Adding annotation list

src/database/db_manager.py

@@ -60,9 +60,7 @@ class DatabaseManager:
         cursor = conn.cursor()
 
         # Check if annotations table exists
-        cursor.execute(
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='annotations'")
-            "SELECT name FROM sqlite_master WHERE type='table' AND name='annotations'"
-        )
         if not cursor.fetchone():
             # Table doesn't exist yet, no migration needed
             return
@@ -203,6 +201,28 @@ 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(
@@ -242,9 +262,7 @@ class DatabaseManager:
             return cursor.lastrowid
         except sqlite3.IntegrityError:
             # Image already exists, return its ID
-            cursor.execute(
+            cursor.execute("SELECT id FROM images WHERE relative_path = ?", (relative_path,))
-                "SELECT id FROM images WHERE relative_path = ?", (relative_path,)
-            )
             row = cursor.fetchone()
             return row["id"] if row else None
         finally:
@@ -255,17 +273,13 @@ class DatabaseManager:
         conn = self.get_connection()
         try:
             cursor = conn.cursor()
-            cursor.execute(
+            cursor.execute("SELECT * FROM images WHERE relative_path = ?", (relative_path,))
-                "SELECT * FROM images WHERE relative_path = ?", (relative_path,)
-            )
             row = cursor.fetchone()
             return dict(row) if row else None
         finally:
             conn.close()
 
-    def get_or_create_image(
+    def get_or_create_image(self, relative_path: str, filename: str, width: int, height: int) -> int:
-        self, relative_path: str, filename: str, width: int, height: int
-    ) -> int:
         """Get existing image or create new one."""
         existing = self.get_image_by_path(relative_path)
         if existing:
@@ -355,16 +369,8 @@ class DatabaseManager:
                         bbox[2],
                         bbox[3],
                         det["confidence"],
-                        (
+                        (json.dumps(det.get("segmentation_mask")) if det.get("segmentation_mask") else None),
-                            json.dumps(det.get("segmentation_mask"))
+                        (json.dumps(det.get("metadata")) if det.get("metadata") else None),
-                            if det.get("segmentation_mask")
-                            else None
-                        ),
-                        (
-                            json.dumps(det.get("metadata"))
-                            if det.get("metadata")
-                            else None
-                        ),
                     ),
                 )
             conn.commit()
@@ -409,15 +415,16 @@ class DatabaseManager:
             if filters:
                 conditions = []
                 for key, value in filters.items():
-                    if (
+                    if key.startswith("d.") or key.startswith("i.") or key.startswith("m."):
-                        key.startswith("d.")
+                        if "like" in value.lower():
-                        or key.startswith("i.")
+                            conditions.append(f"{key} LIKE ?")
-                        or key.startswith("m.")
+                            params.append(value.split(" ")[1])
-                    ):
+                        else:
-                        conditions.append(f"{key} = ?")
+                            conditions.append(f"{key} = ?")
+                            params.append(value)
                     else:
                         conditions.append(f"d.{key} = ?")
-                    params.append(value)
+                        params.append(value)
                 query += " WHERE " + " AND ".join(conditions)
 
             query += " ORDER BY d.detected_at DESC"
@@ -442,18 +449,14 @@ class DatabaseManager:
         finally:
             conn.close()
 
-    def get_detections_for_image(
+    def get_detections_for_image(self, image_id: int, model_id: Optional[int] = None) -> List[Dict]:
-        self, image_id: int, model_id: Optional[int] = None
-    ) -> List[Dict]:
         """Get all detections for a specific image."""
         filters = {"image_id": image_id}
         if model_id:
             filters["model_id"] = model_id
         return self.get_detections(filters)
 
-    def delete_detections_for_image(
+    def delete_detections_for_image(self, image_id: int, model_id: Optional[int] = None) -> int:
-        self, image_id: int, model_id: Optional[int] = None
-    ) -> int:
         """Delete detections tied to a specific image and optional model."""
         conn = self.get_connection()
         try:
@@ -481,6 +484,22 @@ 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()
+            cursor.execute("DELETE FROM detections")
+            conn.commit()
+            return cursor.rowcount
+        finally:
+            conn.close()
+
     # ==================== Statistics Operations ====================
 
     def get_detection_statistics(
@@ -524,9 +543,7 @@ class DatabaseManager:
             """,
                 params,
             )
-            class_counts = {
+            class_counts = {row["class_name"]: row["count"] for row in cursor.fetchall()}
-                row["class_name"]: row["count"] for row in cursor.fetchall()
-            }
 
             # Average confidence
             cursor.execute(
@@ -583,9 +600,7 @@ class DatabaseManager:
 
     # ==================== Export Operations ====================
 
-    def export_detections_to_csv(
+    def export_detections_to_csv(self, output_path: str, filters: Optional[Dict] = None) -> bool:
-        self, output_path: str, filters: Optional[Dict] = None
-    ) -> bool:
         """Export detections to CSV file."""
         try:
             detections = self.get_detections(filters)
@@ -614,9 +629,7 @@ class DatabaseManager:
                 for det in detections:
                     row = {k: det[k] for k in fieldnames if k in det}
                     # Convert segmentation mask list to JSON string for CSV
-                    if row.get("segmentation_mask") and isinstance(
+                    if row.get("segmentation_mask") and isinstance(row["segmentation_mask"], list):
-                        row["segmentation_mask"], list
-                    ):
                         row["segmentation_mask"] = json.dumps(row["segmentation_mask"])
                     writer.writerow(row)
 
@@ -625,9 +638,7 @@ class DatabaseManager:
             print(f"Error exporting to CSV: {e}")
             return False
 
-    def export_detections_to_json(
+    def export_detections_to_json(self, output_path: str, filters: Optional[Dict] = None) -> bool:
-        self, output_path: str, filters: Optional[Dict] = None
-    ) -> bool:
         """Export detections to JSON file."""
         try:
             detections = self.get_detections(filters)
@@ -647,6 +658,75 @@ class DatabaseManager:
 
     # ==================== Annotation Operations ====================
 
+    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,
@@ -785,17 +865,13 @@ class DatabaseManager:
         conn = self.get_connection()
         try:
             cursor = conn.cursor()
-            cursor.execute(
+            cursor.execute("SELECT * FROM object_classes WHERE class_name = ?", (class_name,))
-                "SELECT * FROM object_classes WHERE class_name = ?", (class_name,)
-            )
             row = cursor.fetchone()
             return dict(row) if row else None
         finally:
             conn.close()
 
-    def add_object_class(
+    def add_object_class(self, class_name: str, color: str, description: Optional[str] = None) -> int:
-        self, class_name: str, color: str, description: Optional[str] = None
-    ) -> int:
         """
         Add a new object class.
 
