Major changes in annotation and results showing/storing

src/database/db_manager.py

@@ -40,8 +40,15 @@ class DatabaseManager:
 
         conn = self.get_connection()
         try:
-            # Check if annotations table needs migration
+            # Pre-schema migrations.
+            # These must run BEFORE executing schema.sql because schema.sql may
+            # contain CREATE INDEX statements referencing newly added columns.
+            #
+            # 1) Check if annotations table needs migration (may drop an old table)
             self._migrate_annotations_table(conn)
+            # 2) Ensure images table has the required columns (e.g. 'source')
+            self._migrate_images_table(conn)
+            conn.commit()
 
             # Read schema file and execute
             schema_path = Path(__file__).parent / "schema.sql"
@@ -53,6 +60,19 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def _migrate_images_table(self, conn: sqlite3.Connection) -> None:
+        """Migrate images table to include the 'source' column if missing."""
+
+        cursor = conn.cursor()
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='images'")
+        if not cursor.fetchone():
+            return
+
+        cursor.execute("PRAGMA table_info(images)")
+        columns = {row[1] for row in cursor.fetchall()}
+        if "source" not in columns:
+            cursor.execute("ALTER TABLE images ADD COLUMN source TEXT")
+
     def _migrate_annotations_table(self, conn: sqlite3.Connection) -> None:
         """
         Migrate annotations table from old schema (class_name) to new schema (class_id).
@@ -85,6 +105,103 @@ class DatabaseManager:
         conn.execute("PRAGMA foreign_keys = ON")  # Enable foreign keys
         return conn
 
+    # ==================== Detection Run Operations ====================
+
+    def upsert_detection_run(
+        self,
+        image_id: int,
+        model_id: int,
+        count: int,
+        metadata: Optional[Dict] = None,
+    ) -> bool:
+        """Insert/update a per-image per-model detection run summary.
+
+        This enables the UI to show runs even when zero detections were produced.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                INSERT INTO detection_runs (image_id, model_id, detected_at, count, metadata)
+                VALUES (?, ?, CURRENT_TIMESTAMP, ?, ?)
+                ON CONFLICT(image_id, model_id) DO UPDATE SET
+                    detected_at = CURRENT_TIMESTAMP,
+                    count = excluded.count,
+                    metadata = excluded.metadata
+                """,
+                (
+                    int(image_id),
+                    int(model_id),
+                    int(count),
+                    json.dumps(metadata) if metadata else None,
+                ),
+            )
+            conn.commit()
+            return True
+        finally:
+            conn.close()
+
+    def get_detection_run_summaries(self, limit: int = 500, offset: int = 0) -> List[Dict]:
+        """Return latest detection run summaries grouped by image+model.
+
+        Includes runs with 0 detections.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                SELECT
+                    dr.image_id,
+                    dr.model_id,
+                    dr.detected_at,
+                    dr.count,
+                    dr.metadata,
+                    i.relative_path AS image_path,
+                    i.filename AS image_filename,
+                    m.model_name,
+                    m.model_version,
+                    GROUP_CONCAT(DISTINCT d.class_name) AS classes
+                FROM detection_runs dr
+                JOIN images i ON dr.image_id = i.id
+                JOIN models m ON dr.model_id = m.id
+                LEFT JOIN detections d
+                    ON d.image_id = dr.image_id AND d.model_id = dr.model_id
+                GROUP BY dr.image_id, dr.model_id
+                ORDER BY dr.detected_at DESC
+                LIMIT ? OFFSET ?
+                """,
+                (int(limit), int(offset)),
+            )
+
+            rows: List[Dict] = []
+            for row in cursor.fetchall():
+                item = dict(row)
+                if item.get("metadata"):
+                    try:
+                        item["metadata"] = json.loads(item["metadata"])
+                    except Exception:
+                        item["metadata"] = None
+                rows.append(item)
+            return rows
+        finally:
+            conn.close()
+
+    def get_detection_run_total(self) -> int:
+        """Return total number of detection_runs rows."""
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("SELECT COUNT(*) AS cnt FROM detection_runs")
+            row = cursor.fetchone()
+            return int(row["cnt"] if row and row["cnt"] is not None else 0)
+        finally:
+            conn.close()
+
     # ==================== Model Operations ====================
 
     def add_model(
@@ -201,6 +318,28 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def delete_model(self, model_id: int) -> bool:
+        """Delete a model from the database.
+
+        Note: detections referencing this model are deleted automatically via
+        the `detections.model_id` foreign key (ON DELETE CASCADE).
+
+        Args:
+            model_id: ID of the model to delete.
+
+        Returns:
+            True if a model row was deleted, False otherwise.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM models WHERE id = ?", (model_id,))
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
     # ==================== Image Operations ====================
 
     def add_image(
@@ -211,6 +350,7 @@ class DatabaseManager:
         height: int,
         captured_at: Optional[datetime] = None,
         checksum: Optional[str] = None,
+        source: Optional[str] = None,
     ) -> int:
         """
         Add a new image to the database.
@@ -231,10 +371,10 @@ class DatabaseManager:
             cursor = conn.cursor()
             cursor.execute(
                 """
-                INSERT INTO images (relative_path, filename, width, height, captured_at, checksum)
+                INSERT INTO images (relative_path, filename, width, height, captured_at, checksum, source)
-                VALUES (?, ?, ?, ?, ?, ?)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
                 """,
-                (relative_path, filename, width, height, captured_at, checksum),
+                (relative_path, filename, width, height, captured_at, checksum, source),
             )
             conn.commit()
             return cursor.lastrowid
@@ -264,6 +404,18 @@ class DatabaseManager:
             return existing["id"]
         return self.add_image(relative_path, filename, width, height)
 
+    def set_image_source(self, image_id: int, source: Optional[str]) -> bool:
+        """Set/update the source marker for an image row."""
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("UPDATE images SET source = ? WHERE id = ?", (source, int(image_id)))
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
     # ==================== Detection Operations ====================
 
     def add_detection(
@@ -462,6 +614,30 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def delete_all_detections(self) -> int:
+        """Delete all detections from the database.
+
+        Returns:
+            Number of rows deleted.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            # Also clear detection run summaries so the Results tab does not continue
+            # to show historical runs after detections have been wiped.
+            try:
+                cursor.execute("DELETE FROM detection_runs")
+            except sqlite3.OperationalError:
+                # Backwards-compatible: table may not exist on older DB files.
+                pass
+
+            cursor.execute("DELETE FROM detections")
+            conn.commit()
+            return cursor.rowcount
+        finally:
+            conn.close()
+
     # ==================== Statistics Operations ====================
 
     def get_detection_statistics(
@@ -620,6 +796,153 @@ class DatabaseManager:
 
     # ==================== Annotation Operations ====================
 
+    def get_images_summary(
+        self,
+        name_filter: Optional[str] = None,
+        source_filter: Optional[str] = None,
+        order_by: str = "filename",
+        order_dir: str = "ASC",
+        limit: Optional[int] = None,
+        offset: int = 0,
+    ) -> List[Dict]:
+        """Return all images with annotation counts (including zero).
+
+        This is used by the Annotation tab to populate the image list even when
+        no annotations exist yet.
+
+        Args:
+            name_filter: Optional substring filter applied to filename/relative_path.
+            order_by: One of: 'filename', 'relative_path', 'annotation_count', 'added_at'.
+            order_dir: 'ASC' or 'DESC'.
+            limit: Optional max number of rows.
+            offset: Pagination offset.
+
+        Returns:
+            List of dicts: {id, relative_path, filename, added_at, annotation_count}
+        """
+
+        allowed_order_by = {
+            "filename": "i.filename",
+            "relative_path": "i.relative_path",
+            "annotation_count": "annotation_count",
+            "added_at": "i.added_at",
+        }
+        order_expr = allowed_order_by.get(order_by, "i.filename")
+        dir_norm = str(order_dir).upper().strip()
+        if dir_norm not in {"ASC", "DESC"}:
+            dir_norm = "ASC"
+
+        conn = self.get_connection()
+        try:
+            params: List[Any] = []
+            where_clauses: List[str] = []
+            if name_filter:
+                token = f"%{name_filter}%"
+                where_clauses.append("(i.filename LIKE ? OR i.relative_path LIKE ?)")
+                params.extend([token, token])
+            if source_filter:
+                where_clauses.append("i.source = ?")
+                params.append(source_filter)
+
+            where_sql = ""
+            if where_clauses:
+                where_sql = "WHERE " + " AND ".join(where_clauses)
+
+            limit_sql = ""
+            if limit is not None:
+                limit_sql = " LIMIT ? OFFSET ?"
+                params.extend([int(limit), int(offset)])
+
+            query = f"""
+                SELECT
+                    i.id,
+                    i.relative_path,
+                    i.filename,
+                    i.added_at,
+                    COUNT(a.id) AS annotation_count
+                FROM images i
+                LEFT JOIN annotations a ON a.image_id = i.id
+                {where_sql}
+                GROUP BY i.id
+                ORDER BY {order_expr} {dir_norm}
+                {limit_sql}
+            """
+
+            cursor = conn.cursor()
+            cursor.execute(query, params)
+            return [dict(row) for row in cursor.fetchall()]
+        finally:
+            conn.close()
+
+    def get_annotated_images_summary(
+        self,
+        name_filter: Optional[str] = None,
+        order_by: str = "filename",
+        order_dir: str = "ASC",
+        limit: Optional[int] = None,
+        offset: int = 0,
+    ) -> List[Dict]:
+        """Return images that have at least one manual annotation.
+
+        Args:
+            name_filter: Optional substring filter applied to filename/relative_path.
+            order_by: One of: 'filename', 'relative_path', 'annotation_count', 'added_at'.
+            order_dir: 'ASC' or 'DESC'.
+            limit: Optional max number of rows.
+            offset: Pagination offset.
+
+        Returns:
+            List of dicts: {id, relative_path, filename, added_at, annotation_count}
+        """
+
+        allowed_order_by = {
+            "filename": "i.filename",
+            "relative_path": "i.relative_path",
+            "annotation_count": "annotation_count",
+            "added_at": "i.added_at",
+        }
+        order_expr = allowed_order_by.get(order_by, "i.filename")
+        dir_norm = str(order_dir).upper().strip()
+        if dir_norm not in {"ASC", "DESC"}:
+            dir_norm = "ASC"
+
+        conn = self.get_connection()
+        try:
+            params: List[Any] = []
+            where_sql = ""
+            if name_filter:
+                # Case-insensitive substring search.
+                token = f"%{name_filter}%"
+                where_sql = "WHERE (i.filename LIKE ? OR i.relative_path LIKE ?)"
+                params.extend([token, token])
+
+            limit_sql = ""
+            if limit is not None:
+                limit_sql = " LIMIT ? OFFSET ?"
+                params.extend([int(limit), int(offset)])
+
+            query = f"""
+                SELECT
+                    i.id,
+                    i.relative_path,
+                    i.filename,
+                    i.added_at,
+                    COUNT(a.id) AS annotation_count
+                FROM images i
+                JOIN annotations a ON a.image_id = i.id
+                {where_sql}
+                GROUP BY i.id
+                HAVING annotation_count > 0
+                ORDER BY {order_expr} {dir_norm}
+                {limit_sql}
+            """
+
+            cursor = conn.cursor()
+            cursor.execute(query, params)
+            return [dict(row) for row in cursor.fetchall()]
+        finally:
+            conn.close()
+
     def add_annotation(
         self,
         image_id: int,
@@ -725,6 +1048,27 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def delete_annotations_for_image(self, image_id: int) -> int:
+        """Delete ALL annotations for a specific image.
+
+        This is primarily used for import/overwrite workflows.
+
+        Args:
+            image_id: ID of the image whose annotations should be deleted.
+
+        Returns:
+            Number of rows deleted.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM annotations WHERE image_id = ?", (int(image_id),))
+            conn.commit()
+            return int(cursor.rowcount or 0)
+        finally:
+            conn.close()
+
     # ==================== Object Class Operations ====================
 
     def get_object_classes(self) -> List[Dict]:

src/database/schema.sql

@@ -7,7 +7,7 @@ CREATE TABLE IF NOT EXISTS models (
     model_name TEXT NOT NULL,
     model_version TEXT NOT NULL,
     model_path TEXT NOT NULL,
-    base_model TEXT NOT NULL DEFAULT 'yolov8s.pt',
+    base_model TEXT NOT NULL DEFAULT 'yolo11s.pt',
     created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
     training_params TEXT,  -- JSON string of training parameters
     metrics TEXT,          -- JSON string of validation metrics
@@ -23,7 +23,8 @@ CREATE TABLE IF NOT EXISTS images (
     height INTEGER,
     captured_at TIMESTAMP,
     added_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    checksum TEXT
+    checksum TEXT,
+    source TEXT
 );
 
 -- Detections table: stores detection results
@@ -44,6 +45,19 @@ CREATE TABLE IF NOT EXISTS detections (
     FOREIGN KEY (model_id) REFERENCES models (id) ON DELETE CASCADE
 );
 
+-- Detection runs table: stores per-image per-model run summaries (including 0 detections)
+CREATE TABLE IF NOT EXISTS detection_runs (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    image_id INTEGER NOT NULL,
+    model_id INTEGER NOT NULL,
+    detected_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    count INTEGER NOT NULL DEFAULT 0,
+    metadata TEXT,
+    UNIQUE(image_id, model_id),
+    FOREIGN KEY (image_id) REFERENCES images (id) ON DELETE CASCADE,
+    FOREIGN KEY (model_id) REFERENCES models (id) ON DELETE CASCADE
+);
+
 -- Object classes table: stores annotation class definitions with colors
 CREATE TABLE IF NOT EXISTS object_classes (
     id INTEGER PRIMARY KEY AUTOINCREMENT,
@@ -55,10 +69,7 @@ CREATE TABLE IF NOT EXISTS object_classes (
 
 -- Insert default object classes
 INSERT OR IGNORE INTO object_classes (class_name, color, description) VALUES
-    ('cell', '#FF0000', 'Cell object'),
+    ('terminal', '#FFFF00', 'Axion terminal');
-    ('nucleus', '#00FF00', 'Cell nucleus'),
-    ('mitochondria', '#0000FF', 'Mitochondria'),
-    ('vesicle', '#FFFF00', 'Vesicle');
 
