Create a Python script (monochrome_graph.py) with the following code:

import cv2
import numpy as np
import matplotlib.pyplot as plt

def create_color_mask(image, lower_color, upper_color):
    hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    return cv2.inRange(hsv_image, lower_color, upper_color)

def main():
    # Read the input colored graph image
    input_image = cv2.imread('colored_graph.png')

    # Define color ranges for thresholding
    color_ranges = [
        (np.array([0, 100, 100]), np.array([10, 255, 255])),   # Red
        (np.array([35, 100, 100]), np.array([70, 255, 255])),  # Green
        (np.array([100, 100, 100]), np.array([130, 255, 255])) # Blue
    ]

    # Line styles for each color
    line_styles = ['-', '--', '-.']

    plt.figure()
    for i, (lower_color, upper_color) in enumerate(color_ranges):
        # Create a binary mask for the current color
        mask = create_color_mask(input_image, lower_color, upper_color)

        # Apply edge detection
        edges = cv2.Canny(mask, 50, 150)

        # Detect lines using HoughLinesP
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)

        if lines is not None:
            for line in lines:
                x1, y1, x2, y2 = line[0]
                plt.plot([x1, x2], [y1, y2], line_styles[i % len(line_styles)])

    # Display the monochrome graph
    plt.gca().invert_yaxis()
    plt.axis('off')
    plt.show()

if __name__ == '__main__':
    main()