Create app.py

app.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import pywt
+from skimage import exposure
+import gradio as gr
+from io import BytesIO
+from PIL import Image
+
+def musica_enhancement(image):
+    """
+    Enhances a 16-bit TIFF image using wavelet decomposition, CLAHE, gamma correction,
+    and edge-preserving sharpening.
+
+    Args:
+        image (PIL.Image): Uploaded image.
+
+    Returns:
+        enhanced_image (PIL.Image): Enhanced image.
+        histogram (PIL.Image): Histogram of the enhanced image.
+    """
+    # Convert PIL Image to numpy array
+    img = np.array(image)
+
+    # Handle different image modes
+    if image.mode == 'I;16':
+        # 16-bit image
+        img = img.astype(np.uint16)
+        img_norm = img.astype(np.float32) / 65535.0
+    elif image.mode == 'I':
+        # 32-bit integer image
+        img = img.astype(np.int32)
+        img_norm = img.astype(np.float32) / (2**32 - 1)
+    elif image.mode in ['RGB', 'RGBA']:
+        # Convert to grayscale if it's a color image
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        img_norm = img.astype(np.float32) / 255.0
+    elif image.mode == 'L':
+        # 8-bit grayscale
+        img_norm = img.astype(np.float32) / 255.0
+    else:
+        raise ValueError(f"Unsupported image mode: {image.mode}")
+
+    # 1. Multi-Scale Decomposition (3-level wavelet transform)
+    coeffs = pywt.wavedec2(img_norm, 'bior1.3', level=3)
+    cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs
+
+    # 2. Adaptive Processing per Sub-band
+    cD1 = pywt.threshold(cD1, 0.05 * np.max(cD1), mode='soft')
+    cD2 = pywt.threshold(cD2, 0.07 * np.max(cD2), mode='soft')
+    cH1 = cH1 * 1.2
+    cV1 = cV1 * 1.2
+
+    # 3. Reconstruct Enhanced Image with clipping
+    coeffs_enhanced = [cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1)]
+    img_recon = pywt.waverec2(coeffs_enhanced, 'bior1.3')
+    img_recon = np.clip(img_recon, 0, 1)  # Critical fix
+
+    # 4. Adaptive CLAHE 
+    entropy = -np.sum(img_recon * np.log2(img_recon + 1e-7))  # Now safe
+    clip_limit = 0.02 if entropy > 7 else 0.05
+    img_clahe = exposure.equalize_adapthist(img_recon, clip_limit=clip_limit, kernel_size=64)
+
+    # 5. Gamma correction
+    p5, p95 = np.percentile(img_clahe, (5, 95))
+    gamma = 0.7 if (p95 - p5) < 0.3 else 0.9
+    img_gamma = exposure.adjust_gamma(img_clahe, gamma=gamma)
+
+    # 6. Edge-Preserving Sharpening (convert to BGR first)
+    img_gamma_8bit = (img_gamma * 255).astype(np.uint8)
+    img_bgr = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2BGR)  # Convert to 3-channel
+    img_sharp = cv2.detailEnhance(img_bgr, sigma_s=12, sigma_r=0.15)
+    img_sharp = cv2.cvtColor(img_sharp, cv2.COLOR_BGR2GRAY)  # Convert back to grayscale
+
+    # Convert enhanced image to PIL Image
+    enhanced_image = Image.fromarray(img_sharp)
+
+    # Create histogram plot
+    plt.figure(figsize=(6, 4))
+    plt.hist(img_sharp.ravel(), bins=256, range=(0, 255), color='gray')
+    plt.title('Enhanced Histogram')
+    plt.xlabel('Pixel Intensity')
+    plt.ylabel('Frequency')
+    plt.tight_layout()
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    plt.close()
+    buf.seek(0)
+    histogram = Image.open(buf)
+
+    return enhanced_image, histogram
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Musica Image Enhancement")
+    gr.Markdown(
+        """
+        Upload a 16-bit TIFF image to enhance it using wavelet decomposition, CLAHE,
+        gamma correction, and edge-preserving sharpening.
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                type="pil",
+                label="Upload 16-bit TIFF Image",
+                source="upload",
+                tool="editor",
+            )
+            run_button = gr.Button("Enhance Image")
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Enhanced Image")
+            output_hist = gr.Image(type="pil", label="Enhanced Histogram")
+    
+    run_button.click(
+        fn=musica_enhancement,
+        inputs=input_image,
+        outputs=[output_image, output_hist],
+    )
+
+if __name__ == "__main__":
+    demo.launch()