 -- Annotations table: stores manual annotations
 CREATE TABLE IF NOT EXISTS annotations (
@@ -83,9 +94,13 @@ CREATE INDEX IF NOT EXISTS idx_detections_model_id ON detections(model_id);
 CREATE INDEX IF NOT EXISTS idx_detections_class_name ON detections(class_name);
 CREATE INDEX IF NOT EXISTS idx_detections_detected_at ON detections(detected_at);
 CREATE INDEX IF NOT EXISTS idx_detections_confidence ON detections(confidence);
+CREATE INDEX IF NOT EXISTS idx_detection_runs_image_id ON detection_runs(image_id);
+CREATE INDEX IF NOT EXISTS idx_detection_runs_model_id ON detection_runs(model_id);
+CREATE INDEX IF NOT EXISTS idx_detection_runs_detected_at ON detection_runs(detected_at);
 CREATE INDEX IF NOT EXISTS idx_images_relative_path ON images(relative_path);
 CREATE INDEX IF NOT EXISTS idx_images_added_at ON images(added_at);
+CREATE INDEX IF NOT EXISTS idx_images_source ON images(source);
 CREATE INDEX IF NOT EXISTS idx_annotations_image_id ON annotations(image_id);
 CREATE INDEX IF NOT EXISTS idx_annotations_class_id ON annotations(class_id);
 CREATE INDEX IF NOT EXISTS idx_models_created_at ON models(created_at);
-CREATE INDEX IF NOT EXISTS idx_object_classes_class_name ON object_classes(class_name);
+CREATE INDEX IF NOT EXISTS idx_object_classes_class_name ON object_classes(class_name);

src/gui/main_window.py

@@ -1,6 +1,7 @@
-"""
+"""Main window for the microscopy object detection application."""
-Main window for the microscopy object detection application.
+
-"""
+import shutil
+from pathlib import Path
 
 from PySide6.QtWidgets import (
     QMainWindow,
@@ -20,6 +21,7 @@ from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
 from src.utils.logger import get_logger
 from src.gui.dialogs.config_dialog import ConfigDialog
+from src.gui.dialogs.delete_model_dialog import DeleteModelDialog
 from src.gui.tabs.detection_tab import DetectionTab
 from src.gui.tabs.training_tab import TrainingTab
 from src.gui.tabs.validation_tab import ValidationTab
@@ -91,6 +93,12 @@ class MainWindow(QMainWindow):
         db_stats_action.triggered.connect(self._show_database_stats)
         tools_menu.addAction(db_stats_action)
 
+        tools_menu.addSeparator()
+
+        delete_model_action = QAction("Delete &Model…", self)
+        delete_model_action.triggered.connect(self._show_delete_model_dialog)
+        tools_menu.addAction(delete_model_action)
+
         # Help menu
         help_menu = menubar.addMenu("&Help")
 
@@ -117,10 +125,10 @@ class MainWindow(QMainWindow):
 
             # Add tabs to widget
             self.tab_widget.addTab(self.detection_tab, "Detection")
+            self.tab_widget.addTab(self.results_tab, "Results")
+            self.tab_widget.addTab(self.annotation_tab, "Annotation")
             self.tab_widget.addTab(self.training_tab, "Training")
             self.tab_widget.addTab(self.validation_tab, "Validation")
-            self.tab_widget.addTab(self.results_tab, "Results")
-            self.tab_widget.addTab(self.annotation_tab, "Annotation (Future)")
 
             # Connect tab change signal
             self.tab_widget.currentChanged.connect(self._on_tab_changed)
@@ -152,9 +160,7 @@ class MainWindow(QMainWindow):
         """Center window on screen."""
         screen = self.screen().geometry()
         size = self.geometry()
-        self.move(
+        self.move((screen.width() - size.width()) // 2, (screen.height() - size.height()) // 2)
-            (screen.width() - size.width()) // 2, (screen.height() - size.height()) // 2
-        )
 
     def _restore_window_state(self):
         """Restore window geometry from settings or center window."""
@@ -193,6 +199,10 @@ class MainWindow(QMainWindow):
                 self.training_tab.refresh()
             if hasattr(self, "results_tab"):
                 self.results_tab.refresh()
+            if hasattr(self, "annotation_tab"):
+                self.annotation_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
         except Exception as e:
             logger.error(f"Error applying settings: {e}")
 
@@ -209,6 +219,14 @@ class MainWindow(QMainWindow):
         logger.debug(f"Switched to tab: {tab_name}")
         self._update_status(f"Viewing: {tab_name}")
 
+        # Ensure the Annotation tab always shows up-to-date DB-backed lists.
+        try:
+            current_widget = self.tab_widget.widget(index)
+            if hasattr(self, "annotation_tab") and current_widget is self.annotation_tab:
+                self.annotation_tab.refresh()
+        except Exception as exc:
+            logger.debug(f"Failed to refresh annotation tab on selection: {exc}")
+
     def _show_database_stats(self):
         """Show database statistics dialog."""
         try:
@@ -231,9 +249,229 @@ class MainWindow(QMainWindow):
 
         except Exception as e:
             logger.error(f"Error getting database stats: {e}")
-            QMessageBox.warning(
+            QMessageBox.warning(self, "Error", f"Failed to get database statistics:\n{str(e)}")
-                self, "Error", f"Failed to get database statistics:\n{str(e)}"
+
-            )
+    def _show_delete_model_dialog(self) -> None:
+        """Open the model deletion dialog."""
+        dialog = DeleteModelDialog(self.db_manager, self)
+        if not dialog.exec():
+            return
+
+        model_ids = dialog.selected_model_ids
+        if not model_ids:
+            return
+
+        self._delete_models(model_ids)
+
+    def _delete_models(self, model_ids: list[int]) -> None:
+        """Delete one or more models from the database and remove artifacts from disk."""
+
+        deleted_count = 0
+        removed_paths: list[str] = []
+        remove_errors: list[str] = []
+
+        for model_id in model_ids:
+            model = None
+            try:
+                model = self.db_manager.get_model_by_id(int(model_id))
+            except Exception as exc:
+                logger.error(f"Failed to load model {model_id} before deletion: {exc}")
+
+            if not model:
+                remove_errors.append(f"Model id {model_id} not found in database.")
+                continue
+
+            try:
+                deleted = self.db_manager.delete_model(int(model_id))
+            except Exception as exc:
+                logger.error(f"Failed to delete model {model_id}: {exc}")
+                remove_errors.append(f"Failed to delete model id {model_id} from DB: {exc}")
+                continue
+
+            if not deleted:
+                remove_errors.append(f"Model id {model_id} was not deleted (already removed?).")
+                continue
+
+            deleted_count += 1
+            removed, errors = self._delete_model_artifacts_from_disk(model)
+            removed_paths.extend(removed)
+            remove_errors.extend(errors)
+
+        # Refresh tabs to reflect the deletion(s).
+        try:
+            if hasattr(self, "detection_tab"):
+                self.detection_tab.refresh()
+            if hasattr(self, "results_tab"):
+                self.results_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
+            if hasattr(self, "training_tab"):
+                self.training_tab.refresh()
+        except Exception as exc:
+            logger.warning(f"Failed to refresh tabs after model deletion: {exc}")
+
+        details: list[str] = []
+        if removed_paths:
+            details.append("Removed from disk:\n" + "\n".join(removed_paths))
+        if remove_errors:
+            details.append("\nDisk cleanup warnings:\n" + "\n".join(remove_errors))
+
+        QMessageBox.information(
+            self,
+            "Delete Model",
+            f"Deleted {deleted_count} model(s) from database." + ("\n\n" + "\n".join(details) if details else ""),
+        )
+
+    def _delete_model(self, model_id: int) -> None:
+        """Delete a model from the database and remove its artifacts from disk."""
+
+        model = None
+        try:
+            model = self.db_manager.get_model_by_id(model_id)
+        except Exception as exc:
+            logger.error(f"Failed to load model {model_id} before deletion: {exc}")
+
+        if not model:
+            QMessageBox.warning(self, "Delete Model", "Selected model was not found in the database.")
+            return
+
+        model_path = str(model.get("model_path") or "")
+
+        try:
+            deleted = self.db_manager.delete_model(model_id)
+        except Exception as exc:
+            logger.error(f"Failed to delete model {model_id}: {exc}")
+            QMessageBox.critical(self, "Delete Model", f"Failed to delete model from database:\n{exc}")
+            return
+
+        if not deleted:
+            QMessageBox.warning(self, "Delete Model", "No model was deleted (it may have already been removed).")
+            return
+
+        removed_paths, remove_errors = self._delete_model_artifacts_from_disk(model)
+
+        # Refresh tabs to reflect the deletion.
+        try:
+            if hasattr(self, "detection_tab"):
+                self.detection_tab.refresh()
+            if hasattr(self, "results_tab"):
+                self.results_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
+            if hasattr(self, "training_tab"):
+                self.training_tab.refresh()
+        except Exception as exc:
+            logger.warning(f"Failed to refresh tabs after model deletion: {exc}")
+
+        details = []
+        if model_path:
+            details.append(f"Deleted model record for: {model_path}")
+        if removed_paths:
+            details.append("\nRemoved from disk:\n" + "\n".join(removed_paths))
+        if remove_errors:
+            details.append("\nDisk cleanup warnings:\n" + "\n".join(remove_errors))
+
+        QMessageBox.information(
+            self,
+            "Delete Model",
+            "Model deleted from database." + ("\n\n" + "\n".join(details) if details else ""),
+        )
+
+    def _delete_model_artifacts_from_disk(self, model: dict) -> tuple[list[str], list[str]]:
+        """Best-effort removal of model artifacts on disk.
+
+        Strategy:
+        - Remove run directories inferred from:
+          - model.model_path (…/<run>/weights/*.pt => <run>)
+          - training_params.stage_results[].results.save_dir
+          but only if they are under the configured models directory.
+        - If the weights file itself exists and is outside the models directory, delete only the file.
+
+        Returns:
+            (removed_paths, errors)
+        """
+
+        removed: list[str] = []
+        errors: list[str] = []
+
+        models_root = Path(self.config_manager.get_models_directory() or "data/models").expanduser()
+        try:
+            models_root_resolved = models_root.resolve()
+        except Exception:
+            models_root_resolved = models_root
+
+        inferred_dirs: list[Path] = []
+
+        # 1) From model_path
+        model_path_value = model.get("model_path")
+        if model_path_value:
+            try:
+                p = Path(str(model_path_value)).expanduser()
+                p_resolved = p.resolve() if p.exists() else p
+                if p_resolved.is_file():
+                    if p_resolved.parent.name == "weights" and p_resolved.parent.parent.exists():
+                        inferred_dirs.append(p_resolved.parent.parent)
+                    elif p_resolved.parent.exists():
+                        inferred_dirs.append(p_resolved.parent)
+            except Exception:
+                pass
+
+        # 2) From training_params.stage_results[].results.save_dir
+        training_params = model.get("training_params") or {}
+        if isinstance(training_params, dict):
+            stage_results = training_params.get("stage_results")
+            if isinstance(stage_results, list):
+                for stage in stage_results:
+                    results = (stage or {}).get("results")
+                    save_dir = (results or {}).get("save_dir") if isinstance(results, dict) else None
+                    if not save_dir:
+                        continue
+                    try:
+                        d = Path(str(save_dir)).expanduser()
+                        if d.exists() and d.is_dir():
+                            inferred_dirs.append(d)
+                    except Exception:
+                        continue
+
+        # Deduplicate inferred_dirs
+        unique_dirs: list[Path] = []
+        seen: set[str] = set()
+        for d in inferred_dirs:
+            try:
+                key = str(d.resolve())
+            except Exception:
+                key = str(d)
+            if key in seen:
+                continue
+            seen.add(key)
+            unique_dirs.append(d)
+
+        # Delete directories under models_root
+        for d in unique_dirs:
+            try:
+                d_resolved = d.resolve()
+            except Exception:
+                d_resolved = d
+            try:
+                if d_resolved.exists() and d_resolved.is_dir() and d_resolved.is_relative_to(models_root_resolved):
+                    shutil.rmtree(d_resolved)
+                    removed.append(str(d_resolved))
+            except Exception as exc:
+                errors.append(f"Failed to remove directory {d_resolved}: {exc}")
+
+        # If nothing matched (e.g., model_path outside models_root), delete just the file.
+        if model_path_value:
+            try:
+                p = Path(str(model_path_value)).expanduser()
+                if p.exists() and p.is_file():
+                    p_resolved = p.resolve()
+                    if not p_resolved.is_relative_to(models_root_resolved):
+                        p_resolved.unlink()
+                        removed.append(str(p_resolved))
+            except Exception as exc:
+                errors.append(f"Failed to remove model file {model_path_value}: {exc}")
+
+        return removed, errors
 
     def _show_about(self):
         """Show about dialog."""
@@ -301,6 +539,11 @@ class MainWindow(QMainWindow):
             if hasattr(self, "training_tab"):
                 self.training_tab.shutdown()
             if hasattr(self, "annotation_tab"):
+                # Best-effort refresh so DB-backed UI state is consistent at shutdown.
+                try:
+                    self.annotation_tab.refresh()
+                except Exception:
+                    pass
                 self.annotation_tab.save_state()
 
             logger.info("Application closing")

src/gui/tabs/annotation_tab.py

@@ -13,6 +13,11 @@ from PySide6.QtWidgets import (
     QFileDialog,
     QMessageBox,
     QSplitter,
+    QLineEdit,
+    QTableWidget,
+    QTableWidgetItem,
+    QHeaderView,
+    QAbstractItemView,
 )
 from PySide6.QtCore import Qt, QSettings
 from pathlib import Path
@@ -29,9 +34,7 @@ logger = get_logger(__name__)
 class AnnotationTab(QWidget):
     """Annotation tab for manual image annotation."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
@@ -52,6 +55,32 @@ class AnnotationTab(QWidget):
         self.main_splitter = QSplitter(Qt.Horizontal)
         self.main_splitter.setHandleWidth(10)
 
+        # { Left-most pane: annotated images list
+        annotated_group = QGroupBox("Annotated Images")
+        annotated_layout = QVBoxLayout()
+
+        filter_row = QHBoxLayout()
+        filter_row.addWidget(QLabel("Filter:"))
+        self.annotated_filter_edit = QLineEdit()
+        self.annotated_filter_edit.setPlaceholderText("Type to filter by image name…")
+        self.annotated_filter_edit.textChanged.connect(self._refresh_annotated_images_list)
+        filter_row.addWidget(self.annotated_filter_edit, 1)
+        annotated_layout.addLayout(filter_row)
+
+        self.annotated_images_table = QTableWidget(0, 2)
+        self.annotated_images_table.setHorizontalHeaderLabels(["Image", "Annotations"])
+        self.annotated_images_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
+        self.annotated_images_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)
+        self.annotated_images_table.setSelectionBehavior(QAbstractItemView.SelectRows)
+        self.annotated_images_table.setSelectionMode(QAbstractItemView.SingleSelection)
+        self.annotated_images_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
+        self.annotated_images_table.setSortingEnabled(True)
+        self.annotated_images_table.itemSelectionChanged.connect(self._on_annotated_image_selected)
+        annotated_layout.addWidget(self.annotated_images_table, 1)
+
+        annotated_group.setLayout(annotated_layout)
+        # }
+
         # { Left splitter for image display and zoom info
         self.left_splitter = QSplitter(Qt.Vertical)
         self.left_splitter.setHandleWidth(10)
@@ -62,6 +91,9 @@ class AnnotationTab(QWidget):
 
