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d3560c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | 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']
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