explain the method used fot this code

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

# Read the image    
src = cv2.imread('/Users/abhi/Downloads/Zerodur/Sample 8_Fine.tif')
imgray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
# Blur the image to suppress artefacts
# in the binary image while preserving edges
median = cv2.medianBlur(imgray, 15)
# Do a global thresholding
ret, thresh = cv2.threshold(median, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Morphological operations to reduce noise
kernel = np.ones((5, 5), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=1)

# Copy image to show binary image (findContours manipulates source)
copy = closing.copy()

# Detect just external contours
contours, hierarchy = cv2.findContours(copy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
height, width, channels = src.shape

# Image for displaying contour
drawing = np.zeros((height, width, 3), dtype='uint8')

# Variable to store the largest circle contour
largest_circle_contour = None
largest_circle_area = 0

# Loop through contours to find the largest one
for contour in contours:
    # Check if the contour has at least 5 points
    if len(contour) >= 10:
        # Calculate the area of the contour
        area = cv2.contourArea(contour)
        
        # Calculate the circularity of the contour
        perimeter = cv2.arcLength(contour, True)
        circularity = 4 * np.pi * (area / (perimeter ** 2)) if perimeter > 0 else 0
        
        # Check if the contour is circular enough and has a larger area
        if circularity > 0.1 and area > largest_circle_area:  ########### Adjust circularity threshold as needed #############
            largest_circle_contour = contour
            largest_circle_area = area

# Draw the largest circle contour if found
if largest_circle_contour is not None:
    cv2.drawContours(drawing, [largest_circle_contour], 0, (255, 0, 0), 2)
    (x, y), radius = cv2.minEnclosingCircle(largest_circle_contour)
    center = (int(x), int(y))
    cv2.circle(drawing, center, int(radius), (0, 0, 255), 2)
    try:
        ellipse = cv2.fitEllipse(largest_circle_contour)
        # Check if the ellipse parameters are valid
        if ellipse[1][0] > 0 and ellipse[1][1] > 0:
            cv2.ellipse(drawing, ellipse, (0, 255, 0), 2)
        else:
            print("Invalid ellipse parameters:", ellipse)
    except Exception as e:
        print("Error fitting ellipse:", e)

# Convert images from BGR to RGB for matplotlib
src_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
median_rgb = cv2.cvtColor(median, cv2.COLOR_GRAY2RGB)
thresh_rgb = cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB)
opening_rgb = cv2.cvtColor(opening, cv2.COLOR_GRAY2RGB)
closing_rgb = cv2.cvtColor(closing, cv2.COLOR_GRAY2RGB)
drawing_rgb = cv2.cvtColor(drawing, cv2.COLOR_BGR2RGB)

print("Threshold value (Otsu):", ret)

# Plot the images using matplotlib
plt.figure(figsize=(15, 7.5))

plt.subplot(2, 4, 1)
plt.title('Original Image')
plt.imshow(src_rgb)
plt.axis('off')

plt.subplot(2, 4, 2)
plt.title('Median Blurred Image')
plt.imshow(median_rgb)
plt.axis('off')

plt.subplot(2, 4, 3)
plt.title('Binary Image')
plt.imshow(thresh_rgb, cmap='gray')
plt.axis('off')

plt.subplot(2, 4, 4)
plt.title('Largest Circle Contour')
plt.imshow(drawing_rgb)
plt.axis('off')

plt.tight_layout()
plt.show()