@@ -928,8 +1004,7 @@ class DatabaseManager:
             if not split_map[required]:
                 raise ValueError(
                     "Unable to determine %s image directory under %s. Provide it "
-                    "explicitly via the 'splits' argument."
+                    "explicitly via the 'splits' argument." % (required, dataset_root_path)
-                    % (required, dataset_root_path)
                 )
 
         yaml_splits: Dict[str, str] = {}
@@ -955,11 +1030,7 @@ class DatabaseManager:
         if yaml_splits.get("test"):
             payload["test"] = yaml_splits["test"]
 
-        output_path_obj = (
+        output_path_obj = Path(output_path).expanduser() if output_path else dataset_root_path / "data.yaml"
-            Path(output_path).expanduser()
-            if output_path
-            else dataset_root_path / "data.yaml"
-        )
         output_path_obj.parent.mkdir(parents=True, exist_ok=True)
 
         with open(output_path_obj, "w", encoding="utf-8") as handle:
@@ -1019,15 +1090,9 @@ class DatabaseManager:
         for split_name, options in patterns.items():
             for relative in options:
                 candidate = (dataset_root / relative).resolve()
-                if (
+                if candidate.exists() and candidate.is_dir() and self._directory_has_images(candidate):
-                    candidate.exists()
-                    and candidate.is_dir()
-                    and self._directory_has_images(candidate)
-                ):
                     try:
-                        inferred[split_name] = candidate.relative_to(
+                        inferred[split_name] = candidate.relative_to(dataset_root).as_posix()
-                            dataset_root
-                        ).as_posix()
                     except ValueError:
                         inferred[split_name] = candidate.as_posix()
                     break

src/database/schema.sql

@@ -55,10 +55,7 @@ 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 (

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)
         # }
@@ -85,36 +115,20 @@ 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
@@ -137,12 +151,13 @@ 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 +165,9 @@ class AnnotationTab(QWidget):
         # Restore splitter positions from settings
         self._restore_state()
 
+        # Populate list on startup.
+        self._refresh_annotated_images_list()
+
     def _load_image(self):
         """Load and display an image file."""
         # Get last opened directory from QSettings
@@ -180,12 +198,24 @@ 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,
@@ -199,6 +229,9 @@ 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 +239,7 @@ 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 +327,9 @@ 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 +374,7 @@ 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 +387,7 @@ 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 +420,7 @@ 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 +448,11 @@ 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 +460,9 @@ 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 +485,13 @@ 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 +515,7 @@ 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 +567,176 @@ 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_annotated_images_summary(name_filter=name_filter)
+        except Exception as exc:
+            logger.error(f"Failed to load annotated 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
+
+        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,
@@ -35,9 +35,7 @@ logger = get_logger(__name__)
 class ResultsTab(QWidget):
     """Results tab showing detection history and preview overlays."""
 
-    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
@@ -67,28 +65,32 @@ class ResultsTab(QWidget):
         self.refresh_btn = QPushButton("Refresh")
         self.refresh_btn.clicked.connect(self.refresh)
         controls_layout.addWidget(self.refresh_btn)
+
+        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)
 
         self.results_table = QTableWidget(0, 5)
-        self.results_table.setHorizontalHeaderLabels(
+        self.results_table.setHorizontalHeaderLabels(["Image", "Model", "Detections", "Classes", "Last Updated"])
-            ["Image", "Model", "Detections", "Classes", "Last Updated"]
+        self.results_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
-        )
+        self.results_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.Stretch)
-        self.results_table.horizontalHeader().setSectionResizeMode(
+        self.results_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeToContents)
-            0, QHeaderView.Stretch
+        self.results_table.horizontalHeader().setSectionResizeMode(3, QHeaderView.Stretch)
-        )
+        self.results_table.horizontalHeader().setSectionResizeMode(4, QHeaderView.ResizeToContents)
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            1, QHeaderView.Stretch
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            2, QHeaderView.ResizeToContents
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            3, QHeaderView.Stretch
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            4, QHeaderView.ResizeToContents
-        )
         self.results_table.setSelectionBehavior(QAbstractItemView.SelectRows)
         self.results_table.setSelectionMode(QAbstractItemView.SingleSelection)
         self.results_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
@@ -106,6 +108,8 @@ class ResultsTab(QWidget):
         preview_layout = QVBoxLayout()
 
         self.preview_canvas = AnnotationCanvasWidget()
+        # Auto-zoom so newly loaded images fill the available preview viewport.
+        self.preview_canvas.set_auto_fit_to_view(True)
         self.preview_canvas.set_polyline_enabled(False)
         self.preview_canvas.set_show_bboxes(True)
         preview_layout.addWidget(self.preview_canvas)
@@ -119,9 +123,7 @@ class ResultsTab(QWidget):
         self.show_bboxes_checkbox.stateChanged.connect(self._toggle_bboxes)
         self.show_confidence_checkbox = QCheckBox("Show Confidence")
         self.show_confidence_checkbox.setChecked(False)
-        self.show_confidence_checkbox.stateChanged.connect(
+        self.show_confidence_checkbox.stateChanged.connect(self._apply_detection_overlays)
-            self._apply_detection_overlays
-        )
         toggles_layout.addWidget(self.show_masks_checkbox)
         toggles_layout.addWidget(self.show_bboxes_checkbox)
         toggles_layout.addWidget(self.show_confidence_checkbox)
@@ -144,6 +146,41 @@ 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."""
         self._load_detection_summary()
@@ -153,6 +190,8 @@ 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)
 
     def _load_detection_summary(self):
         """Load latest detection summaries grouped by image + model."""
@@ -169,8 +208,7 @@ class ResultsTab(QWidget):
                         "image_id": det["image_id"],
                         "model_id": det["model_id"],
                         "image_path": det.get("image_path"),
-                        "image_filename": det.get("image_filename")
+                        "image_filename": det.get("image_filename") or det.get("image_path"),
-                        or det.get("image_path"),
                         "model_name": det.get("model_name", ""),
                         "model_version": det.get("model_version", ""),
                         "last_detected": det.get("detected_at"),
@@ -183,8 +221,7 @@ class ResultsTab(QWidget):
 
                 entry["count"] += 1
                 if det.get("detected_at") and (
-                    not entry.get("last_detected")
+                    not entry.get("last_detected") or str(det.get("detected_at")) > str(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"):
@@ -214,9 +251,7 @@ class ResultsTab(QWidget):
 
         for row, entry in enumerate(self.detection_summary):
             model_label = f"{entry['model_name']} {entry['model_version']}".strip()
-            class_list = (
+            class_list = ", ".join(sorted(entry["classes"])) if entry["classes"] else "-"
-                ", ".join(sorted(entry["classes"])) if entry["classes"] else "-"
-            )
 
             items = [
                 QTableWidgetItem(entry.get("image_filename", "")),
@@ -276,6 +311,231 @@ 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

@@ -10,6 +10,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 import yaml
+import numpy as np
 from PySide6.QtCore import Qt, QThread, Signal
 from PySide6.QtWidgets import (
     QWidget,
@@ -91,10 +92,7 @@ class TrainingWorker(QThread):
                 },
             }
         ]
-        computed_total = sum(
+        computed_total = sum(max(0, int((stage.get("params") or {}).get("epochs", 0))) for stage in self.stage_plan)
-            max(0, int((stage.get("params") or {}).get("epochs", 0)))
-            for stage in self.stage_plan
-        )
         self.total_epochs = total_epochs if total_epochs else computed_total or epochs
         self._stop_requested = False
 
@@ -201,9 +199,7 @@ class TrainingWorker(QThread):
 class TrainingTab(QWidget):
     """Training tab for model training."""
 
