Update app.py
Browse files
app.py
CHANGED
@@ -24,11 +24,15 @@ def add_noise(image, password):
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def remove_noise(noisy_image, password):
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"""Attempt to remove noise from the image using the same password."""
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seed = hash_password(password) % (2**32)
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np.random.seed(seed) # Reset the seed to generate the same noise parameters
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mean = np.random.uniform(-0.05, 0.05)
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var = np.random.uniform(0.001, 0.01)
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image = noisy_image.convert('L')
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image_np = np.array(image)
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sigma_est = np.mean(estimate_sigma(image_np, multichannel=False))
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denoised_image = denoise_nl_means(image_np, h=1.15 * sigma_est, fast_mode=True,
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@@ -36,6 +40,7 @@ def remove_noise(noisy_image, password):
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denoised_image = (255 * denoised_image).astype(np.uint8) # Scale back to 0-255
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return Image.fromarray(denoised_image)
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# Define Gradio interface with tabs for adding and removing noise
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with gr.Blocks() as interface:
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gr.Markdown("### Image Noise Encryption and Decryption App")
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def remove_noise(noisy_image, password):
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"""Attempt to remove noise from the image using the same password."""
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# Check if the noisy_image is a numpy array and convert it to PIL Image if so
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if isinstance(noisy_image, np.ndarray):
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noisy_image = Image.fromarray(noisy_image.astype('uint8'))
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seed = hash_password(password) % (2**32)
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np.random.seed(seed) # Reset the seed to generate the same noise parameters
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mean = np.random.uniform(-0.05, 0.05)
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var = np.random.uniform(0.001, 0.01)
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image = noisy_image.convert('L') # Ensure image is in grayscale
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image_np = np.array(image)
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sigma_est = np.mean(estimate_sigma(image_np, multichannel=False))
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denoised_image = denoise_nl_means(image_np, h=1.15 * sigma_est, fast_mode=True,
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denoised_image = (255 * denoised_image).astype(np.uint8) # Scale back to 0-255
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return Image.fromarray(denoised_image)
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# Define Gradio interface with tabs for adding and removing noise
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with gr.Blocks() as interface:
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gr.Markdown("### Image Noise Encryption and Decryption App")
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