Update src/ai_processor.py

src/ai_processor.py

@@ -1196,142 +1196,142 @@ Automated analysis provides quantitative measurements; verify via clinical exami
         seg_adjust: float = 0.0,
         manual_mask_path: Optional[str] = None,
     ) -> Dict:
-    """
-    Analyze a wound image and return a dictionary with visual analysis, report and paths.
-    """
-    try:
-        # Normalize input to PIL
-        if isinstance(image, str):
-            if not os.path.exists(image):
-                raise ValueError(f"Image file not found: {image}")
-            image_pil = Image.open(image)
-        elif isinstance(image, Image.Image):
-            image_pil = image
-        elif isinstance(image, np.ndarray):
-            image_pil = Image.fromarray(image)
-        else:
-            raise ValueError(f"Unsupported image type: {type(image)}")
-
-        # Run the standard pipeline
-        result = self.full_analysis_pipeline(image_pil, questionnaire_data or {})
-
-        # If neither manual mask nor adjustment specified, return as is
-        if (not manual_mask_path) and (abs(seg_adjust) < 1e-5):
-            return result
-
-        # Extract visual analysis and calibration from result
-        visual = result.get("visual_analysis", {}) or {}
-        px_per_cm = float(visual.get("px_per_cm", DEFAULT_PX_PER_CM))
-
-        # Attempt to load the ROI mask generated by the pipeline
-        roi_mask_path = visual.get("roi_mask_path")
-        mask_img = None
-
-        # Use manual mask if provided
-        if manual_mask_path:
-            try:
-                if os.path.exists(manual_mask_path):
-                    mask_img = Image.open(manual_mask_path)
-                else:
-                    logging.warning(f"Manual mask path does not exist: {manual_mask_path}")
-            except Exception as e:
-                logging.warning(f"Failed to load manual mask: {e}")
-        elif roi_mask_path and os.path.exists(roi_mask_path):
-            # Otherwise load the automatically generated ROI mask
-            try:
-                mask_img = Image.open(roi_mask_path)
-            except Exception as e:
-                logging.warning(f"Failed to load ROI mask for adjustment: {e}")
-
-        if mask_img is not None:
-            # Convert to numpy for processing
-            mask_np = np.array(mask_img.convert("L"))
-
-            # If adjustment is requested and no manual mask override
-            if (manual_mask_path is None) and (abs(seg_adjust) >= 1e-5):
-                # Determine the number of iterations based on percentage; roughly 5% increments
-                iter_count = max(1, int(round(abs(seg_adjust) / 5)))
-                kernel = np.ones((3, 3), np.uint8)
+        """
+        Analyze a wound image and return a dictionary with visual analysis, report and paths.
+        """
+        try:
+            # Normalize input to PIL
+            if isinstance(image, str):
+                if not os.path.exists(image):
+                    raise ValueError(f"Image file not found: {image}")
+                image_pil = Image.open(image)
+            elif isinstance(image, Image.Image):
+                image_pil = image
+            elif isinstance(image, np.ndarray):
+                image_pil = Image.fromarray(image)
+            else:
+                raise ValueError(f"Unsupported image type: {type(image)}")
+    
+            # Run the standard pipeline
+            result = self.full_analysis_pipeline(image_pil, questionnaire_data or {})
+    
+            # If neither manual mask nor adjustment specified, return as is
+            if (not manual_mask_path) and (abs(seg_adjust) < 1e-5):
+                return result
+    
+            # Extract visual analysis and calibration from result
+            visual = result.get("visual_analysis", {}) or {}
+            px_per_cm = float(visual.get("px_per_cm", DEFAULT_PX_PER_CM))
+    
+            # Attempt to load the ROI mask generated by the pipeline
+            roi_mask_path = visual.get("roi_mask_path")
+            mask_img = None
+    
+            # Use manual mask if provided
+            if manual_mask_path:
                 try:
-                    if seg_adjust > 0:
-                        # Dilate (expand) mask
-                        mask_np = cv2.dilate((mask_np > 127).astype(np.uint8), kernel, iterations=iter_count)
+                    if os.path.exists(manual_mask_path):
+                        mask_img = Image.open(manual_mask_path)
                     else:
-                        # Erode (shrink) mask
-                        mask_np = cv2.erode((mask_np > 127).astype(np.uint8), kernel, iterations=iter_count)
+                        logging.warning(f"Manual mask path does not exist: {manual_mask_path}")
