not

import cv2
import numpy as np

# Function to calculate the midpoint of a line segment
def midpoint(p1, p2):
    return ((p1[0] + p2[0]) // 2, (p1[1] + p2[1]) // 2)

# Load the image
image = cv2.imread('tray_image.jpg')

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

# Apply GaussianBlur to reduce noise
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Apply thresholding to get a binary image
_, binary = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY_INV)

# Find contours in the binary image
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Iterate through contours and extract midpoints of each edge
for contour in contours:
    # Approximate the contour to get polygonal curves
    epsilon = 0.01 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)
    
    # Draw the contour on the original image (for visualization)
    cv2.drawContours(image, [approx], -1, (0, 255, 0), 2)
    
    # Iterate through the vertices of the polygon
    for i in range(len(approx)):
        # Get the current point and the next point (looping around)
        p1 = tuple(approx[i][0])
        p2 = tuple(approx[(i + 1) % len(approx)][0])
        
        # Calculate the midpoint
        mid = midpoint(p1, p2)
        
        # Draw the midpoint on the image
        cv2.circle(image, mid, 5, (255, 0, 0), -1)
        
        # Print the coordinates of the midpoint
        print(f"Midpoint between {p1} and {p2}: {mid}")

# Display the result
cv2.imshow('Detected Objects with Midpoints', image)
cv2.waitKey(0)
cv2.destroyAllWindows()