         # Use the AnnotationCanvasWidget
         self.annotation_canvas = AnnotationCanvasWidget()
+        # Auto-zoom so newly loaded images fill the available canvas viewport.
+        # (Matches the behavior used in ResultsTab.)
+        self.annotation_canvas.set_auto_fit_to_view(True)
         self.annotation_canvas.zoom_changed.connect(self._on_zoom_changed)
         self.annotation_canvas.annotation_drawn.connect(self._on_annotation_drawn)
         # Selection of existing polylines (when tool is not in drawing mode)
@@ -72,9 +104,7 @@ class AnnotationTab(QWidget):
         self.left_splitter.addWidget(canvas_group)
 
         # Controls info
-        controls_info = QLabel(
+        controls_info = QLabel("Zoom: Mouse wheel or +/- keys | Drawing: Enable pen and drag mouse")
-            "Zoom: Mouse wheel or +/- keys | Drawing: Enable pen and drag mouse"
-        )
         controls_info.setStyleSheet("QLabel { color: #888; font-style: italic; }")
         self.left_splitter.addWidget(controls_info)
         # }
@@ -85,47 +115,47 @@ class AnnotationTab(QWidget):
 
         # Annotation tools section
         self.annotation_tools = AnnotationToolsWidget(self.db_manager)
-        self.annotation_tools.polyline_enabled_changed.connect(
+        self.annotation_tools.polyline_enabled_changed.connect(self.annotation_canvas.set_polyline_enabled)
-            self.annotation_canvas.set_polyline_enabled
+        self.annotation_tools.polyline_pen_color_changed.connect(self.annotation_canvas.set_polyline_pen_color)
-        )
+        self.annotation_tools.polyline_pen_width_changed.connect(self.annotation_canvas.set_polyline_pen_width)
-        self.annotation_tools.polyline_pen_color_changed.connect(
-            self.annotation_canvas.set_polyline_pen_color
-        )
-        self.annotation_tools.polyline_pen_width_changed.connect(
-            self.annotation_canvas.set_polyline_pen_width
-        )
         # Show / hide bounding boxes
-        self.annotation_tools.show_bboxes_changed.connect(
+        self.annotation_tools.show_bboxes_changed.connect(self.annotation_canvas.set_show_bboxes)
-            self.annotation_canvas.set_show_bboxes
-        )
         # RDP simplification controls
-        self.annotation_tools.simplify_on_finish_changed.connect(
+        self.annotation_tools.simplify_on_finish_changed.connect(self._on_simplify_on_finish_changed)
-            self._on_simplify_on_finish_changed
+        self.annotation_tools.simplify_epsilon_changed.connect(self._on_simplify_epsilon_changed)
-        )
-        self.annotation_tools.simplify_epsilon_changed.connect(
-            self._on_simplify_epsilon_changed
-        )
         # Class selection and class-color changes
         self.annotation_tools.class_selected.connect(self._on_class_selected)
         self.annotation_tools.class_color_changed.connect(self._on_class_color_changed)
-        self.annotation_tools.clear_annotations_requested.connect(
+        self.annotation_tools.clear_annotations_requested.connect(self._on_clear_annotations)
-            self._on_clear_annotations
-        )
         # Delete selected annotation on canvas
-        self.annotation_tools.delete_selected_annotation_requested.connect(
+        self.annotation_tools.delete_selected_annotation_requested.connect(self._on_delete_selected_annotation)
-            self._on_delete_selected_annotation
-        )
         self.right_splitter.addWidget(self.annotation_tools)
 
         # Image loading section
         load_group = QGroupBox("Image Loading")
         load_layout = QVBoxLayout()
 
-        # Load image button
+        # Buttons row
         button_layout = QHBoxLayout()
         self.load_image_btn = QPushButton("Load Image")
         self.load_image_btn.clicked.connect(self._load_image)
         button_layout.addWidget(self.load_image_btn)
+
+        self.import_images_btn = QPushButton("Import Images")
+        self.import_images_btn.setToolTip(
+            "Import one or more images into the database.\n" "Images already present in the DB are skipped."
+        )
+        self.import_images_btn.clicked.connect(self._import_images)
+        button_layout.addWidget(self.import_images_btn)
+
+        self.import_annotations_btn = QPushButton("Import Annotations")
+        self.import_annotations_btn.setToolTip(
+            "Import YOLO .txt annotation files and register them with their corresponding images.\n"
+            "Existing annotations for those images will be overwritten."
+        )
+        self.import_annotations_btn.clicked.connect(self._import_annotations)
+        button_layout.addWidget(self.import_annotations_btn)
+
         button_layout.addStretch()
         load_layout.addLayout(button_layout)
 
@@ -137,12 +167,13 @@ class AnnotationTab(QWidget):
         self.right_splitter.addWidget(load_group)
         # }
 
-        # Add both splitters to the main horizontal splitter
+        # Add list + both splitters to the main horizontal splitter
+        self.main_splitter.addWidget(annotated_group)
         self.main_splitter.addWidget(self.left_splitter)
         self.main_splitter.addWidget(self.right_splitter)
 
-        # Set initial sizes: 75% for left (image), 25% for right (controls)
+        # Set initial sizes: list (left), canvas (middle), controls (right)
-        self.main_splitter.setSizes([750, 250])
+        self.main_splitter.setSizes([320, 650, 280])
 
         layout.addWidget(self.main_splitter)
         self.setLayout(layout)
@@ -150,6 +181,375 @@ class AnnotationTab(QWidget):
         # Restore splitter positions from settings
         self._restore_state()
 
+        # Populate list on startup.
+        self._refresh_annotated_images_list()
+
+    def _import_images(self) -> None:
+        """Import one or more images into the database and refresh the list."""
+
+        settings = QSettings("microscopy_app", "object_detection")
+        last_dir = settings.value("annotation_tab/last_image_import_directory", None)
+
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        if last_dir and Path(str(last_dir)).exists():
+            start_dir = str(last_dir)
+        elif repo_root and Path(repo_root).exists():
+            start_dir = repo_root
+        else:
+            start_dir = str(Path.home())
+
+        # Build filter string for supported extensions
+        patterns = " ".join(f"*{ext}" for ext in Image.SUPPORTED_EXTENSIONS)
+        file_paths, _ = QFileDialog.getOpenFileNames(
+            self,
+            "Select Image File(s)",
+            start_dir,
+            f"Images ({patterns})",
+        )
+
+        if not file_paths:
+            return
+
+        try:
+            settings.setValue("annotation_tab/last_image_import_directory", str(Path(file_paths[0]).parent))
+            # Keep compatibility with the existing image resolver fallback (it checks last_directory).
+            settings.setValue("annotation_tab/last_directory", str(Path(file_paths[0]).parent))
+        except Exception:
+            pass
+
+        imported = 0
+        tagged_existing = 0
+        skipped = 0
+        errors: list[str] = []
+
+        for fp in file_paths:
+            try:
+                img_path = Path(fp)
+                img = Image(str(img_path))
+                relative_path = self._compute_relative_path_for_repo(img_path)
+
+                # Skip if already present
+                existing = self.db_manager.get_image_by_path(relative_path)
+                if existing:
+                    # If the image already exists (e.g. created earlier by other workflows),
+                    # tag it as being managed by the Annotation tab so it becomes visible
+                    # in the left list.
+                    try:
+                        self.db_manager.set_image_source(int(existing["id"]), "annotation_tab")
+                        tagged_existing += 1
+                    except Exception:
+                        # If tagging fails, fall back to treating as skipped.
+                        skipped += 1
+                    continue
+
+                image_id = self.db_manager.add_image(
+                    relative_path,
+                    img_path.name,
+                    img.width,
+                    img.height,
+                    source="annotation_tab",
+                )
+                try:
+                    # In case the DB row was created by an older schema/migration path.
+                    self.db_manager.set_image_source(image_id, "annotation_tab")
+                except Exception:
+                    pass
+                imported += 1
+            except ImageLoadError as exc:
+                skipped += 1
+                errors.append(f"Failed to load image {fp}: {exc}")
+            except Exception as exc:
+                skipped += 1
+                errors.append(f"Failed to import image {fp}: {exc}")
+
+        self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
+        msg = (
+            f"Imported: {imported}\n"
+            f"Already in DB (tagged for Annotation tab): {tagged_existing}\n"
+            f"Skipped (errors): {skipped}"
+        )
+        if errors:
+            details = "\n".join(errors[:25])
+            if len(errors) > 25:
+                details += f"\n... and {len(errors) - 25} more"
+            msg += "\n\nDetails:\n" + details
+        QMessageBox.information(self, "Import Images", msg)
+
+    # ==================== Import annotations (YOLO .txt) ====================
+
+    def _import_annotations(self) -> None:
+        """Import YOLO segmentation/bbox annotations from one or more .txt files."""
+
+        settings = QSettings("microscopy_app", "object_detection")
+        last_dir = settings.value("annotation_tab/last_annotation_directory", None)
+
+        # Default start dir: repo root if set, otherwise last used, otherwise home.
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        if last_dir and Path(str(last_dir)).exists():
+            start_dir = str(last_dir)
+        elif repo_root and Path(repo_root).exists():
+            start_dir = repo_root
+        else:
+            start_dir = str(Path.home())
+
+        file_paths, _ = QFileDialog.getOpenFileNames(
+            self,
+            "Select YOLO Annotation File(s)",
+            start_dir,
+            "YOLO annotations (*.txt)",
+        )
+
+        if not file_paths:
+            return
+
+        # Persist last annotation directory for the next import.
+        try:
+            settings.setValue("annotation_tab/last_annotation_directory", str(Path(file_paths[0]).parent))
+        except Exception:
+            pass
+
+        imported_images = 0
+        imported_annotations = 0
+        overwritten_images = 0
+        skipped = 0
+        errors: list[str] = []
+
+        for label_file in file_paths:
+            label_path = Path(label_file)
+            try:
+                image_path = self._infer_corresponding_image_path(label_path)
+                if not image_path:
+                    skipped += 1
+                    errors.append(f"Image not found for label file: {label_path}")
+                    continue
+
+                # Load image to obtain width/height for DB entry.
+                img = Image(str(image_path))
+
+                # Store in DB using a repo-relative path if possible.
+                relative_path = self._compute_relative_path_for_repo(image_path)
+                image_id = self.db_manager.get_or_create_image(relative_path, image_path.name, img.width, img.height)
+                try:
+                    self.db_manager.set_image_source(image_id, "annotation_tab")
+                except Exception:
+                    pass
+
+                # Overwrite existing annotations for this image.
+                try:
+                    deleted = self.db_manager.delete_annotations_for_image(image_id)
+                except AttributeError:
+                    # Safety fallback if older DBManager is used.
+                    deleted = 0
+                if deleted > 0:
+                    overwritten_images += 1
+
+                # Parse YOLO lines and insert as annotations.
+                parsed = self._parse_yolo_annotation_file(label_path)
+                if not parsed:
+                    # Empty/invalid label file: treat as "clear" operation (already deleted above)
+                    imported_images += 1
+                    continue
+
+                db_classes = self.db_manager.get_object_classes() or []
+                classes_by_index = {idx: row for idx, row in enumerate(db_classes)}
+
+                for class_index, bbox, poly in parsed:
+                    class_row = classes_by_index.get(int(class_index))
+                    if not class_row:
+                        skipped += 1
+                        errors.append(
+                            f"Unknown class index {class_index} in {label_path.name}. "
+                            "Create object classes in the UI first (class index is based on DB ordering)."
+                        )
+                        continue
+
+                    ann_id = self.db_manager.add_annotation(
+                        image_id=image_id,
+                        class_id=int(class_row["id"]),
+                        bbox=bbox,
+                        annotator="import",
+                        segmentation_mask=poly,
+                        verified=False,
+                    )
+                    if ann_id:
+                        imported_annotations += 1
+
+                imported_images += 1
+
+                # If we imported for the currently open image, reload.
+                if self.current_image_id and int(self.current_image_id) == int(image_id):
+                    self._load_annotations_for_current_image()
+
+            except ImageLoadError as exc:
+                skipped += 1
+                errors.append(f"Failed to load image for {label_path.name}: {exc}")
+            except Exception as exc:
+                skipped += 1
+                errors.append(f"Import failed for {label_path.name}: {exc}")
+
+        # Refresh annotated images list.
+        self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
+        summary = (
+            f"Imported files: {len(file_paths)}\n"
+            f"Images processed: {imported_images}\n"
+            f"Annotations inserted: {imported_annotations}\n"
+            f"Images overwritten (had existing annotations): {overwritten_images}\n"
+            f"Skipped: {skipped}"
+        )
+        if errors:
+            # Cap error details to avoid huge dialogs.
+            details = "\n".join(errors[:25])
+            if len(errors) > 25:
+                details += f"\n... and {len(errors) - 25} more"
+            summary += "\n\nDetails:\n" + details
+
+        QMessageBox.information(self, "Import Annotations", summary)
+
+    def _infer_corresponding_image_path(self, label_path: Path) -> Path | None:
+        """Infer image path from YOLO label file path.
+
+        Requirement: image(s) live in an `images/` folder located in the label file's parent directory.
+        Example:
+            /dataset/train/labels/img123.txt -> /dataset/train/images/img123.(any supported ext)
+        """
+
+        parent = label_path.parent
+        images_dir = parent.parent / "images"
+        stem = label_path.stem
+
+        # 1) Direct stem match in images dir (any supported extension)
+        for ext in Image.SUPPORTED_EXTENSIONS:
+            candidate = images_dir / f"{stem}{ext}"
+            if candidate.exists() and candidate.is_file():
+                return candidate
+
+        # 2) Fallback: repository-root search by filename
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        if repo_root:
+            root = Path(repo_root).expanduser()
+            try:
+                if root.exists():
+                    for ext in Image.SUPPORTED_EXTENSIONS:
+                        filename = f"{stem}{ext}"
+                        for match in root.rglob(filename):
+                            if match.is_file():
+                                return match.resolve()
+            except Exception:
+                pass
+
+        return None
+
+    def _compute_relative_path_for_repo(self, image_path: Path) -> str:
+        """Compute a stable `relative_path` suitable for DB storage.
+
+        Policy:
+        - If an image repository root is configured and the image is under it, store a repo-relative path.
+        - Otherwise, store an absolute resolved path so the image can be reopened later.
+        """
+
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        try:
+            if repo_root:
+                repo_root_path = Path(repo_root).expanduser().resolve()
+                img_resolved = image_path.expanduser().resolve()
+                if img_resolved.is_relative_to(repo_root_path):
+                    return img_resolved.relative_to(repo_root_path).as_posix()
+        except Exception:
+            pass
+        try:
+            return str(image_path.expanduser().resolve())
+        except Exception:
+            return str(image_path)
+
+    def _parse_yolo_annotation_file(
+        self, label_path: Path
+    ) -> list[tuple[int, tuple[float, float, float, float], list[list[float]] | None]]:
+        """Parse a YOLO .txt label file.
+
+        Supports:
+          - YOLO segmentation polygon format: "class x1 y1 x2 y2 ..." (normalized)
+          - YOLO bbox format: "class x_center y_center width height" (normalized)
+
+        Returns:
+            List of (class_index, bbox_xyxy_norm, segmentation_mask_db)
+            Where segmentation_mask_db is [[y_norm, x_norm], ...] or None.
+        """
+
+        out: list[tuple[int, tuple[float, float, float, float], list[list[float]] | None]] = []
+        try:
+            raw = label_path.read_text(encoding="utf-8").splitlines()
+        except OSError as exc:
+            logger.error(f"Failed to read label file {label_path}: {exc}")
+            return out
+
+        for line in raw:
+            stripped = line.strip()
+            if not stripped:
+                continue
+            parts = stripped.split()
+            if len(parts) < 5:
+                # not enough for bbox
+                continue
+
+            try:
+                class_idx = int(float(parts[0]))
+                coords = [float(x) for x in parts[1:]]
+            except Exception:
+                continue
+
+            # Segmentation polygon format (>= 6 values)
+            if len(coords) >= 6:
+                # bbox is not explicitly present in this format in our importer; compute from polygon.
+                xs = coords[0::2]
+                ys = coords[1::2]
+                if not xs or not ys:
+                    continue
+                x_min, x_max = min(xs), max(xs)
+                y_min, y_max = min(ys), max(ys)
+                bbox = (
+                    self._clamp01(x_min),
+                    self._clamp01(y_min),
+                    self._clamp01(x_max),
+                    self._clamp01(y_max),
+                )
+
+                # Convert to DB polyline convention: [[y_norm, x_norm], ...]
+                poly: list[list[float]] = []
+                for x, y in zip(xs, ys):
+                    poly.append([self._clamp01(float(y)), self._clamp01(float(x))])
+                # Ensure closure for consistency (optional)
+                if poly and poly[0] != poly[-1]:
+                    poly.append(list(poly[0]))
+                out.append((class_idx, bbox, poly))
+                continue
+
+            # bbox format: xc yc w h
+            if len(coords) >= 4:
+                xc, yc, w, h = coords[:4]
+                x_min = xc - w / 2.0
+                y_min = yc - h / 2.0
+                x_max = xc + w / 2.0
+                y_max = yc + h / 2.0
+                bbox = (
+                    self._clamp01(float(x_min)),
+                    self._clamp01(float(y_min)),
+                    self._clamp01(float(x_max)),
+                    self._clamp01(float(y_max)),
+                )
+                out.append((class_idx, bbox, None))
+
+        return out
+
+    @staticmethod
+    def _clamp01(value: float) -> float:
+        if value < 0.0:
+            return 0.0
+        if value > 1.0:
+            return 1.0
+        return float(value)
+
     def _load_image(self):
         """Load and display an image file."""
         # Get last opened directory from QSettings
@@ -180,18 +580,35 @@ class AnnotationTab(QWidget):
             self.current_image_path = file_path
 
