Finish validation tab

src/database/db_manager.py

@@ -462,6 +462,22 @@ 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(

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'),
-    ('nucleus', '#00FF00', 'Cell nucleus'),
-    ('mitochondria', '#0000FF', 'Mitochondria'),
-    ('vesicle', '#FFFF00', 'Vesicle');
+    ('terminal', '#FFFF00', 'Axion terminal');
 
 -- Annotations table: stores manual annotations
 CREATE TABLE IF NOT EXISTS annotations (

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,
@@ -65,6 +65,22 @@ 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)
 
@@ -130,6 +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()
@@ -139,6 +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."""
@@ -258,6 +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/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__(
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
+    def __init__(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")
-        group_layout = QVBoxLayout()
-        label = QLabel(
-            "Validation functionality will be implemented here.\n\n"
-            "Features:\n"
-            "- Model validation\n"
-            "- Metrics visualization\n"
-            "- Confusion matrix\n"
-            "- Precision-Recall curves"
-        )
-        group_layout.addWidget(label)
-        group.setLayout(group_layout)
+        # ===== Header controls =====
+        header = QGroupBox("Validation")
+        header_layout = QVBoxLayout()
+        header_row = QHBoxLayout()
 
-        layout.addWidget(group)
-        layout.addStretch()
-        self.setLayout(layout)
+        header_row.addWidget(QLabel("Select model:"))
+
+        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."""
+        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/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()

tests/show_yolo_seg.py

@@ -189,25 +189,30 @@ 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)
+    out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
+    if 0:
+        plt.imshow(out_rgb.transpose(1, 0, 2))
+    else:
+        plt.imshow(out_rgb)
+
+    for label in labels:
+        lclass, coords = label
+        # print(lclass, coords)
         bbox = coords[:4]
-    print("bbox", bbox)
+        # 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)
+        # 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)
+        # print("pl", coords[4:])
+        # print("pl", polyline)
 
         # Convert BGR -> RGB for matplotlib display
         # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
-    out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         # out_rgb = Image()
-    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
-    plt.imshow(out_rgb)
         plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
         if 0:
             plt.plot(