import numpy as np
import matplotlib.pyplot as plt
from skimage import io
from skimage.filters import threshold_otsu, gaussian
from skimage.morphology import closing, square
from skimage.measure import label, regionprops
from skimage.color import rgb2gray
import matplotlib.patches as patches
from sklearn.cluster import KMeans

def get_heights(filename: str):
    image = io.imread(filename)
    im = rgb2gray(image)
    binary_im = im > threshold_otsu(im)
    smooth_im = gaussian(binary_im.astype(float), sigma=1)
    morph_im = closing(smooth_im > 0.5, square(3))
    
    labeled = label(morph_im)
    bar_dims = [r.bbox for r in regionprops(labeled)]
    bar_dims.sort(key=lambda x: x[1])  # left to right
    
    bars = bar_dims[1:]  # skip logo
    bar_heights_raw = []
    print(len(bars))
    for bar in bars:
        top, left, bottom, right = bar
        effective_height = bottom - top  # use bounding box height directly
        bar_heights_raw.append(effective_height)

    # Cluster measured heights to 8 clusters representing discrete bar levels
    bar_heights_raw_np = np.array(bar_heights_raw).reshape(-1, 1)
    
    kmeans = KMeans(n_clusters=8, random_state=0).fit(bar_heights_raw_np)
    cluster_centers = np.sort(kmeans.cluster_centers_.flatten())
    
    # Assign each bar height to closest cluster center index (0 to 7)
    predicted_levels = []
    for h in bar_heights_raw:
        diffs = np.abs(cluster_centers - h)
        closest_cluster = np.argmin(diffs)
        predicted_levels.append(int(closest_cluster))
    

    print((predicted_levels))
    return predicted_levels