             # Store the directory for next time
-            settings.setValue(
+            settings.setValue("annotation_tab/last_directory", str(Path(file_path).parent))
-                "annotation_tab/last_directory", str(Path(file_path).parent)
-            )
 
             # Get or create image in database
-            relative_path = str(Path(file_path).name)  # Simplified for now
+            repo_root = self.config_manager.get_image_repository_path()
+            relative_path: str
+            try:
+                if repo_root:
+                    repo_root_path = Path(repo_root).expanduser().resolve()
+                    file_resolved = Path(file_path).expanduser().resolve()
+                    if file_resolved.is_relative_to(repo_root_path):
+                        relative_path = file_resolved.relative_to(repo_root_path).as_posix()
+                    else:
+                        # Fallback: store filename only to avoid leaking absolute paths.
+                        relative_path = file_resolved.name
+                else:
+                    relative_path = str(Path(file_path).name)
+            except Exception:
+                relative_path = str(Path(file_path).name)
             self.current_image_id = self.db_manager.get_or_create_image(
                 relative_path,
                 Path(file_path).name,
                 self.current_image.width,
                 self.current_image.height,
             )
+            # Mark as managed by Annotation tab so it appears in the left list.
+            try:
+                self.db_manager.set_image_source(int(self.current_image_id), "annotation_tab")
+            except Exception:
+                pass
 
             # Display image using the AnnotationCanvasWidget
             self.annotation_canvas.load_image(self.current_image)
@@ -199,6 +616,9 @@ class AnnotationTab(QWidget):
             # Load and display any existing annotations for this image
             self._load_annotations_for_current_image()
 
+            # Update annotated images list (newly annotated image added/selected).
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
             # Update info label
             self._update_image_info()
 
@@ -206,9 +626,7 @@ class AnnotationTab(QWidget):
 
         except ImageLoadError as e:
             logger.error(f"Failed to load image: {e}")
-            QMessageBox.critical(
+            QMessageBox.critical(self, "Error Loading Image", f"Failed to load image:\n{str(e)}")
-                self, "Error Loading Image", f"Failed to load image:\n{str(e)}"
-            )
         except Exception as e:
             logger.error(f"Unexpected error loading image: {e}")
             QMessageBox.critical(self, "Error", f"Unexpected error:\n{str(e)}")
@@ -296,6 +714,9 @@ class AnnotationTab(QWidget):
             # Reload annotations from DB and redraw (respecting current class filter)
             self._load_annotations_for_current_image()
 
+            # Update list counts.
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
         except Exception as e:
             logger.error(f"Failed to save annotation: {e}")
             QMessageBox.critical(self, "Error", f"Failed to save annotation:\n{str(e)}")
@@ -340,9 +761,7 @@ class AnnotationTab(QWidget):
         if not self.current_image_id:
             return
 
-        logger.debug(
+        logger.debug(f"Class color changed; reloading annotations for image ID {self.current_image_id}")
-            f"Class color changed; reloading annotations for image ID {self.current_image_id}"
-        )
         self._load_annotations_for_current_image()
 
     def _on_class_selected(self, class_data):
@@ -355,9 +774,7 @@ class AnnotationTab(QWidget):
         if class_data:
             logger.debug(f"Object class selected: {class_data['class_name']}")
         else:
-            logger.debug(
+            logger.debug('No class selected ("-- Select Class --"), showing all annotations')
-                'No class selected ("-- Select Class --"), showing all annotations'
-            )
 
         # Changing the class filter invalidates any previous selection
         self.selected_annotation_ids = []
@@ -390,9 +807,7 @@ class AnnotationTab(QWidget):
             question = "Are you sure you want to delete the selected annotation?"
             title = "Delete Annotation"
         else:
-            question = (
+            question = f"Are you sure you want to delete the {count} selected annotations?"
-                f"Are you sure you want to delete the {count} selected annotations?"
-            )
             title = "Delete Annotations"
 
         reply = QMessageBox.question(
@@ -420,13 +835,11 @@ class AnnotationTab(QWidget):
                 QMessageBox.warning(
                     self,
                     "Partial Failure",
-                    "Some annotations could not be deleted:\n"
+                    "Some annotations could not be deleted:\n" + ", ".join(str(a) for a in failed_ids),
-                    + ", ".join(str(a) for a in failed_ids),
                 )
             else:
                 logger.info(
-                    f"Deleted {count} annotation(s): "
+                    f"Deleted {count} annotation(s): " + ", ".join(str(a) for a in self.selected_annotation_ids)
-                    + ", ".join(str(a) for a in self.selected_annotation_ids)
                 )
 
             # Clear selection and reload annotations for the current image from DB
@@ -434,6 +847,9 @@ class AnnotationTab(QWidget):
             self.annotation_tools.set_has_selected_annotation(False)
             self._load_annotations_for_current_image()
 
+            # Update list counts.
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
         except Exception as e:
             logger.error(f"Failed to delete annotations: {e}")
             QMessageBox.critical(
@@ -456,17 +872,13 @@ class AnnotationTab(QWidget):
             return
 
         try:
-            self.current_annotations = self.db_manager.get_annotations_for_image(
+            self.current_annotations = self.db_manager.get_annotations_for_image(self.current_image_id)
-                self.current_image_id
-            )
             # New annotations loaded; reset any selection
             self.selected_annotation_ids = []
             self.annotation_tools.set_has_selected_annotation(False)
             self._redraw_annotations_for_current_filter()
         except Exception as e:
-            logger.error(
+            logger.error(f"Failed to load annotations for image {self.current_image_id}: {e}")
-                f"Failed to load annotations for image {self.current_image_id}: {e}"
-            )
             QMessageBox.critical(
                 self,
                 "Error",
@@ -490,10 +902,7 @@ class AnnotationTab(QWidget):
         drawn_count = 0
         for ann in self.current_annotations:
             # Filter by class if one is selected
-            if (
+            if selected_class_id is not None and ann.get("class_id") != selected_class_id:
-                selected_class_id is not None
-                and ann.get("class_id") != selected_class_id
-            ):
                 continue
 
             if ann.get("segmentation_mask"):
@@ -545,22 +954,185 @@ class AnnotationTab(QWidget):
         settings = QSettings("microscopy_app", "object_detection")
 
         # Save main splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/main_splitter_state", self.main_splitter.saveState())
-            "annotation_tab/main_splitter_state", self.main_splitter.saveState()
-        )
 
         # Save left splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/left_splitter_state", self.left_splitter.saveState())
-            "annotation_tab/left_splitter_state", self.left_splitter.saveState()
-        )
 
         # Save right splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/right_splitter_state", self.right_splitter.saveState())
-            "annotation_tab/right_splitter_state", self.right_splitter.saveState()
-        )
 
         logger.debug("Saved annotation tab splitter states")
 
     def refresh(self):
         """Refresh the tab."""
-        pass
+        self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
+    # ==================== Annotated images list ====================
+
+    def _refresh_annotated_images_list(self, select_image_id: int | None = None) -> None:
+        """Reload annotated-images list from the database."""
+        if not hasattr(self, "annotated_images_table"):
+            return
+
+        # Preserve selection if possible
+        desired_id = select_image_id if select_image_id is not None else self.current_image_id
+
+        name_filter = ""
+        if hasattr(self, "annotated_filter_edit"):
+            name_filter = self.annotated_filter_edit.text().strip()
+
+        try:
+            rows = self.db_manager.get_images_summary(name_filter=name_filter, source_filter="annotation_tab")
+        except Exception as exc:
+            logger.error(f"Failed to load images summary: {exc}")
+            rows = []
+
+        sorting_enabled = self.annotated_images_table.isSortingEnabled()
+        self.annotated_images_table.setSortingEnabled(False)
+        self.annotated_images_table.blockSignals(True)
+        try:
+            self.annotated_images_table.setRowCount(len(rows))
+            for r, entry in enumerate(rows):
+                image_name = str(entry.get("filename") or "")
+                count = int(entry.get("annotation_count") or 0)
+                rel_path = str(entry.get("relative_path") or "")
+
+                name_item = QTableWidgetItem(image_name)
+                # Tooltip shows full path of the image (best-effort: repository_root + relative_path)
+                full_path = rel_path
+                repo_root = self.config_manager.get_image_repository_path()
+                if repo_root and rel_path and not Path(rel_path).is_absolute():
+                    try:
+                        full_path = str((Path(repo_root) / rel_path).resolve())
+                    except Exception:
+                        full_path = str(Path(repo_root) / rel_path)
+                name_item.setToolTip(full_path)
+                name_item.setData(Qt.UserRole, int(entry.get("id")))
+                name_item.setData(Qt.UserRole + 1, rel_path)
+
+                count_item = QTableWidgetItem()
+                # Use EditRole to ensure numeric sorting.
+                count_item.setData(Qt.EditRole, count)
+                count_item.setData(Qt.UserRole, int(entry.get("id")))
+                count_item.setData(Qt.UserRole + 1, rel_path)
+
+                self.annotated_images_table.setItem(r, 0, name_item)
+                self.annotated_images_table.setItem(r, 1, count_item)
+
+            # Re-select desired row
+            if desired_id is not None:
+                for r in range(self.annotated_images_table.rowCount()):
+                    item = self.annotated_images_table.item(r, 0)
+                    if item and item.data(Qt.UserRole) == desired_id:
+                        self.annotated_images_table.selectRow(r)
+                        break
+        finally:
+            self.annotated_images_table.blockSignals(False)
+            self.annotated_images_table.setSortingEnabled(sorting_enabled)
+
+    def _on_annotated_image_selected(self) -> None:
+        """When user clicks an item in the list, load that image in the annotation canvas."""
+        selected = self.annotated_images_table.selectedItems()
+        if not selected:
+            return
+
+        # Row selection -> take the first column item
+        row = self.annotated_images_table.currentRow()
+        item = self.annotated_images_table.item(row, 0)
+        if not item:
+            return
+
+        image_id = item.data(Qt.UserRole)
+        rel_path = item.data(Qt.UserRole + 1) or ""
+        if not image_id:
+            return
+
+        image_path = self._resolve_image_path_for_relative_path(rel_path)
+        if not image_path:
+            QMessageBox.warning(
+                self,
+                "Image Not Found",
+                "Unable to locate image on disk for:\n"
+                f"{rel_path}\n\n"
+                "Tip: set Settings → Image repository path to the folder containing your images.",
+            )
+            return
+
+        try:
+            self.current_image = Image(image_path)
+            self.current_image_path = image_path
+            self.current_image_id = int(image_id)
+            self.annotation_canvas.load_image(self.current_image)
+            self._load_annotations_for_current_image()
+            self._update_image_info()
+        except ImageLoadError as exc:
+            logger.error(f"Failed to load image '{image_path}': {exc}")
+            QMessageBox.critical(self, "Error Loading Image", f"Failed to load image:\n{exc}")
+        except Exception as exc:
+            logger.error(f"Unexpected error loading image '{image_path}': {exc}")
+            QMessageBox.critical(self, "Error", f"Unexpected error:\n{exc}")
+
+    def _resolve_image_path_for_relative_path(self, relative_path: str) -> str | None:
+        """Best-effort conversion from a DB relative_path to an on-disk file path."""
+
+        rel = (relative_path or "").strip()
+        if not rel:
+            return None
+
+        candidates: list[Path] = []
+
+        # 1) Repository root + relative
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        if repo_root:
+            candidates.append(Path(repo_root) / rel)
+
+        # 2) If the DB path is absolute, try it directly.
+        candidates.append(Path(rel))
+
+        # 3) Try the directory of the currently loaded image (helps when DB stores only filenames)
+        if self.current_image_path:
+            try:
+                candidates.append(Path(self.current_image_path).expanduser().resolve().parent / Path(rel).name)
+            except Exception:
+                pass
+
+        # 4) Try the last directory used by the annotation file picker
+        try:
+            settings = QSettings("microscopy_app", "object_detection")
+            last_dir = settings.value("annotation_tab/last_directory", None)
+            if last_dir:
+                candidates.append(Path(str(last_dir)) / Path(rel).name)
+        except Exception:
+            pass
+
+        # 4b) Try the last directory used by the image import picker
+        try:
+            settings = QSettings("microscopy_app", "object_detection")
+            last_import_dir = settings.value("annotation_tab/last_image_import_directory", None)
+            if last_import_dir:
+                candidates.append(Path(str(last_import_dir)) / Path(rel).name)
+        except Exception:
+            pass
+
+        for p in candidates:
+            try:
+                expanded = p.expanduser()
+                if expanded.exists() and expanded.is_file():
+                    return str(expanded.resolve())
+            except Exception:
+                continue
+
+        # 5) Fallback: search by filename within repository root.
+        filename = Path(rel).name
+        if repo_root and filename:
+            root = Path(repo_root).expanduser()
+            try:
+                if root.exists():
+                    for match in root.rglob(filename):
+                        if match.is_file():
+                            return str(match.resolve())
+            except Exception as exc:
+                logger.debug(f"Search for {filename} under {root} failed: {exc}")
+
+        return None

src/gui/tabs/results_tab.py

@@ -3,7 +3,7 @@ Results tab for browsing stored detections and visualizing overlays.
 """
 
