app.py

import cv2
import numpy as np

def watershed_segmentation(input_image):
    # Convert the image to grayscale
    gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
    
    # Apply adaptive thresholding
    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)

    # Morphological operations to remove small noise - use morphologyEx
    kernel = np.ones((3, 3), np.uint8)
    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
    
    # Identify sure background area
    sure_bg = cv2.dilate(opening, kernel, iterations=3)

    # Find sure foreground area using distance transform
    dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
    ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)

    # Find unknown region
    sure_fg = np.uint8(sure_fg)
    unknown = cv2.subtract(sure_bg, sure_fg)
    
    # Marker labeling
    ret, markers = cv2.connectedComponents(sure_fg)
    
    # Add one to all labels so that sure background is not 0, but 1
    markers = markers + 1
    
    # Mark the unknown region with zero
    markers[unknown == 255] = 0
    
    # Apply watershed
    cv2.watershed(input_image, markers)
    input_image[markers == -1] = [0, 0, 255]  # boundary region
    
    return input_image