画像セグメンテーションをマスターする: デジタル時代でも伝統的な技術がどのように輝き続けるのか

# Thresholding Segmentation
def thresholding(img):
    # Convert image to grayscale
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    # Apply Otsu's thresholding
    _, thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)

    return thresh

# Edge Detection Segmentation
def edge_detection(img):
    # Convert image to grayscale
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    # Apply Gaussian blur
    gray = cv.GaussianBlur(gray, (3, 3), 0)

    # Calculate lower and upper thresholds for Canny edge detection
    sigma = 0.33
    v = np.median(gray)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))

    # Apply Canny edge detection
    edges = cv.Canny(gray, lower, upper)

    # Dilate the edges to fill gaps
    kernel = np.ones((5, 5), np.uint8)
    dilated_edges = cv.dilate(edges, kernel, iterations=2)

    # Clean the edges using morphological opening
    cleaned_edges = cv.morphologyEx(dilated_edges, cv.MORPH_OPEN, kernel, iterations=1)

    # Find connected components and filter out small components
    num_labels, labels, stats, _ = cv.connectedComponentsWithStats(
        cleaned_edges, connectivity=8
    )
    min_size = 500
    filtered_mask = np.zeros_like(cleaned_edges)
    for i in range(1, num_labels):
        if stats[i, cv.CC_STAT_AREA] >= min_size:
            filtered_mask[labels == i] = 255

    # Find contours of the filtered mask
    contours, _ = cv.findContours(
        filtered_mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE
    )

    # Create a filled mask using the contours
    filled_mask = np.zeros_like(gray)
    cv.drawContours(filled_mask, contours, -1, (255), thickness=cv.FILLED)

    # Perform morphological closing to fill holes
    final_filled_image = cv.morphologyEx(
        filled_mask, cv.MORPH_CLOSE, kernel, iterations=2
    )

    # Dilate the final filled image to smooth the edges
    final_filled_image = cv.dilate(final_filled_image, kernel, iterations=1)

    return final_filled_image

# Region-Based Segmentation
def region_based(img):
    # Convert image to grayscale
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    # Apply Otsu's thresholding
    _, thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)

    # Apply morphological opening to remove noise
    kernel = np.ones((3, 3), np.uint8)
    opening = cv.morphologyEx(thresh, cv.MORPH_OPEN, kernel, iterations=2)

    # Dilate the opening to get the background
    sure_bg = cv.dilate(opening, kernel, iterations=3)

    # Calculate the distance transform
    dist_transform = cv.distanceTransform(opening, cv.DIST_L2, 5)

    # Threshold the distance transform to get the foreground
    _, sure_fg = cv.threshold(dist_transform, 0.2 * dist_transform.max(), 255, 0)
    sure_fg = np.uint8(sure_fg)

    # Find the unknown region
    unknown = cv.subtract(sure_bg, sure_fg)

    # Label the markers for watershed algorithm
    _, markers = cv.connectedComponents(sure_fg)
    markers = markers + 1
    markers[unknown == 255] = 0

    # Apply watershed algorithm
    markers = cv.watershed(img, markers)

    # Create a mask for the segmented region
    mask = np.zeros_like(gray, dtype=np.uint8)
    mask[markers == 1] = 255

    return mask

# Clustering Segmentation
def clustering(img):
    # Convert image to grayscale
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    # Reshape the image
    Z = gray.reshape((-1, 3))
    Z = np.float32(Z)

    # Define the criteria for k-means clustering
    criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)

    # Set the number of clusters
    K = 2

    # Perform k-means clustering
    _, label, center = cv.kmeans(Z, K, None, criteria, 10, cv.KMEANS_RANDOM_CENTERS)

    # Convert the center values to uint8
    center = np.uint8(center)

    # Reshape the result
    res = center[label.flatten()]
    res = res.reshape((gray.shape))

    # Apply thresholding to the result
    _, res = cv.threshold(res, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)

    return res

def calculate_f1_score(ground_image, segmented_image):
    ground_image = ground_image.flatten()
    segmented_image = segmented_image.flatten()
    return f1_score(ground_image, segmented_image, average="weighted")