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from PySide6.QtWidgets import (
     QWidget,
@@ -40,6 +40,12 @@ class ResultsTab(QWidget):
         self.db_manager = db_manager
         self.config_manager = config_manager
 
+        # Pagination
+        self.page_size = 200
+        self.current_page = 0  # 0-based
+        self.total_runs = 0
+        self.total_pages = 0
+
         self.detection_summary: List[Dict] = []
         self.current_selection: Optional[Dict] = None
         self.current_image: Optional[Image] = None
@@ -65,6 +71,36 @@ class ResultsTab(QWidget):
         self.refresh_btn = QPushButton("Refresh")
         self.refresh_btn.clicked.connect(self.refresh)
         controls_layout.addWidget(self.refresh_btn)
+
+        self.prev_page_btn = QPushButton("◀ Prev")
+        self.prev_page_btn.setToolTip("Previous page")
+        self.prev_page_btn.clicked.connect(self._prev_page)
+        controls_layout.addWidget(self.prev_page_btn)
+
+        self.next_page_btn = QPushButton("Next ▶")
+        self.next_page_btn.setToolTip("Next page")
+        self.next_page_btn.clicked.connect(self._next_page)
+        controls_layout.addWidget(self.next_page_btn)
+
+        self.page_label = QLabel("Page 1/1")
+        self.page_label.setMinimumWidth(100)
+        controls_layout.addWidget(self.page_label)
+
+        self.delete_all_btn = QPushButton("Delete All Detections")
+        self.delete_all_btn.setToolTip(
+            "Permanently delete ALL detections from the database.\n" "This cannot be undone."
+        )
+        self.delete_all_btn.clicked.connect(self._delete_all_detections)
+        controls_layout.addWidget(self.delete_all_btn)
+
+        self.export_labels_btn = QPushButton("Export Labels")
+        self.export_labels_btn.setToolTip(
+            "Export YOLO .txt labels for the selected image/model run.\n"
+            "Output path is inferred from the image path (images/ -> labels/)."
+        )
+        self.export_labels_btn.clicked.connect(self._export_labels_for_current_selection)
+        controls_layout.addWidget(self.export_labels_btn)
+
         controls_layout.addStretch()
         left_layout.addLayout(controls_layout)
 
@@ -130,8 +166,45 @@ class ResultsTab(QWidget):
         layout.addWidget(splitter)
         self.setLayout(layout)
 
+    def _delete_all_detections(self):
+        """Delete all detections from the database after user confirmation."""
+        confirm = QMessageBox.warning(
+            self,
+            "Delete All Detections",
+            "This will permanently delete ALL detections from the database.\n\n"
+            "This action cannot be undone.\n\n"
+            "Do you want to continue?",
+            QMessageBox.Yes | QMessageBox.No,
+            QMessageBox.No,
+        )
+
+        if confirm != QMessageBox.Yes:
+            return
+
+        try:
+            deleted = self.db_manager.delete_all_detections()
+        except Exception as exc:
+            logger.error(f"Failed to delete all detections: {exc}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to delete detections:\n{exc}",
+            )
+            return
+
+        QMessageBox.information(
+            self,
+            "Delete All Detections",
+            f"Deleted {deleted} detection(s) from the database.",
+        )
+
+        # Reset UI state.
+        self.refresh()
+
     def refresh(self):
         """Refresh the detection list and preview."""
+        # Reset to first page on refresh.
+        self.current_page = 0
         self._load_detection_summary()
         self._populate_results_table()
         self.current_selection = None
@@ -139,58 +212,138 @@ class ResultsTab(QWidget):
         self.current_detections = []
         self.preview_canvas.clear()
         self.summary_label.setText("Select a detection result to preview.")
+        if hasattr(self, "export_labels_btn"):
+            self.export_labels_btn.setEnabled(False)
+
+        self._update_pagination_controls()
+
+    def _prev_page(self):
+        """Go to previous results page."""
+        if self.current_page <= 0:
+            return
+        self.current_page -= 1
+        self._load_detection_summary()
+        self._populate_results_table()
+        self._update_pagination_controls()
+
+    def _next_page(self):
+        """Go to next results page."""
+        if self.total_pages and self.current_page >= (self.total_pages - 1):
+            return
+        self.current_page += 1
+        self._load_detection_summary()
+        self._populate_results_table()
+        self._update_pagination_controls()
+
+    def _update_pagination_controls(self):
+        """Update pagination label/button enabled state."""
+
+        # Default state (safe)
+        total_pages = max(int(self.total_pages or 0), 1)
+        current_page = min(max(int(self.current_page or 0), 0), total_pages - 1)
+        self.current_page = current_page
+
+        self.page_label.setText(f"Page {current_page + 1}/{total_pages}")
+        self.prev_page_btn.setEnabled(current_page > 0)
+        self.next_page_btn.setEnabled(current_page < (total_pages - 1))
 
     def _load_detection_summary(self):
         """Load latest detection summaries grouped by image + model."""
         try:
-            detections = self.db_manager.get_detections(limit=500)
+            # Prefer run summaries (supports zero-detection runs). Fall back to legacy
-            summary_map: Dict[tuple, Dict] = {}
+            # detection aggregation if the DB/table isn't available.
+            try:
+                self.total_runs = int(self.db_manager.get_detection_run_total())
+            except Exception:
+                self.total_runs = 0
 
-            for det in detections:
+            self.total_pages = (self.total_runs + self.page_size - 1) // self.page_size if self.total_runs > 0 else 1
-                key = (det["image_id"], det["model_id"])
+
-                metadata = det.get("metadata") or {}
+            offset = int(self.current_page) * int(self.page_size)
-                entry = summary_map.setdefault(
+            runs = self.db_manager.get_detection_run_summaries(
-                    key,
+                limit=int(self.page_size),
+                offset=offset,
+            )
+            summary: List[Dict] = []
+            for run in runs:
+                meta = run.get("metadata") or {}
+                classes_raw = run.get("classes")
+                classes = set()
+                if isinstance(classes_raw, str) and classes_raw.strip():
+                    classes = {c.strip() for c in classes_raw.split(",") if c.strip()}
+
+                summary.append(
                     {
-                        "image_id": det["image_id"],
+                        "image_id": run.get("image_id"),
-                        "model_id": det["model_id"],
+                        "model_id": run.get("model_id"),
-                        "image_path": det.get("image_path"),
+                        "image_path": run.get("image_path"),
-                        "image_filename": det.get("image_filename") or det.get("image_path"),
+                        "image_filename": run.get("image_filename") or run.get("image_path"),
-                        "model_name": det.get("model_name", ""),
+                        "model_name": run.get("model_name", ""),
-                        "model_version": det.get("model_version", ""),
+                        "model_version": run.get("model_version", ""),
-                        "last_detected": det.get("detected_at"),
+                        "last_detected": run.get("detected_at"),
-                        "count": 0,
+                        "count": int(run.get("count") or 0),
-                        "classes": set(),
+                        "classes": classes,
-                        "source_path": metadata.get("source_path"),
+                        "source_path": meta.get("source_path"),
-                        "repository_root": metadata.get("repository_root"),
+                        "repository_root": meta.get("repository_root"),
-                    },
+                    }
                 )
 
-                entry["count"] += 1
+            self.detection_summary = summary
-                if det.get("detected_at") and (
-                    not entry.get("last_detected") or str(det.get("detected_at")) > str(entry.get("last_detected"))
-                ):
-                    entry["last_detected"] = det.get("detected_at")
-                if det.get("class_name"):
-                    entry["classes"].add(det["class_name"])
-                if metadata.get("source_path") and not entry.get("source_path"):
-                    entry["source_path"] = metadata.get("source_path")
-                if metadata.get("repository_root") and not entry.get("repository_root"):
-                    entry["repository_root"] = metadata.get("repository_root")
-
-            self.detection_summary = sorted(
-                summary_map.values(),
-                key=lambda x: str(x.get("last_detected") or ""),
-                reverse=True,
-            )
         except Exception as e:
-            logger.error(f"Failed to load detection summary: {e}")
+            logger.error(f"Failed to load detection run summaries, falling back: {e}")
-            QMessageBox.critical(
+            # Disable pagination if we can't page via detection_runs.
-                self,
+            self.total_runs = 0
-                "Error",
+            self.total_pages = 1
-                f"Failed to load detection results:\n{str(e)}",
+            self.current_page = 0
-            )
+            try:
-            self.detection_summary = []
+                detections = self.db_manager.get_detections(limit=int(self.page_size))
+                summary_map: Dict[tuple, Dict] = {}
+
+                for det in detections:
+                    key = (det["image_id"], det["model_id"])
+                    metadata = det.get("metadata") or {}
+                    entry = summary_map.setdefault(
+                        key,
+                        {
+                            "image_id": det["image_id"],
+                            "model_id": det["model_id"],
+                            "image_path": det.get("image_path"),
+                            "image_filename": det.get("image_filename") or det.get("image_path"),
+                            "model_name": det.get("model_name", ""),
+                            "model_version": det.get("model_version", ""),
+                            "last_detected": det.get("detected_at"),
+                            "count": 0,
+                            "classes": set(),
+                            "source_path": metadata.get("source_path"),
+                            "repository_root": metadata.get("repository_root"),
+                        },
+                    )
+
+                    entry["count"] += 1
+                    if det.get("detected_at") and (
+                        not entry.get("last_detected") or str(det.get("detected_at")) > str(entry.get("last_detected"))
+                    ):
+                        entry["last_detected"] = det.get("detected_at")
+                    if det.get("class_name"):
+                        entry["classes"].add(det["class_name"])
+                    if metadata.get("source_path") and not entry.get("source_path"):
+                        entry["source_path"] = metadata.get("source_path")
+                    if metadata.get("repository_root") and not entry.get("repository_root"):
+                        entry["repository_root"] = metadata.get("repository_root")
+
+                self.detection_summary = sorted(
+                    summary_map.values(),
+                    key=lambda x: str(x.get("last_detected") or ""),
+                    reverse=True,
+                )
+            except Exception as inner:
+                logger.error(f"Failed to load detection summary: {inner}")
+                QMessageBox.critical(
+                    self,
+                    "Error",
+                    f"Failed to load detection results:\n{str(inner)}",
+                )
+                self.detection_summary = []
 
