Update app.py

app.py

@@ -11,76 +11,54 @@ from PIL import Image
 print("Gradio version:", gr.__version__)
 
 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)
+    # Debugging: Print image properties
+    print(f"Uploaded image shape: {img.shape}, dtype: {img.dtype}")
+    print(f"Image min: {img.min()}, max: {img.max()}")
+
+    # Check if the image is grayscale
+    if len(img.shape) != 2:
+        raise ValueError("Uploaded image must be a grayscale image.")
+
+    # Ensure the image has 16-bit depth
+    if img.dtype != np.uint16:
+        raise ValueError("Uploaded image must be a 16-bit TIFF image.")
+
+    # Normalize to [0, 1]
+    img_norm = img.astype(np.float32) / 65535.0
+    print(f"Normalized image min: {img_norm.min()}, max: {img_norm.max()}")
+
+    # Wavelet decomposition and reconstruction
     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
+    img_recon = np.clip(img_recon, 0, 1)
+    print(f"Reconstructed image min: {img_recon.min()}, max: {img_recon.max()}")
 
-    # 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)
+    # CLAHE
+    img_clahe = exposure.equalize_adapthist(img_recon, clip_limit=0.02, 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)
+    # Gamma correction
+    img_gamma = exposure.adjust_gamma(img_clahe, gamma=0.9)
 
-    # 6. Edge-Preserving Sharpening (convert to BGR first)
+    # Convert to 8-bit
     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
+    print(f"Final image min: {img_gamma_8bit.min()}, max: {img_gamma_8bit.max()}")
 
-    # Convert enhanced image to PIL Image
-    enhanced_image = Image.fromarray(img_sharp)
+    # Convert to RGB for better viewing compatibility
+    img_rgb = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2RGB)
+    enhanced_image = Image.fromarray(img_rgb)
 
     # Create histogram plot
     plt.figure(figsize=(6, 4))
-    plt.hist(img_sharp.ravel(), bins=256, range=(0, 255), color='gray')
+    plt.hist(img_gamma_8bit.ravel(), bins=256, range=(0, 255), color='gray')
     plt.title('Enhanced Histogram')
     plt.xlabel('Pixel Intensity')
     plt.ylabel('Frequency')
@@ -93,6 +71,7 @@ def musica_enhancement(image):
 
     return enhanced_image, histogram
 
+
 # Define Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Musica Image Enhancement")