50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
#
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# Demo 3b: One-step recursive normalized cuts with Ncut metric
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#
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# author: Christos Choutouridis <cchoutou@ece.auth.gr>
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# date: 06/07/2025
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#
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try:
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from scipy.io import loadmat
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import matplotlib.pyplot as plt
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import numpy as np
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#Project requirements
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from image_to_graph import image_to_graph
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from normalized_cuts import n_cuts, calculate_n_cut_value
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except ImportError as e:
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print("Missing package:", e)
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print("Run: pip install -r requirements.txt")
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exit(1)
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def plot_split(image, labels, title, fname):
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M, N, _ = image.shape
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segmented = labels.reshape(M, N)
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plt.imshow(segmented, cmap='tab10', vmin=0, vmax=1)
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plt.title(title)
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plt.axis('off')
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plt.tight_layout()
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plt.savefig(fname)
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print(f"Saved: {fname}")
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plt.close()
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def run_demo3b():
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data = loadmat("dip_hw_3.mat")
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for name in ["d2a", "d2b"]:
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img = data[name]
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print(f"\n=== Image {name} ===")
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affinity = image_to_graph(img)
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labels = n_cuts(affinity, k=2)
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ncut_val = calculate_n_cut_value(affinity, labels)
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print(f" Ncut value: {ncut_val:.4f}")
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plot_split(img, labels, f"{name} - one step n_cuts (Ncut={ncut_val:.4f})", f"plots/demo3b_{name}_ncut.png")
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if __name__ == '__main__':
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run_demo3b()
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