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1
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import numpy as np
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2
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from PIL import Image
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3
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from image_processing import find_overlap
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4
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from gamma_correction import adjust_gamma_with_gradient
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5
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def gradient_gamma_correction_overlap(img1, img2, overlap_width, start_gamma=1.0, end_gamma=0.5):
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img1_overlap = img1.crop((img1.width - overlap_width, 0, img1.width, img1.height)).convert('RGB')
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img2_overlap = img2.crop((0, 0, overlap_width, img2.height)).convert('RGB')
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9
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img1_overlap_corrected = adjust_gamma_with_gradient(np.array(img1_overlap), start_gamma, end_gamma)
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img2_overlap_corrected = adjust_gamma_with_gradient(np.array(img2_overlap), end_gamma, start_gamma)
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12
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img1.paste(Image.fromarray(img1_overlap_corrected), (img1.width - overlap_width, 0))
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img2.paste(Image.fromarray(img2_overlap_corrected), (0, 0))
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15
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return img1, img2
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def main():
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image_left_path = 'ImageLeft0.png' # Replace with your image path
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image_right_path = 'ImageRight0.png' # Replace with your image path
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image_left = Image.open(image_left_path)
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image_right = Image.open(image_right_path)
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overlap_width = find_overlap(image_left, image_right)
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image_left_corrected, image_right_corrected = gradient_gamma_correction_overlap(
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image_left, image_right, overlap_width
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)
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28
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image_left_corrected.save('imageleft1.png')
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image_right_corrected.save('imageright1.png')
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31
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32
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if __name__ == "__main__":
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33
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main()
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