Adding important file

config/app_config.yaml

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

src/gui/tabs/training_tab.py

@@ -1303,6 +1303,14 @@ class TrainingTab(QWidget):
         sample_image = self._find_first_image(images_dir)
         if not sample_image:
             return False
+
+        # Do not force an RGB cache for TIFF datasets.
+        # We handle grayscale/16-bit TIFFs via runtime Ultralytics patches that:
+        # - load TIFFs with `tifffile`
+        # - replicate grayscale to 3 channels without quantization
+        # - normalize uint16 correctly during training
+        if sample_image.suffix.lower() in {".tif", ".tiff"}:
+            return False
         try:
             img = Image(sample_image)
             return img.pil_image.mode.upper() != "RGB"

src/model/yolo_wrapper.py

@@ -1,9 +1,13 @@
-"""
+"""YOLO model wrapper for the microscopy object detection application.
-YOLO model wrapper for the microscopy object detection application.
+
-Provides a clean interface to YOLOv8 for training, validation, and inference.
+Notes on 16-bit TIFF support:
+- Ultralytics training defaults assume 8-bit images and normalize by dividing by 255.
+- This project can patch Ultralytics at runtime to decode TIFFs via `tifffile` and
+  normalize `uint16` correctly.
+
+See [`apply_ultralytics_16bit_tiff_patches()`](src/utils/ultralytics_16bit_patch.py:1).
 """
 
-from ultralytics import YOLO
 from pathlib import Path
 from typing import Optional, List, Dict, Callable, Any
 import torch
@@ -11,6 +15,7 @@ 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__)
@@ -31,6 +36,9 @@ class YOLOWrapper:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         logger.info(f"YOLOWrapper initialized with device: {self.device}")
 
+        # Apply Ultralytics runtime patches early (before first import/instantiation of YOLO datasets/trainers).
+        apply_ultralytics_16bit_tiff_patches()
+
     def load_model(self) -> bool:
         """
         Load YOLO model from path.
@@ -40,6 +48,9 @@ class YOLOWrapper:
         """
         try:
             logger.info(f"Loading YOLO model from {self.model_path}")
+            # Import YOLO lazily to ensure runtime patches are applied first.
+            from ultralytics import YOLO
+
             self.model = YOLO(self.model_path)
             self.model.to(self.device)
             logger.info("Model loaded successfully")
@@ -89,6 +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,

src/utils/image.py

@@ -313,7 +313,8 @@ class Image:
         """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)"
         )

src/utils/ultralytics_16bit_patch.py

@@ -0,0 +1,165 @@
+"""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
+
+
+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 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
+    ) -> 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.
+        """
+
+        ext = os.path.splitext(filename)[1].lower()
+        if ext in (".tif", ".tiff"):
+            arr = tifffile.imread(filename)
+
+            # 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]
+
+            # Ultralytics expects BGR ordering when `channels=3`.
+            # For grayscale data we replicate channels (no scaling, no quantization).
+            if flags != cv2.IMREAD_GRAYSCALE:
+                if arr.shape[2] == 1:
+                    arr = np.repeat(arr, 3, axis=2)
+                elif arr.shape[2] >= 3:
+                    arr = arr[:, :, :3]
+
+            # Ensure contiguous array for downstream OpenCV ops.
+            return np.ascontiguousarray(arr)
+
+        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.
+        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
+    )