-    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
@@ -337,18 +333,14 @@ class TrainingTab(QWidget):
         self.model_version_edit = QLineEdit("v1")
         form_layout.addRow("Version:", self.model_version_edit)
 
-        default_base_model = self.config_manager.get(
+        default_base_model = self.config_manager.get("models.default_base_model", "yolov8s-seg.pt")
-            "models.default_base_model", "yolov8s-seg.pt"
-        )
         base_model_choices = self.config_manager.get("models.base_model_choices", [])
 
         self.base_model_combo = QComboBox()
         self.base_model_combo.addItem("Custom path…", "")
         for choice in base_model_choices:
             self.base_model_combo.addItem(choice, choice)
-        self.base_model_combo.currentIndexChanged.connect(
+        self.base_model_combo.currentIndexChanged.connect(self._on_base_model_preset_changed)
-            self._on_base_model_preset_changed
-        )
         form_layout.addRow("Base Model Preset:", self.base_model_combo)
 
         base_model_layout = QHBoxLayout()
@@ -434,12 +426,8 @@ class TrainingTab(QWidget):
         group_layout = QVBoxLayout()
 
         self.two_stage_checkbox = QCheckBox("Enable staged head-only + full fine-tune")
-        two_stage_defaults = (
+        two_stage_defaults = training_defaults.get("two_stage", {}) if training_defaults else {}
-            training_defaults.get("two_stage", {}) if training_defaults else {}
+        self.two_stage_checkbox.setChecked(bool(two_stage_defaults.get("enabled", False)))
-        )
-        self.two_stage_checkbox.setChecked(
-            bool(two_stage_defaults.get("enabled", False))
-        )
         self.two_stage_checkbox.toggled.connect(self._on_two_stage_toggled)
         group_layout.addWidget(self.two_stage_checkbox)
 
@@ -501,9 +489,7 @@ class TrainingTab(QWidget):
         stage2_group.setLayout(stage2_form)
         controls_layout.addWidget(stage2_group)
 
-        helper_label = QLabel(
+        helper_label = QLabel("When enabled, staged hyperparameters override the global epochs/patience/lr.")
-            "When enabled, staged hyperparameters override the global epochs/patience/lr."
-        )
         helper_label.setWordWrap(True)
         controls_layout.addWidget(helper_label)
 
@@ -548,9 +534,7 @@ class TrainingTab(QWidget):
             if normalized == preset_value:
                 target_index = idx
                 break
-            if normalized.endswith(f"/{preset_value}") or normalized.endswith(
+            if normalized.endswith(f"/{preset_value}") or normalized.endswith(f"\\{preset_value}"):
-                f"\\{preset_value}"
-            ):
                 target_index = idx
                 break
         self.base_model_combo.blockSignals(True)
@@ -638,9 +622,7 @@ class TrainingTab(QWidget):
 
     def _browse_dataset(self):
         """Open a file dialog to manually select data.yaml."""
-        start_dir = self.config_manager.get(
+        start_dir = self.config_manager.get("training.last_dataset_dir", "data/datasets")
-            "training.last_dataset_dir", "data/datasets"
-        )
         start_path = Path(start_dir).expanduser()
         if not start_path.exists():
             start_path = Path.cwd()
@@ -676,9 +658,7 @@ class TrainingTab(QWidget):
             return
         except Exception as exc:
             logger.exception("Unexpected error while generating data.yaml")
-            self._display_dataset_error(
+            self._display_dataset_error("Unexpected error while generating data.yaml. Check logs for details.")
-                "Unexpected error while generating data.yaml. Check logs for details."
-            )
             QMessageBox.critical(
                 self,
                 "data.yaml Generation Failed",
@@ -755,13 +735,9 @@ class TrainingTab(QWidget):
         self.selected_dataset = info
 
         self.dataset_root_label.setText(info["root"])  # type: ignore[arg-type]
-        self.train_count_label.setText(
+        self.train_count_label.setText(self._format_split_info(info["splits"].get("train")))
-            self._format_split_info(info["splits"].get("train"))
-        )
         self.val_count_label.setText(self._format_split_info(info["splits"].get("val")))
-        self.test_count_label.setText(
+        self.test_count_label.setText(self._format_split_info(info["splits"].get("test")))
-            self._format_split_info(info["splits"].get("test"))
-        )
         self.num_classes_label.setText(str(info["num_classes"]))
         class_names = ", ".join(info["class_names"]) or "–"
         self.class_names_label.setText(class_names)
@@ -815,18 +791,12 @@ class TrainingTab(QWidget):
                 if split_path.exists():
                     split_info["count"] = self._count_images(split_path)
                     if split_info["count"] == 0:
-                        warnings.append(
+                        warnings.append(f"No images found for {split_name} split at {split_path}")
-                            f"No images found for {split_name} split at {split_path}"
-                        )
                 else:
-                    warnings.append(
+                    warnings.append(f"{split_name.capitalize()} path does not exist: {split_path}")
-                        f"{split_name.capitalize()} path does not exist: {split_path}"
-                    )
             else:
                 if split_name in ("train", "val"):
-                    warnings.append(
+                    warnings.append(f"{split_name.capitalize()} split missing in data.yaml")
-                        f"{split_name.capitalize()} split missing in data.yaml"
-                    )
             splits[split_name] = split_info
 
         names_list = self._normalize_class_names(data.get("names"))
@@ -844,9 +814,7 @@ class TrainingTab(QWidget):
         if not names_list and nc_value:
             names_list = [f"class_{idx}" for idx in range(int(nc_value))]
         elif nc_value and len(names_list) not in (0, int(nc_value)):
-            warnings.append(
+            warnings.append(f"Number of class names ({len(names_list)}) does not match nc={nc_value}")
-                f"Number of class names ({len(names_list)}) does not match nc={nc_value}"
-            )
 
         dataset_name = data.get("name") or base_path.name
 
@@ -898,16 +866,12 @@ class TrainingTab(QWidget):
 
         class_index_map = self._build_class_index_map(dataset_info)
         if not class_index_map:
-            self._append_training_log(
+            self._append_training_log("Skipping label export: dataset classes do not match database entries.")
-                "Skipping label export: dataset classes do not match database entries."
-            )
             return
 
         dataset_root_str = dataset_info.get("root")
         dataset_yaml_path = dataset_info.get("yaml_path")
-        dataset_yaml = (
+        dataset_yaml = Path(dataset_yaml_path).expanduser() if dataset_yaml_path else None
-            Path(dataset_yaml_path).expanduser() if dataset_yaml_path else None
-        )
         dataset_root: Optional[Path]
         if dataset_root_str:
             dataset_root = Path(dataset_root_str).resolve()
@@ -941,7 +905,9 @@ class TrainingTab(QWidget):
             if stats["registered_images"]:
                 message += f" {stats['registered_images']} image(s) had database-backed annotations."
             if stats["missing_records"]:
-                message += f" {stats['missing_records']} image(s) had no database entry; empty label files were written."
+                message += (
+                    f" {stats['missing_records']} image(s) had no database entry; empty label files were written."
+                )
             split_messages.append(message)
 
