Finish validation tab

config/app_config.yaml

@@ -1,57 +0,0 @@
-database:
-  path: data/detections.db
-image_repository:
-  base_path: ''
-  allowed_extensions:
-  - .jpg
-  - .jpeg
-  - .png
-  - .tif
-  - .tiff
-  - .bmp
-models:
-  default_base_model: yolov8s-seg.pt
-  models_directory: data/models
-  base_model_choices:
-  - yolov8s-seg.pt
-  - yolo11s-seg.pt
-training:
-  default_epochs: 100
-  default_batch_size: 16
-  default_imgsz: 1024
-  default_patience: 50
-  default_lr0: 0.01
-  two_stage:
-    enabled: false
-    stage1:
-      epochs: 20
-      lr0: 0.0005
-      patience: 10
-      freeze: 10
-    stage2:
-      epochs: 150
-      lr0: 0.0003
-      patience: 30
-  last_dataset_yaml: /home/martin/code/object_detection/data/datasets/data.yaml
-  last_dataset_dir: /home/martin/code/object_detection/data/datasets
-detection:
-  default_confidence: 0.25
-  default_iou: 0.45
-  max_batch_size: 100
-visualization:
-  bbox_colors:
-    organelle: '#FF6B6B'
-    membrane_branch: '#4ECDC4'
-    default: '#00FF00'
-  bbox_thickness: 2
-  font_size: 12
-export:
-  formats:
-  - csv
-  - json
-  - excel
-  default_format: csv
-logging:
-  level: INFO
-  file: logs/app.log
-  format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

pyproject.toml

@@ -82,12 +82,12 @@ include-package-data = true
 "src.database" = ["*.sql"]
 
 [tool.black]
-line-length = 88
+line-length = 120
 target-version = ['py38', 'py39', 'py310', 'py311']
 include = '\.pyi?$'
 
 [tool.pylint.messages_control]
-max-line-length = 88
+max-line-length = 120
 
 [tool.mypy]
 python_version = "3.8"

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
@@ -242,9 +240,7 @@ 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 +251,13 @@ 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 +347,8 @@ 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()
@@ -409,12 +393,13 @@ 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} = ?")
+                            params.append(value)
                     else:
                         conditions.append(f"d.{key} = ?")
                         params.append(value)
@@ -442,18 +427,14 @@ 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 +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(
@@ -524,9 +521,7 @@ 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 +578,7 @@ 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 +607,7 @@ 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 +616,7 @@ 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)
@@ -785,17 +774,13 @@ 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 +913,7 @@ 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 +939,7 @@ 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 +999,9 @@ 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/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,
@@ -34,7 +35,7 @@ from PySide6.QtWidgets import (
 from src.database.db_manager import DatabaseManager
 from src.model.yolo_wrapper import YOLOWrapper
 from src.utils.config_manager import ConfigManager
-from src.utils.image import Image, convert_grayscale_to_rgb_preserve_range
+from src.utils.image import Image
 from src.utils.logger import get_logger
 
 
@@ -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
 
@@ -1303,6 +1267,14 @@ class TrainingTab(QWidget):
         sample_image = self._find_first_image(images_dir)
         if not sample_image:
             return False
+
+        # Do not force an RGB cache for TIFF datasets.
+        # We handle grayscale/16-bit TIFFs via runtime Ultralytics patches that:
+        # - load TIFFs with `tifffile`
+        # - replicate grayscale to 3 channels without quantization
+        # - normalize uint16 correctly during training
+        if sample_image.suffix.lower() in {".tif", ".tiff"}:
+            return False
         try:
             img = Image(sample_image)
             return img.pil_image.mode.upper() != "RGB"
@@ -1368,7 +1340,7 @@ class TrainingTab(QWidget):
                 img_obj = Image(src)
                 pil_img = img_obj.pil_image
                 if len(pil_img.getbands()) == 1:
-                    rgb_img = convert_grayscale_to_rgb_preserve_range(pil_img)
+                    rgb_img = img_obj.convert_grayscale_to_rgb_preserve_range()
                 else:
                     rgb_img = pil_img.convert("RGB")
                 rgb_img.save(dst)
@@ -1455,15 +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)
         )
 
@@ -1472,16 +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
@@ -1490,9 +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))
@@ -1520,9 +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)
 
@@ -1558,9 +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()
 
@@ -1577,16 +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))
 
@@ -1613,9 +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()
@@ -1661,9 +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):
@@ -1706,9 +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."""
+        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."""
@@ -250,12 +288,10 @@ class AnnotationCanvasWidget(QWidget):
             # Get image data in a format compatible with Qt
             if self.current_image.channels in (3, 4):
                 image_data = self.current_image.get_rgb()
-                height, width = image_data.shape[:2]
             else:
-                image_data = self.current_image.get_grayscale()
+                image_data = self.current_image.get_qt_rgb()
-                height, width = image_data.shape
 
-            image_data = np.ascontiguousarray(image_data)
+            height, width = image_data.shape[:2]
             bytes_per_line = image_data.strides[0]
 
             qimage = QImage(
@@ -263,7 +299,7 @@ class AnnotationCanvasWidget(QWidget):
                 width,
                 height,
                 bytes_per_line,
-                self.current_image.qtimage_format,
+                QImage.Format_RGBX32FPx4,  # self.current_image.qtimage_format,
             ).copy()  # Copy so Qt owns the buffer even after numpy array goes out of scope
 
             self.original_pixmap = QPixmap.fromImage(qimage)
@@ -291,22 +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
@@ -392,16 +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.
@@ -423,9 +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
@@ -626,11 +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
 
@@ -690,15 +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,
@@ -711,8 +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 = []
@@ -752,9 +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, "
@@ -774,7 +783,7 @@ class AnnotationCanvasWidget(QWidget):
         self,
         polyline: List[List[float]],
         color: str,
-        width: int = 3,
+        width: int = 1,
         annotation_id: Optional[int] = None,
     ):
         """
@@ -812,17 +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,
@@ -846,9 +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)
@@ -869,15 +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

