.

sorghum_pipeline/output/manager.py

@@ -153,6 +153,14 @@ class OutputManager:
             if isinstance(size_img, np.ndarray) and size_img.size > 0:
                 titled = self._add_title_banner(size_img, 'Morphology Size')
                 cv2.imwrite(str(results_dir / 'size.size_analysis.png'), titled)
+            else:
+                # Fallback: synthesize a simple size analysis from the mask if available
+                mask_for_size = plant_data.get('mask')
+                base_img_for_size = plant_data.get('composite')
+                if isinstance(mask_for_size, np.ndarray) and mask_for_size.size > 0:
+                    synthesized = self._create_size_analysis_from_mask(mask_for_size, base_img_for_size)
+                    titled = self._add_title_banner(synthesized, 'Morphology Size')
+                    cv2.imwrite(str(results_dir / 'size.size_analysis.png'), titled)
         except Exception as e:
             logger.error(f"Failed to save size analysis: {e}")
     
@@ -221,4 +229,52 @@ class OutputManager:
         cv2.putText(composed, text, (x+1, y+1), font, font_scale, (0, 0, 0), thickness+1, cv2.LINE_AA)
         cv2.putText(composed, text, (x, y), font, font_scale, (0, 80, 0), thickness+1, cv2.LINE_AA)
 
-        return composed
+        return composed
+
+    def _create_size_analysis_from_mask(self, mask: np.ndarray, base_image: Any = None) -> np.ndarray:
+        """Create a simple size analysis visualization from a binary mask.
+        Draws contours and prints pixel area. If base_image is provided, overlays on it; otherwise uses a white canvas.
+        """
+        if mask is None or mask.size == 0:
+            return np.zeros((1, 1, 3), dtype=np.uint8)
+
+        # Prepare base image
+        if isinstance(base_image, np.ndarray) and base_image.size > 0:
+            img = base_image
+            if img.dtype != np.uint8:
+                img = self._normalize_to_uint8(img.astype(np.float64))
+            if img.ndim == 2:
+                base_bgr = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            elif img.ndim == 3 and img.shape[2] == 3:
+                base_bgr = img.copy()
+            elif img.ndim == 3 and img.shape[2] == 4:
+                base_bgr = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
+            else:
+                norm = self._normalize_to_uint8(img.astype(np.float64))
+                base_bgr = cv2.cvtColor(norm, cv2.COLOR_GRAY2BGR)
+        else:
+            h, w = mask.shape[:2]
+            base_bgr = np.full((h, w, 3), 255, dtype=np.uint8)
+
+        # Ensure binary mask
+        if mask.ndim == 3 and mask.shape[2] == 3:
+            gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+        else:
+            gray = mask.astype(np.uint8)
+        _, bin_mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
+
+        # Contours and area
+        contours, _ = cv2.findContours(bin_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cv2.drawContours(base_bgr, contours, -1, (0, 0, 255), 1)
+        area_px = int(cv2.countNonZero(bin_mask))
+
+        # Bounding box for the largest contour
+        if contours:
+            largest = max(contours, key=cv2.contourArea)
+            x, y, w, h = cv2.boundingRect(largest)
+            cv2.rectangle(base_bgr, (x, y), (x + w, y + h), (255, 0, 0), 1)
+
+        # Put area text
+        cv2.putText(base_bgr, f"Area: {area_px} px", (10, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2, cv2.LINE_AA)
+
+        return base_bgr