Practical Segmentation Project

import cv2
import numpy as np

def count_and_measure_objects(image_path):
    """
    Segments an image and counts distinct objects,
    reporting their size and position.
    """
    image = cv2.imread(image_path)
    if image is None:
        print("Could not load image")
        return

    h, w = image.shape[:2]
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (7, 7), 0)

    # Segment
    _, binary = cv2.threshold(blurred, 0, 255,
                              cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    kernel = np.ones((5, 5), np.uint8)
    cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
    cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_CLOSE, kernel)

    contours, _ = cv2.findContours(
        cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
    )

    print(f"=== Object Analysis: {image_path} ===")
    result = image.copy()
    objects = []

    for i, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if area < 500:
            continue
        x, y, cw, ch = cv2.boundingRect(contour)
        cx = x + cw // 2
        cy = y + ch // 2
        perimeter = cv2.arcLength(contour, True)
        objects.append({"id": i, "area": area, "x": cx, "y": cy, "w": cw, "h": ch})
        cv2.rectangle(result, (x,y), (x+cw,y+ch), (0,255,0), 2)
        cv2.putText(result, f"#{len(objects)}", (x, y-5),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1)

    print(f"Objects found: {len(objects)}")
    for obj in objects:
        print(f"  Object {obj['id']}: area={obj['area']:.0f}px, centre=({obj['x']},{obj['y']})")

    cv2.imwrite("objects_counted.jpg", result)
    return objects

count_and_measure_objects("your_photo.jpg")