     def _populate_results_table(self):
         """Populate the table widget with detection summaries."""
@@ -258,6 +411,231 @@ class ResultsTab(QWidget):
         self._load_detections_for_selection(entry)
         self._apply_detection_overlays()
         self._update_summary_label(entry)
+        if hasattr(self, "export_labels_btn"):
+            self.export_labels_btn.setEnabled(True)
+
+    def _export_labels_for_current_selection(self):
+        """Export YOLO label file(s) for the currently selected image/model."""
+        if not self.current_selection:
+            QMessageBox.information(self, "Export Labels", "Select a detection result first.")
+            return
+
+        entry = self.current_selection
+
+        image_path_str = self._resolve_image_path(entry)
+        if not image_path_str:
+            QMessageBox.warning(
+                self,
+                "Export Labels",
+                "Unable to locate the image file for this detection; cannot infer labels path.",
+            )
+            return
+
+        # Ensure we have the detections for the selection.
+        if not self.current_detections:
+            self._load_detections_for_selection(entry)
+
+        if not self.current_detections:
+            QMessageBox.information(
+                self,
+                "Export Labels",
+                "No detections found for this image/model selection.",
+            )
+            return
+
+        image_path = Path(image_path_str)
+        try:
+            label_path = self._infer_yolo_label_path(image_path)
+        except Exception as exc:
+            logger.error(f"Failed to infer label path for {image_path}: {exc}")
+            QMessageBox.critical(
+                self,
+                "Export Labels",
+                f"Failed to infer export path for labels:\n{exc}",
+            )
+            return
+
+        class_map = self._build_detection_class_index_map(self.current_detections)
+        if not class_map:
+            QMessageBox.warning(
+                self,
+                "Export Labels",
+                "Unable to build class->index mapping (missing class names).",
+            )
+            return
+
+        lines_written = 0
+        skipped = 0
+        label_path.parent.mkdir(parents=True, exist_ok=True)
+        try:
+            with open(label_path, "w", encoding="utf-8") as handle:
+                print("writing to", label_path)
+                for det in self.current_detections:
+                    yolo_line = self._format_detection_as_yolo_line(det, class_map)
+                    if not yolo_line:
+                        skipped += 1
+                        continue
+                    handle.write(yolo_line + "\n")
+                    lines_written += 1
+        except OSError as exc:
+            logger.error(f"Failed to write labels file {label_path}: {exc}")
+            QMessageBox.critical(
+                self,
+                "Export Labels",
+                f"Failed to write label file:\n{label_path}\n\n{exc}",
+            )
+            return
+
+        return
+        # Optional: write a classes.txt next to the labels root to make the mapping discoverable.
+        # This is not required by Ultralytics (data.yaml usually holds class names), but helps reuse.
+        try:
+            classes_txt = label_path.parent.parent / "classes.txt"
+            classes_txt.parent.mkdir(parents=True, exist_ok=True)
+            inv = {idx: name for name, idx in class_map.items()}
+            with open(classes_txt, "w", encoding="utf-8") as handle:
+                for idx in range(len(inv)):
+                    handle.write(f"{inv[idx]}\n")
+        except Exception:
+            # Non-fatal
+            pass
+
+        QMessageBox.information(
+            self,
+            "Export Labels",
+            f"Exported {lines_written} label line(s) to:\n{label_path}\n\nSkipped {skipped} invalid detection(s).",
+        )
+
+    def _infer_yolo_label_path(self, image_path: Path) -> Path:
+        """Infer a YOLO label path from an image path.
+
+        If the image lives under an `images/` directory (anywhere in the path), we mirror the
+        subpath under a sibling `labels/` directory at the same level.
+
+        Example:
+            /dataset/train/images/sub/img.jpg -> /dataset/train/labels/sub/img.txt
+        """
+
+        resolved = image_path.expanduser().resolve()
+
+        # Find the nearest ancestor directory named 'images'
+        images_dir: Optional[Path] = None
+        for parent in [resolved.parent, *resolved.parents]:
+            if parent.name.lower() == "images":
+                images_dir = parent
+                break
+
+        if images_dir is not None:
+            rel = resolved.relative_to(images_dir)
+            labels_dir = images_dir.parent / "labels"
+            return (labels_dir / rel).with_suffix(".txt")
+
+        # Fallback: create a local sibling labels folder next to the image.
+        return (resolved.parent / "labels" / resolved.name).with_suffix(".txt")
+
+    def _build_detection_class_index_map(self, detections: List[Dict]) -> Dict[str, int]:
+        """Build a stable class_name -> YOLO class index mapping.
+
+        Preference order:
+        1) Database object_classes table (alphabetical class_name order)
+        2) Fallback to class_name values present in the detections (alphabetical)
+        """
+
+        names: List[str] = []
+        try:
+            db_classes = self.db_manager.get_object_classes() or []
+            names = [str(row.get("class_name")) for row in db_classes if row.get("class_name")]
+        except Exception:
+            names = []
+
+        if not names:
+            observed = sorted({str(det.get("class_name")) for det in detections if det.get("class_name")})
+            names = list(observed)
+
+        return {name: idx for idx, name in enumerate(names)}
+
+    def _format_detection_as_yolo_line(self, det: Dict, class_map: Dict[str, int]) -> Optional[str]:
+        """Convert a detection row to a YOLO label line.
+
+        - If segmentation_mask is present, exports segmentation polygon format:
+            class x1 y1 x2 y2 ...
+          (normalized coordinates)
+        - Otherwise exports bbox format:
+            class x_center y_center width height
+          (normalized coordinates)
+        """
+
+        class_name = det.get("class_name")
+        if not class_name or class_name not in class_map:
+            return None
+        class_idx = class_map[class_name]
+
+        mask = det.get("segmentation_mask")
+        polygon = self._convert_segmentation_mask_to_polygon(mask)
+        if polygon:
+            coords = " ".join(f"{value:.6f}" for value in polygon)
+            return f"{class_idx} {coords}".strip()
+
+        bbox = self._convert_bbox_to_yolo_xywh(det)
+        if bbox is None:
+            return None
+        x_center, y_center, width, height = bbox
+        return f"{class_idx} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}"
+
+    def _convert_bbox_to_yolo_xywh(self, det: Dict) -> Optional[Tuple[float, float, float, float]]:
+        """Convert stored xyxy (normalized) bbox to YOLO xywh (normalized)."""
+
+        x_min = det.get("x_min")
+        y_min = det.get("y_min")
+        x_max = det.get("x_max")
+        y_max = det.get("y_max")
+        if any(v is None for v in (x_min, y_min, x_max, y_max)):
+            return None
+
+        try:
+            x_min_f = self._clamp01(float(x_min))
+            y_min_f = self._clamp01(float(y_min))
+            x_max_f = self._clamp01(float(x_max))
+            y_max_f = self._clamp01(float(y_max))
+        except (TypeError, ValueError):
+            return None
+
+        width = max(0.0, x_max_f - x_min_f)
+        height = max(0.0, y_max_f - y_min_f)
+        if width <= 0.0 or height <= 0.0:
+            return None
+
+        x_center = x_min_f + width / 2.0
+        y_center = y_min_f + height / 2.0
+        return x_center, y_center, width, height
+
+    def _convert_segmentation_mask_to_polygon(self, mask_data) -> List[float]:
+        """Convert stored segmentation_mask [[x,y], ...] to YOLO polygon coords [x1,y1,...]."""
+
+        if not isinstance(mask_data, list):
+            return []
+
+        coords: List[float] = []
+        for point in mask_data:
+            if not isinstance(point, (list, tuple)) or len(point) < 2:
+                continue
+            try:
+                x = self._clamp01(float(point[0]))
+                y = self._clamp01(float(point[1]))
+            except (TypeError, ValueError):
+                continue
+            coords.extend([x, y])
+
+        # Need at least 3 points => 6 values.
+        return coords if len(coords) >= 6 else []
+
+    @staticmethod
+    def _clamp01(value: float) -> float:
+        if value < 0.0:
+            return 0.0
+        if value > 1.0:
+            return 1.0
+        return value
 
     def _load_detections_for_selection(self, entry: Dict):
         """Load detection records for the selected image/model pair."""

src/gui/tabs/training_tab.py

@@ -905,9 +905,14 @@ class TrainingTab(QWidget):
             if stats["registered_images"]:
                 message += f" {stats['registered_images']} image(s) had database-backed annotations."
             if stats["missing_records"]:
-                message += (
+                preserved = stats.get("preserved_existing_labels", 0)
-                    f" {stats['missing_records']} image(s) had no database entry; empty label files were written."
+                if preserved:
-                )
+                    message += (
+                        f" {stats['missing_records']} image(s) had no database annotations; "
+                        f"preserved {preserved} existing label file(s) (no overwrite)."
+                    )
+                else:
+                    message += f" {stats['missing_records']} image(s) had no database annotations; empty label files were written."
             split_messages.append(message)
 
         for msg in split_messages:
@@ -929,6 +934,7 @@ class TrainingTab(QWidget):
         processed_images = 0
         registered_images = 0
         missing_records = 0
+        preserved_existing_labels = 0
         total_annotations = 0
 
         for image_file in images_dir.rglob("*"):
@@ -950,6 +956,12 @@ class TrainingTab(QWidget):
             else:
                 missing_records += 1
 
+            # If the database has no entry for this image, do not overwrite an existing label file
+            # with an empty one (preserve any manually created labels on disk).
+            if not found and label_path.exists():
+                preserved_existing_labels += 1
+                continue
+
             annotations_written = 0
             with open(label_path, "w", encoding="utf-8") as handle:
                 for entry in annotation_entries:
@@ -979,6 +991,7 @@ class TrainingTab(QWidget):
             "processed_images": processed_images,
             "registered_images": registered_images,
             "missing_records": missing_records,
+            "preserved_existing_labels": preserved_existing_labels,
             "total_annotations": total_annotations,
         }
 
@@ -1008,6 +1021,10 @@ class TrainingTab(QWidget):
         resolved_image = image_path.resolve()
         candidates: List[str] = []
 
+        # Some DBs store absolute paths in `images.relative_path`.
+        # Include the absolute resolved path as a lookup candidate.
+        candidates.append(resolved_image.as_posix())
+
         for base in (dataset_root, images_dir):
             try:
                 relative = resolved_image.relative_to(base.resolve()).as_posix()
@@ -1032,6 +1049,13 @@ class TrainingTab(QWidget):
             return False, []
 
         annotations = self.db_manager.get_annotations_for_image(image_row["id"]) or []
+
+        # Treat "found" as "has database-backed annotations".
+        # If the image exists in DB but has no annotations yet, we don't want to overwrite
+        # an existing label file on disk with an empty one.
+        if not annotations:
+            return False, []
+
         yolo_entries: List[Dict[str, Any]] = []
 
         for ann in annotations:

src/gui/tabs/validation_tab.py

@@ -2,45 +2,554 @@
 Validation tab for the microscopy object detection application.
 """
 
-from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QGroupBox
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+from PySide6.QtCore import Qt, QSize
+from PySide6.QtGui import QPainter, QPixmap
+from PySide6.QtWidgets import (
+    QWidget,
+    QVBoxLayout,
+    QLabel,
+    QGroupBox,
+    QHBoxLayout,
+    QPushButton,
+    QComboBox,
+    QFormLayout,
+    QScrollArea,
+    QGridLayout,
+    QFrame,
+    QTableWidget,
+    QTableWidgetItem,
+    QHeaderView,
+    QSplitter,
+    QListWidget,
+    QListWidgetItem,
+    QAbstractItemView,
+    QGraphicsView,
+    QGraphicsScene,
+    QGraphicsPixmapItem,
+)
 
 from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
+from src.utils.logger import get_logger
+
+
+logger = get_logger(__name__)
+
+
+@dataclass(frozen=True)
+class _PlotItem:
+    label: str
+    path: Path
+
+
+class _ZoomableImageView(QGraphicsView):
+    """Zoomable image viewer.
+
+    - Mouse wheel: zoom in/out
+    - Left mouse drag: pan (ScrollHandDrag)
+    """
+
+    def __init__(self, parent: Optional[QWidget] = None):
+        super().__init__(parent)
+        self._scene = QGraphicsScene(self)
+        self.setScene(self._scene)
+        self._pixmap_item = QGraphicsPixmapItem()
+        self._scene.addItem(self._pixmap_item)
+
+        # QGraphicsView render hints are QPainter.RenderHints.
+        self.setRenderHints(self.renderHints() | QPainter.RenderHint.SmoothPixmapTransform)
+        self.setDragMode(QGraphicsView.DragMode.ScrollHandDrag)
+        self.setTransformationAnchor(QGraphicsView.ViewportAnchor.AnchorUnderMouse)
+        self.setResizeAnchor(QGraphicsView.ViewportAnchor.AnchorUnderMouse)
+
+        self._has_pixmap = False
+
+    def clear(self) -> None:
+        self._pixmap_item.setPixmap(QPixmap())
+        self._scene.setSceneRect(0, 0, 1, 1)
+        self.resetTransform()
+        self._has_pixmap = False
+
+    def set_pixmap(self, pixmap: QPixmap, *, fit: bool = True) -> None:
+        self._pixmap_item.setPixmap(pixmap)
+        self._scene.setSceneRect(pixmap.rect())
+        self._has_pixmap = not pixmap.isNull()
+        self.resetTransform()
+        if fit and self._has_pixmap:
+            self.fitInView(self._pixmap_item, Qt.AspectRatioMode.KeepAspectRatio)
+
+    def wheelEvent(self, event) -> None:  # type: ignore[override]
+        if not self._has_pixmap:
+            return
+        zoom_in_factor = 1.25
+        zoom_out_factor = 1.0 / zoom_in_factor
+        factor = zoom_in_factor if event.angleDelta().y() > 0 else zoom_out_factor
+        self.scale(factor, factor)
 
 
 class ValidationTab(QWidget):
-    """Validation tab placeholder."""
+    """Validation tab that shows stored validation metrics + plots for a selected model."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
 
+        self._models: List[Dict[str, Any]] = []
+        self._selected_model_id: Optional[int] = None
+        self._plot_widgets: List[QWidget] = []
+        self._plot_items: List[_PlotItem] = []
+
         self._setup_ui()
+        self.refresh()
 
     def _setup_ui(self):
         """Setup user interface."""
-        layout = QVBoxLayout()
+        layout = QVBoxLayout(self)
 
-        group = QGroupBox("Validation")
+        # ===== Header controls =====
-        group_layout = QVBoxLayout()
+        header = QGroupBox("Validation")
-        label = QLabel(
+        header_layout = QVBoxLayout()
-            "Validation functionality will be implemented here.\n\n"
+        header_row = QHBoxLayout()
-            "Features:\n"
-            "- Model validation\n"
-            "- Metrics visualization\n"
-            "- Confusion matrix\n"
-            "- Precision-Recall curves"
-        )
-        group_layout.addWidget(label)
-        group.setLayout(group_layout)
 
-        layout.addWidget(group)
+        header_row.addWidget(QLabel("Select model:"))
-        layout.addStretch()
+
-        self.setLayout(layout)
+        self.model_combo = QComboBox()
+        self.model_combo.setMinimumWidth(420)
+        self.model_combo.currentIndexChanged.connect(self._on_model_selected)
+        header_row.addWidget(self.model_combo, 1)
+
+        self.refresh_btn = QPushButton("Refresh")
+        self.refresh_btn.clicked.connect(self.refresh)
+        header_row.addWidget(self.refresh_btn)
+        header_row.addStretch()
+
+        header_layout.addLayout(header_row)
+        self.header_status = QLabel("No models loaded.")
+        self.header_status.setWordWrap(True)
+        header_layout.addWidget(self.header_status)
+        header.setLayout(header_layout)
+        layout.addWidget(header)
+
+        # ===== Metrics =====
+        metrics_group = QGroupBox("Validation Metrics")
+        metrics_layout = QVBoxLayout()
+
+        self.metrics_form = QFormLayout()
+        self.metric_labels: Dict[str, QLabel] = {}
+        for key in ("mAP50", "mAP50-95", "precision", "recall", "fitness"):
+            value_label = QLabel("–")
+            value_label.setTextInteractionFlags(Qt.TextSelectableByMouse)
+            self.metric_labels[key] = value_label
+            self.metrics_form.addRow(f"{key}:", value_label)
+        metrics_layout.addLayout(self.metrics_form)
+
+        self.per_class_table = QTableWidget(0, 3)
+        self.per_class_table.setHorizontalHeaderLabels(["Class", "AP", "AP50"])
+        self.per_class_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
+        self.per_class_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)
+        self.per_class_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeToContents)
+        self.per_class_table.setEditTriggers(QTableWidget.NoEditTriggers)
+        self.per_class_table.setMinimumHeight(160)
+        metrics_layout.addWidget(QLabel("Per-class metrics (if available):"))
+        metrics_layout.addWidget(self.per_class_table)
+
+        metrics_group.setLayout(metrics_layout)
+        layout.addWidget(metrics_group)
+
+        # ===== Plots =====
+        plots_group = QGroupBox("Validation Plots")
+        plots_layout = QVBoxLayout()
+
+        self.plots_status = QLabel("Select a model to see validation plots.")
+        self.plots_status.setWordWrap(True)
+        plots_layout.addWidget(self.plots_status)
+
+        self.plots_splitter = QSplitter(Qt.Orientation.Horizontal)
+
+        # Left: selected image viewer
+        left_widget = QWidget()
+        left_layout = QVBoxLayout(left_widget)
+        left_layout.setContentsMargins(0, 0, 0, 0)
+
+        self.selected_plot_title = QLabel("No image selected.")
+        self.selected_plot_title.setWordWrap(True)
+        self.selected_plot_title.setTextInteractionFlags(Qt.TextSelectableByMouse)
+        left_layout.addWidget(self.selected_plot_title)
+
+        self.plot_view = _ZoomableImageView()
+        self.plot_view.setMinimumHeight(360)
+        left_layout.addWidget(self.plot_view, 1)
+
+        self.selected_plot_path = QLabel("")
+        self.selected_plot_path.setWordWrap(True)
+        self.selected_plot_path.setStyleSheet("color: #888;")
+        self.selected_plot_path.setTextInteractionFlags(Qt.TextSelectableByMouse)
+        left_layout.addWidget(self.selected_plot_path)
+
+        # Right: scrollable list
+        right_widget = QWidget()
+        right_layout = QVBoxLayout(right_widget)
+        right_layout.setContentsMargins(0, 0, 0, 0)
+        right_layout.addWidget(QLabel("Images:"))
+
+        self.plots_list = QListWidget()
+        self.plots_list.setSelectionMode(QAbstractItemView.SelectionMode.SingleSelection)
+        self.plots_list.setIconSize(QSize(160, 160))
+        self.plots_list.itemSelectionChanged.connect(self._on_plot_item_selected)
+        right_layout.addWidget(self.plots_list, 1)
+
+        self.plots_splitter.addWidget(left_widget)
+        self.plots_splitter.addWidget(right_widget)
+        self.plots_splitter.setStretchFactor(0, 3)
+        self.plots_splitter.setStretchFactor(1, 1)
+        plots_layout.addWidget(self.plots_splitter, 1)
+
+        plots_group.setLayout(plots_layout)
+        layout.addWidget(plots_group, 1)
+
+        layout.addStretch(0)
+
+        self._clear_metrics()
+        self._clear_plots()
+
+    # ==================== Public API ====================
 