@@ -1,16 +1,21 @@
-"""
+"""YOLO model wrapper for the microscopy object detection application.
-YOLO model wrapper for the microscopy object detection application.
+
-Provides a clean interface to YOLOv8 for training, validation, and inference.
+Notes on 16-bit TIFF support:
+- Ultralytics training defaults assume 8-bit images and normalize by dividing by 255.
+- This project can patch Ultralytics at runtime to decode TIFFs via `tifffile` and
+  normalize `uint16` correctly.
+
+See [`apply_ultralytics_16bit_tiff_patches()`](src/utils/ultralytics_16bit_patch.py:1).
 """
 
-from ultralytics import YOLO
 from pathlib import Path
 from typing import Optional, List, Dict, Callable, Any
 import torch
 import tempfile
 import os
-from src.utils.image import Image, convert_grayscale_to_rgb_preserve_range
+from src.utils.image import Image
 from src.utils.logger import get_logger
+from src.utils.ultralytics_16bit_patch import apply_ultralytics_16bit_tiff_patches
 
 
 logger = get_logger(__name__)
@@ -31,6 +36,9 @@ class YOLOWrapper:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"YOLOWrapper initialized with device: {self.device}")
 
+        # Apply Ultralytics runtime patches early (before first import/instantiation of YOLO datasets/trainers).
+        apply_ultralytics_16bit_tiff_patches()
+
     def load_model(self) -> bool:
         """
         Load YOLO model from path.
@@ -40,6 +48,9 @@ class YOLOWrapper:
         """
         try:
             logger.info(f"Loading YOLO model from {self.model_path}")
+            # Import YOLO lazily to ensure runtime patches are applied first.
+            from ultralytics import YOLO
+
             self.model = YOLO(self.model_path)
             self.model.to(self.device)
             logger.info("Model loaded successfully")
@@ -85,9 +96,17 @@ 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.
+            kwargs.setdefault("mosaic", 0.0)
+            kwargs.setdefault("mixup", 0.0)
+            kwargs.setdefault("cutmix", 0.0)
+            kwargs.setdefault("copy_paste", 0.0)
+            kwargs.setdefault("hsv_h", 0.0)
+            kwargs.setdefault("hsv_s", 0.0)
+            kwargs.setdefault("hsv_v", 0.0)
 
             # Train the model
             results = self.model.train(
@@ -128,9 +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)
@@ -169,17 +186,18 @@ 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(f"Running inference on {source}")
+            logger.info(f"Running inference on {source} -> prepared_source {prepared_source}")
             results = self.model.predict(
-                source=prepared_source,
+                source=source,
                 conf=conf,
                 iou=iou,
                 save=save,
                 save_txt=save_txt,
                 save_conf=save_conf,
                 device=self.device,
+                imgsz=imgsz,
                 **kwargs,
             )
 
@@ -195,13 +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.
 
@@ -236,21 +250,13 @@ class YOLOWrapper:
             if source_path.is_file():
                 try:
                     img_obj = Image(source_path)
-                    pil_img = img_obj.pil_image
-                    if len(pil_img.getbands()) == 1:
-                        rgb_img = convert_grayscale_to_rgb_preserve_range(pil_img)
-                    else:
-                        rgb_img = pil_img.convert("RGB")
-
                     suffix = source_path.suffix or ".png"
                     tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
                     tmp_path = tmp.name
                     tmp.close()
-                    rgb_img.save(tmp_path)
+                    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(
@@ -263,9 +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,
@@ -298,9 +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
@@ -310,11 +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
 
@@ -347,21 +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:
@@ -374,9 +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
@@ -390,9 +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/config_manager.py

@@ -54,7 +54,7 @@ class ConfigManager:
                 "models_directory": "data/models",
                 "base_model_choices": [
                     "yolov8s-seg.pt",
-                    "yolov11s-seg.pt",
+                    "yolo11s-seg.pt",
                 ],
             },
             "training": {
@@ -225,6 +225,4 @@ class ConfigManager:
 
     def get_allowed_extensions(self) -> list:
         """Get list of allowed image file extensions."""
-        return self.get(
+        return self.get("image_repository.allowed_extensions", Image.SUPPORTED_EXTENSIONS)
-            "image_repository.allowed_extensions", Image.SUPPORTED_EXTENSIONS
-        )

src/utils/create_mask_from_detection.py

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

src/utils/image.py

@@ -6,16 +6,55 @@ import cv2
 import numpy as np
 from pathlib import Path
 from typing import Optional, Tuple, Union
-from PIL import Image as PILImage
 
 from src.utils.logger import get_logger
 from src.utils.file_utils import validate_file_path, is_image_file
 
 from PySide6.QtGui import QImage
 
+from tifffile import imread, imwrite
+
 logger = get_logger(__name__)
 
 
+def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
+    """
+    Convert a grayscale image to a pseudo-RGB image using a gamma correction.
+
+    Args:
+        arr: Input grayscale image as numpy array
+
+    Returns:
+        Pseudo-RGB image as numpy array
+    """
+    if arr.ndim != 2:
+        raise ValueError("Input array must be a grayscale image with shape (H, W)")
+
+    a1 = arr.copy().astype(np.float32)
+    a1 -= np.percentile(a1, 2)
+    a1[a1 < 0] = 0
+    p999 = np.percentile(a1, 99.9)
+    a1[a1 > p999] = p999
+    a1 /= a1.max()
+
+    if 1:
+        a2 = a1.copy()
+        a2 = a2**gamma
+        a2 /= a2.max()
+
+        a3 = a1.copy()
+        p9999 = np.percentile(a3, 99.99)
+        a3[a3 > p9999] = p9999
+        a3 /= a3.max()
+
+    # return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # return np.stack([a2, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    out = np.stack([a1, a2, a3], axis=0)
+    # print(any(np.isnan(out).flatten()))
+
+    return out
+
+
 class ImageLoadError(Exception):
     """Exception raised when an image cannot be loaded."""
 
