from PIL import Image

def load_and_crop_image(path="Carson_map.png", crop_box=(15, 15, 1000, 950)):
    img = Image.open(path).convert("RGB")
    cropped_img = img.crop(crop_box)
    return cropped_img


from sklearn.cluster import KMeans
import numpy as np

def cluster_image(cropped_img, n_clusters=6):
    img_array = np.array(cropped_img)
    pixels = img_array.reshape(-1, 3)
    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(pixels)
    labels = kmeans.labels_.reshape(img_array.shape[:2])
    return labels


from collections import Counter
import numpy as np

def build_parcel_map(clustered_img, grid_size=20):
    height, width = clustered_img.shape
    n_rows = height // grid_size
    n_cols = width // grid_size
    parcel_map = np.zeros((n_rows, n_cols), dtype=int)

    for i in range(n_rows):
        for j in range(n_cols):
            patch = clustered_img[i*grid_size:(i+1)*grid_size, j*grid_size:(j+1)*grid_size].flatten()
            dominant = Counter(patch).most_common(1)[0][0]
            parcel_map[i, j] = dominant

    return parcel_map, n_rows, n_cols


import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.colors import ListedColormap

def plot_parcel_map(parcel_map, cluster_labels, land_colors, title="25×25 Land Parcels by Land Type"):
    cmap = ListedColormap(land_colors)
    plt.figure(figsize=(10, 8))
    plt.imshow(parcel_map, cmap=cmap, origin='upper')
    legend_patches = [mpatches.Patch(color=land_colors[i], label=cluster_labels[i]) for i in cluster_labels]
    plt.legend(handles=legend_patches, bbox_to_anchor=(1.05, 1), loc='upper left', title="Land Type")
    plt.title(title)
    plt.axis('off')
    plt.tight_layout()
    plt.show()

def plot_parcel_map_to_file(parcel_map, cluster_labels, land_colors, save_path="clustered_map.png", title="25×25 Land Parcels by Land Type"):
    cmap = ListedColormap(land_colors)
    fig, ax = plt.subplots(figsize=(10, 8))
    cax = ax.imshow(parcel_map, cmap=cmap, origin='upper')
    legend_patches = [mpatches.Patch(color=land_colors[i], label=cluster_labels[i]) for i in cluster_labels]
    ax.legend(handles=legend_patches, bbox_to_anchor=(1.05, 1), loc='upper left', title="Land Type")
    ax.set_title(title)
    ax.axis('off')
    plt.tight_layout()
    plt.savefig(save_path)
    plt.close(fig)


def get_cluster_labels():
    return {
        0: 'Pasture/Desert',
        1: 'Productive Grass',
        2: 'Pasture/Desert',
        3: 'Riparian Sensitive Zone',
        4: 'Rocky Area',
        5: 'Water'
    }


def get_land_colors():
    return ['#dfb867', '#a0ca76', '#dfb867', '#5b8558', '#888888', '#3a75a8']