Contour-Based Extraction

import cv2
import numpy as np
import os

def extract_objects(image_path, output_dir="objects", min_area=1000):
    """Extract all significant objects from an image as individual files."""
    os.makedirs(output_dir, exist_ok=True)

    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    _, binary = cv2.threshold(blurred, 0, 255,
                              cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

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

    print(f"Found {len(contours)} contours")
    extracted = 0

    for i, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if area < min_area:
            continue

        # Get bounding rectangle
        x, y, w, h = cv2.boundingRect(contour)

        # Create object mask from contour
        mask = np.zeros_like(gray)
        cv2.drawContours(mask, [contour], -1, 255, -1)  # Filled contour

        # Extract with transparent background (BGRA)
        object_bgra = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
        object_bgra[:, :, 3] = mask  # Alpha channel = mask

        # Crop to bounding rect
        crop = object_bgra[y:y+h, x:x+w]
        cv2.imwrite(f"{output_dir}/object_{extracted:03d}.png", crop)

        print(f"  Object {extracted}: area={area:.0f}, size={w}x{h}px")
        extracted += 1

    print(f"Extracted {extracted} objects to '{output_dir}/'")

extract_objects("photo.jpg")