import cv2
import numpy as np
import matplotlib.pyplot as plt

# Read the image
image = cv2.imread("image.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Perform connected components analysis
num_labels, labels_im = cv2.connectedComponents(gray)

# Create a copy of the original image for drawing
result_image = image.copy()

# Perform corner detection for each connected component
for label in range(1, num_labels):  # Start from 1 to skip the background
    # Create a mask for the current component
    component_mask = np.uint8(labels_im == label)

    # Apply the mask to the grayscale image
    component_gray = cv2.bitwise_and(gray, gray, mask=component_mask)

    # Perform corner detection on the component
    corners = cv2.goodFeaturesToTrack(component_gray, 1, 0.01, 10)

    M = cv2.moments(component_mask)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    if corners is not None:
        corners = np.int0(corners)
        for corner in corners:
            x, y = corner.ravel()
            cv2.arrowedLine(result_image, (cX, cY), (x, y), (0, 255, 0), 3)

# Display the result
plt.figure(figsize=(12, 6))
plt.subplot(121), plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), plt.title(
    "Original Image"
)
plt.subplot(122), plt.imshow(cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)), plt.title(
    "Corners Detected"
)
plt.tight_layout()
plt.show()