Update app.py

app.py

@@ -10,114 +10,122 @@ from datetime import datetime
 
 def apply_color_transformation(image, transform_type):
     """Apply different color transformations to the image"""
-    if len(image.shape) == 3:
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    
-    if transform_type == "Original":
-        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    elif transform_type == "Grayscale":
-        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    elif transform_type == "Binary":
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
-        return binary
-    elif transform_type == "CLAHE":
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
-        return clahe.apply(gray)
-    return image
+    try:
+        if len(image.shape) == 3:
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        
+        if transform_type == "Original":
+            return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        elif transform_type == "Grayscale":
+            return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        elif transform_type == "Binary":
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+            return binary
+        elif transform_type == "CLAHE":
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
+            return clahe.apply(gray)
+        return image
+    except Exception as e:
+        print(f"Error in apply_color_transformation: {e}")
+        return None
 
 def process_image(image, transform_type):
     """Process uploaded image and extract cell features"""
-    if image is None:
-        return None, None, None, None
-    
-    # Store original image for color transformations
-    original_image = image.copy()
-    
-    # Process image as before
-    if len(image.shape) == 3:
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    
-    # Basic preprocessing
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
-    enhanced = clahe.apply(gray)
-    blurred = cv2.medianBlur(enhanced, 5)
-    
-    # [Rest of the processing code remains the same until visualization]
-    
-    # Create enhanced visualization with timestamp
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    vis_img = image.copy()
-    contours = measure.find_contours(markers, 0.5)
-    
-    # Draw contours and cell IDs with improved visibility
-    for contour in contours:
-        coords = contour.astype(int)
-        cv2.drawContours(vis_img, [coords], -1, (0,255,0), 2)  # Thicker lines
-    
-    for region in measure.regionprops(markers):
-        if region.area >= 50:
-            y, x = region.centroid
-            # Add white background for better text visibility
-            cv2.putText(vis_img, str(region.label), 
-                       (int(x), int(y)),
-                       cv2.FONT_HERSHEY_SIMPLEX, 
-                       0.5, (255,255,255), 2)  # White outline
-            cv2.putText(vis_img, str(region.label), 
-                       (int(x), int(y)),
-                       cv2.FONT_HERSHEY_SIMPLEX, 
-                       0.5, (0,0,255), 1)  # Red text
-    
-    # Add timestamp and cell count
-    cv2.putText(vis_img, f"Analyzed: {timestamp}", 
-                (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 
-                0.7, (255,255,255), 2)
-    
-    # Create summary plots with improved styling
-    plt.style.use('seaborn')
-    fig, axes = plt.subplots(2, 2, figsize=(15, 12))
-    fig.suptitle('Cell Analysis Results', fontsize=16, y=0.95)
-    
-    df = pd.DataFrame(features)
-    if not df.empty:
-        # Distribution plots
-        df['area'].hist(ax=axes[0,0], bins=20, color='skyblue', edgecolor='black')
-        axes[0,0].set_title('Cell Size Distribution')
-        axes[0,0].set_xlabel('Area')
-        axes[0,0].set_ylabel('Count')
+    try:
+        if image is None:
+            return None, None, None, None
         
-        df['circularity'].hist(ax=axes[0,1], bins=20, color='lightgreen', edgecolor='black')
-        axes[0,1].set_title('Circularity Distribution')
-        axes[0,1].set_xlabel('Circularity')
-        axes[0,1].set_ylabel('Count')
+        # Store original image for color transformations
+        original_image = image.copy()
         
-        # Scatter plots
-        axes[1,0].scatter(df['circularity'], df['mean_intensity'], 
-                         alpha=0.6, c='purple')
-        axes[1,0].set_title('Circularity vs Intensity')
-        axes[1,0].set_xlabel('Circularity')
-        axes[1,0].set_ylabel('Mean Intensity')
+        # Process image as before
+        if len(image.shape) == 3:
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
         
