import numpy as np
import cv2
from skimage.morphology import skeletonize

def crack_metrics(mask: np.ndarray):
    # mask: [H,W] 0/1
    area_px = int(mask.sum())

    # largest connected component
    num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask.astype(np.uint8), connectivity=8)
    largest_cc = 0
    if num_labels > 1:
        # ignore background label 0
        largest_cc = int(stats[1:, cv2.CC_STAT_AREA].max())

    # length estimate via skeletonization
    skel = skeletonize(mask.astype(bool))
    length_px = int(skel.sum())

    return {
        "area_px": area_px,
        "length_px": length_px,
        "largest_component_px": largest_cc,
    }

def severity_from_metrics(m):
    # Tune these thresholds on your validation set
    area = m["area_px"]
    length = m["length_px"]
    largest = m["largest_component_px"]

    score = 0
    if area > 1500: score += 1
    if area > 6000: score += 1
    if length > 600: score += 1
    if length > 2000: score += 1
    if largest > 2500: score += 1

    if score >= 4:
        return "High"
    if score >= 2:
        return "Medium"
    return "Low"