93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
#
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# Digital Image Processing HW01 assignment demo file
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#
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#
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# author: Christos Choutouridis <cchoutou@ece.auth.gr>
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# date: 29/04/2025
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#
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try:
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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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 to install.")
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exit(1)
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from hist_utils import calculate_hist_of_img
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from hist_modif import perform_hist_eq, perform_hist_matching
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# Define filenames
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input_filename = "input_img.jpg"
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ref_filename = "ref_img.jpg"
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# Load images
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input_img = Image.open(input_filename).convert("L")
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ref_img = Image.open(ref_filename).convert("L")
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# Convert to numpy arrays in [0,1]
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input_array = np.array(input_img).astype(float) / 255.0
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ref_array = np.array(ref_img).astype(float) / 255.0
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# Create output directory
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os.makedirs("demo_outputs", exist_ok=True)
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def plot_comparison(input_array, output_array, title, filename):
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"""
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Create a 2x2 plot: input image, output image, input histogram, output histogram.
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Save it to filename.
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"""
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fig, axes = plt.subplots(2, 2, figsize=(10, 8))
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# Plot input image
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axes[0, 0].imshow(input_array, cmap="gray", vmin=0, vmax=1)
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axes[0, 0].set_title("Input Image")
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axes[0, 0].axis("off")
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# Plot output image
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axes[0, 1].imshow(output_array, cmap="gray", vmin=0, vmax=1)
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axes[0, 1].set_title("Output Image")
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axes[0, 1].axis("off")
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# Plot input histogram
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input_hist = calculate_hist_of_img(input_array, return_normalized=True)
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axes[1, 0].bar(list(input_hist.keys()), list(input_hist.values()), width=0.01)
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axes[1, 0].set_title("Input Histogram")
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# Plot output histogram
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output_hist = calculate_hist_of_img(output_array, return_normalized=True)
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axes[1, 1].bar(list(output_hist.keys()), list(output_hist.values()), width=0.01)
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axes[1, 1].set_title("Output Histogram")
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# Set overall title
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fig.suptitle(title, fontsize=16)
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# Adjust layout
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plt.tight_layout(rect=[0, 0, 1, 0.95])
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# Save
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plt.savefig(filename)
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plt.close()
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# Modes to test
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modes = ["greedy", "non-greedy", "post-disturbance"]
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# Run equalization
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for mode in modes:
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print("Perform histogram equalization in mode: ", mode)
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equalized_img = perform_hist_eq(input_array, mode)
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out_filename = f"demo_outputs/equalization_{mode}.png"
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plot_comparison(input_array, equalized_img, f"Histogram Equalization ({mode})", out_filename)
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# Run matching
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for mode in modes:
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print("Perform histogram matching in mode: ", mode)
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matched_img = perform_hist_matching(input_array, ref_array, mode)
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out_filename = f"demo_outputs/matching_{mode}.png"
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plot_comparison(input_array, matched_img, f"Histogram Matching ({mode})", out_filename)
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