         for msg in split_messages:
@@ -973,9 +939,7 @@ class TrainingTab(QWidget):
                 continue
 
             processed_images += 1
-            label_path = (labels_dir / image_file.relative_to(images_dir)).with_suffix(
+            label_path = (labels_dir / image_file.relative_to(images_dir)).with_suffix(".txt")
-                ".txt"
-            )
             label_path.parent.mkdir(parents=True, exist_ok=True)
 
             found, annotation_entries = self._fetch_annotations_for_image(
@@ -991,25 +955,23 @@ class TrainingTab(QWidget):
                 for entry in annotation_entries:
                     polygon = entry.get("polygon") or []
                     if polygon:
+                        print(image_file, polygon[:4], polygon[-2:], entry.get("bbox"))
+                        # coords = " ".join(f"{value:.6f}" for value in entry.get("bbox"))
+                        # coords += " "
                         coords = " ".join(f"{value:.6f}" for value in polygon)
                         handle.write(f"{entry['class_idx']} {coords}\n")
                         annotations_written += 1
                     elif entry.get("bbox"):
                         x_center, y_center, width, height = entry["bbox"]
-                        handle.write(
+                        handle.write(f"{entry['class_idx']} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n")
-                            f"{entry['class_idx']} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n"
-                        )
                         annotations_written += 1
-
             total_annotations += annotations_written
 
         cache_reset_root = labels_dir.parent
         self._invalidate_split_cache(cache_reset_root)
 
         if processed_images == 0:
-            self._append_training_log(
+            self._append_training_log(f"[{split_name}] No images found to export labels for.")
-                f"[{split_name}] No images found to export labels for."
-            )
             return None
 
         return {
@@ -1135,6 +1097,10 @@ class TrainingTab(QWidget):
                 xs.append(x_val)
                 ys.append(y_val)
 
+            if any(np.abs(np.array(coords[:2]) - np.array(coords[-2:])) < 1e-5):
+                print("Closing polygon")
+                coords.extend(coords[:2])
+
             if len(coords) < 6:
                 continue
 
@@ -1147,6 +1113,11 @@ class TrainingTab(QWidget):
                     + abs((min(ys) if ys else 0.0) - y_min)
                     + abs((max(ys) if ys else 0.0) - y_max)
                 )
+                width = max(0.0, x_max - x_min)
+                height = max(0.0, y_max - y_min)
+                x_center = x_min + width / 2.0
+                y_center = y_min + height / 2.0
+                score = (x_center, y_center, width, height)
 
             candidates.append((score, coords))
 
@@ -1164,13 +1135,10 @@ class TrainingTab(QWidget):
             return 1.0
         return value
 
-    def _prepare_dataset_for_training(
+    def _prepare_dataset_for_training(self, dataset_yaml: Path, dataset_info: Optional[Dict[str, Any]] = None) -> Path:
-        self, dataset_yaml: Path, dataset_info: Optional[Dict[str, Any]] = None
-    ) -> Path:
         dataset_info = dataset_info or (
             self.selected_dataset
-            if self.selected_dataset
+            if self.selected_dataset and self.selected_dataset.get("yaml_path") == str(dataset_yaml)
-            and self.selected_dataset.get("yaml_path") == str(dataset_yaml)
             else self._parse_dataset_yaml(dataset_yaml)
         )
 
@@ -1189,14 +1157,10 @@ class TrainingTab(QWidget):
         cache_root = self._get_rgb_cache_root(dataset_yaml)
         rgb_yaml = cache_root / "data.yaml"
         if rgb_yaml.exists():
-            self._append_training_log(
+            self._append_training_log(f"Detected grayscale dataset; reusing RGB cache at {cache_root}")
-                f"Detected grayscale dataset; reusing RGB cache at {cache_root}"
-            )
             return rgb_yaml
 
-        self._append_training_log(
+        self._append_training_log(f"Detected grayscale dataset; creating RGB cache at {cache_root}")
-            f"Detected grayscale dataset; creating RGB cache at {cache_root}"
-        )
         self._build_rgb_dataset(cache_root, dataset_info)
         return rgb_yaml
 
@@ -1463,15 +1427,12 @@ class TrainingTab(QWidget):
 
         dataset_path = Path(dataset_yaml).expanduser()
         if not dataset_path.exists():
-            QMessageBox.warning(
+            QMessageBox.warning(self, "Invalid Dataset", "Selected data.yaml file does not exist.")
-                self, "Invalid Dataset", "Selected data.yaml file does not exist."
-            )
             return
 
         dataset_info = (
             self.selected_dataset
-            if self.selected_dataset
+            if self.selected_dataset and self.selected_dataset.get("yaml_path") == str(dataset_path)
-            and self.selected_dataset.get("yaml_path") == str(dataset_path)
             else self._parse_dataset_yaml(dataset_path)
         )
 
@@ -1480,16 +1441,12 @@ class TrainingTab(QWidget):
 
         dataset_to_use = self._prepare_dataset_for_training(dataset_path, dataset_info)
         if dataset_to_use != dataset_path:
-            self._append_training_log(
+            self._append_training_log(f"Using RGB-converted dataset at {dataset_to_use.parent}")
-                f"Using RGB-converted dataset at {dataset_to_use.parent}"
-            )
 
         params = self._collect_training_params()
         stage_plan = self._compose_stage_plan(params)
         params["stage_plan"] = stage_plan
-        total_planned_epochs = (
+        total_planned_epochs = self._calculate_total_stage_epochs(stage_plan) or params["epochs"]
-            self._calculate_total_stage_epochs(stage_plan) or params["epochs"]
-        )
         params["total_planned_epochs"] = total_planned_epochs
         self._active_training_params = params
         self._training_cancelled = False
@@ -1498,9 +1455,7 @@ class TrainingTab(QWidget):
             self._append_training_log("Two-stage fine-tuning schedule:")
             self._log_stage_plan(stage_plan)
 
-        self._append_training_log(
+        self._append_training_log(f"Starting training run '{params['run_name']}' using {params['base_model']}")
-            f"Starting training run '{params['run_name']}' using {params['base_model']}"
-        )
 
         self.training_progress_bar.setVisible(True)
         self.training_progress_bar.setMaximum(max(1, total_planned_epochs))
@@ -1528,9 +1483,7 @@ class TrainingTab(QWidget):
     def _stop_training(self):
         if self.training_worker and self.training_worker.isRunning():
             self._training_cancelled = True
-            self._append_training_log(
+            self._append_training_log("Stop requested. Waiting for the current epoch to finish...")
-                "Stop requested. Waiting for the current epoch to finish..."
-            )
             self.training_worker.stop()
             self.stop_training_button.setEnabled(False)
 
