")

if __name__ == "__main__":

    plot_blending_mechanics_unique()

</code></pre>