     def refresh(self):
         """Refresh the tab."""
-        pass
+        self._load_models()
+        self._populate_model_combo()
+        self._restore_or_select_default_model()
+
+    # ==================== Internal: models ====================
+
+    def _load_models(self) -> None:
+        try:
+            self._models = self.db_manager.get_models() or []
+        except Exception as exc:
+            logger.error("Failed to load models: %s", exc)
+            self._models = []
+
+    def _populate_model_combo(self) -> None:
+        self.model_combo.blockSignals(True)
+        self.model_combo.clear()
+        self.model_combo.addItem("Select a model…", None)
+
+        for model in self._models:
+            model_id = model.get("id")
+            name = (model.get("model_name") or "").strip()
+            version = (model.get("model_version") or "").strip()
+            created_at = model.get("created_at")
+            label = f"{name} {version}".strip()
+            if created_at:
+                label = f"{label}  ({created_at})"
+            self.model_combo.addItem(label, model_id)
+
+        self.model_combo.blockSignals(False)
+
+        if self._models:
+            self.header_status.setText(f"Loaded {len(self._models)} model(s).")
+        else:
+            self.header_status.setText("No models found. Train a model first.")
+
+    def _restore_or_select_default_model(self) -> None:
+        if not self._models:
+            self._selected_model_id = None
+            self._clear_metrics()
+            self._clear_plots()
+            return
+
+        # Keep selection if still present.
+        if self._selected_model_id is not None:
+            for idx in range(1, self.model_combo.count()):
+                if self.model_combo.itemData(idx) == self._selected_model_id:
+                    self.model_combo.setCurrentIndex(idx)
+                    return
+
+        # Otherwise select the newest model (top of get_models ORDER BY created_at DESC).
+        first_model_id = self.model_combo.itemData(1) if self.model_combo.count() > 1 else None
+        if first_model_id is not None:
+            self.model_combo.setCurrentIndex(1)
+
+    def _on_model_selected(self, index: int) -> None:
+        model_id = self.model_combo.itemData(index)
+        if not model_id:
+            self._selected_model_id = None
+            self._clear_metrics()
+            self._clear_plots()
+            self.plots_status.setText("Select a model to see validation plots.")
+            return
+
+        self._selected_model_id = int(model_id)
+        model = self._get_model_by_id(self._selected_model_id)
+        if not model:
+            self._clear_metrics()
+            self._clear_plots()
+            self.plots_status.setText("Selected model not found.")
+            return
+
+        self._render_metrics(model)
+        self._render_plots(model)
+
+    def _get_model_by_id(self, model_id: int) -> Optional[Dict[str, Any]]:
+        for model in self._models:
+            if model.get("id") == model_id:
+                return model
+        try:
+            return self.db_manager.get_model_by_id(model_id)
+        except Exception:
+            return None
+
+    # ==================== Internal: metrics ====================
+
+    def _clear_metrics(self) -> None:
+        for label in self.metric_labels.values():
+            label.setText("–")
+        self.per_class_table.setRowCount(0)
+
+    def _render_metrics(self, model: Dict[str, Any]) -> None:
+        self._clear_metrics()
+
+        metrics: Dict[str, Any] = model.get("metrics") or {}
+        # Training tab stores metrics under results['metrics'] in training results payload.
+        if isinstance(metrics, dict) and "metrics" in metrics and isinstance(metrics.get("metrics"), dict):
+            metrics = metrics.get("metrics") or {}
+
+        def set_metric(key: str, value: Any) -> None:
+            if key not in self.metric_labels:
+                return
+            if value is None:
+                self.metric_labels[key].setText("–")
+                return
+            try:
+                self.metric_labels[key].setText(f"{float(value):.4f}")
+            except Exception:
+                self.metric_labels[key].setText(str(value))
+
+        set_metric("mAP50", metrics.get("mAP50"))
+        set_metric("mAP50-95", metrics.get("mAP50-95") or metrics.get("mAP50_95") or metrics.get("mAP50-95"))
+        set_metric("precision", metrics.get("precision"))
+        set_metric("recall", metrics.get("recall"))
+        set_metric("fitness", metrics.get("fitness"))
+
+        # Optional per-class metrics
+        class_metrics = metrics.get("class_metrics") if isinstance(metrics, dict) else None
+        if isinstance(class_metrics, dict) and class_metrics:
+            items = sorted(class_metrics.items(), key=lambda kv: str(kv[0]))
+            self.per_class_table.setRowCount(len(items))
+            for row, (cls_name, cls_stats) in enumerate(items):
+                ap = (cls_stats or {}).get("ap")
+                ap50 = (cls_stats or {}).get("ap50")
+                self.per_class_table.setItem(row, 0, QTableWidgetItem(str(cls_name)))
+                self.per_class_table.setItem(row, 1, QTableWidgetItem(self._format_float(ap)))
+                self.per_class_table.setItem(row, 2, QTableWidgetItem(self._format_float(ap50)))
+        else:
+            self.per_class_table.setRowCount(0)
+
+    @staticmethod
+    def _format_float(value: Any) -> str:
+        if value is None:
+            return "–"
+        try:
+            return f"{float(value):.4f}"
+        except Exception:
+            return str(value)
+
+    # ==================== Internal: plots ====================
+
+    def _clear_plots(self) -> None:
+        # Remove legacy grid widgets (from the initial implementation).
+        for widget in self._plot_widgets:
+            widget.setParent(None)
+            widget.deleteLater()
+        self._plot_widgets = []
+
+        self._plot_items = []
+
+        if hasattr(self, "plots_list"):
+            self.plots_list.blockSignals(True)
+            self.plots_list.clear()
+            self.plots_list.blockSignals(False)
+
+        if hasattr(self, "plot_view"):
+            self.plot_view.clear()
+        if hasattr(self, "selected_plot_title"):
+            self.selected_plot_title.setText("No image selected.")
+        if hasattr(self, "selected_plot_path"):
+            self.selected_plot_path.setText("")
+
+    def _render_plots(self, model: Dict[str, Any]) -> None:
+        self._clear_plots()
+
+        plot_dirs = self._infer_run_directories(model)
+        plot_items = self._discover_plot_items(plot_dirs)
+
+        if not plot_items:
+            dirs_text = "\n".join(str(p) for p in plot_dirs if p)
+            self.plots_status.setText(
+                "No validation plot images found for this model.\n\n"
+                "Searched directories:\n" + (dirs_text or "(none)")
+            )
+            return
+
+        self._plot_items = list(plot_items)
+        self.plots_status.setText(f"Found {len(plot_items)} plot image(s). Select one to view/zoom.")
+
+        self.plots_list.blockSignals(True)
+        self.plots_list.clear()
+        for idx, item in enumerate(self._plot_items):
+            qitem = QListWidgetItem(item.label)
+            qitem.setData(Qt.ItemDataRole.UserRole, idx)
+
+            pix = QPixmap(str(item.path))
+            if not pix.isNull():
+                thumb = pix.scaled(
+                    self.plots_list.iconSize(),
+                    Qt.AspectRatioMode.KeepAspectRatio,
+                    Qt.TransformationMode.SmoothTransformation,
+                )
+                qitem.setIcon(thumb)
+            self.plots_list.addItem(qitem)
+        self.plots_list.blockSignals(False)
+
+        if self.plots_list.count() > 0:
+            self.plots_list.setCurrentRow(0)
+
+    def _on_plot_item_selected(self) -> None:
+        if not self._plot_items:
+            return
+
+        selected = self.plots_list.selectedItems()
+        if not selected:
+            return
+
+        idx = selected[0].data(Qt.ItemDataRole.UserRole)
+        try:
+            idx_int = int(idx)
+        except Exception:
+            return
+        if idx_int < 0 or idx_int >= len(self._plot_items):
+            return
+
+        plot = self._plot_items[idx_int]
+        self.selected_plot_title.setText(plot.label)
+        self.selected_plot_path.setText(str(plot.path))
+
+        pix = QPixmap(str(plot.path))
+        if pix.isNull():
+            self.plot_view.clear()
+            return
+        self.plot_view.set_pixmap(pix, fit=True)
+
+    def _infer_run_directories(self, model: Dict[str, Any]) -> List[Path]:
+        dirs: List[Path] = []
+
+        # 1) Infer from model_path: .../<run>/weights/best.pt -> <run>
+        model_path = model.get("model_path")
+        if model_path:
+            try:
+                p = Path(str(model_path)).expanduser()
+                if p.name.lower().endswith(".pt"):
+                    # If it lives under weights/, use parent.parent.
+                    if p.parent.name == "weights" and p.parent.parent.exists():
+                        dirs.append(p.parent.parent)
+                    elif p.parent.exists():
+                        dirs.append(p.parent)
+            except Exception:
+                pass
+
+        # 2) Look at training_params.stage_results[].results.save_dir
+        training_params = model.get("training_params") or {}
+        stage_results = None
+        if isinstance(training_params, dict):
+            stage_results = training_params.get("stage_results")
+        if isinstance(stage_results, list):
+            for stage in stage_results:
+                results = (stage or {}).get("results")
+                save_dir = (results or {}).get("save_dir") if isinstance(results, dict) else None
+                if save_dir:
+                    try:
+                        save_path = Path(str(save_dir)).expanduser()
+                        if save_path.exists():
+                            dirs.append(save_path)
+                    except Exception:
+                        continue
+
+        # Deduplicate while preserving order.
+        unique: List[Path] = []
+        seen: set[str] = set()
+        for d in dirs:
+            try:
+                resolved = str(d.resolve())
+            except Exception:
+                resolved = str(d)
+            if resolved not in seen and d.exists() and d.is_dir():
+                seen.add(resolved)
+                unique.append(d)
+        return unique
+
+    def _discover_plot_items(self, directories: Sequence[Path]) -> List[_PlotItem]:
+        # Prefer canonical Ultralytics filenames first, then fall back to any png/jpg.
+        preferred_names = [
+            "results.png",
+            "results.jpg",
+            "confusion_matrix.png",
+            "confusion_matrix_normalized.png",
+            "labels.jpg",
+            "labels.png",
+            "BoxPR_curve.png",
+            "BoxP_curve.png",
+            "BoxR_curve.png",
+            "BoxF1_curve.png",
+            "MaskPR_curve.png",
+            "MaskP_curve.png",
+            "MaskR_curve.png",
+            "MaskF1_curve.png",
+            "val_batch0_pred.jpg",
+            "val_batch0_labels.jpg",
+        ]
+
+        found: List[_PlotItem] = []
+        seen: set[str] = set()
+
+        for d in directories:
+            # 1) Preferred
+            for name in preferred_names:
+                p = d / name
+                if p.exists() and p.is_file():
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{name} (from {d.name})", path=p))
+
+            # 2) Curated globs
+            for pattern in ("train_batch*.jpg", "val_batch*.jpg", "*curve*.png"):
+                for p in sorted(d.glob(pattern)):
+                    if not p.is_file():
+                        continue
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{p.name} (from {d.name})", path=p))
+
+            # 3) Fallback: any top-level png/jpg (excluding weights dir contents)
+            for ext in ("*.png", "*.jpg", "*.jpeg", "*.webp"):
+                for p in sorted(d.glob(ext)):
+                    if not p.is_file():
+                        continue
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{p.name} (from {d.name})", path=p))
+
+        # Keep list bounded to avoid UI overload for huge runs.
+        return found[:60]

src/model/inference.py

@@ -84,25 +84,19 @@ class InferenceEngine:
 
             # 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(
+                deleted_count = self.db_manager.delete_detections_for_image(image_id, self.model_id)
-                    image_id, self.model_id
-                )
                 if detections:
                     detection_records = []
                     for det in detections:
                         # Use normalized bbox from detection
-                        bbox_normalized = det[
+                        bbox_normalized = det["bbox_normalized"]  # [x_min, y_min, x_max, y_max]
-                            "bbox_normalized"
-                        ]  # [x_min, y_min, x_max, y_max]
 
                         metadata = {
                             "class_id": det["class_id"],
                             "source_path": str(Path(image_path).resolve()),
                         }
                         if repository_root:
-                            metadata["repository_root"] = str(
+                            metadata["repository_root"] = str(Path(repository_root).resolve())
-                                Path(repository_root).resolve()
-                            )
 
                         record = {
                             "image_id": image_id,
@@ -115,16 +109,27 @@ class InferenceEngine:
                         }
                         detection_records.append(record)
 
-                    inserted_count = self.db_manager.add_detections_batch(
+                    inserted_count = self.db_manager.add_detections_batch(detection_records)
-                        detection_records
+                    logger.info(f"Saved {inserted_count} detections to database (replaced {deleted_count})")
-                    )
-                    logger.info(
-                        f"Saved {inserted_count} detections to database (replaced {deleted_count})"
-                    )
                 else:
-                    logger.info(
+                    logger.info(f"Detection run removed {deleted_count} stale entries but produced no new detections")
-                        f"Detection run removed {deleted_count} stale entries but produced no new detections"
+
+                # Always store a run summary so the Results tab can show zero-detection runs.
+                try:
+                    run_metadata = {
+                        "source_path": str(Path(image_path).resolve()),
+                    }
+                    if repository_root:
+                        run_metadata["repository_root"] = str(Path(repository_root).resolve())
+                    self.db_manager.upsert_detection_run(
+                        image_id=image_id,
+                        model_id=self.model_id,
+                        count=len(detections),
+                        metadata=run_metadata,
                     )
+                except Exception as exc:
+                    # Non-fatal: detection records may still be present.
+                    logger.warning(f"Failed to store detection run summary: {exc}")
 
             return {
                 "success": True,
@@ -232,9 +237,7 @@ class InferenceEngine:
             for det in detections:
                 # Get color for this class
                 class_name = det["class_name"]
-                color_hex = bbox_colors.get(
+                color_hex = bbox_colors.get(class_name, bbox_colors.get("default", "#00FF00"))
-                    class_name, bbox_colors.get("default", "#00FF00")
-                )
                 color = self._hex_to_bgr(color_hex)
 