@@ -54,7 +93,6 @@ class Image:
         """
         self.path = Path(image_path)
         self._data: Optional[np.ndarray] = None
-        self._pil_image: Optional[PILImage.Image] = None
         self._width: int = 0
         self._height: int = 0
         self._channels: int = 0
@@ -80,11 +118,14 @@ class Image:
         if not is_image_file(str(self.path), self.SUPPORTED_EXTENSIONS):
             ext = self.path.suffix.lower()
             raise ImageLoadError(
-                f"Unsupported image format: {ext}. "
+                f"Unsupported image format: {ext}. " f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
-                f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
             )
 
         try:
+            if self.path.suffix.lower() in [".tif", ".tiff"]:
+                self._data = imread(str(self.path))
+            else:
+                # raise NotImplementedError("RGB is not implemented")
                 # Load with OpenCV (returns BGR format)
                 self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
 
@@ -92,23 +133,19 @@ class Image:
                 raise ImageLoadError(f"Failed to load image with OpenCV: {self.path}")
 
             # Extract metadata
+            # print(self._data.shape)
+            if len(self._data.shape) == 2:
                 self._height, self._width = self._data.shape[:2]
-            self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
+                self._channels = 1
+            else:
+                self._height, self._width = self._data.shape[1:]
+                self._channels = self._data.shape[0]
+            # self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
             self._format = self.path.suffix.lower().lstrip(".")
             self._size_bytes = self.path.stat().st_size
             self._dtype = self._data.dtype
 
-            # Load PIL version for compatibility (convert BGR to RGB)
+            if 0:
-            if self._channels == 3:
-                rgb_data = cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
-                self._pil_image = PILImage.fromarray(rgb_data)
-            elif self._channels == 4:
-                rgba_data = cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
-                self._pil_image = PILImage.fromarray(rgba_data)
-            else:
-                # Grayscale
-                self._pil_image = PILImage.fromarray(self._data)
-
                 logger.info(
                     f"Successfully loaded image: {self.path.name} "
                     f"({self._width}x{self._height}, {self._channels} channels, "
@@ -131,18 +168,6 @@ class Image:
             raise ImageLoadError("Image data not available")
         return self._data
 
-    @property
-    def pil_image(self) -> PILImage.Image:
-        """
-        Get image data as PIL Image (RGB or grayscale).
-
-        Returns:
-            PIL Image object
-        """
-        if self._pil_image is None:
-            raise ImageLoadError("PIL image not available")
-        return self._pil_image
-
     @property
     def width(self) -> int:
         """Get image width in pixels."""
@@ -187,6 +212,7 @@ class Image:
     @property
     def dtype(self) -> np.dtype:
         """Get the data type of the image array."""
+
         if self._dtype is None:
             raise ImageLoadError("Image dtype not available")
         return self._dtype
@@ -206,8 +232,10 @@ class Image:
         elif self._channels == 1:
             if self._dtype == np.uint16:
                 return QImage.Format_Grayscale16
-            else:
+            elif self._dtype == np.uint8:
                 return QImage.Format_Grayscale8
+            elif self._dtype == np.float32:
+                return QImage.Format_BGR30
         else:
             raise ImageLoadError(f"Unsupported number of channels: {self._channels}")
 
@@ -218,12 +246,36 @@ class Image:
         Returns:
             Image data in RGB format as numpy array
         """
-        if self._channels == 3:
+        if self.channels == 1:
-            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
+            img = get_pseudo_rgb(self.data)
+            self._dtype = img.dtype
+            return img, True
+
+        elif self._channels == 3:
+            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, pseudo = self.get_rgb()
+
+        if pseudo:
+            img = np.zeros((self.height, self.width, 4), dtype=np.float32)
+            img[..., 0] = _img[0]  # R gradient
+            img[..., 1] = _img[1]  # G gradient
+            img[..., 2] = _img[2]  # B constant
+            img[..., 3] = 1.0  # A = 1.0 (opaque)
+
+            return np.ascontiguousarray(img)
+        else:
+            return np.ascontiguousarray(_img)
 
     def get_grayscale(self) -> np.ndarray:
         """
@@ -277,11 +329,26 @@ class Image:
         """
         return self._channels >= 3
 
+    def save(self, path: Union[str, Path], pseudo_rgb: bool = True) -> None:
+
+        if self.channels == 1:
+            if pseudo_rgb:
+                img = get_pseudo_rgb(self.data)
+                print("Image.save", img.shape)
+            else:
+                img = np.repeat(self.data, 3, axis=2)
+
+        else:
+            raise NotImplementedError("Only grayscale images are supported for now.")
+
+        imwrite(path, data=img)
+
     def __repr__(self) -> str:
         """String representation of the Image object."""
         return (
             f"Image(path='{self.path.name}', "
-            f"shape=({self._width}x{self._height}x{self._channels}), "
+            # Display as HxWxC to match the conventional NumPy shape semantics.
+            f"shape=({self._height}x{self._width}x{self._channels}), "
             f"format={self._format}, "
             f"size={self.size_mb:.2f}MB)"
         )
@@ -291,38 +358,13 @@ class Image:
         return self.__repr__()
 
 
-def convert_grayscale_to_rgb_preserve_range(
+if __name__ == "__main__":
-    pil_image: PILImage.Image,
+    import argparse
-) -> PILImage.Image:
-    """Convert a single-channel PIL image to RGB while preserving dynamic range.
 