                 except Exception as e:
-                    logging.warning(f"Segmentation adjustment failed: {e}")
-            else:
-                # If manual mask provided, binarize it directly
-                mask_np = (mask_np > 127).astype(np.uint8)
-
-            # Recalculate length, width and area using the adjusted or manual mask
-            try:
-                length_cm, breadth_cm, area_cm2 = self._refine_metrics_from_mask(mask_np, px_per_cm)
-                visual["length_cm"] = length_cm
-                visual["breadth_cm"] = breadth_cm
-                visual["surface_area_cm2"] = area_cm2
-                # Indicate that segmentation was refined manually or adjusted
-                visual["segmentation_refined"] = bool(manual_mask_path) or (abs(seg_adjust) >= 1e-5)
-            except Exception as e:
-                logging.warning(f"Failed to recalculate metrics from mask: {e}")
-
-            # --- NEW: if a manual mask was supplied, create & store a manual overlay and update paths ---
-            if manual_mask_path:
+                    logging.warning(f"Failed to load manual mask: {e}")
+            elif roi_mask_path and os.path.exists(roi_mask_path):
+                # Otherwise load the automatically generated ROI mask
                 try:
-                    # Base image for overlay
-                    base_rgb = np.array(image_pil.convert("RGB"))
-                    base_bgr = cv2.cvtColor(base_rgb, cv2.COLOR_RGB2BGR)
-                    h, w = base_bgr.shape[:2]
-
-                    # Ensure mask matches base size
-                    if mask_np.shape[:2] != (h, w):
-                        mask_np = cv2.resize(mask_np.astype(np.uint8), (w, h), interpolation=cv2.INTER_NEAREST)
-
-                    # Decide output directory
-                    out_dir = os.path.dirname(roi_mask_path or result.get("saved_image_path") or manual_mask_path)
-                    if not out_dir or not os.path.exists(out_dir):
-                        out_dir = os.getcwd()
-
-                    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-
-                    # Save a clean binary manual mask (0/255)
-                    manual_mask_save = os.path.join(out_dir, f"manual_mask_{ts}.png")
-                    cv2.imwrite(manual_mask_save, (mask_np * 255).astype(np.uint8))
-
-                    # Build red overlay with white contour
-                    red = np.zeros_like(base_bgr); red[:] = (0, 0, 255)
-                    alpha = 0.55
-                    tinted = cv2.addWeighted(base_bgr, 1 - alpha, red, alpha, 0)
-                    mask3 = cv2.merge([(mask_np * 255).astype(np.uint8)] * 3)
-                    overlay = np.where(mask3 > 0, tinted, base_bgr)
-
-                    cnts, _ = cv2.findContours((mask_np * 255).astype(np.uint8),
-                                               cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-                    if cnts:
-                        cv2.drawContours(overlay, cnts, -1, (255, 255, 255), 2)
-
-                    manual_overlay_path = os.path.join(out_dir, f"segmentation_manual_{ts}.png")
-                    cv2.imwrite(manual_overlay_path, overlay)
-
-                    # Update paths so the UI shows the MANUAL overlay/mask
-                    visual["roi_mask_path"] = manual_mask_save
-                    visual["segmentation_image_path"] = manual_overlay_path
-                    visual["segmentation_roi_path"] = manual_overlay_path  # alias some UIs read
-                    visual["segmentation_refined_type"] = "manual"
-                    visual["manual_mask_used"] = True
+                    mask_img = Image.open(roi_mask_path)
                 except Exception as e:
-                    logging.warning(f"Failed to generate manual segmentation overlay: {e}")
-
-        result["visual_analysis"] = visual
-        return result
-    except Exception as e:
-        logging.error(f"Wound analysis error: {e}")
-        return {
-            "success": False,
-            "error": str(e),
-            "visual_analysis": {},
-            "report": f"Analysis initialization failed: {str(e)}",
-            "saved_image_path": None,
-            "guideline_context": "",
-        }
+                    logging.warning(f"Failed to load ROI mask for adjustment: {e}")
+    
+            if mask_img is not None:
+                # Convert to numpy for processing
+                mask_np = np.array(mask_img.convert("L"))
+    
+                # If adjustment is requested and no manual mask override
+                if (manual_mask_path is None) and (abs(seg_adjust) >= 1e-5):
+                    # Determine the number of iterations based on percentage; roughly 5% increments
+                    iter_count = max(1, int(round(abs(seg_adjust) / 5)))
+                    kernel = np.ones((3, 3), np.uint8)
+                    try:
+                        if seg_adjust > 0:
+                            # Dilate (expand) mask
+                            mask_np = cv2.dilate((mask_np > 127).astype(np.uint8), kernel, iterations=iter_count)
+                        else:
+                            # Erode (shrink) mask
+                            mask_np = cv2.erode((mask_np > 127).astype(np.uint8), kernel, iterations=iter_count)
+                    except Exception as e:
+                        logging.warning(f"Segmentation adjustment failed: {e}")
+                else:
+                    # If manual mask provided, binarize it directly
+                    mask_np = (mask_np > 127).astype(np.uint8)
+    
+                # Recalculate length, width and area using the adjusted or manual mask
+                try:
+                    length_cm, breadth_cm, area_cm2 = self._refine_metrics_from_mask(mask_np, px_per_cm)
+                    visual["length_cm"] = length_cm
+                    visual["breadth_cm"] = breadth_cm
+                    visual["surface_area_cm2"] = area_cm2
+                    # Indicate that segmentation was refined manually or adjusted
+                    visual["segmentation_refined"] = bool(manual_mask_path) or (abs(seg_adjust) >= 1e-5)
+                except Exception as e:
+                    logging.warning(f"Failed to recalculate metrics from mask: {e}")
+    
+                # --- NEW: if a manual mask was supplied, create & store a manual overlay and update paths ---
+                if manual_mask_path:
+                    try:
+                        # Base image for overlay
+                        base_rgb = np.array(image_pil.convert("RGB"))
+                        base_bgr = cv2.cvtColor(base_rgb, cv2.COLOR_RGB2BGR)
+                        h, w = base_bgr.shape[:2]
+    
+                        # Ensure mask matches base size
+                        if mask_np.shape[:2] != (h, w):
+                            mask_np = cv2.resize(mask_np.astype(np.uint8), (w, h), interpolation=cv2.INTER_NEAREST)
+    
+                        # Decide output directory
+                        out_dir = os.path.dirname(roi_mask_path or result.get("saved_image_path") or manual_mask_path)
+                        if not out_dir or not os.path.exists(out_dir):
+                            out_dir = os.getcwd()
+    
+                        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    
+                        # Save a clean binary manual mask (0/255)
+                        manual_mask_save = os.path.join(out_dir, f"manual_mask_{ts}.png")
+                        cv2.imwrite(manual_mask_save, (mask_np * 255).astype(np.uint8))
+    
+                        # Build red overlay with white contour
+                        red = np.zeros_like(base_bgr); red[:] = (0, 0, 255)
+                        alpha = 0.55
+                        tinted = cv2.addWeighted(base_bgr, 1 - alpha, red, alpha, 0)
+                        mask3 = cv2.merge([(mask_np * 255).astype(np.uint8)] * 3)
+                        overlay = np.where(mask3 > 0, tinted, base_bgr)
+    
+                        cnts, _ = cv2.findContours((mask_np * 255).astype(np.uint8),
+                                                   cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                        if cnts:
+                            cv2.drawContours(overlay, cnts, -1, (255, 255, 255), 2)
+    
+                        manual_overlay_path = os.path.join(out_dir, f"segmentation_manual_{ts}.png")
+                        cv2.imwrite(manual_overlay_path, overlay)
+    
+                        # Update paths so the UI shows the MANUAL overlay/mask
+                        visual["roi_mask_path"] = manual_mask_save
+                        visual["segmentation_image_path"] = manual_overlay_path
+                        visual["segmentation_roi_path"] = manual_overlay_path  # alias some UIs read
+                        visual["segmentation_refined_type"] = "manual"
+                        visual["manual_mask_used"] = True
+                    except Exception as e:
+                        logging.warning(f"Failed to generate manual segmentation overlay: {e}")
+    
+            result["visual_analysis"] = visual
+            return result
+        except Exception as e:
+            logging.error(f"Wound analysis error: {e}")
+            return {
+                "success": False,
+                "error": str(e),
+                "visual_analysis": {},
+                "report": f"Analysis initialization failed: {str(e)}",
+                "saved_image_path": None,
+                "guideline_context": "",
+            }