@@ -1566,9 +1519,7 @@ class TrainingTab(QWidget):
 
         if worker.isRunning():
             if not worker.wait(wait_timeout_ms):
-                logger.warning(
+                logger.warning("Training worker did not finish within %sms", wait_timeout_ms)
-                    "Training worker did not finish within %sms", wait_timeout_ms
-                )
 
         worker.deleteLater()
 
@@ -1585,16 +1536,12 @@ class TrainingTab(QWidget):
         self._set_training_state(False)
         self.training_progress_bar.setVisible(False)
 
-    def _on_training_progress(
+    def _on_training_progress(self, current_epoch: int, total_epochs: int, metrics: Dict[str, Any]):
-        self, current_epoch: int, total_epochs: int, metrics: Dict[str, Any]
-    ):
         self.training_progress_bar.setMaximum(total_epochs)
         self.training_progress_bar.setValue(current_epoch)
         parts = [f"Epoch {current_epoch}/{total_epochs}"]
         if metrics:
-            metric_text = ", ".join(
+            metric_text = ", ".join(f"{key}: {value:.4f}" for key, value in metrics.items())
-                f"{key}: {value:.4f}" for key, value in metrics.items()
-            )
             parts.append(metric_text)
         self._append_training_log(" | ".join(parts))
 
@@ -1621,9 +1568,7 @@ class TrainingTab(QWidget):
                 f"Model trained but not registered: {exc}",
             )
         else:
-            QMessageBox.information(
+            QMessageBox.information(self, "Training Complete", "Training finished successfully.")
-                self, "Training Complete", "Training finished successfully."
-            )
 
     def _on_training_error(self, message: str):
         self._cleanup_training_worker()
@@ -1669,9 +1614,7 @@ class TrainingTab(QWidget):
             metrics=results.get("metrics"),
         )
 
-        self._append_training_log(
+        self._append_training_log(f"Registered model '{params['model_name']}' (ID {model_id}) at {model_path}")
-            f"Registered model '{params['model_name']}' (ID {model_id}) at {model_path}"
-        )
         self._active_training_params = None
 
     def _set_training_state(self, is_training: bool):
@@ -1714,9 +1657,7 @@ class TrainingTab(QWidget):
 
     def _browse_save_dir(self):
         start_path = self.save_dir_edit.text().strip() or "data/models"
-        directory = QFileDialog.getExistingDirectory(
+        directory = QFileDialog.getExistingDirectory(self, "Select Save Directory", start_path)
-            self, "Select Save Directory", start_path
-        )
         if directory:
             self.save_dir_edit.setText(directory)
 

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/gui/widgets/annotation_canvas_widget.py

@@ -18,7 +18,7 @@ from PySide6.QtGui import (
     QPaintEvent,
     QPolygonF,
 )
-from PySide6.QtCore import Qt, QEvent, Signal, QPoint, QPointF, QRect
+from PySide6.QtCore import Qt, QEvent, Signal, QPoint, QPointF, QRect, QTimer
 from typing import Any, Dict, List, Optional, Tuple
 
 from src.utils.image import Image, ImageLoadError
@@ -79,9 +79,7 @@ def rdp(points: List[Tuple[float, float]], epsilon: float) -> List[Tuple[float,
     return [start, end]
 
 
-def simplify_polyline(
+def simplify_polyline(points: List[Tuple[float, float]], epsilon: float) -> List[Tuple[float, float]]:
-    points: List[Tuple[float, float]], epsilon: float
-) -> List[Tuple[float, float]]:
     """
     Simplify a polyline with RDP while preserving closure semantics.
 
@@ -145,6 +143,10 @@ class AnnotationCanvasWidget(QWidget):
         self.zoom_step = 0.1
         self.zoom_wheel_step = 0.15
 
+        # Auto-fit behavior (opt-in): when enabled, newly loaded images (and resizes)
+        # will scale to fill the available viewport while preserving aspect ratio.
+        self._auto_fit_to_view: bool = False
+
         # Drawing / interaction state
         self.is_drawing = False
         self.polyline_enabled = False
@@ -175,6 +177,35 @@ class AnnotationCanvasWidget(QWidget):
 
         self._setup_ui()
 
+    def set_auto_fit_to_view(self, enabled: bool):
+        """Enable/disable automatic zoom-to-fit behavior."""
+        self._auto_fit_to_view = bool(enabled)
+        if self._auto_fit_to_view and self.original_pixmap is not None:
+            QTimer.singleShot(0, self.fit_to_view)
+
+    def fit_to_view(self, padding_px: int = 6):
+        """Zoom the image so it fits the scroll area's viewport (aspect preserved)."""
+        if self.original_pixmap is None:
+            return
+
+        viewport = self.scroll_area.viewport().size()
+        available_w = max(1, int(viewport.width()) - int(padding_px))
+        available_h = max(1, int(viewport.height()) - int(padding_px))
+
+        img_w = max(1, int(self.original_pixmap.width()))
+        img_h = max(1, int(self.original_pixmap.height()))
+
+        scale_w = available_w / img_w
+        scale_h = available_h / img_h
+        new_scale = min(scale_w, scale_h)
+        new_scale = max(self.zoom_min, min(self.zoom_max, float(new_scale)))
+
+        if abs(new_scale - self.zoom_scale) < 1e-4:
+            return
+
+        self.zoom_scale = new_scale
+        self._apply_zoom()
+
     def _setup_ui(self):
         """Setup user interface."""
         layout = QVBoxLayout()
@@ -187,9 +218,7 @@ class AnnotationCanvasWidget(QWidget):
 
         self.canvas_label = QLabel("No image loaded")
         self.canvas_label.setAlignment(Qt.AlignCenter)
-        self.canvas_label.setStyleSheet(
+        self.canvas_label.setStyleSheet("QLabel { background-color: #2b2b2b; color: #888; }")
-            "QLabel { background-color: #2b2b2b; color: #888; }"
-        )
         self.canvas_label.setScaledContents(False)
         self.canvas_label.setMouseTracking(True)
 
@@ -212,9 +241,18 @@ class AnnotationCanvasWidget(QWidget):
         self.zoom_scale = 1.0
         self.clear_annotations()
         self._display_image()
-        logger.debug(
+
-            f"Loaded image into annotation canvas: {image.width}x{image.height}"
+        # Defer fit-to-view until the widget has a valid viewport size.
-        )
+        if self._auto_fit_to_view:
+            QTimer.singleShot(0, self.fit_to_view)
+
+        logger.debug(f"Loaded image into annotation canvas: {image.width}x{image.height}")
+
+    def resizeEvent(self, event):
+        """Optionally keep the image fitted when the widget is resized."""
+        super().resizeEvent(event)
+        if self._auto_fit_to_view and self.original_pixmap is not None:
+            QTimer.singleShot(0, self.fit_to_view)
 
     def clear(self):
         """Clear the displayed image and all annotations."""
@@ -289,22 +327,14 @@ class AnnotationCanvasWidget(QWidget):
             scaled_width,
             scaled_height,
             Qt.KeepAspectRatio,
-            (
+            (Qt.SmoothTransformation if self.zoom_scale >= 1.0 else Qt.FastTransformation),
-                Qt.SmoothTransformation
-                if self.zoom_scale >= 1.0
-                else Qt.FastTransformation
-            ),
         )
 
         scaled_annotations = self.annotation_pixmap.scaled(
             scaled_width,
             scaled_height,
             Qt.KeepAspectRatio,
-            (
+            (Qt.SmoothTransformation if self.zoom_scale >= 1.0 else Qt.FastTransformation),
-                Qt.SmoothTransformation
-                if self.zoom_scale >= 1.0
-                else Qt.FastTransformation
-            ),
         )
 