-    Args:
+    parser = argparse.ArgumentParser()
-        pil_image: Single-channel PIL image (e.g., 16-bit grayscale).
+    parser.add_argument("--path", type=str, required=True)
+    args = parser.parse_args()
 
-    Returns:
+    img = Image(args.path)
-        PIL Image in RGB mode with intensities normalized to 0-255.
+    img.save(args.path + "test.tif")
-    """
+    print(img)
-
-    if pil_image.mode == "RGB":
-        return pil_image
-
-    grayscale = np.array(pil_image)
-    if grayscale.ndim == 3:
-        grayscale = grayscale[:, :, 0]
-
-    original_dtype = grayscale.dtype
-    grayscale = grayscale.astype(np.float32)
-
-    if grayscale.size == 0:
-        return PILImage.new("RGB", pil_image.size, color=(0, 0, 0))
-
-    if np.issubdtype(original_dtype, np.integer):
-        denom = float(max(np.iinfo(original_dtype).max, 1))
-    else:
-        max_val = float(grayscale.max())
-        denom = max(max_val, 1.0)
-
-    grayscale = np.clip(grayscale / denom, 0.0, 1.0)
-    grayscale_u8 = (grayscale * 255.0).round().astype(np.uint8)
-    rgb_arr = np.repeat(grayscale_u8[:, :, None], 3, axis=2)
-    return PILImage.fromarray(rgb_arr, mode="RGB")

src/utils/image_converters.py

@@ -12,23 +12,38 @@ class UT:
     Operetta files along with rois drawn in ImageJ
     """
 
-    def __init__(self, roifile_fn: Path):
+    def __init__(self, roifile_fn: Path, no_labels: bool):
         self.roifile_fn = roifile_fn
+        print("is file", self.roifile_fn.is_file())
+        self.rois = None
+        if no_labels:
             self.rois = ImagejRoi.fromfile(self.roifile_fn)
-        self.stem = self.roifile_fn.stem.strip("-RoiSet")
+            print(self.roifile_fn.stem)
+            print(self.roifile_fn.parent.parts[-1])
+            if "Roi-" in self.roifile_fn.stem:
+                self.stem = self.roifile_fn.stem.split("Roi-")[1]
+            else:
+                self.stem = self.roifile_fn.parent.parts[-1]
+
+        else:
+            self.roifile_fn = roifile_fn / roifile_fn.parts[-1]
+            self.stem = self.roifile_fn.stem
+
+            print(self.roifile_fn)
+
+        print(self.stem)
         self.image, self.image_props = self._load_images()
 
     def _load_images(self):
         """Loading sequence of tif files
         array sequence is CZYX
         """
-        print(self.roifile_fn.parent, self.stem)
+        print("Loading images:", self.roifile_fn.parent, self.stem)
-        fns = list(self.roifile_fn.parent.glob(f"{self.stem}*.tif*"))
+        fns = list(self.roifile_fn.parent.glob(f"{self.stem.lower()}*.tif*"))
         stems = [fn.stem.split(self.stem)[-1] for fn in fns]
         n_ch = len(set([stem.split("-ch")[-1].split("t")[0] for stem in stems]))
         n_p = len(set([stem.split("-")[0] for stem in stems]))
         n_t = len(set([stem.split("t")[1] for stem in stems]))
-        print(n_ch, n_p, n_t)
 
         with TiffFile(fns[0]) as tif:
             img = tif.asarray()
@@ -42,6 +57,7 @@ class UT:
                 "height": h,
                 "dtype": dtype,
             }
+        print("Image props", self.image_props)
 
         image_stack = np.zeros((n_ch, n_p, w, h), dtype=dtype)
         for fn in fns:
@@ -49,7 +65,7 @@ class UT:
                 img = tif.asarray()
                 stem = fn.stem.split(self.stem)[-1]
                 ch = int(stem.split("-ch")[-1].split("t")[0])
-                p = int(stem.split("-")[0].lstrip("p"))
+                p = int(stem.split("-")[0].split("p")[1])
                 t = int(stem.split("t")[1])
                 print(fn.stem, "ch", ch, "p", p, "t", t)
                 image_stack[ch - 1, p - 1] = img
@@ -82,10 +98,19 @@ class UT:
     ):
         """Export rois to a file"""
         with open(path / subfolder / f"{self.stem}.txt", "w") as f:
-            for roi in self.rois:
+            for i, roi in enumerate(self.rois):
-                # TODO add image coordinates normalization
+                rc = roi.subpixel_coordinates
-                coords = ""
+                if rc is None:
-                for x, y in roi.subpixel_coordinates:
+                    print(f"No coordinates: {self.roifile_fn}, element {i}, out of {len(self.rois)}")
+                    continue
+                xmn, ymn = rc.min(axis=0)
+                xmx, ymx = rc.max(axis=0)
+                xc = (xmn + xmx) / 2
+                yc = (ymn + ymx) / 2
+                bw = xmx - xmn
+                bh = ymx - ymn
+                coords = f"{xc/self.width} {yc/self.height} {bw/self.width} {bh/self.height} "
+                for x, y in rc:
                     coords += f"{x/self.width} {y/self.height} "
                 f.write(f"{class_index} {coords}\n")
 
@@ -104,6 +129,7 @@ class UT:
             self.image = np.max(self.image[channel], axis=0)
             print(self.image.shape)
 
+        print(path / subfolder / f"{self.stem}.tif")
         with TiffWriter(path / subfolder / f"{self.stem}.tif") as tif:
             tif.write(self.image)
 
@@ -112,11 +138,31 @@ if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("input", type=Path)
+    parser.add_argument("-i", "--input", nargs="*", type=Path)
-    parser.add_argument("output", type=Path)
+    parser.add_argument("-o", "--output", type=Path)
+    parser.add_argument(
+        "--no-labels",
+        action="store_false",
+        help="Source does not have labels, export only images",
+    )
     args = parser.parse_args()
 