                 # Draw segmentation mask if available and requested
@@ -243,10 +246,7 @@ class InferenceEngine:
                     if mask_normalized and len(mask_normalized) > 0:
                         # Convert normalized coordinates to absolute pixels
                         mask_points = np.array(
-                            [
+                            [[int(pt[0] * width), int(pt[1] * height)] for pt in mask_normalized],
-                                [int(pt[0] * width), int(pt[1] * height)]
-                                for pt in mask_normalized
-                            ],
                             dtype=np.int32,
                         )
 
@@ -270,9 +270,7 @@ class InferenceEngine:
                 label = f"{class_name} {det['confidence']:.2f}"
 
                 # Draw label background
-                (label_w, label_h), baseline = cv2.getTextSize(
+                (label_w, label_h), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
-                    label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1
-                )
                 cv2.rectangle(
                     img,
                     (x1, y1 - label_h - baseline - 5),

src/model/yolo_wrapper.py

@@ -186,7 +186,7 @@ class YOLOWrapper:
                 raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         prepared_source, cleanup_path = self._prepare_source(source)
-        imgsz = 1088
+        imgsz = 640  # 1088
         try:
             logger.info(f"Running inference on {source} -> prepared_source {prepared_source}")
             results = self.model.predict(

src/utils/create_mask_from_detection.py

@@ -0,0 +1,103 @@
+import numpy as np
+
+from pathlib import Path
+from skimage.draw import polygon
+from tifffile import TiffFile
+
+from src.database.db_manager import DatabaseManager
+
+
+def read_image(image_path: Path) -> np.ndarray:
+    metadata = {}
+    with TiffFile(image_path) as tif:
+        image = tif.asarray()
+        metadata = tif.imagej_metadata
+    return image, metadata
+
+
+def main():
+
+    polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
+    image = np.zeros((100, 100), dtype=np.uint8)
+    rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
+    image[rr, cc] = 255
+
+
+if __name__ == "__main__":
+
+    db = DatabaseManager()
+    model_name = "c17"
+    model_id = db.get_models(filters={"model_name": model_name})[0]["id"]
+    print(f"Model name {model_name}, id {model_id}")
+    detections = db.get_detections(filters={"model_id": model_id})
+
+    file_stems = set()
+
+    for detection in detections:
+        file_stems.add(detection["image_filename"].split("_")[0])
+
+    print("Files:", file_stems)
+
+    for stem in file_stems:
+        print(stem)
+        detections = db.get_detections(filters={"model_id": model_id, "i.filename": f"LIKE %{stem}%"})
+        annotations = []
+        for detection in detections:
+            source_path = Path(detection["metadata"]["source_path"])
+            image, metadata = read_image(source_path)
+
+            offset = np.array(list(map(int, metadata["tile_section"].split(","))))[::-1]
+            scale = np.array(list(map(int, metadata["patch_size"].split(","))))[::-1]
+            # tile_size = np.array(list(map(int, metadata["tile_size"].split(","))))
+            segmentation = np.array(detection["segmentation_mask"])  # * tile_size
+
+            # print(source_path, image, metadata, segmentation.shape)
+            # print(offset)
+            # print(scale)
+            # print(segmentation)
+
+            # segmentation = (segmentation + offset * tile_size) / (tile_size * scale)
+            segmentation = (segmentation + offset) / scale
+
+            yolo_annotation = f"{detection['metadata']['class_id']} " + " ".join(
+                [f"{x:.6f} {y:.6f}" for x, y in segmentation]
+            )
+            annotations.append(yolo_annotation)
+            # print(segmentation)
+            # print(yolo_annotation)
+
+            # aa
+            print(
+                "  ",
+                detection["model_name"],
+                detection["image_id"],
+                detection["image_filename"],
+                source_path,
+                metadata["label_path"],
+            )
+            # section_i_section_j = detection["image_filename"].split("_")[1].split(".")[0]
+            # print("    ", section_i_section_j)
+
+        label_path = metadata["label_path"]
+        print("  ", label_path)
+        with open(label_path, "w") as f:
+            f.write("\n".join(annotations))
+
+    exit()
+
+    for detection in detections:
+        print(detection["model_name"], detection["image_id"], detection["image_filename"])
+
+    print(detections[0])
+    # polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
+
+    # image = np.zeros((100, 100), dtype=np.uint8)
+
+    # rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
+
+    # image[rr, cc] = 255
+
+    # import matplotlib.pyplot as plt
+
+    # plt.imshow(image, cmap='gray')
+    # plt.show()

src/utils/image.py

@@ -6,6 +6,9 @@ import cv2
 import numpy as np
 from pathlib import Path
 from typing import Optional, Tuple, Union
+from skimage.restoration import rolling_ball
+from skimage.filters import threshold_otsu
+from scipy.ndimage import median_filter, gaussian_filter
 
 from src.utils.logger import get_logger
 from src.utils.file_utils import validate_file_path, is_image_file
@@ -37,6 +40,8 @@ def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
     a1[a1 > p999] = p999
     a1 /= a1.max()
 
+    # print("Using get_pseudo_rgb")
+
     if 1:
         a2 = a1.copy()
         a2 = a2**gamma
@@ -46,14 +51,75 @@ def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
         p9999 = np.percentile(a3, 99.99)
         a3[a3 > p9999] = p9999
         a3 /= a3.max()
+        out = np.stack([a1, a2, a3], axis=0)
+
+    else:
+        out = np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+
+    return out
 
     # return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
     # return np.stack([a2, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
-    out = np.stack([a1, a2, a3], axis=0)
     # print(any(np.isnan(out).flatten()))
 
+
+def _get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.3) -> np.ndarray:
+    """
+    Convert a grayscale image to a pseudo-RGB image using a gamma correction.
+
+    Args:
+        arr: Input grayscale image as numpy array
+
+    Returns:
+        Pseudo-RGB image as numpy array
+    """
+    if arr.ndim != 2:
+        raise ValueError("Input array must be a grayscale image with shape (H, W)")
+
+    radius = 80
+    # bg = rolling_ball(arr, radius=radius)
+    a1 = arr.copy().astype(np.float32)
+    a1 = a1.astype(np.float32)
+    # a1 -= bg
+    # a1[a1 < 0] = 0
+    # a1 -= np.percentile(a1, 2)
+    # a1[a1 < 0] = 0
+    p999 = np.percentile(a1, 99.99)
+    a1[a1 > p999] = p999
+    a1 /= a1.max()
+
+    print("Using get_pseudo_rgb")
+
+    if 1:
+        a2 = a1.copy()
+        _a2 = a2**gamma
+        thr = threshold_otsu(_a2)
+        mask = gaussian_filter((_a2 > thr).astype(np.float32), sigma=5)
+        mask[mask > 0.0001] = 1
+        mask[mask <= 0.0001] = 0
+        a2 *= mask
+
+        # bg2 = rolling_ball(a2, radius=radius)
+        # a2 -= bg2
+        a2 -= np.percentile(a2, 2)
+        a2[a2 < 0] = 0
+        a2 /= a2.max()
+
+        a3 = a1.copy()
+        p9999 = np.percentile(a3, 99.99)
+        a3[a3 > p9999] = p9999
+        a3 /= a3.max()
+        out = np.stack([a1, a2, _a2], axis=0)
+
+    else:
+        out = np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+
     return out
 
+    # return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # return np.stack([a2, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # print(any(np.isnan(out).flatten()))
+
 
 class ImageLoadError(Exception):
     """Exception raised when an image cannot be loaded."""
@@ -330,17 +396,20 @@ class Image:
         return self._channels >= 3
 
     def save(self, path: Union[str, Path], pseudo_rgb: bool = True) -> None:
-
+        # print("Image.save", self.data.shape)
         if self.channels == 1:
+            # print("Image.save grayscale")
             if pseudo_rgb:
                 img = get_pseudo_rgb(self.data)
-                print("Image.save", img.shape)
+                # print("Image.save", img.shape)
+
             else:
                 img = np.repeat(self.data, 3, axis=2)
-
+                # print("Image.save no pseudo", img.shape)
         else:
             raise NotImplementedError("Only grayscale images are supported for now.")
 
+        # print("Image.save imwrite", img.shape)
         imwrite(path, data=img)
 
     def __repr__(self) -> str:

src/utils/image_converters.py

@@ -97,7 +97,7 @@ class UT:
         class_index: int = 0,
     ):
         """Export rois to a file"""
-        with open(path / subfolder / f"{self.stem}.txt", "w") as f:
+        with open(path / subfolder / f"{PREFIX}-{self.stem}.txt", "w") as f:
             for i, roi in enumerate(self.rois):
                 rc = roi.subpixel_coordinates
                 if rc is None:
@@ -129,8 +129,8 @@ class UT:
             self.image = np.max(self.image[channel], axis=0)
             print(self.image.shape)
 
-        print(path / subfolder / f"{self.stem}.tif")
+        print(path / subfolder / f"{PREFIX}_{self.stem}.tif")
-        with TiffWriter(path / subfolder / f"{self.stem}.tif") as tif:
+        with TiffWriter(path / subfolder / f"{PREFIX}-{self.stem}.tif") as tif:
             tif.write(self.image)
 
 
@@ -145,11 +145,19 @@ if __name__ == "__main__":
         action="store_false",
         help="Source does not have labels, export only images",
     )
+    parser.add_argument("--prefix", help="Prefix for output files")
     args = parser.parse_args()
 
+    PREFIX = args.prefix
     # print(args)
     # aa
 
+    # for path in args.input:
+    #     print(path)
+    #     ut = UT(path, args.no_labels)
+    #     ut.export_image(args.output, plane_mode="max projection", channel=0)
+    #     ut.export_rois(args.output, class_index=0)
+
     for path in args.input:
         print("Path:", path)
         if not args.no_labels:

src/utils/image_splitter.py

@@ -5,7 +5,7 @@ from tifffile import imread, imwrite
 from shapely.geometry import LineString
 from copy import deepcopy
 from scipy.ndimage import zoom
-
+from skimage.restoration import rolling_ball
 
 # debug
 from src.utils.image import Image
@@ -160,8 +160,11 @@ class YoloLabelReader:
 
 
 class ImageSplitter:
-    def __init__(self, image_path: Path, label_path: Path):
+    def __init__(self, image_path: Path, label_path: Path, subtract_bg: bool = False):
         self.image = imread(image_path)
+        if subtract_bg:
+            self.image = self.image - rolling_ball(self.image, radius=12)
+            self.image[self.image < 0] = 0
         self.image_path = image_path
         self.label_path = label_path
         if not label_path.exists():
@@ -273,13 +276,14 @@ def main(args):
     if args.output:
         args.output.mkdir(exist_ok=True, parents=True)
         (args.output / "images").mkdir(exist_ok=True)
-        (args.output / "images-zoomed").mkdir(exist_ok=True)
+        # (args.output / "images-zoomed").mkdir(exist_ok=True)
         (args.output / "labels").mkdir(exist_ok=True)
 
     for image_path in (args.input / "images").glob("*.tif"):
         data = ImageSplitter(
             image_path=image_path,
             label_path=(args.input / "labels" / image_path.stem).with_suffix(".txt"),
+            subtract_bg=args.subtract_bg,
         )
 
         if args.split_around_label:
@@ -332,10 +336,20 @@ def main(args):
 
                 if labels is not None:
                     with open(args.output / "labels" / f"{image_path.stem}_{tile_reference}.txt", "w") as f:
+                        print(
+                            f"Writing {len(labels)} labels to {args.output / 'labels' / f'{image_path.stem}_{tile_reference}.txt'}"
+                        )
                         for label in labels:
                             # label.offset_label(tile.shape[1], tile.shape[0])
                             f.write(label.to_string() + "\n")
 
+                            # { debug
+                            if debug:
+                                print(label.to_string())
+                            # } debug
+            # break
+        # break
+
 
 if __name__ == "__main__":
     import argparse
@@ -363,6 +377,7 @@ if __name__ == "__main__":
         default=67,
         help="Padding around the label when splitting around the label.",
     )
+    parser.add_argument("-bg", "--subtract-bg", action="store_true", help="Subtract background from the image.")
     args = parser.parse_args()
 
     main(args)

tests/show_yolo_seg.py

@@ -189,33 +189,38 @@ def main():
         # continue and just show image
     out = draw_annotations(img.copy(), labels, alpha=args.alpha, draw_bbox_for_poly=(not args.no_bbox))
 
-    lclass, coords = labels[0]
-    print(lclass, coords)
-    bbox = coords[:4]
-    print("bbox", bbox)
-    bbox = np.array(bbox) * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
-    yc, xc, h, w = bbox
-    print("bbox", bbox)
-
-    # polyline = np.array(coords[4:]).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
-    polyline = np.array(coords).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
-    print("pl", coords[4:])
-    print("pl", polyline)
-
-    # Convert BGR -> RGB for matplotlib display
-    # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
     out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    # out_rgb = Image()
     plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
-    plt.imshow(out_rgb)
-    plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
     if 0:
-        plt.plot(
+        plt.imshow(out_rgb.transpose(1, 0, 2))
-            [yc - h / 2, yc - h / 2, yc + h / 2, yc + h / 2, yc - h / 2],
+    else:
-            [xc - w / 2, xc + w / 2, xc + w / 2, xc - w / 2, xc - w / 2],
+        plt.imshow(out_rgb)
-            "r",
+
-            linewidth=2,
+    for label in labels:
-        )
+        lclass, coords = label
+        # print(lclass, coords)
+        bbox = coords[:4]
+        # print("bbox", bbox)
+        bbox = np.array(bbox) * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        yc, xc, h, w = bbox
+        # print("bbox", bbox)
+
+        # polyline = np.array(coords[4:]).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+        polyline = np.array(coords).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+        # print("pl", coords[4:])
+        # print("pl", polyline)
+
+        # Convert BGR -> RGB for matplotlib display
+        # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+        # out_rgb = Image()
+        plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
+        if 0:
+            plt.plot(
+                [yc - h / 2, yc - h / 2, yc + h / 2, yc + h / 2, yc - h / 2],
+                [xc - w / 2, xc + w / 2, xc + w / 2, xc - w / 2, xc - w / 2],
+                "r",
+                linewidth=2,
+            )
 
     # plt.axis("off")
     plt.title(f"{img_path.name} ({lbl_path.name})")