I am working on a project involving thousands of images containing irregular-shaped adjacent cells. While I have extensive experience with Python, my image-processing expertise is limited. My objectives for this project are as follows:

1. Cell Identification: Detect and identify all individual cells within each image.
2. Cell Labeling: Assign a specific code or unique identifier to each detected cell and annotate the cell with this identifier at its center. Additionally, calculate and display the surface area of each cell.
3. Edge Cell Detection: Ignore cells located at the edges of the images that are incomplete or partially visible.

import cv2
import numpy as np

# Load the image
image = cv2.imread('cells.tif')

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

# Apply thresholding
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# Find contours
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Iterate through contours to identify shapes and calculate their areas
shapeCount=0
for contour in contours:
    # Approximate the contour to a simpler shape
    epsilon = 0.04 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)
    
    # Calculate the area
    area = cv2.contourArea(contour)

    # Draw the contour and label the shape on the original image
    cv2.drawContours(image, [contour], -1, (0, 255, 0), 2)
    cv2.putText(image, f"{shapeCount} -- {int(area)}", (contour[0][0][0] - 10, contour[0][0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
    shapeCount +=1

# Display the image with shapes identified
cv2.imshow('Shapes', image)
cv2.waitKey(0)
cv2.destroyAllWindows()