-    for rfn in args.input.glob("*.zip"):
+    # print(args)
-        ut = UT(rfn)
+    # aa
+
+    for path in args.input:
+        print("Path:", path)
+        if not args.no_labels:
+            print("No labels")
+            ut = UT(path, args.no_labels)
+            ut.export_image(args.output, plane_mode="max projection", channel=0)
+
+        else:
+            for rfn in Path(path).glob("*.zip"):
+                # if Path(path).suffix == ".zip":
+                print("Roi FN:", rfn)
+                ut = UT(rfn, args.no_labels)
                 ut.export_rois(args.output, class_index=0)
                 ut.export_image(args.output, plane_mode="max projection", channel=0)
+
+        print()

src/utils/image_splitter.py

@@ -0,0 +1,368 @@
+import numpy as np
+
+from pathlib import Path
+from tifffile import imread, imwrite
+from shapely.geometry import LineString
+from copy import deepcopy
+from scipy.ndimage import zoom
+
+
+# debug
+from src.utils.image import Image
+from show_yolo_seg import draw_annotations
+
+import pylab as plt
+import cv2
+
+
+class Label:
+    def __init__(self, yolo_annotation: str):
+        class_id, bbox, polygon = self.parse_yolo_annotation(yolo_annotation)
+        self.class_id = class_id
+        self.bbox = bbox
+        self.polygon = polygon
+
+    def parse_yolo_annotation(self, yolo_annotation: str):
+        class_id, *coords = yolo_annotation.split()
+        class_id = int(class_id)
+        bbox = np.array(coords[:4], dtype=np.float32)
+        polygon = np.array(coords[4:], dtype=np.float32).reshape(-1, 2) if len(coords) > 4 else None
+        if not any(np.isclose(polygon[0], polygon[-1])):
+            polygon = np.vstack([polygon, polygon[0]])
+        return class_id, bbox, polygon
+
+    def offset_label(
+        self,
+        img_w,
+        img_h,
+        distance: float = 1.0,
+        cap_style: int = 2,
+        join_style: int = 2,
+    ):
+        if self.polygon is None:
+            self.bbox = np.array(
+                [
+                    self.bbox[0] - distance if self.bbox[0] - distance > 0 else 0,
+                    self.bbox[1] - distance if self.bbox[1] - distance > 0 else 0,
+                    self.bbox[2] + distance if self.bbox[2] + distance < 1 else 1,
+                    self.bbox[3] + distance if self.bbox[3] + distance < 1 else 1,
+                ],
+                dtype=np.float32,
+            )
+            return self.bbox
+
+        def coords_are_normalized(coords):
+            # If every coordinate is between 0 and 1 (inclusive-ish), assume normalized
+            print(coords)
+            # if not coords:
+            #     return False
+            return all(max(coords.flatten)) <= 1.001
+
+        def poly_to_pts(coords, img_w, img_h):
+            # coords: [x1 y1 x2 y2 ...] either normalized or absolute
+            # if coords_are_normalized(coords):
+            coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(coords))]
+            pts = np.array(coords, dtype=np.int32).reshape(-1, 2)
+            return pts
+
+        pts = poly_to_pts(self.polygon, img_w, img_h)
+        line = LineString(pts)
+        # Buffer distance in pixels
+        buffered = line.buffer(distance=distance, cap_style=cap_style, join_style=join_style)
+        self.polygon = np.array(buffered.exterior.coords, dtype=np.float32) / (img_w, img_h)
+        xmn, ymn = self.polygon.min(axis=0)
+        xmx, ymx = self.polygon.max(axis=0)
+        xc = (xmn + xmx) / 2
+        yc = (ymn + ymx) / 2
+        bw = xmx - xmn
+        bh = ymx - ymn
+        self.bbox = np.array([xc, yc, bw, bh], dtype=np.float32)
+
+        return self.bbox, self.polygon
+
+    def translate(self, x, y, scale_x, scale_y):
+        self.bbox[0] -= x
+        self.bbox[0] *= scale_x
+        self.bbox[1] -= y
+        self.bbox[1] *= scale_y
+        self.bbox[2] *= scale_x
+        self.bbox[3] *= scale_y
+        if self.polygon is not None:
+            self.polygon[:, 0] -= x
+            self.polygon[:, 0] *= scale_x
+            self.polygon[:, 1] -= y
+            self.polygon[:, 1] *= scale_y
+
+    def in_range(self, hrange, wrange):
+        xc, yc, h, w = self.bbox
+        x1 = xc - w / 2
+        y1 = yc - h / 2
+        x2 = xc + w / 2
+        y2 = yc + h / 2
+        truth_val = (
+            xc >= wrange[0]
+            and x1 <= wrange[1]
+            and x2 >= wrange[0]
+            and x2 <= wrange[1]
+            and y1 >= hrange[0]
+            and y1 <= hrange[1]
+            and y2 >= hrange[0]
+            and y2 <= hrange[1]
+        )
+
+        print(x1, x2, wrange, y1, y2, hrange, truth_val)
+        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
+        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}"
+
+    def __str__(self):
+        return f"Class: {self.class_id}, BBox: {self.bbox}, Polygon: {self.polygon}"
+
+
+class YoloLabelReader:
+    def __init__(self, label_path: Path):
+        self.label_path = label_path
+        self.labels = self._read_labels()
+
+    def _read_labels(self):
+        with open(self.label_path, "r") as f:
+            labels = [Label(line) for line in f.readlines()]
+
+        return labels
+
+    def get_labels(self, hrange, wrange):
+        """hrange and wrange are tuples of (start, end) normalized to [0, 1]"""
+        labels = []
+        # print(hrange, wrange)
+        for lbl in self.labels:
+            # print(lbl)
+            if lbl.in_range(hrange, wrange):
+                labels.append(lbl)
+        return labels if len(labels) > 0 else None
+
+    def __get_item__(self, index):
+        return self.labels[index]
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __iter__(self):
+        return iter(self.labels)
+
+
+class ImageSplitter:
+    def __init__(self, image_path: Path, label_path: Path):
+        self.image = imread(image_path)
+        self.image_path = image_path
+        self.label_path = label_path
+        if not label_path.exists():
+            print(f"Label file {label_path} not found")
+            self.labels = None