         # Composite image and annotations
@@ -390,16 +420,11 @@ class AnnotationCanvasWidget(QWidget):
         y = (pos.y() - offset_y) / self.zoom_scale
 
         # Check bounds
-        if (
+        if 0 <= x < self.original_pixmap.width() and 0 <= y < self.original_pixmap.height():
-            0 <= x < self.original_pixmap.width()
-            and 0 <= y < self.original_pixmap.height()
-        ):
             return (int(x), int(y))
         return None
 
-    def _find_polyline_at(
+    def _find_polyline_at(self, img_x: float, img_y: float, threshold_px: float = 5.0) -> Optional[int]:
-        self, img_x: float, img_y: float, threshold_px: float = 5.0
-    ) -> Optional[int]:
         """
         Find index of polyline whose geometry is within threshold_px of (img_x, img_y).
         Returns the index in self.polylines, or None if none is close enough.
@@ -421,9 +446,7 @@ class AnnotationCanvasWidget(QWidget):
 
             # Precise distance to all segments
             for (x1, y1), (x2, y2) in zip(polyline[:-1], polyline[1:]):
-                d = perpendicular_distance(
+                d = perpendicular_distance((img_x, img_y), (float(x1), float(y1)), (float(x2), float(y2)))
-                    (img_x, img_y), (float(x1), float(y1)), (float(x2), float(y2))
-                )
                 if d < best_dist:
                     best_dist = d
                     best_index = idx
@@ -624,11 +647,7 @@ class AnnotationCanvasWidget(QWidget):
 
     def mouseMoveEvent(self, event: QMouseEvent):
         """Handle mouse move events for drawing."""
-        if (
+        if not self.is_drawing or not self.polyline_enabled or self.annotation_pixmap is None:
-            not self.is_drawing
-            or not self.polyline_enabled
-            or self.annotation_pixmap is None
-        ):
             super().mouseMoveEvent(event)
             return
 
@@ -688,15 +707,10 @@ class AnnotationCanvasWidget(QWidget):
 
             if len(simplified) >= 2:
                 # Store polyline and redraw all annotations
-                self._add_polyline(
+                self._add_polyline(simplified, self.polyline_pen_color, self.polyline_pen_width)
-                    simplified, self.polyline_pen_color, self.polyline_pen_width
-                )
 
                 # Convert to normalized coordinates for metadata + signal
-                normalized_stroke = [
+                normalized_stroke = [self._image_to_normalized_coords(int(x), int(y)) for (x, y) in simplified]
-                    self._image_to_normalized_coords(int(x), int(y))
-                    for (x, y) in simplified
-                ]
                 self.all_strokes.append(
                     {
                         "points": normalized_stroke,
@@ -709,8 +723,7 @@ class AnnotationCanvasWidget(QWidget):
                 # Emit signal with normalized coordinates
                 self.annotation_drawn.emit(normalized_stroke)
                 logger.debug(
-                    f"Completed stroke with {len(simplified)} points "
+                    f"Completed stroke with {len(simplified)} points " f"(normalized len={len(normalized_stroke)})"
-                    f"(normalized len={len(normalized_stroke)})"
                 )
 
         self.current_stroke = []
@@ -750,9 +763,7 @@ class AnnotationCanvasWidget(QWidget):
 
             # Store polyline as [y_norm, x_norm] to match DB convention and
             # the expectations of draw_saved_polyline().
-            normalized_polyline = [
+            normalized_polyline = [[y / img_height, x / img_width] for (x, y) in polyline]
-                [y / img_height, x / img_width] for (x, y) in polyline
-            ]
 
             logger.debug(
                 f"Polyline {idx}: {len(polyline)} points, "
@@ -772,7 +783,7 @@ class AnnotationCanvasWidget(QWidget):
         self,
         polyline: List[List[float]],
         color: str,
-        width: int = 3,
+        width: int = 1,
         annotation_id: Optional[int] = None,
     ):
         """
@@ -810,17 +821,13 @@ class AnnotationCanvasWidget(QWidget):
 
         # Store and redraw using common pipeline
         pen_color = QColor(color)
-        pen_color.setAlpha(128)  # Add semi-transparency
+        pen_color.setAlpha(255)  # Add semi-transparency
         self._add_polyline(img_coords, pen_color, width, annotation_id=annotation_id)
 
         # Store in all_strokes for consistency (uses normalized coordinates)
-        self.all_strokes.append(
+        self.all_strokes.append({"points": polyline, "color": color, "alpha": 255, "width": width})
-            {"points": polyline, "color": color, "alpha": 128, "width": width}
-        )
 
-        logger.debug(
+        logger.debug(f"Drew saved polyline with {len(polyline)} points in color {color}")
-            f"Drew saved polyline with {len(polyline)} points in color {color}"
-        )
 
     def draw_saved_bbox(
         self,
@@ -844,9 +851,7 @@ class AnnotationCanvasWidget(QWidget):
             return
 
         if len(bbox) != 4:
-            logger.warning(
+            logger.warning(f"Invalid bounding box format: expected 4 values, got {len(bbox)}")
-                f"Invalid bounding box format: expected 4 values, got {len(bbox)}"
-            )
             return
 
         # Convert normalized coordinates to image coordinates (for logging/debug)
@@ -867,15 +872,11 @@ class AnnotationCanvasWidget(QWidget):
         # in _redraw_annotations() together with all polylines.
         pen_color = QColor(color)
         pen_color.setAlpha(128)  # Add semi-transparency
-        self.bboxes.append(
+        self.bboxes.append([float(x_min_norm), float(y_min_norm), float(x_max_norm), float(y_max_norm)])
-            [float(x_min_norm), float(y_min_norm), float(x_max_norm), float(y_max_norm)]
-        )
         self.bbox_meta.append({"color": pen_color, "width": int(width), "label": label})
 
         # Store in all_strokes for consistency
-        self.all_strokes.append(
+        self.all_strokes.append({"bbox": bbox, "color": color, "alpha": 128, "width": width, "label": label})
-            {"bbox": bbox, "color": color, "alpha": 128, "width": width, "label": label}
-        )
 
         # Redraw overlay (polylines + all bounding boxes)
         self._redraw_annotations()

src/model/yolo_wrapper.py

@@ -96,9 +96,7 @@ class YOLOWrapper:
 
         try:
             logger.info(f"Starting training: {name}")
-            logger.info(
+            logger.info(f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}")
-                f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}"
-            )
 
