Iam trying to detect Tile corners and edges but there is an issue of light reflection intensity as well as the shadows.Since iam using image processing technique so i want to sort it effectively like this and my code works on some images of tiles but doesnt work on many of them.
[Results on processing1](https://i.sstatic.net/YFhdHjdx.png)
Results on processing2(https://i.sstatic.net/82XqXEPT.png)
[Results on processing3](https://i.sstatic.net/2cPGR0M6.jpg)
I have shared my code below along with desired results i want to achieve like.

Code

# Import necessary libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
import os

# Function to calculate the distance between two points
def point_distance(x1, y1, x2, y2):
    return np.sqrt((x1 - x2)**2 + (y1 - y2)**2)

# Function to check if a point is on a line segment
def is_point_on_line(x, y, x1, y1, x2, y2, tolerance=10):
    dist1 = point_distance(x, y, x1, y1)
    dist2 = point_distance(x, y, x2, y2)
    line_length = point_distance(x1, y1, x2, y2)

    # Check if the sum of distances from the point to the two ends is approximately equal to the line length
    return abs(dist1 + dist2 - line_length) < tolerance

# Function to find intersections and ensure the point is on both line segments
def find_intersections(lines):
    intersections = []
    for i, line1 in enumerate(lines):
        for line2 in lines[i+1:]:
            x1, y1, x2, y2 = line1[0]
            x3, y3, x4, y4 = line2[0]

            # Calculate the intersection point using line equations
            denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
            if denom == 0:  # Lines are parallel
                continue
            intersect_x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / denom
            intersect_y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / denom

            # Check if the intersection point is on both line segments
            if is_point_on_line(intersect_x, intersect_y, x1, y1, x2, y2) and \
               is_point_on_line(intersect_x, intersect_y, x3, y3, x4, y4):
                intersections.append((int(intersect_x), int(intersect_y)))

    return intersections

# Load images from the Tiles folder
tiles_folder = "hard"  # Adjust this path if necessary
output_folder = "Processed_Tiles"  # Name of the output folder
os.makedirs(output_folder, exist_ok=True)  # Create output folder if it doesn't exist
images = [f for f in os.listdir(tiles_folder) if f.endswith(('.png', '.jpg', '.jpeg'))]

# Process each image
for image_filename in images:
    image_path = os.path.join(tiles_folder, image_filename)
    image = cv2.imread(image_path)

    # Convert image to RGB for visualization with matplotlib
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Apply Gaussian Blur to reduce noise and improve edge detection
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)

    # Use Canny edge detection
    edges = cv2.Canny(blurred, 50, 200)

    # Apply dilation to thicken edges for better line detection
    dilated = cv2.dilate(edges, None, iterations=1)

    # Detect lines using Hough Line Transform
    lines = cv2.HoughLinesP(dilated, 1, np.pi/180, threshold=100, minLineLength=50, maxLineGap=20)

    # Draw the detected lines on the image
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(image_rgb, (x1, y1), (x2, y2), (0, 255, 0), 3)  # Thicker lines

        # Find intersections and draw them
        intersections = find_intersections(lines)
        for point in intersections:
            cv2.circle(image_rgb, point, 5, (255, 0, 0), -1)  # Larger and clearer circles for intersections

    # Convert image back to BGR format for saving
    image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)

    # Save the processed image to the output folder
    output_path = os.path.join(output_folder, f"processed_{image_filename}")
    cv2.imwrite(output_path, image_bgr)

    # Optionally display the result using matplotlib
    plt.figure(figsize=(10, 10))
    plt.imshow(image_rgb)
    plt.title(f'Detected Lines and Intersections in {image_filename}')
    plt.axis('off')
    plt.show()

print("Processing complete. Processed images saved in:", output_folder)

Desired Result:

Desired Output:

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