+        else:
+            self.labels = YoloLabelReader(label_path)
+
+    def split_into_tiles(self, patch_size: tuple = (2, 2)):
+        """Split image into patches of size patch_size"""
+        hstep, wstep = (
+            self.image.shape[0] // patch_size[0],
+            self.image.shape[1] // patch_size[1],
+        )
+        h, w = self.image.shape[:2]
+
+        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)
+                tile = self.image[i * hstep : (i + 1) * hstep, j * wstep : (j + 1) * wstep]
+
+                labels = None
+                if self.labels is not None:
+                    labels = deepcopy(self.labels.get_labels(hrange, wrange))
+                    print(id(labels))
+
+                    if labels is not None:
+                        print(hrange[0], wrange[0])
+                        for l in labels:
+                            print(l.bbox)
+                        [l.translate(wrange[0], hrange[0], 2, 2) for l in labels]
+                        print("translated")
+                        for l in labels:
+                            print(l.bbox)
+
+                # print(labels)
+                yield tile_reference, tile, labels, metadata
+
+    def split_respective_to_label(self, padding: int = 67):
+        if self.labels is None:
+            raise ValueError("No labels found. Only images having labels can be split.")
+
+        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 = [
+                int(np.round(f))
+                for f in [
+                    xc_norm * self.image.shape[1],
+                    yc_norm * self.image.shape[0],
+                    h_norm * self.image.shape[0],
+                    w_norm * self.image.shape[1],
+                ]
+            ]  # image coords
+
+            # print("img coords:", xc, yc, h, w)
+            pad_xneg = padding + 1  # int(w / 2) + padding
+            pad_xpos = padding  # int(w / 2) + padding
+            pad_yneg = padding + 1  # int(h / 2) + padding
+            pad_ypos = padding  # int(h / 2) + padding
+            if xc - pad_xneg < 0:
+                pad_xneg = xc
+            if pad_xpos + xc > self.image.shape[1]:
+                pad_xpos = self.image.shape[1] - xc
+            if yc - pad_yneg < 0:
+                pad_yneg = yc
+            if pad_ypos + yc > self.image.shape[0]:
+                pad_ypos = self.image.shape[0] - yc
+
+            # print("pads:", pad_xneg, pad_xpos, pad_yneg, pad_ypos)
+
+            tile = self.image[
+                yc - pad_yneg : yc + pad_ypos,
+                xc - pad_xneg : xc + pad_xpos,
+            ]
+            ny, nx = tile.shape
+            x_offset = pad_xneg
+            y_offset = pad_yneg
+
+            # print("tile shape:", tile.shape)
+
+            yolo_annotation = f"{label.class_id} "  # {x_offset/nx} {y_offset/ny} {h_norm} {w_norm} "
+            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], metadata
+
+
+def main(args):
+
+    if args.output:
+        args.output.mkdir(exist_ok=True, parents=True)
+        (args.output / "images").mkdir(exist_ok=True)
+        (args.output / "images-zoomed").mkdir(exist_ok=True)
+        (args.output / "labels").mkdir(exist_ok=True)
+
+    for image_path in (args.input / "images").glob("*.tif"):
+        data = ImageSplitter(
+            image_path=image_path,
+            label_path=(args.input / "labels" / image_path.stem).with_suffix(".txt"),
+        )
+
+        if args.split_around_label:
+            data = data.split_respective_to_label(padding=args.padding)
+        else:
+            data = data.split_into_tiles(patch_size=args.patch_size)
+
+        for tile_reference, tile, labels, metadata in data:
+            print()
+            print(tile_reference, tile.shape, labels, metadata)  # len(labels) if labels else None)
+
+            # { debug
+            debug = False
+            if debug:
+                plt.figure(figsize=(10, 10 * tile.shape[0] / tile.shape[1]))
+                if labels is None:
+                    plt.imshow(tile, cmap="gray")
+                    plt.axis("off")
+                    plt.title(f"{image_path.name} ({tile_reference})")
+                    plt.show()
+                    continue
+
+                print(labels[0].bbox)
+                # Draw annotations
+                out = draw_annotations(
+                    cv2.cvtColor((tile / tile.max() * 255).astype(np.uint8), cv2.COLOR_GRAY2BGR),
+                    [l.to_string() for l in labels],
+                    alpha=0.1,
+                )
+
+                # Convert BGR -> RGB for matplotlib display
+                out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+                plt.imshow(out_rgb)
+                plt.axis("off")
+                plt.title(f"{image_path.name} ({tile_reference})")
+                plt.show()
+            # } debug
+
+            if args.output:
+                # imwrite(args.output / "images" / f"{image_path.stem}_{tile_reference}.tif", tile, metadata=metadata)
+                scale = 5
+                tile_zoomed = zoom(tile, zoom=scale)
+                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])
+                            f.write(label.to_string() + "\n")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", type=Path)
+    parser.add_argument("-o", "--output", type=Path)
+    parser.add_argument(
+        "-p",
+        "--patch-size",
+        nargs=2,
+        type=int,
+        default=[2, 2],
+        help="Number of patches along height and width, rows and columns, respectively",
+    )
+    parser.add_argument(
+        "-sal",
+        "--split-around-label",
+        action="store_true",
+        help="If enabled, the image will be split around the label and for each label, a separate image will be created.",
+    )
+    parser.add_argument(
+        "--padding",
+        type=int,
+        default=67,
+        help="Padding around the label when splitting around the label.",
+    )
+    args = parser.parse_args()
+
+    main(args)

