Update app.py

app.py

@@ -1,21 +1,65 @@
-import gradio as gr
 import cv2
 import numpy as np
-from ultralytics import YOLO
+import gradio as gr
+import pywt
+from skimage import exposure
+
+def musica_enhancement(img):
+    # Convert from RGB to grayscale if needed
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    
+    # Normalize to [0, 1]
+    img_norm = img.astype(np.float32) / 255.0
+
+    # 1. Wavelet Decomposition
+    coeffs = pywt.wavedec2(img_norm, 'bior1.3', level=3)
+    cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs
+
+    # 2. Frequency Band Processing
+    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. Reconstruction 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)
 
-model = YOLO('best.pt')
+    # 4. Adaptive CLAHE
+    entropy = -np.sum(img_recon * np.log2(img_recon + 1e-7))
+    clip_limit = 0.02 if entropy > 7 else 0.05
+    img_clahe = exposure.equalize_adapthist(img_recon, clip_limit=clip_limit, kernel_size=64)
 
-def predict_image(img): 
-        
-  model = YOLO('best.pt')
-  result = model.predict(img,conf=0.2)
-  res_plotted = result[0].plot()
-  #cv2.imshow( res_plotted)
-  return res_plotted
+    # 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 Enhancement
+    img_gamma_8bit = (img_gamma * 255).astype(np.uint8)
+    img_bgr = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2BGR)
+    img_sharp = cv2.detailEnhance(img_bgr, sigma_s=12, sigma_r=0.15)
+    img_sharp = cv2.cvtColor(img_sharp, cv2.COLOR_BGR2RGB)
+    
+    return img_sharp
 
+def process_image(input_img):
+    enhanced_img = musica_enhancement(input_img)
+    return input_img, enhanced_img
 
-image = gr.inputs.Image()
+# Gradio Interface
+demo = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(label="Upload X-ray"),
+    outputs=[
+        gr.Image(label="Original Image"), 
+        gr.Image(label="Enhanced Image")
+    ],
+    examples=[["./sample_xray.jpg"]],
+    title="X-ray Enhancement (MUSICA®-Style)",
+    description="Medical X-ray enhancement using multi-scale wavelet processing and adaptive contrast adjustment"
+)
 
-label = gr.outputs.Label('ok')
-gr.Interface(fn=predict_image, inputs=image, outputs=image).launch(debug='True')
+demo.launch()