-        # Add box plot
-        df.boxplot(column=['area', 'circularity'], ax=axes[1,1])
-        axes[1,1].set_title('Feature Distributions')
-    else:
-        for ax in axes.flat:
-            ax.text(0.5, 0.5, 'No cells detected', ha='center', va='center')
-    
-    plt.tight_layout()
-    
-    # Apply color transformation to original image
-    transformed_image = apply_color_transformation(original_image, transform_type)
-    
-    return (
-        cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB),
-        transformed_image,
-        fig,
-        df
-    )
+        # Basic preprocessing
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
+        enhanced = clahe.apply(gray)
+        blurred = cv2.medianBlur(enhanced, 5)
+        
+        # [Rest of the processing code remains the same until visualization]
+        
+        # Create enhanced visualization with timestamp
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        vis_img = image.copy()
+        contours = measure.find_contours(markers, 0.5)
+        
+        # Draw contours and cell IDs with improved visibility
+        for contour in contours:
+            coords = contour.astype(int)
+            cv2.drawContours(vis_img, [coords], -1, (0,255,0), 2)  # Thicker lines
+        
+        for region in measure.regionprops(markers):
+            if region.area >= 50:
+                y, x = region.centroid
+                # Add white background for better text visibility
+                cv2.putText(vis_img, str(region.label), 
+                           (int(x), int(y)),
+                           cv2.FONT_HERSHEY_SIMPLEX, 
+                           0.5, (255,255,255), 2)  # White outline
+                cv2.putText(vis_img, str(region.label), 
+                           (int(x), int(y)),
+                           cv2.FONT_HERSHEY_SIMPLEX, 
+                           0.5, (0,0,255), 1)  # Red text
+        
+        # Add timestamp and cell count
+        cv2.putText(vis_img, f"Analyzed: {timestamp}", 
+                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 
+                    0.7, (255,255,255), 2)
+        
+        # Create summary plots with improved styling
+        plt.style.use('seaborn')
+        fig, axes = plt.subplots(2, 2, figsize=(15, 12))
+        fig.suptitle('Cell Analysis Results', fontsize=16, y=0.95)
+        
+        df = pd.DataFrame(features)
+        if not df.empty:
+            # Distribution plots
+            df['area'].hist(ax=axes[0,0], bins=20, color='skyblue', edgecolor='black')
+            axes[0,0].set_title('Cell Size Distribution')
+            axes[0,0].set_xlabel('Area')
+            axes[0,0].set_ylabel('Count')
+            
+            df['circularity'].hist(ax=axes[0,1], bins=20, color='lightgreen', edgecolor='black')
+            axes[0,1].set_title('Circularity Distribution')
+            axes[0,1].set_xlabel('Circularity')
+            axes[0,1].set_ylabel('Count')
+            
+            # Scatter plots
+            axes[1,0].scatter(df['circularity'], df['mean_intensity'], 
+                             alpha=0.6, c='purple')
+            axes[1,0].set_title('Circularity vs Intensity')
+            axes[1,0].set_xlabel('Circularity')
+            axes[1,0].set_ylabel('Mean Intensity')
+            
+            # Add box plot
+            df.boxplot(column=['area', 'circularity'], ax=axes[1,1])
+            axes[1,1].set_title('Feature Distributions')
+        else:
+            for ax in axes.flat:
+                ax.text(0.5, 0.5, 'No cells detected', ha='center', va='center')
+        
+        plt.tight_layout()
+        
+        # Apply color transformation to original image
+        transformed_image = apply_color_transformation(original_image, transform_type)
+        
+        return (
+            cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB),
+            transformed_image,
+            fig,
+            df
+        )
+    except Exception as e:
+        print(f"Error in process_image: {e}")
+        return None, None, None, None
 
 # Create enhanced Gradio interface
 with gr.Blocks(title="Advanced Cell Analysis Tool", theme=gr.themes.Soft()) as demo:
@@ -201,4 +209,7 @@ with gr.Blocks(title="Advanced Cell Analysis Tool", theme=gr.themes.Soft()) as d
 
 # Launch the demo
 if __name__ == "__main__":
-    demo.launch()
+    try:
+        demo.launch()
+    except Exception as e:
+        print(f"Error launching Gradio interface: {e}")