src/utils/show_yolo_seg.py

@@ -0,0 +1 @@
+../../tests/show_yolo_seg.py

src/utils/ultralytics_16bit_patch.py

@@ -0,0 +1,157 @@
+"""Ultralytics runtime patches for 16-bit TIFF training.
+
+Goals:
+- Use `tifffile` to decode `.tif/.tiff` reliably (OpenCV can silently drop bit-depth depending on codec).
+- Preserve 16-bit data through the dataloader as `uint16` tensors.
+- Fix Ultralytics trainer normalization (default divides by 255) to scale `uint16` correctly.
+- Avoid uint8-forcing augmentations by recommending/setting hyp values (handled by caller).
+
+This module is intended to be imported/called **before** instantiating/using YOLO.
+"""
+
+from __future__ import annotations
+
+from typing import Optional
+
+from src.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def apply_ultralytics_16bit_tiff_patches(*, force: bool = False) -> None:
+    """Apply runtime monkey-patches to Ultralytics to better support 16-bit TIFFs.
+
+    This function is safe to call multiple times.
+
+    Args:
+        force: If True, re-apply patches even if already applied.
+    """
+
+    # Import inside function to ensure patching occurs before YOLO model/dataset is created.
+    import os
+
+    import cv2
+    import numpy as np
+
+    # import tifffile
+    import torch
+    from src.utils.image import Image
+
+    from ultralytics.utils import patches as ul_patches
+
+    already_patched = getattr(ul_patches.imread, "__name__", "") == "tifffile_imread"
+    if already_patched and not force:
+        return
+
+    _original_imread = ul_patches.imread
+
+    def tifffile_imread(filename: str, flags: int = cv2.IMREAD_COLOR, pseudo_rgb: bool = True) -> Optional[np.ndarray]:
+        """Replacement for [`ultralytics.utils.patches.imread()`](venv/lib/python3.12/site-packages/ultralytics/utils/patches.py:20).
+
+        - For `.tif/.tiff`, uses `tifffile.imread()` and preserves dtype (e.g. uint16).
+        - For other formats, falls back to Ultralytics' original implementation.
+        - Always returns HWC (3 dims). For grayscale, returns (H, W, 1) or (H, W, 3) depending on requested flags.
+        """
+        # print("here")
+        # return _original_imread(filename, flags)
+        ext = os.path.splitext(filename)[1].lower()
+        if ext in (".tif", ".tiff"):
+            arr = Image(filename).get_qt_rgb()[:, :, :3]
+
+            # Normalize common shapes:
+            # - (H, W) -> (H, W, 1)
+            # - (C, H, W) -> (H, W, C) (heuristic)
+            if arr is None:
+                return None
+            if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[0] < arr.shape[1]:
+                arr = np.transpose(arr, (1, 2, 0))
+            if arr.ndim == 2:
+                arr = arr[..., None]
+
+            # Ensure contiguous array for downstream OpenCV ops.
+            # logger.info(f"Loading with monkey-patched imread: {filename}")
+            arr = arr.astype(np.float32)
+            arr /= arr.max()
+            arr *= 2**8 - 1
+            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}")
+        return _original_imread(filename, flags)
+
+    # Patch the canonical reference.
+    ul_patches.imread = tifffile_imread
+
+    # Patch common module-level imports (some Ultralytics modules do `from ... import imread`).
+    # Importing these modules is safe and helps ensure the patched function is used.
+    try:
+        import ultralytics.data.base as _ul_base
+
+        _ul_base.imread = tifffile_imread
+    except Exception:
+        pass
+    try:
+        import ultralytics.data.loaders as _ul_loaders
+
+        _ul_loaders.imread = tifffile_imread
+    except Exception:
+        pass
+
+    # Patch trainer normalization: default divides by 255 regardless of input dtype.
+    from ultralytics.models.yolo.detect import train as detect_train
+
+    _orig_preprocess_batch = detect_train.DetectionTrainer.preprocess_batch
+
+    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")
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                batch[k] = v.to(self.device, non_blocking=self.device.type == "cuda")
+
+        img = batch.get("img")
+        if isinstance(img, torch.Tensor):
+            # Decide scaling denom based on dtype (avoid expensive reductions if possible).
+            if img.dtype == torch.uint8:
+                denom = 255.0
+            elif img.dtype == torch.uint16:
+                denom = 65535.0
+            elif img.dtype.is_floating_point:
+                # Assume already in 0-1 range if float.
+                denom = 1.0
+            else:
+                # Generic integer fallback.
+                try:
+                    denom = float(torch.iinfo(img.dtype).max)
+                except Exception:
+                    denom = 255.0
+
+            batch["img"] = img.float() / denom
+
+        # Multi-scale branch copied from upstream to avoid re-introducing `/255` scaling.
+        if getattr(self.args, "multi_scale", False):
+            import math
+            import random
+
+            import torch.nn as nn
+
+            imgs = batch["img"]
+            sz = (
+                random.randrange(int(self.args.imgsz * 0.5), int(self.args.imgsz * 1.5 + self.stride))
+                // self.stride
+                * self.stride
+            )
+            sf = sz / max(imgs.shape[2:])
+            if sf != 1:
+                ns = [math.ceil(x * sf / self.stride) * self.stride for x in imgs.shape[2:]]
+                imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
+            batch["img"] = imgs
+
+        return batch
+
+    detect_train.DetectionTrainer.preprocess_batch = preprocess_batch_16bit
+
+    # Tag function to make it easier to detect patch state.
+    setattr(detect_train.DetectionTrainer.preprocess_batch, "_ultralytics_16bit_patch", True)

tests/show_yolo_seg.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""
+show_yolo_seg.py
+
+Usage:
+    python show_yolo_seg.py /path/to/image.jpg /path/to/labels.txt
+
+Supports:
+ - Segmentation polygons: "class x1 y1 x2 y2 ... xn yn"
+ - YOLO bbox lines as fallback: "class x_center y_center width height"
+Coordinates can be normalized [0..1] or absolute pixels (auto-detected).
+"""
+import sys
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+from pathlib import Path
+import random
+from shapely.geometry import LineString
+
+from src.utils.image import Image
+
+
+def parse_label_line(line):
+    parts = line.strip().split()
+    if not parts:
+        return None
+    cls = int(float(parts[0]))
+    coords = [float(x) for x in parts[1:]]
+    return cls, coords
+
+
+def coords_are_normalized(coords):
+    # If every coordinate is between 0 and 1 (inclusive-ish), assume normalized
+    if not coords:
+        return False
+    return max(coords) <= 1.001
+
+
+def yolo_bbox_to_xyxy(coords, img_w, img_h):
+    # coords: [xc, yc, w, h] normalized or absolute
+    xc, yc, w, h = coords[:4]
+    if max(coords) <= 1.001:
+        xc *= img_w
+        yc *= img_h
+        w *= img_w
+        h *= img_h
+    x1 = int(round(xc - w / 2))
+    y1 = int(round(yc - h / 2))
+    x2 = int(round(xc + w / 2))
+    y2 = int(round(yc + h / 2))
+    return x1, y1, x2, y2
+
+
+def poly_to_pts(coords, img_w, img_h):
+    # coords: [x1 y1 x2 y2 ...] either normalized or absolute
+    if coords_are_normalized(coords[4:]):
+        coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(coords))]
+    pts = np.array(coords, dtype=np.int32).reshape(-1, 2)
+    return pts
+
+
+def random_color_for_class(cls):
+    random.seed(cls)  # deterministic per class
+    return (
+        0,
+        0,
+        255,
+    )  # tuple(int(x) for x in np.array([random.randint(0, 255) for _ in range(3)]))
+
+
+def draw_annotations(img, labels, alpha=0.4, draw_bbox_for_poly=True):
+    # img: BGR numpy array
+    overlay = img.copy()
+    h, w = img.shape[:2]
+    for line in labels:
+        if isinstance(line, str):
+            cls, coords = parse_label_line(line)
+        if isinstance(line, tuple):
+            cls, coords = line
+
+        if not coords:
+            continue
+        # polygon case (>=6 coordinates)
+        if len(coords) >= 6:
+            color = random_color_for_class(cls)
+
+            x1, y1, x2, y2 = yolo_bbox_to_xyxy(coords[:4], w, h)
+            print(x1, y1, x2, y2)
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 1)
+
+            pts = poly_to_pts(coords[4:], w, h)
+            # line = LineString(pts)
+            # # Buffer distance in pixels
+            # buffered = line.buffer(3, cap_style=2, join_style=2)
+            # coords = np.array(buffered.exterior.coords, dtype=np.int32)
+            # cv2.fillPoly(overlay, [coords], color=(255, 255, 255))
+
+            # fill on overlay
+            cv2.fillPoly(overlay, [pts], color)
+            # outline on base image
+            cv2.polylines(img, [pts], isClosed=True, color=color, thickness=1)
+            # put class text at first point
+            x, y = int(pts[0, 0]), int(pts[0, 1]) - 6
+            if 0:
+                cv2.putText(
+                    img,
+                    str(cls),
+                    (x, max(6, y)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6,
+                    (255, 255, 255),
+                    2,
+                    cv2.LINE_AA,
+                )
+
+        # YOLO bbox case (4 coords)
+        elif len(coords) == 4:
+            x1, y1, x2, y2 = yolo_bbox_to_xyxy(coords, w, h)
+            color = random_color_for_class(cls)
+            cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(
+                img,
+                str(cls),
+                (x1, max(6, y1 - 4)),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.6,
+                (255, 255, 255),
+                2,
+                cv2.LINE_AA,
+            )
+        else:
+            # Unknown / invalid format, skip
+            continue
+
+    # blend overlay for filled polygons
+    cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0, img)
+    return img
+
+
+def load_labels_file(label_path):
+    labels = []
+    with open(label_path, "r") as f:
+        for raw in f:
+            line = raw.strip()
+            if not line:
+                continue
+            parsed = parse_label_line(line)
+            if parsed:
+                labels.append(parsed)
+    return labels
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Show YOLO segmentation / polygon annotations")
+    parser.add_argument("image", type=str, help="Path to image file")
+    parser.add_argument("--labels", type=str, help="Path to YOLO label file (polygons)")
+    parser.add_argument("--alpha", type=float, default=0.4, help="Polygon fill alpha (0..1)")
+    parser.add_argument("--no-bbox", action="store_true", help="Don't draw bounding boxes for polygons")
+    args = parser.parse_args()
+
+    print(args)
+
+    img_path = Path(args.image)
+    if args.labels:
+        lbl_path = Path(args.labels)
+    else:
+        lbl_path = img_path.with_suffix(".txt")
+        lbl_path = Path(str(lbl_path).replace("images", "labels"))
+
+    if not img_path.exists():
+        print("Image not found:", img_path)
+        sys.exit(1)
+    if not lbl_path.exists():
+        print("Label file not found:", lbl_path)
+        sys.exit(1)
+
+    # img = cv2.imread(str(img_path), cv2.IMREAD_COLOR)
+    img = (Image(img_path).get_qt_rgb() * 255).astype(np.uint8)
+
+    if img is None:
+        print("Could not load image:", img_path)
+        sys.exit(1)
+
+    labels = load_labels_file(str(lbl_path))
+    if not labels:
+        print("No labels parsed from", lbl_path)
+        # continue and just show image
+    out = draw_annotations(img.copy(), labels, alpha=args.alpha, draw_bbox_for_poly=(not args.no_bbox))
+
+    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)
+        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})")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()