             # Defaults for 16-bit safety: disable augmentations that force uint8 and HSV ops that assume 0..255.
             # Users can override by passing explicit kwargs.
@@ -149,9 +147,7 @@ class YOLOWrapper:
 
         try:
             logger.info(f"Starting validation on {split} split")
-            results = self.model.val(
+            results = self.model.val(data=data_yaml, split=split, device=self.device, **kwargs)
-                data=data_yaml, split=split, device=self.device, **kwargs
-            )
 
             logger.info("Validation completed successfully")
             return self._format_validation_results(results)
@@ -190,11 +186,9 @@ class YOLOWrapper:
                 raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         prepared_source, cleanup_path = self._prepare_source(source)
-
+        imgsz = 1088
         try:
-            logger.info(
+            logger.info(f"Running inference on {source} -> prepared_source {prepared_source}")
-                f"Running inference on {source} -> prepared_source {prepared_source}"
-            )
             results = self.model.predict(
                 source=source,
                 conf=conf,
@@ -203,6 +197,7 @@ class YOLOWrapper:
                 save_txt=save_txt,
                 save_conf=save_conf,
                 device=self.device,
+                imgsz=imgsz,
                 **kwargs,
             )
 
@@ -218,13 +213,9 @@ class YOLOWrapper:
                 try:
                     os.remove(cleanup_path)
                 except OSError as cleanup_error:
-                    logger.warning(
+                    logger.warning(f"Failed to delete temporary RGB image {cleanup_path}: {cleanup_error}")
-                        f"Failed to delete temporary RGB image {cleanup_path}: {cleanup_error}"
-                    )
 
-    def export(
+    def export(self, format: str = "onnx", output_path: Optional[str] = None, **kwargs) -> str:
-        self, format: str = "onnx", output_path: Optional[str] = None, **kwargs
-    ) -> str:
         """
         Export model to different format.
 
@@ -265,9 +256,7 @@ class YOLOWrapper:
                     tmp.close()
                     img_obj.save(tmp_path)
                     cleanup_path = tmp_path
-                    logger.info(
+                    logger.info(f"Converted image {source_path} to RGB for inference at {tmp_path}")
-                        f"Converted image {source_path} to RGB for inference at {tmp_path}"
-                    )
                     return tmp_path, cleanup_path
                 except Exception as convert_error:
                     logger.warning(
@@ -280,9 +269,7 @@ class YOLOWrapper:
         """Format training results into dictionary."""
         try:
             # Get the results dict
-            results_dict = (
+            results_dict = results.results_dict if hasattr(results, "results_dict") else {}
-                results.results_dict if hasattr(results, "results_dict") else {}
-            )
 
             formatted = {
                 "success": True,
@@ -315,9 +302,7 @@ class YOLOWrapper:
                 "mAP50-95": float(box_metrics.map),
                 "precision": float(box_metrics.mp),
                 "recall": float(box_metrics.mr),
-                "fitness": (
+                "fitness": (float(results.fitness) if hasattr(results, "fitness") else 0.0),
-                    float(results.fitness) if hasattr(results, "fitness") else 0.0
-                ),
             }
 
             # Add per-class metrics if available
@@ -327,11 +312,7 @@ class YOLOWrapper:
                     if idx < len(box_metrics.ap):
                         class_metrics[name] = {
                             "ap": float(box_metrics.ap[idx]),
-                            "ap50": (
+                            "ap50": (float(box_metrics.ap50[idx]) if hasattr(box_metrics, "ap50") else 0.0),
-                                float(box_metrics.ap50[idx])
-                                if hasattr(box_metrics, "ap50")
-                                else 0.0
-                            ),
                         }
                 formatted["class_metrics"] = class_metrics
 
@@ -364,21 +345,15 @@ class YOLOWrapper:
                         "class_id": int(boxes.cls[i]),
                         "class_name": result.names[int(boxes.cls[i])],
                         "confidence": float(boxes.conf[i]),
-                        "bbox_normalized": [
+                        "bbox_normalized": [float(v) for v in xyxyn],  # [x_min, y_min, x_max, y_max]
-                            float(v) for v in xyxyn
+                        "bbox_absolute": [float(v) for v in boxes.xyxy[i].cpu().numpy()],  # Absolute pixels
-                        ],  # [x_min, y_min, x_max, y_max]
-                        "bbox_absolute": [
-                            float(v) for v in boxes.xyxy[i].cpu().numpy()
-                        ],  # Absolute pixels
                     }
 
                     # Extract segmentation mask if available
                     if has_masks:
                         try:
                             # Get the mask for this detection
-                            mask_data = result.masks.xy[
+                            mask_data = result.masks.xy[i]  # Polygon coordinates in absolute pixels
-                                i
-                            ]  # Polygon coordinates in absolute pixels
 
                             # Convert to normalized coordinates
                             if len(mask_data) > 0:
@@ -391,9 +366,7 @@ class YOLOWrapper:
                             else:
                                 detection["segmentation_mask"] = None
                         except Exception as mask_error:
-                            logger.warning(
+                            logger.warning(f"Error extracting mask for detection {i}: {mask_error}")
-                                f"Error extracting mask for detection {i}: {mask_error}"
-                            )
                             detection["segmentation_mask"] = None
                     else:
                         detection["segmentation_mask"] = None
@@ -407,9 +380,7 @@ class YOLOWrapper:
             return []
 
     @staticmethod
-    def convert_bbox_format(
+    def convert_bbox_format(bbox: List[float], format_from: str = "xywh", format_to: str = "xyxy") -> List[float]:
-        bbox: List[float], format_from: str = "xywh", format_to: str = "xyxy"
-    ) -> List[float]:
         """
         Convert bounding box between formats.
 

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

@@ -37,7 +37,7 @@ def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
     a1[a1 > p999] = p999
     a1 /= a1.max()
 
-    if 0:
+    if 1:
         a2 = a1.copy()
         a2 = a2**gamma
         a2 /= a2.max()
@@ -47,9 +47,12 @@ def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
         a3[a3 > p9999] = p9999
         a3 /= a3.max()
 
-    return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # 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)
-    # return np.stack([a1, a2, a3], axis=0)
+    out = np.stack([a1, a2, a3], axis=0)
+    # print(any(np.isnan(out).flatten()))
+
+    return out
 
 
 class ImageLoadError(Exception):
@@ -122,7 +125,7 @@ class Image:
             if self.path.suffix.lower() in [".tif", ".tiff"]:
                 self._data = imread(str(self.path))
             else:
-                raise NotImplementedError("RGB is not implemented")
+                # raise NotImplementedError("RGB is not implemented")
                 # Load with OpenCV (returns BGR format)
                 self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
 
@@ -246,27 +249,33 @@ class Image:
         if self.channels == 1:
             img = get_pseudo_rgb(self.data)
             self._dtype = img.dtype
-            return img
+            return img, True
-        raise NotImplementedError
 
-        if self._channels == 3:
+        elif self._channels == 3:
-            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
+            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB), False
         elif self._channels == 4:
-            return cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
+            return cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA), False
+
         else:
-            return self._data
+            raise NotImplementedError
+
+        # else:
+        #     return self._data
 
     def get_qt_rgb(self) -> np.ascontiguousarray:
         # we keep data as (C, H, W)
-        _img = self.get_rgb()
+        _img, pseudo = self.get_rgb()
 
