Update app.py

app.py

@@ -69,7 +69,7 @@ class DicomAnalyzer:
                 dtype=cv2.CV_8U
             )
             if len(normalized.shape) == 2:
-                normalized = cv2.cvtColor(normalized, cv2.COLOR_GRAY2RGB)
+                normalized = cv2.cvtColor(normalized, cv2.COLOR_GRAY2BGR)
             return normalized
         except Exception as e:
             print(f"Error normalizing image: {str(e)}")
@@ -121,64 +121,64 @@ class DicomAnalyzer:
             return self.display_image
 
     def analyze_roi(self, evt: gr.SelectData):
-    try:
-        if self.current_image is None:
-            return None, "No image loaded"
-
-        # Get the clicked coordinates
-        clicked_x = evt.index[0]
-        clicked_y = evt.index[1]
-
-        # Adjust for pan and zoom
-        x = int((clicked_x + self.pan_x) / self.zoom_factor)
-        y = int((clicked_y + self.pan_y) / self.zoom_factor)
-
-        # Ensure coordinates are within image bounds
-        height, width = self.current_image.shape[:2]
-        x = max(0, min(x, width-1))
-        y = max(0, min(y, height-1))
-
-        # Create mask for ROI
-        mask = np.zeros_like(self.current_image, dtype=np.uint8)
-        y_indices, x_indices = np.ogrid[:self.current_image.shape[0], :self.current_image.shape[1]]
-        radius = self.circle_diameter / 2
-        distance_from_center = np.sqrt(
-            (x_indices - x)**2 + (y_indices - y)**2
-        )
-        mask[distance_from_center <= radius] = 1
-
-        # Calculate statistics
-        roi_pixels = self.current_image[mask == 1]
-        pixel_spacing = float(self.dicom_data.PixelSpacing[0])
-        area_pixels = np.sum(mask)
-        area_mm2 = area_pixels * (pixel_spacing ** 2)
-        mean = np.mean(roi_pixels)
-        stddev = np.std(roi_pixels)
-        min_val = np.min(roi_pixels)
-        max_val = np.max(roi_pixels)
-
-        # Adjust coordinates to match ImageJ coordinate system
-        # ImageJ coordinates start from top-left
-        imagej_x = x
-        imagej_y = y
-
-        result = {
-            'Area (mm²)': f"{area_mm2:.3f}",
-            'Mean': f"{mean:.3f}",
-            'StdDev': f"{stddev:.3f}",
-            'Min': f"{min_val:.3f}",
-            'Max': f"{max_val:.3f}",
-            'Point': f"({imagej_x}, {imagej_y})"
-        }
-        
-        self.results.append(result)
-        self.marks.append((x, y, self.circle_diameter))
-        print(f"ROI analyzed at point ({imagej_x}, {imagej_y})")
+        try:
+            if self.current_image is None:
+                return None, "No image loaded"
+
+            # Get the clicked coordinates
+            clicked_x = evt.index[0]
+            clicked_y = evt.index[1]
+
+            # Adjust for pan and zoom
+            x = int((clicked_x + self.pan_x) / self.zoom_factor)
+            y = int((clicked_y + self.pan_y) / self.zoom_factor)
+
+            # Ensure coordinates are within image bounds
+            height, width = self.current_image.shape[:2]
+            x = max(0, min(x, width-1))
+            y = max(0, min(y, height-1))
+
+            # Create mask for ROI
+            mask = np.zeros_like(self.current_image, dtype=np.uint8)
+            y_indices, x_indices = np.ogrid[:self.current_image.shape[0], :self.current_image.shape[1]]
+            radius = self.circle_diameter / 2
+            distance_from_center = np.sqrt(
+                (x_indices - x)**2 + (y_indices - y)**2
+            )
+            mask[distance_from_center <= radius] = 1
+
+            # Calculate statistics
+            roi_pixels = self.current_image[mask == 1]
+            pixel_spacing = float(self.dicom_data.PixelSpacing[0])
+            area_pixels = np.sum(mask)
+            area_mm2 = area_pixels * (pixel_spacing ** 2)
+            mean = np.mean(roi_pixels)
+            stddev = np.std(roi_pixels)
+            min_val = np.min(roi_pixels)
+            max_val = np.max(roi_pixels)
+
+            # Adjust coordinates to match ImageJ coordinate system
+            # ImageJ coordinates start from top-left
+            imagej_x = x
+            imagej_y = y
+
+            result = {
+                'Area (mm²)': f"{area_mm2:.3f}",
+                'Mean': f"{mean:.3f}",
+                'StdDev': f"{stddev:.3f}",
+                'Min': f"{min_val:.3f}",
+                'Max': f"{max_val:.3f}",
+                'Point': f"({imagej_x}, {imagej_y})"
+            }
+            
+            self.results.append(result)
+            self.marks.append((x, y, self.circle_diameter))
+            print(f"ROI analyzed at point ({imagej_x}, {imagej_y})")
 
-        return self.update_display(), self.format_results()
-    except Exception as e:
-        print(f"Error analyzing ROI: {str(e)}")
-        return self.display_image, f"Error analyzing ROI: {str(e)}"
+            return self.update_display(), self.format_results()
+        except Exception as e:
+            print(f"Error analyzing ROI: {str(e)}")
+            return self.display_image, f"Error analyzing ROI: {str(e)}"
 
     def update_display(self):
         try: