Updating image splitter

Reviewed-on: #2

config/app_config.yaml

@@ -1,41 +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
-training:
-  default_epochs: 100
-  default_batch_size: 16
-  default_imgsz: 640
-  default_patience: 50
-  default_lr0: 0.01
-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

@@ -10,6 +10,14 @@ from typing import List, Dict, Optional, Tuple, Any, Union
 from pathlib import Path
 import csv
 import hashlib
+import yaml
+
+from src.utils.logger import get_logger
+from src.utils.image import Image
+
+IMAGE_EXTENSIONS = tuple(Image.SUPPORTED_EXTENSIONS)
+
+logger = get_logger(__name__)
 
 
 class DatabaseManager:
@@ -443,6 +451,25 @@ class DatabaseManager:
             filters["model_id"] = model_id
         return self.get_detections(filters)
 
+    def delete_detections_for_image(
+        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:
+            cursor = conn.cursor()
+            if model_id is not None:
+                cursor.execute(
+                    "DELETE FROM detections WHERE image_id = ? AND model_id = ?",
+                    (image_id, model_id),
+                )
+            else:
+                cursor.execute("DELETE FROM detections WHERE image_id = ?", (image_id,))
+            conn.commit()
+            return cursor.rowcount
+        finally:
+            conn.close()
+
     def delete_detections_for_model(self, model_id: int) -> int:
         """Delete all detections for a specific model."""
         conn = self.get_connection()
@@ -861,6 +888,187 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    # ==================== Dataset Utilities ====================
+
+    def compose_data_yaml(
+        self,
+        dataset_root: str,
+        output_path: Optional[str] = None,
+        splits: Optional[Dict[str, str]] = None,
+    ) -> str:
+        """
+        Compose a YOLO data.yaml file based on dataset folders and database metadata.
+
+        Args:
+            dataset_root: Base directory containing the dataset structure.
+            output_path: Optional output path; defaults to <dataset_root>/data.yaml.
+            splits: Optional mapping overriding train/val/test image directories (relative
+                to dataset_root or absolute paths).
+
+        Returns:
+            Path to the generated YAML file.
+        """
+        dataset_root_path = Path(dataset_root).expanduser()
+        if not dataset_root_path.exists():
+            raise ValueError(f"Dataset root does not exist: {dataset_root_path}")
+        dataset_root_path = dataset_root_path.resolve()
+
+        split_map: Dict[str, str] = {key: "" for key in ("train", "val", "test")}
+        if splits:
+            for key, value in splits.items():
+                if key in split_map and value:
+                    split_map[key] = value
+
+        inferred = self._infer_split_dirs(dataset_root_path)
+        for key in split_map:
+            if not split_map[key]:
+                split_map[key] = inferred.get(key, "")
+
+        for required in ("train", "val"):
+            if not split_map[required]:
+                raise ValueError(
+                    "Unable to determine %s image directory under %s. Provide it "
+                    "explicitly via the 'splits' argument."
+                    % (required, dataset_root_path)
+                )
+
+        yaml_splits: Dict[str, str] = {}
+        for key, value in split_map.items():
+            if not value:
+                continue
+            yaml_splits[key] = self._normalize_split_value(value, dataset_root_path)
+
+        class_names = self._fetch_annotation_class_names()
+        if not class_names:
+            class_names = [cls["class_name"] for cls in self.get_object_classes()]
+        if not class_names:
+            raise ValueError("No object classes available to populate data.yaml")
+
+        names_map = {idx: name for idx, name in enumerate(class_names)}
+        payload: Dict[str, Any] = {
+            "path": dataset_root_path.as_posix(),
+            "train": yaml_splits["train"],
+            "val": yaml_splits["val"],
+            "names": names_map,
+            "nc": len(class_names),
+        }
+        if yaml_splits.get("test"):
+            payload["test"] = yaml_splits["test"]
+
+        output_path_obj = (
+            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:
+            yaml.safe_dump(payload, handle, sort_keys=False)
+
+        logger.info(f"Generated data.yaml at {output_path_obj}")
+        return output_path_obj.as_posix()
+
+    def _fetch_annotation_class_names(self) -> List[str]:
+        """Return class names referenced by annotations (ordered by class ID)."""
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                SELECT DISTINCT c.id, c.class_name
+                FROM annotations a
+                JOIN object_classes c ON a.class_id = c.id
+                ORDER BY c.id
+                """
+            )
+            rows = cursor.fetchall()
+            return [row["class_name"] for row in rows]
+        finally:
+            conn.close()
+
+    def _infer_split_dirs(self, dataset_root: Path) -> Dict[str, str]:
+        """Infer train/val/test image directories relative to dataset_root."""
+        patterns = {
+            "train": [
+                "train/images",
+                "training/images",
+                "images/train",
+                "images/training",
+                "train",
+                "training",
+            ],
+            "val": [
+                "val/images",
+                "validation/images",
+                "images/val",
+                "images/validation",
+                "val",
+                "validation",
+            ],
+            "test": [
+                "test/images",
+                "testing/images",
+                "images/test",
+                "images/testing",
+                "test",
+                "testing",
+            ],
+        }
+
+        inferred: Dict[str, str] = {key: "" for key in patterns}
+        for split_name, options in patterns.items():
+            for relative in options:
+                candidate = (dataset_root / relative).resolve()
+                if (
+                    candidate.exists()
+                    and candidate.is_dir()
+                    and self._directory_has_images(candidate)
+                ):
+                    try:
+                        inferred[split_name] = candidate.relative_to(
+                            dataset_root
+                        ).as_posix()
+                    except ValueError:
+                        inferred[split_name] = candidate.as_posix()
+                    break
+        return inferred
+
+    def _normalize_split_value(self, split_value: str, dataset_root: Path) -> str:
+        """Validate and normalize a split directory to a YAML-friendly string."""
+        split_path = Path(split_value).expanduser()
+        if not split_path.is_absolute():
+            split_path = (dataset_root / split_path).resolve()
+        else:
+            split_path = split_path.resolve()
+
+        if not split_path.exists() or not split_path.is_dir():
+            raise ValueError(f"Split directory not found: {split_path}")
+
+        if not self._directory_has_images(split_path):
+            raise ValueError(f"No images found under {split_path}")
+
+        try:
+            return split_path.relative_to(dataset_root).as_posix()
+        except ValueError:
+            return split_path.as_posix()
+
+    @staticmethod
+    def _directory_has_images(directory: Path, max_checks: int = 2000) -> bool:
+        """Return True if directory tree contains at least one image file."""
+        checked = 0
+        try:
+            for file_path in directory.rglob("*"):
+                if not file_path.is_file():
+                    continue
+                if file_path.suffix.lower() in IMAGE_EXTENSIONS:
+                    return True
+                checked += 1
+                if checked >= max_checks:
+                    break
+        except Exception:
+            return False
+        return False
+
     @staticmethod
     def calculate_checksum(file_path: str) -> str:
         """Calculate MD5 checksum of a file."""

src/gui/main_window.py

@@ -297,7 +297,9 @@ class MainWindow(QMainWindow):
             # Save window state before closing
             self._save_window_state()
 
-            # Save annotation tab state if it exists
+            # Persist tab state and stop background work before exit
+            if hasattr(self, "training_tab"):
+                self.training_tab.shutdown()
             if hasattr(self, "annotation_tab"):
                 self.annotation_tab.save_state()
 

src/gui/tabs/annotation_tab.py

@@ -168,7 +168,7 @@ class AnnotationTab(QWidget):
             self,
             "Select Image",
             start_dir,
-            "Images (*.jpg *.jpeg *.png *.tif *.tiff *.bmp)",
+            "Images (*" + " *".join(Image.SUPPORTED_EXTENSIONS) + ")",
         )
 
         if not file_path:

src/gui/tabs/detection_tab.py

@@ -20,12 +20,14 @@ from PySide6.QtWidgets import (
 )
 from PySide6.QtCore import Qt, QThread, Signal
 from pathlib import Path
+from typing import Optional
 
 from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
 from src.utils.logger import get_logger
 from src.utils.file_utils import get_image_files
 from src.model.inference import InferenceEngine
+from src.utils.image import Image
 
 
 logger = get_logger(__name__)
@@ -147,30 +149,66 @@ class DetectionTab(QWidget):
         self.model_combo.currentIndexChanged.connect(self._on_model_changed)
 
     def _load_models(self):
-        """Load available models from database."""
+        """Load available models from database and local storage."""
         try:
-            models = self.db_manager.get_models()
             self.model_combo.clear()
+            models = self.db_manager.get_models()
+            has_models = False
 
-            if not models:
-                self.model_combo.addItem("No models available", None)
-                self._set_buttons_enabled(False)
-                return
+            known_paths = set()
 
-            # Add base model option
+            # Add base model option first (always available)
             base_model = self.config_manager.get(
                 "models.default_base_model", "yolov8s-seg.pt"
             )
-            self.model_combo.addItem(
-                f"Base Model ({base_model})", {"id": 0, "path": base_model}
-            )
+            if base_model:
+                base_data = {
+                    "id": 0,
+                    "path": base_model,
+                    "model_name": Path(base_model).stem or "Base Model",
+                    "model_version": "pretrained",
+                    "base_model": base_model,
+                    "source": "base",
+                }
+                self.model_combo.addItem(f"Base Model ({base_model})", base_data)
+                known_paths.add(self._normalize_model_path(base_model))
+                has_models = True
 
-            # Add trained models
+            # Add trained models from database
             for model in models:
                 display_name = f"{model['model_name']} v{model['model_version']}"
-                self.model_combo.addItem(display_name, model)
+                model_data = {**model, "path": model.get("model_path")}
+                normalized = self._normalize_model_path(model_data.get("path"))
+                if normalized:
+                    known_paths.add(normalized)
+                self.model_combo.addItem(display_name, model_data)
+                has_models = True
 
-            self._set_buttons_enabled(True)
+            # Discover local model files not yet in the database
+            local_models = self._discover_local_models()
+            for model_path in local_models:
+                normalized = self._normalize_model_path(model_path)
+                if normalized in known_paths:
+                    continue
+
+                display_name = f"Local Model ({Path(model_path).stem})"
+                model_data = {
+                    "id": None,
+                    "path": str(model_path),
+                    "model_name": Path(model_path).stem,
+                    "model_version": "local",
+                    "base_model": Path(model_path).stem,
+                    "source": "local",
+                }
+                self.model_combo.addItem(display_name, model_data)
+                known_paths.add(normalized)
+                has_models = True
+
+            if not has_models:
+                self.model_combo.addItem("No models available", None)
+                self._set_buttons_enabled(False)
+            else:
+                self._set_buttons_enabled(True)
 
         except Exception as e:
             logger.error(f"Error loading models: {e}")
@@ -199,7 +237,7 @@ class DetectionTab(QWidget):
             self,
             "Select Image",
             start_dir,
-            "Images (*.jpg *.jpeg *.png *.tif *.tiff *.bmp)",
+            "Images (*" + " *".join(Image.SUPPORTED_EXTENSIONS) + ")",
         )
 
         if not file_path:
@@ -249,25 +287,39 @@ class DetectionTab(QWidget):
                 QMessageBox.warning(self, "No Model", "Please select a model first.")
                 return
 
-            model_path = model_data["path"]
-            model_id = model_data["id"]
+            model_path = model_data.get("path")
+            if not model_path:
+                QMessageBox.warning(
+                    self, "Invalid Model", "Selected model is missing a file path."
+                )
+                return
 
-            # Ensure we have a valid model ID (create entry for base model if needed)
-            if model_id == 0:
-                # Create database entry for base model
-                base_model = self.config_manager.get(
-                    "models.default_base_model", "yolov8s-seg.pt"
-                )
-                model_id = self.db_manager.add_model(
-                    model_name="Base Model",
-                    model_version="pretrained",
-                    model_path=base_model,
-                    base_model=base_model,
+            if not Path(model_path).exists():
+                QMessageBox.critical(
+                    self,
+                    "Model Not Found",
+                    f"The selected model file could not be found:\n{model_path}",
                 )
+                return
+
+            model_id = model_data.get("id")
+
+            # Ensure we have a database entry for the selected model
+            if model_id in (None, 0):
+                model_id = self._ensure_model_record(model_data)
+                if not model_id:
+                    QMessageBox.critical(
+                        self,
+                        "Model Registration Failed",
+                        "Unable to register the selected model in the database.",
+                    )
+                    return
+
+            normalized_model_path = self._normalize_model_path(model_path) or model_path
 
             # Create inference engine
             self.inference_engine = InferenceEngine(
-                model_path, self.db_manager, model_id
+                normalized_model_path, self.db_manager, model_id
             )
 
             # Get confidence threshold
@@ -338,6 +390,76 @@ class DetectionTab(QWidget):
         self.batch_btn.setEnabled(enabled)
         self.model_combo.setEnabled(enabled)
 
+    def _discover_local_models(self) -> list:
+        """Scan the models directory for standalone .pt files."""
+        models_dir = self.config_manager.get_models_directory()
+        if not models_dir:
+            return []
+
+        models_path = Path(models_dir)
+        if not models_path.exists():
+            return []
+
+        try:
+            return sorted(
+                [p for p in models_path.rglob("*.pt") if p.is_file()],
+                key=lambda p: str(p).lower(),
+            )
+        except Exception as e:
+            logger.warning(f"Error discovering local models: {e}")
+            return []
+
+    def _normalize_model_path(self, path_value) -> str:
+        """Return a normalized absolute path string for comparison."""
+        if not path_value:
+            return ""
+        try:
+            return str(Path(path_value).resolve())
+        except Exception:
+            return str(path_value)
+
+    def _ensure_model_record(self, model_data: dict) -> Optional[int]:
+        """Ensure a database record exists for the selected model."""
+        model_path = model_data.get("path")
+        if not model_path:
+            return None
+
+        normalized_target = self._normalize_model_path(model_path)
+
+        try:
+            existing_models = self.db_manager.get_models()
+            for model in existing_models:
+                existing_path = model.get("model_path")
+                if not existing_path:
+                    continue
+                normalized_existing = self._normalize_model_path(existing_path)
+                if (
+                    normalized_existing == normalized_target
+                    or existing_path == model_path
+                ):
+                    return model["id"]
+
+            model_name = (
+                model_data.get("model_name") or Path(model_path).stem or "Custom Model"
+            )
+            model_version = (
+                model_data.get("model_version") or model_data.get("source") or "local"
+            )
+            base_model = model_data.get(
+                "base_model",
+                self.config_manager.get("models.default_base_model", "yolov8s-seg.pt"),
+            )
+
+            return self.db_manager.add_model(
+                model_name=model_name,
+                model_version=model_version,
+                model_path=normalized_target,
+                base_model=base_model,
+            )
+        except Exception as e:
+            logger.error(f"Failed to ensure model record for {model_path}: {e}")
+            return None
+
     def refresh(self):
         """Refresh the tab."""
         self._load_models()

src/gui/tabs/results_tab.py

@@ -1,15 +1,39 @@
 """
-Results tab for the microscopy object detection application.
+Results tab for browsing stored detections and visualizing overlays.
 """
 
-from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QGroupBox
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from PySide6.QtWidgets import (
+    QWidget,
+    QVBoxLayout,
+    QHBoxLayout,
+    QLabel,
+    QGroupBox,
+    QPushButton,
+    QSplitter,
+    QTableWidget,
+    QTableWidgetItem,
+    QHeaderView,
+    QAbstractItemView,
+    QMessageBox,
+    QCheckBox,
+)
+from PySide6.QtCore import Qt
 
 from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
+from src.utils.logger import get_logger
+from src.utils.image import Image, ImageLoadError
+from src.gui.widgets import AnnotationCanvasWidget
+
+
+logger = get_logger(__name__)
 
 
 class ResultsTab(QWidget):
-    """Results tab placeholder."""
+    """Results tab showing detection history and preview overlays."""
 
     def __init__(
         self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
@@ -18,29 +42,398 @@ class ResultsTab(QWidget):
         self.db_manager = db_manager
         self.config_manager = config_manager
 
+        self.detection_summary: List[Dict] = []
+        self.current_selection: Optional[Dict] = None
+        self.current_image: Optional[Image] = None
+        self.current_detections: List[Dict] = []
+        self._image_path_cache: Dict[str, str] = {}
+
         self._setup_ui()
+        self.refresh()
 
     def _setup_ui(self):
         """Setup user interface."""
         layout = QVBoxLayout()
 
-        group = QGroupBox("Results")
-        group_layout = QVBoxLayout()
-        label = QLabel(
-            "Results viewer will be implemented here.\n\n"
-            "Features:\n"
-            "- Detection history browser\n"
-            "- Advanced filtering\n"
-            "- Statistics dashboard\n"
-            "- Export functionality"
-        )
-        group_layout.addWidget(label)
-        group.setLayout(group_layout)
+        # Splitter for list + preview
+        splitter = QSplitter(Qt.Horizontal)
 
-        layout.addWidget(group)
-        layout.addStretch()
+        # Left pane: detection list
+        left_container = QWidget()
+        left_layout = QVBoxLayout()
+        left_layout.setContentsMargins(0, 0, 0, 0)
+
+        controls_layout = QHBoxLayout()
+        self.refresh_btn = QPushButton("Refresh")
+        self.refresh_btn.clicked.connect(self.refresh)
+        controls_layout.addWidget(self.refresh_btn)
+        controls_layout.addStretch()
+        left_layout.addLayout(controls_layout)
+
+        self.results_table = QTableWidget(0, 5)
+        self.results_table.setHorizontalHeaderLabels(
+            ["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(
+            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)
+        self.results_table.itemSelectionChanged.connect(self._on_result_selected)
+
+        left_layout.addWidget(self.results_table)
+        left_container.setLayout(left_layout)
+
+        # Right pane: preview canvas and controls
+        right_container = QWidget()
+        right_layout = QVBoxLayout()
+        right_layout.setContentsMargins(0, 0, 0, 0)
+
+        preview_group = QGroupBox("Detection Preview")
+        preview_layout = QVBoxLayout()
+
+        self.preview_canvas = AnnotationCanvasWidget()
+        self.preview_canvas.set_polyline_enabled(False)
+        self.preview_canvas.set_show_bboxes(True)
+        preview_layout.addWidget(self.preview_canvas)
+
+        toggles_layout = QHBoxLayout()
+        self.show_masks_checkbox = QCheckBox("Show Masks")
+        self.show_masks_checkbox.setChecked(False)
+        self.show_masks_checkbox.stateChanged.connect(self._apply_detection_overlays)
+        self.show_bboxes_checkbox = QCheckBox("Show Bounding Boxes")
+        self.show_bboxes_checkbox.setChecked(True)
+        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._apply_detection_overlays
+        )
+        toggles_layout.addWidget(self.show_masks_checkbox)
+        toggles_layout.addWidget(self.show_bboxes_checkbox)
+        toggles_layout.addWidget(self.show_confidence_checkbox)
+        toggles_layout.addStretch()
+        preview_layout.addLayout(toggles_layout)
+
+        self.summary_label = QLabel("Select a detection result to preview.")
+        self.summary_label.setWordWrap(True)
+        preview_layout.addWidget(self.summary_label)
+
+        preview_group.setLayout(preview_layout)
+        right_layout.addWidget(preview_group)
+        right_container.setLayout(right_layout)
+
+        splitter.addWidget(left_container)
+        splitter.addWidget(right_container)
+        splitter.setStretchFactor(0, 1)
+        splitter.setStretchFactor(1, 2)
+
+        layout.addWidget(splitter)
         self.setLayout(layout)
 
     def refresh(self):
-        """Refresh the tab."""
-        pass
+        """Refresh the detection list and preview."""
+        self._load_detection_summary()
+        self._populate_results_table()
+        self.current_selection = None
+        self.current_image = None
+        self.current_detections = []
+        self.preview_canvas.clear()
+        self.summary_label.setText("Select a detection result to preview.")
+
+    def _load_detection_summary(self):
+        """Load latest detection summaries grouped by image + model."""
+        try:
+            detections = self.db_manager.get_detections(limit=500)
+            summary_map: Dict[tuple, Dict] = {}
+
+            for det in detections:
+                key = (det["image_id"], det["model_id"])
+                metadata = det.get("metadata") or {}
+                entry = summary_map.setdefault(
+                    key,
+                    {
+                        "image_id": det["image_id"],
+                        "model_id": det["model_id"],
+                        "image_path": det.get("image_path"),
+                        "image_filename": det.get("image_filename")
+                        or det.get("image_path"),
+                        "model_name": det.get("model_name", ""),
+                        "model_version": det.get("model_version", ""),
+                        "last_detected": det.get("detected_at"),
+                        "count": 0,
+                        "classes": set(),
+                        "source_path": metadata.get("source_path"),
+                        "repository_root": metadata.get("repository_root"),
+                    },
+                )
+
+                entry["count"] += 1
+                if det.get("detected_at") and (
+                    not entry.get("last_detected")
+                    or str(det.get("detected_at")) > str(entry.get("last_detected"))
+                ):
+                    entry["last_detected"] = det.get("detected_at")
+                if det.get("class_name"):
+                    entry["classes"].add(det["class_name"])
+                if metadata.get("source_path") and not entry.get("source_path"):
+                    entry["source_path"] = metadata.get("source_path")
+                if metadata.get("repository_root") and not entry.get("repository_root"):
+                    entry["repository_root"] = metadata.get("repository_root")
+
+            self.detection_summary = sorted(
+                summary_map.values(),
+                key=lambda x: str(x.get("last_detected") or ""),
+                reverse=True,
+            )
+        except Exception as e:
+            logger.error(f"Failed to load detection summary: {e}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to load detection results:\n{str(e)}",
+            )
+            self.detection_summary = []
+
+    def _populate_results_table(self):
+        """Populate the table widget with detection summaries."""
+        self.results_table.setRowCount(len(self.detection_summary))
+
+        for row, entry in enumerate(self.detection_summary):
+            model_label = f"{entry['model_name']} {entry['model_version']}".strip()
+            class_list = (
+                ", ".join(sorted(entry["classes"])) if entry["classes"] else "-"
+            )
+
+            items = [
+                QTableWidgetItem(entry.get("image_filename", "")),
+                QTableWidgetItem(model_label),
+                QTableWidgetItem(str(entry.get("count", 0))),
+                QTableWidgetItem(class_list),
+                QTableWidgetItem(str(entry.get("last_detected") or "")),
+            ]
+
+            for col, item in enumerate(items):
+                item.setData(Qt.UserRole, row)
+                self.results_table.setItem(row, col, item)
+
+        self.results_table.clearSelection()
+
+    def _on_result_selected(self):
+        """Handle selection changes in the detection table."""
+        selected_items = self.results_table.selectedItems()
+        if not selected_items:
+            return
+
+        row = selected_items[0].data(Qt.UserRole)
+        if row is None or row >= len(self.detection_summary):
+            return
+
+        entry = self.detection_summary[row]
+        if (
+            self.current_selection
+            and self.current_selection.get("image_id") == entry["image_id"]
+            and self.current_selection.get("model_id") == entry["model_id"]
+        ):
+            return
+
+        self.current_selection = entry
+
+        image_path = self._resolve_image_path(entry)
+        if not image_path:
+            QMessageBox.warning(
+                self,
+                "Image Not Found",
+                "Unable to locate the image file for this detection.",
+            )
+            return
+
+        try:
+            self.current_image = Image(image_path)
+            self.preview_canvas.load_image(self.current_image)
+        except ImageLoadError as e:
+            logger.error(f"Failed to load image '{image_path}': {e}")
+            QMessageBox.critical(
+                self,
+                "Image Error",
+                f"Failed to load image for preview:\n{str(e)}",
+            )
+            return
+
+        self._load_detections_for_selection(entry)
+        self._apply_detection_overlays()
+        self._update_summary_label(entry)
+
+    def _load_detections_for_selection(self, entry: Dict):
+        """Load detection records for the selected image/model pair."""
+        self.current_detections = []
+        if not entry:
+            return
+
+        try:
+            filters = {"image_id": entry["image_id"], "model_id": entry["model_id"]}
+            self.current_detections = self.db_manager.get_detections(filters)
+        except Exception as e:
+            logger.error(f"Failed to load detections for preview: {e}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to load detections for this image:\n{str(e)}",
+            )
+            self.current_detections = []
+
+    def _apply_detection_overlays(self):
+        """Draw detections onto the preview canvas based on current toggles."""
+        self.preview_canvas.clear_annotations()
+        self.preview_canvas.set_show_bboxes(self.show_bboxes_checkbox.isChecked())
+
+        if not self.current_detections or not self.current_image:
+            return
+
+        for det in self.current_detections:
+            color = self._get_class_color(det.get("class_name"))
+
+            if self.show_masks_checkbox.isChecked() and det.get("segmentation_mask"):
+                mask_points = self._convert_mask(det["segmentation_mask"])
+                if mask_points:
+                    self.preview_canvas.draw_saved_polyline(mask_points, color)
+
+            bbox = [
+                det.get("x_min"),
+                det.get("y_min"),
+                det.get("x_max"),
+                det.get("y_max"),
+            ]
+            if all(v is not None for v in bbox):
+                label = None
+                if self.show_confidence_checkbox.isChecked():
+                    confidence = det.get("confidence")
+                    if confidence is not None:
+                        label = f"{confidence:.2f}"
+                self.preview_canvas.draw_saved_bbox(bbox, color, label=label)
+
+    def _convert_mask(self, mask_points: List[List[float]]) -> List[List[float]]:
+        """Convert stored [x, y] masks to [y, x] format for the canvas."""
+        converted = []
+        for point in mask_points:
+            if len(point) >= 2:
+                x, y = point[0], point[1]
+                converted.append([y, x])
+        return converted
+
+    def _toggle_bboxes(self):
+        """Update bounding box visibility on the canvas."""
+        self.preview_canvas.set_show_bboxes(self.show_bboxes_checkbox.isChecked())
+        # Re-render to respect show/hide when toggled
+        self._apply_detection_overlays()
+
+    def _update_summary_label(self, entry: Dict):
+        """Display textual summary for the selected detection run."""
+        classes = ", ".join(sorted(entry.get("classes", []))) or "-"
+        summary_text = (
+            f"Image: {entry.get('image_filename', 'unknown')}\n"
+            f"Model: {entry.get('model_name', '')} {entry.get('model_version', '')}\n"
+            f"Detections: {entry.get('count', 0)}\n"
+            f"Classes: {classes}\n"
+            f"Last Updated: {entry.get('last_detected', 'n/a')}"
+        )
+        self.summary_label.setText(summary_text)
+
+    def _resolve_image_path(self, entry: Dict) -> Optional[str]:
+        """Resolve an image path using metadata, cache, and repository hints."""
+        relative_path = entry.get("image_path") if entry else None
+        cache_key = relative_path or entry.get("source_path")
+        if cache_key and cache_key in self._image_path_cache:
+            cached = Path(self._image_path_cache[cache_key])
+            if cached.exists():
+                return self._image_path_cache[cache_key]
+            del self._image_path_cache[cache_key]
+
+        candidates = []
+        source_path = entry.get("source_path") if entry else None
+        if source_path:
+            candidates.append(Path(source_path))
+
+        repo_roots = []
+        if entry.get("repository_root"):
+            repo_roots.append(entry["repository_root"])
+        config_repo = self.config_manager.get_image_repository_path()
+        if config_repo:
+            repo_roots.append(config_repo)
+
+        for root in repo_roots:
+            if relative_path:
+                candidates.append(Path(root) / relative_path)
+
+        if relative_path:
+            candidates.append(Path(relative_path))
+
+        for candidate in candidates:
+            try:
+                if candidate and candidate.exists():
+                    resolved = str(candidate.resolve())
+                    if cache_key:
+                        self._image_path_cache[cache_key] = resolved
+                    return resolved
+            except Exception:
+                continue
+
+        # Fallback: search by filename in known roots
+        filename = Path(relative_path).name if relative_path else None
+        if filename:
+            search_roots = [Path(root) for root in repo_roots if root]
+            if not search_roots:
+                search_roots = [Path("data")]
+            match = self._search_in_roots(filename, search_roots)
+            if match:
+                resolved = str(match.resolve())
+                if cache_key:
+                    self._image_path_cache[cache_key] = resolved
+                return resolved
+
+        return None
+
+    def _search_in_roots(self, filename: str, roots: List[Path]) -> Optional[Path]:
+        """Search for a file name within a list of root directories."""
+        for root in roots:
+            try:
+                if not root.exists():
+                    continue
+                for candidate in root.rglob(filename):
+                    return candidate
+            except Exception as e:
+                logger.debug(f"Error searching for {filename} in {root}: {e}")
+        return None
+
+    def _get_class_color(self, class_name: Optional[str]) -> str:
+        """Return consistent color hex for a class name."""
+        if not class_name:
+            return "#FF6B6B"
+
+        color_map = self.config_manager.get_bbox_colors()
+        if class_name in color_map:
+            return color_map[class_name]
+
+        # Deterministic fallback color based on hash
+        palette = [
+            "#FF6B6B",
+            "#4ECDC4",
+            "#FFD166",
+            "#1D3557",
+            "#F4A261",
+            "#E76F51",
+        ]
+        return palette[hash(class_name) % len(palette)]

src/gui/widgets/annotation_canvas_widget.py

@@ -16,8 +16,9 @@ from PySide6.QtGui import (
     QKeyEvent,
     QMouseEvent,
     QPaintEvent,
+    QPolygonF,
 )
-from PySide6.QtCore import Qt, QEvent, Signal, QPoint
+from PySide6.QtCore import Qt, QEvent, Signal, QPoint, QPointF, QRect
 from typing import Any, Dict, List, Optional, Tuple
 
 from src.utils.image import Image, ImageLoadError
@@ -246,15 +247,13 @@ class AnnotationCanvasWidget(QWidget):
             return
 
         try:
-            # Get RGB image data
-            if self.current_image.channels == 3:
+            # 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, channels = image_data.shape
             else:
-                image_data = self.current_image.get_grayscale()
-                height, width = image_data.shape
+                image_data = self.current_image.get_qt_rgb()
 
-            image_data = np.ascontiguousarray(image_data)
+            height, width = image_data.shape[:2]
             bytes_per_line = image_data.strides[0]
 
             qimage = QImage(
@@ -262,8 +261,8 @@ 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)
 
@@ -496,8 +495,10 @@ class AnnotationCanvasWidget(QWidget):
                 )
 
             painter.setPen(pen)
-            for (x1, y1), (x2, y2) in zip(polyline[:-1], polyline[1:]):
-                painter.drawLine(int(x1), int(y1), int(x2), int(y2))
+            # Use QPolygonF for efficient polygon rendering (single call vs N-1 calls)
+            # drawPolygon() automatically closes the shape, ensuring proper visual closure
+            polygon = QPolygonF([QPointF(x, y) for x, y in polyline])
+            painter.drawPolygon(polygon)
 
         # Draw bounding boxes (dashed) if enabled
         if self.show_bboxes and self.original_pixmap is not None and self.bboxes:
@@ -529,6 +530,40 @@ class AnnotationCanvasWidget(QWidget):
                 painter.setPen(pen)
                 painter.drawRect(x_min, y_min, rect_width, rect_height)
 
+                label_text = meta.get("label")
+                if label_text:
+                    painter.save()
+                    font = painter.font()
+                    font.setPointSizeF(max(10.0, width + 4))
+                    painter.setFont(font)
+                    metrics = painter.fontMetrics()
+                    text_width = metrics.horizontalAdvance(label_text)
+                    text_height = metrics.height()
+                    padding = 4
+                    bg_width = text_width + padding * 2
+                    bg_height = text_height + padding * 2
+                    canvas_width = self.original_pixmap.width()
+                    canvas_height = self.original_pixmap.height()
+                    bg_x = max(0, min(x_min, canvas_width - bg_width))
+                    bg_y = y_min - bg_height
+                    if bg_y < 0:
+                        bg_y = min(y_min, canvas_height - bg_height)
+                        bg_y = max(0, bg_y)
+                    background_rect = QRect(bg_x, bg_y, bg_width, bg_height)
+                    background_color = QColor(pen_color)
+                    background_color.setAlpha(220)
+                    painter.fillRect(background_rect, background_color)
+                    text_color = QColor(0, 0, 0)
+                    if background_color.lightness() < 128:
+                        text_color = QColor(255, 255, 255)
+                    painter.setPen(text_color)
+                    painter.drawText(
+                        background_rect.adjusted(padding, padding, -padding, -padding),
+                        Qt.AlignLeft | Qt.AlignVCenter,
+                        label_text,
+                    )
+                    painter.restore()
+
         painter.end()
 
         self._update_display()
@@ -787,7 +822,13 @@ class AnnotationCanvasWidget(QWidget):
             f"Drew saved polyline with {len(polyline)} points in color {color}"
         )
 
-    def draw_saved_bbox(self, bbox: List[float], color: str, width: int = 3):
+    def draw_saved_bbox(
+        self,
+        bbox: List[float],
+        color: str,
+        width: int = 3,
+        label: Optional[str] = None,
+    ):
         """
         Draw a bounding box from database coordinates onto the annotation canvas.
 
@@ -796,6 +837,7 @@ class AnnotationCanvasWidget(QWidget):
                   in normalized coordinates (0-1)
             color: Color hex string (e.g., '#FF0000')
             width: Line width in pixels
+            label: Optional text label to render near the bounding box
         """
         if not self.annotation_pixmap or not self.original_pixmap:
             logger.warning("Cannot draw bounding box: no image loaded")
@@ -828,11 +870,11 @@ class AnnotationCanvasWidget(QWidget):
         self.bboxes.append(
             [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)})
+        self.bbox_meta.append({"color": pen_color, "width": int(width), "label": label})
 
         # Store in all_strokes for consistency
         self.all_strokes.append(
-            {"bbox": bbox, "color": color, "alpha": 128, "width": width}
+            {"bbox": bbox, "color": color, "alpha": 128, "width": width, "label": label}
         )
 
         # Redraw overlay (polylines + all bounding boxes)

src/gui/widgets/image_display_widget.py

@@ -137,7 +137,7 @@ class ImageDisplayWidget(QWidget):
                 height,
                 bytes_per_line,
                 self.current_image.qtimage_format,
-            )
+            ).copy()  # Copy to ensure Qt owns its memory after this scope
 
             # Convert to pixmap
             pixmap = QPixmap.fromImage(qimage)

src/model/inference.py

@@ -5,12 +5,12 @@ Handles detection inference and result storage.
 
 from typing import List, Dict, Optional, Callable
 from pathlib import Path
-from PIL import Image
 import cv2
 import numpy as np
 
 from src.model.yolo_wrapper import YOLOWrapper
 from src.database.db_manager import DatabaseManager
+from src.utils.image import Image
 from src.utils.logger import get_logger
 from src.utils.file_utils import get_relative_path
 
@@ -42,6 +42,7 @@ class InferenceEngine:
         relative_path: str,
         conf: float = 0.25,
         save_to_db: bool = True,
+        repository_root: Optional[str] = None,
     ) -> Dict:
         """
         Detect objects in a single image.
@@ -51,49 +52,79 @@ class InferenceEngine:
             relative_path: Relative path from repository root
             conf: Confidence threshold
             save_to_db: Whether to save results to database
+            repository_root: Base directory used to compute relative_path (if known)
 
         Returns:
             Dictionary with detection results
         """
         try:
+            # Normalize storage path (fall back to absolute path when repo root is unknown)
+            stored_relative_path = relative_path
+            if not repository_root:
+                stored_relative_path = str(Path(image_path).resolve())
+
             # Get image dimensions
-            img = Image.open(image_path)
-            width, height = img.size
-            img.close()
+            img = Image(image_path)
+            width = img.width
+            height = img.height
 
             # Perform detection
             detections = self.yolo.predict(image_path, conf=conf)
 
             # Add/get image in database
             image_id = self.db_manager.get_or_create_image(
-                relative_path=relative_path,
+                relative_path=stored_relative_path,
                 filename=Path(image_path).name,
                 width=width,
                 height=height,
             )
 
-            # Save detections to database
-            if save_to_db and detections:
-                detection_records = []
-                for det in detections:
-                    # Use normalized bbox from detection
-                    bbox_normalized = det[
-                        "bbox_normalized"
-                    ]  # [x_min, y_min, x_max, y_max]
+            inserted_count = 0
+            deleted_count = 0
 
-                    record = {
-                        "image_id": image_id,
-                        "model_id": self.model_id,
-                        "class_name": det["class_name"],
-                        "bbox": tuple(bbox_normalized),
-                        "confidence": det["confidence"],
-                        "segmentation_mask": det.get("segmentation_mask"),
-                        "metadata": {"class_id": det["class_id"]},
-                    }
-                    detection_records.append(record)
+            # Save detections to database, replacing any previous results for this image/model
+            if save_to_db:
+                deleted_count = self.db_manager.delete_detections_for_image(
+                    image_id, self.model_id
+                )
+                if detections:
+                    detection_records = []
+                    for det in detections:
+                        # Use normalized bbox from detection
+                        bbox_normalized = det[
+                            "bbox_normalized"
+                        ]  # [x_min, y_min, x_max, y_max]
 
-                self.db_manager.add_detections_batch(detection_records)
-                logger.info(f"Saved {len(detection_records)} detections to database")
+                        metadata = {
+                            "class_id": det["class_id"],
+                            "source_path": str(Path(image_path).resolve()),
+                        }
+                        if repository_root:
+                            metadata["repository_root"] = str(
+                                Path(repository_root).resolve()
+                            )
+
+                        record = {
+                            "image_id": image_id,
+                            "model_id": self.model_id,
+                            "class_name": det["class_name"],
+                            "bbox": tuple(bbox_normalized),
+                            "confidence": det["confidence"],
+                            "segmentation_mask": det.get("segmentation_mask"),
+                            "metadata": metadata,
+                        }
+                        detection_records.append(record)
+
+                    inserted_count = self.db_manager.add_detections_batch(
+                        detection_records
+                    )
+                    logger.info(
+                        f"Saved {inserted_count} detections to database (replaced {deleted_count})"
+                    )
+                else:
+                    logger.info(
+                        f"Detection run removed {deleted_count} stale entries but produced no new detections"
+                    )
 
             return {
                 "success": True,
@@ -142,7 +173,12 @@ class InferenceEngine:
             rel_path = get_relative_path(image_path, repository_root)
 
             # Perform detection
-            result = self.detect_single(image_path, rel_path, conf)
+            result = self.detect_single(
+                image_path,
+                rel_path,
+                conf=conf,
+                repository_root=repository_root,
+            )
             results.append(result)
 
             # Update progress

src/model/yolo_wrapper.py

@@ -1,13 +1,21 @@
-"""
-YOLO model wrapper for the microscopy object detection application.
-Provides a clean interface to YOLOv8 for training, validation, and inference.
+"""YOLO model wrapper for the microscopy object detection application.
+
+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
 from src.utils.logger import get_logger
+from src.utils.ultralytics_16bit_patch import apply_ultralytics_16bit_tiff_patches
 
 
 logger = get_logger(__name__)
@@ -28,6 +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.
@@ -37,6 +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")
@@ -55,6 +69,7 @@ class YOLOWrapper:
         save_dir: str = "data/models",
         name: str = "custom_model",
         resume: bool = False,
+        callbacks: Optional[Dict[str, Callable]] = None,
         **kwargs,
     ) -> Dict[str, Any]:
         """
@@ -69,13 +84,15 @@ class YOLOWrapper:
             save_dir: Directory to save trained model
             name: Name for the training run
             resume: Resume training from last checkpoint
+            callbacks: Optional Ultralytics callback dictionary
             **kwargs: Additional training arguments
 
         Returns:
             Dictionary with training results
         """
         if self.model is None:
-            self.load_model()
+            if not self.load_model():
+                raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         try:
             logger.info(f"Starting training: {name}")
@@ -83,6 +100,16 @@ class YOLOWrapper:
                 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(
                 data=data_yaml,
@@ -117,7 +144,8 @@ class YOLOWrapper:
             Dictionary with validation metrics
         """
         if self.model is None:
-            self.load_model()
+            if not self.load_model():
+                raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         try:
             logger.info(f"Starting validation on {split} split")
@@ -158,10 +186,15 @@ class YOLOWrapper:
             List of detection dictionaries
         """
         if self.model is None:
-            self.load_model()
+            if not self.load_model():
+                raise RuntimeError(f"Failed to load model from {self.model_path}")
+
+        prepared_source, cleanup_path = self._prepare_source(source)
 
         try:
-            logger.info(f"Running inference on {source}")
+            logger.info(
+                f"Running inference on {source} -> prepared_source {prepared_source}"
+            )
             results = self.model.predict(
                 source=source,
                 conf=conf,
@@ -180,6 +213,14 @@ class YOLOWrapper:
         except Exception as e:
             logger.error(f"Error during inference: {e}")
             raise
+        finally:
+            if 0:  # cleanup_path:
+                try:
+                    os.remove(cleanup_path)
+                except OSError as cleanup_error:
+                    logger.warning(
+                        f"Failed to delete temporary RGB image {cleanup_path}: {cleanup_error}"
+                    )
 
     def export(
         self, format: str = "onnx", output_path: Optional[str] = None, **kwargs
@@ -196,7 +237,8 @@ class YOLOWrapper:
             Path to exported model
         """
         if self.model is None:
-            self.load_model()
+            if not self.load_model():
+                raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         try:
             logger.info(f"Exporting model to {format} format")
@@ -208,6 +250,32 @@ class YOLOWrapper:
             logger.error(f"Error exporting model: {e}")
             raise
 
+    def _prepare_source(self, source):
+        """Convert single-channel images to RGB temporarily for inference."""
+        cleanup_path = None
+
+        if isinstance(source, (str, Path)):
+            source_path = Path(source)
+            if source_path.is_file():
+                try:
+                    img_obj = Image(source_path)
+                    suffix = source_path.suffix or ".png"
+                    tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+                    tmp_path = tmp.name
+                    tmp.close()
+                    img_obj.save(tmp_path)
+                    cleanup_path = tmp_path
+                    logger.info(
+                        f"Converted image {source_path} to RGB for inference at {tmp_path}"
+                    )
+                    return tmp_path, cleanup_path
+                except Exception as convert_error:
+                    logger.warning(
+                        f"Failed to preprocess {source_path} as RGB, continuing with original file: {convert_error}"
+                    )
+
+        return source, cleanup_path
+
     def _format_training_results(self, results) -> Dict[str, Any]:
         """Format training results into dictionary."""
         try:

src/utils/config_manager.py

@@ -7,6 +7,7 @@ import yaml
 from pathlib import Path
 from typing import Any, Dict, Optional
 from src.utils.logger import get_logger
+from src.utils.image import Image
 
 
 logger = get_logger(__name__)
@@ -46,18 +47,15 @@ class ConfigManager:
             "database": {"path": "data/detections.db"},
             "image_repository": {
                 "base_path": "",
-                "allowed_extensions": [
-                    ".jpg",
-                    ".jpeg",
-                    ".png",
-                    ".tif",
-                    ".tiff",
-                    ".bmp",
-                ],
+                "allowed_extensions": Image.SUPPORTED_EXTENSIONS,
             },
             "models": {
                 "default_base_model": "yolov8s-seg.pt",
                 "models_directory": "data/models",
+                "base_model_choices": [
+                    "yolov8s-seg.pt",
+                    "yolo11s-seg.pt",
+                ],
             },
             "training": {
                 "default_epochs": 100,
@@ -65,6 +63,20 @@ class ConfigManager:
                 "default_imgsz": 640,
                 "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,
+                    },
+                },
             },
             "detection": {
                 "default_confidence": 0.25,
@@ -213,6 +225,4 @@ class ConfigManager:
 
     def get_allowed_extensions(self) -> list:
         """Get list of allowed image file extensions."""
-        return self.get(
-            "image_repository.allowed_extensions", [".jpg", ".jpeg", ".png"]
-        )
+        return self.get("image_repository.allowed_extensions", Image.SUPPORTED_EXTENSIONS)

src/utils/file_utils.py

@@ -28,7 +28,9 @@ def get_image_files(
         List of absolute paths to image files
     """
     if allowed_extensions is None:
-        allowed_extensions = [".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp"]
+        from src.utils.image import Image
+
+        allowed_extensions = Image.SUPPORTED_EXTENSIONS
 
     # Normalize extensions to lowercase
     allowed_extensions = [ext.lower() for ext in allowed_extensions]
@@ -204,7 +206,9 @@ def is_image_file(
         True if file is an image
     """
     if allowed_extensions is None:
-        allowed_extensions = [".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp"]
+        from src.utils.image import Image
+
+        allowed_extensions = Image.SUPPORTED_EXTENSIONS
 
     extension = Path(file_path).suffix.lower()
     return extension in [ext.lower() for ext in allowed_extensions]

src/utils/image.py

@@ -6,16 +6,52 @@ 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 0:
+        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)
+    # return np.stack([a1, a2, a3], axis=0)
+
+
 class ImageLoadError(Exception):
     """Exception raised when an image cannot be loaded."""
 
@@ -54,7 +90,6 @@ 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
@@ -80,40 +115,39 @@ 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"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
+                f"Unsupported image format: {ext}. " f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
             )
 
         try:
-            # Load with OpenCV (returns BGR format)
-            self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
+            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)
 
             if self._data is None:
                 raise ImageLoadError(f"Failed to load image with OpenCV: {self.path}")
 
             # Extract metadata
-            self._height, self._width = self._data.shape[:2]
-            self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
+            # print(self._data.shape)
+            if len(self._data.shape) == 2:
+                self._height, self._width = self._data.shape[:2]
+                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 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, "
-                f"{self._format.upper()})"
-            )
+            if 0:
+                logger.info(
+                    f"Successfully loaded image: {self.path.name} "
+                    f"({self._width}x{self._height}, {self._channels} channels, "
+                    f"{self._format.upper()})"
+                )
 
         except Exception as e:
             logger.error(f"Error loading image {self.path}: {e}")
@@ -131,18 +165,6 @@ 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 +209,7 @@ 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 +229,10 @@ 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}")
 
@@ -218,6 +243,12 @@ class Image:
         Returns:
             Image data in RGB format as numpy array
         """
+        if self.channels == 1:
+            img = get_pseudo_rgb(self.data)
+            self._dtype = img.dtype
+            return img
+        raise NotImplementedError
+
         if self._channels == 3:
             return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
         elif self._channels == 4:
@@ -225,6 +256,18 @@ class Image:
         else:
             return self._data
 
+    def get_qt_rgb(self) -> np.ascontiguousarray:
+        # we keep data as (C, H, W)
+        _img = self.get_rgb()
+
+        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)
+
     def get_grayscale(self) -> np.ndarray:
         """
         Get image as grayscale numpy array.
@@ -277,11 +320,26 @@ 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)"
         )
@@ -289,3 +347,15 @@ class Image:
     def __str__(self) -> str:
         """String representation of the Image object."""
         return self.__repr__()
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path", type=str, required=True)
+    args = parser.parse_args()
+
+    img = Image(args.path)
+    img.save(args.path + "test.tif")
+    print(img)

src/utils/image_converters.py

@@ -0,0 +1,168 @@
+import numpy as np
+
+from roifile import ImagejRoi
+from tifffile import TiffFile, TiffWriter
+from pathlib import Path
+
+
+class UT:
+    """
+    Docstring for UT
+
+    Operetta files along with rois drawn in ImageJ
+    """
+
+    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)
+            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("Loading images:", self.roifile_fn.parent, self.stem)
+        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]))
+
+        with TiffFile(fns[0]) as tif:
+            img = tif.asarray()
+            w, h = img.shape
+            dtype = img.dtype
+            self.image_props = {
+                "channels": n_ch,
+                "planes": n_p,
+                "tiles": n_t,
+                "width": w,
+                "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:
+            with TiffFile(fn) as tif:
+                img = tif.asarray()
+                stem = fn.stem.split(self.stem)[-1]
+                ch = int(stem.split("-ch")[-1].split("t")[0])
+                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
+
+        print(image_stack.shape)
+
+        return image_stack, self.image_props
+
+    @property
+    def width(self):
+        return self.image_props["width"]
+
+    @property
+    def height(self):
+        return self.image_props["height"]
+
+    @property
+    def nchannels(self):
+        return self.image_props["channels"]
+
+    @property
+    def nplanes(self):
+        return self.image_props["planes"]
+
+    def export_rois(
+        self,
+        path: Path,
+        subfolder: str = "labels",
+        class_index: int = 0,
+    ):
+        """Export rois to a file"""
+        with open(path / subfolder / f"{self.stem}.txt", "w") as f:
+            for i, roi in enumerate(self.rois):
+                rc = roi.subpixel_coordinates
+                if rc is None:
+                    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")
+
+        return
+
+    def export_image(
+        self,
+        path: Path,
+        subfolder: str = "images",
+        plane_mode: str = "max projection",
+        channel: int = 0,
+    ):
+        """Export image to a file"""
+
+        if plane_mode == "max projection":
+            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)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", nargs="*", 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()
+
+    # print(args)
+    # 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 +1,353 @@
+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):
+        if bbox is None:
+            bbox = self.bbox
+        if polygon is None:
+            polygon = self.polygon
+        coords = " ".join([f"{x:.6f}" for x in self.bbox])
+        if self.polygon is not None:
+            coords += " " + " ".join([f"{x:.6f} {y:.6f}" for x, y in self.polygon])
+        return f"{self.class_id} {coords}"
+
+    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]):
+                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
+
+    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)
+
+            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} "
+            print(yolo_annotation)
+            yolo_annotation += " ".join(
+                [
+                    f"{(x*self.image.shape[1]-(xc - x_offset))/nx:.6f} {(y*self.image.shape[0]-(yc-y_offset))/ny:.6f}"
+                    for x, y in label.polygon
+                ]
+            )
+            new_label = Label(yolo_annotation=yolo_annotation)
+
+            yield tile_reference, tile, [new_label]
+
+
+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 in data:
+            print()
+            print(tile_reference, tile.shape, labels)  # 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)
+                scale = 5
+                tile_zoomed = zoom(tile, zoom=scale)
+                imwrite(args.output / "images-zoomed" / f"{image_path.stem}_{tile_reference}.tif", tile_zoomed)
+
+                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 +1,156 @@
+"""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**16 - 1
+            arr = arr.astype(np.uint16)
+            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 +1,223 @@
+#!/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))
+
+    lclass, coords = labels[0]
+    print(lclass, coords)
+    bbox = coords[:4]
+    print("bbox", bbox)
+    bbox = np.array(bbox) * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+    yc, xc, h, w = bbox
+    print("bbox", bbox)
+    polyline = np.array(coords[4:]).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+    print("pl", coords[4:])
+    print("pl", polyline)
+
+    # Convert BGR -> RGB for matplotlib display
+    # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+    out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # out_rgb = Image()
+    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
+    plt.imshow(out_rgb)
+    plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
+    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()

tests/test_image.py

@@ -27,7 +27,7 @@ class TestImage:
 
     def test_supported_extensions(self):
         """Test that supported extensions are correctly defined."""
-        expected_extensions = [".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp"]
+        expected_extensions = Image.SUPPORTED_EXTENSIONS
         assert Image.SUPPORTED_EXTENSIONS == expected_extensions
 
     def test_image_properties(self, tmp_path):