Adding result shower

src/model/inference.py

@@ -42,6 +42,7 @@ class InferenceEngine:
         relative_path: str,
         conf: float = 0.25,
         save_to_db: bool = True,
+        repository_root: Optional[str] = None,
     ) -> Dict:
         """
         Detect objects in a single image.
@@ -51,11 +52,17 @@ class InferenceEngine:
             relative_path: Relative path from repository root
             conf: Confidence threshold
             save_to_db: Whether to save results to database
+            repository_root: Base directory used to compute relative_path (if known)
 
         Returns:
             Dictionary with detection results
         """
         try:
+            # Normalize storage path (fall back to absolute path when repo root is unknown)
+            stored_relative_path = relative_path
+            if not repository_root:
+                stored_relative_path = str(Path(image_path).resolve())
+
             # Get image dimensions
             img = Image.open(image_path)
             width, height = img.size
@@ -66,34 +73,58 @@ class InferenceEngine:
 
             # Add/get image in database
             image_id = self.db_manager.get_or_create_image(
-                relative_path=relative_path,
+                relative_path=stored_relative_path,
                 filename=Path(image_path).name,
                 width=width,
                 height=height,
             )
 
-            # Save detections to database
-            if save_to_db and detections:
-                detection_records = []
-                for det in detections:
-                    # Use normalized bbox from detection
-                    bbox_normalized = det[
-                        "bbox_normalized"
-                    ]  # [x_min, y_min, x_max, y_max]
+            inserted_count = 0
+            deleted_count = 0
 
-                    record = {
-                        "image_id": image_id,
-                        "model_id": self.model_id,
-                        "class_name": det["class_name"],
-                        "bbox": tuple(bbox_normalized),
-                        "confidence": det["confidence"],
-                        "segmentation_mask": det.get("segmentation_mask"),
-                        "metadata": {"class_id": det["class_id"]},
-                    }
-                    detection_records.append(record)
+            # Save detections to database, replacing any previous results for this image/model
+            if save_to_db:
+                deleted_count = self.db_manager.delete_detections_for_image(
+                    image_id, self.model_id
+                )
+                if detections:
+                    detection_records = []
+                    for det in detections:
+                        # Use normalized bbox from detection
+                        bbox_normalized = det[
+                            "bbox_normalized"
+                        ]  # [x_min, y_min, x_max, y_max]
 
-                self.db_manager.add_detections_batch(detection_records)
-                logger.info(f"Saved {len(detection_records)} detections to database")
+                        metadata = {
+                            "class_id": det["class_id"],
+                            "source_path": str(Path(image_path).resolve()),
+                        }
+                        if repository_root:
+                            metadata["repository_root"] = str(
+                                Path(repository_root).resolve()
+                            )
+
+                        record = {
+                            "image_id": image_id,
+                            "model_id": self.model_id,
+                            "class_name": det["class_name"],
+                            "bbox": tuple(bbox_normalized),
+                            "confidence": det["confidence"],
+                            "segmentation_mask": det.get("segmentation_mask"),
+                            "metadata": metadata,
+                        }
+                        detection_records.append(record)
+
+                    inserted_count = self.db_manager.add_detections_batch(
+                        detection_records
+                    )
+                    logger.info(
+                        f"Saved {inserted_count} detections to database (replaced {deleted_count})"
+                    )
+                else:
+                    logger.info(
+                        f"Detection run removed {deleted_count} stale entries but produced no new detections"
+                    )
 
             return {
                 "success": True,
@@ -142,7 +173,12 @@ class InferenceEngine:
             rel_path = get_relative_path(image_path, repository_root)
 
             # Perform detection
-            result = self.detect_single(image_path, rel_path, conf)
+            result = self.detect_single(
+                image_path,
+                rel_path,
+                conf=conf,
+                repository_root=repository_root,
+            )
             results.append(result)
 
             # Update progress