-        img = np.zeros((self.height, self.width, 4), dtype=np.float32)
+        if pseudo:
-        img[..., 0] = _img[0]  # R gradient
+            img = np.zeros((self.height, self.width, 4), dtype=np.float32)
-        img[..., 1] = _img[1]  # G gradient
+            img[..., 0] = _img[0]  # R gradient
-        img[..., 2] = _img[2]  # B constant
+            img[..., 1] = _img[1]  # G gradient
-        img[..., 3] = 1.0  # A = 1.0 (opaque)
+            img[..., 2] = _img[2]  # B constant
+            img[..., 3] = 1.0  # A = 1.0 (opaque)
 
-        return np.ascontiguousarray(img)
+            return np.ascontiguousarray(img)
+        else:
+            return np.ascontiguousarray(_img)
 
     def get_grayscale(self) -> np.ndarray:
         """

src/utils/image_splitter.py

@@ -114,11 +114,12 @@ class Label:
         return truth_val
 
     def to_string(self, bbox: list = None, polygon: list = None):
+        coords = ""
         if bbox is None:
             bbox = self.bbox
+            # coords += " ".join([f"{x:.6f}" for x in self.bbox])
         if polygon is None:
             polygon = self.polygon
-        coords = " ".join([f"{x:.6f}" for x in self.bbox])
         if self.polygon is not None:
             coords += " " + " ".join([f"{x:.6f} {y:.6f}" for x, y in self.polygon])
         return f"{self.class_id} {coords}"
@@ -179,6 +180,13 @@ class ImageSplitter:
 
         for i in range(patch_size[0]):
             for j in range(patch_size[1]):
+                metadata = {
+                    "image_path": str(self.image_path),
+                    "label_path": str(self.label_path),
+                    "tile_section": f"{i}, {j}",
+                    "tile_size": f"{hstep}, {wstep}",
+                    "patch_size": f"{patch_size[0]}, {patch_size[1]}",
+                }
                 tile_reference = f"i{i}j{j}"
                 hrange = (i * hstep / h, (i + 1) * hstep / h)
                 wrange = (j * wstep / w, (j + 1) * wstep / w)
@@ -199,7 +207,7 @@ class ImageSplitter:
                             print(l.bbox)
 
                 # print(labels)
-                yield tile_reference, tile, labels
+                yield tile_reference, tile, labels, metadata
 
     def split_respective_to_label(self, padding: int = 67):
         if self.labels is None:
@@ -208,6 +216,7 @@ class ImageSplitter:
         for i, label in enumerate(self.labels):
             tile_reference = f"_lbl-{i+1:02d}"
             # print(label.bbox)
+            metadata = {"image_path": str(self.image_path), "label_path": str(self.label_path), "label_index": str(i)}
 
             xc_norm, yc_norm, h_norm, w_norm = label.bbox  # normalized coords
             xc, yc, h, w = [
@@ -246,17 +255,17 @@ class ImageSplitter:
 
             # print("tile shape:", tile.shape)
 
-            yolo_annotation = f"{label.class_id} {x_offset/nx} {y_offset/ny} {h_norm} {w_norm} "
+            yolo_annotation = f"{label.class_id} "  # {x_offset/nx} {y_offset/ny} {h_norm} {w_norm} "
-            print(yolo_annotation)
             yolo_annotation += " ".join(
                 [
                     f"{(x*self.image.shape[1]-(xc - x_offset))/nx:.6f} {(y*self.image.shape[0]-(yc-y_offset))/ny:.6f}"
                     for x, y in label.polygon
                 ]
             )
+            print(yolo_annotation)
             new_label = Label(yolo_annotation=yolo_annotation)
 
-            yield tile_reference, tile, [new_label]
+            yield tile_reference, tile, [new_label], metadata
 
 
 def main(args):
@@ -278,9 +287,9 @@ def main(args):
         else:
             data = data.split_into_tiles(patch_size=args.patch_size)
 
-        for tile_reference, tile, labels in data:
+        for tile_reference, tile, labels, metadata in data:
             print()
-            print(tile_reference, tile.shape, labels)  # len(labels) if labels else None)
+            print(tile_reference, tile.shape, labels, metadata)  # len(labels) if labels else None)
 
             # { debug
             debug = False
@@ -310,15 +319,21 @@ def main(args):
             # } debug
 
             if args.output:
-                imwrite(args.output / "images" / f"{image_path.stem}_{tile_reference}.tif", tile)
+                # imwrite(args.output / "images" / f"{image_path.stem}_{tile_reference}.tif", tile, metadata=metadata)
                 scale = 5
                 tile_zoomed = zoom(tile, zoom=scale)
-                imwrite(args.output / "images-zoomed" / f"{image_path.stem}_{tile_reference}.tif", tile_zoomed)
+                metadata["scale"] = scale
+                imwrite(
+                    args.output / "images" / f"{image_path.stem}_{tile_reference}.tif",
+                    tile_zoomed,
+                    metadata=metadata,
+                    imagej=True,
+                )
 
                 if labels is not None:
                     with open(args.output / "labels" / f"{image_path.stem}_{tile_reference}.txt", "w") as f:
                         for label in labels:
-                            label.offset_label(tile.shape[1], tile.shape[0])
+                            # label.offset_label(tile.shape[1], tile.shape[0])
                             f.write(label.to_string() + "\n")
 
 

src/utils/ultralytics_16bit_patch.py

@@ -72,8 +72,9 @@ def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
             # logger.info(f"Loading with monkey-patched imread: {filename}")
             arr = arr.astype(np.float32)
             arr /= arr.max()
-            arr *= 2**16 - 1
+            arr *= 2**8 - 1
-            arr = arr.astype(np.uint16)
+            arr = arr.astype(np.uint8)
+            # print(arr.shape, arr.dtype, any(np.isnan(arr).flatten()), np.where(np.isnan(arr)), arr.min(), arr.max())
             return np.ascontiguousarray(arr)
 
         # logger.info(f"Loading with original imread: {filename}")
@@ -105,7 +106,7 @@ def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
     def preprocess_batch_16bit(self, batch: dict) -> dict:  # type: ignore[override]
         # Start from upstream behavior to keep device placement + multiscale identical,
         # but replace the 255 division with dtype-aware scaling.
-        logger.info(f"Preprocessing batch with monkey-patched preprocess_batch")
+        # logger.info(f"Preprocessing batch with monkey-patched preprocess_batch")
         for k, v in batch.items():
             if isinstance(v, torch.Tensor):
                 batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")

tests/show_yolo_seg.py

@@ -189,30 +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]])
-    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)
+    if 0:
-    plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
+        plt.imshow(out_rgb.transpose(1, 0, 2))
-    plt.plot(
+    else:
-        [yc - h / 2, yc - h / 2, yc + h / 2, yc + h / 2, yc - h / 2],
+        plt.imshow(out_rgb)
-        [xc - w / 2, xc + w / 2, xc + w / 2, xc - w / 2, xc - w / 2],
+
-        "r",
+    for label in labels:
-        linewidth=2,
+        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})")