Adding annotation list

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'

docs/16BIT_TIFF_SUPPORT.md

@@ -1,300 +0,0 @@
-# 16-bit TIFF Support for YOLO Object Detection
-
-## Overview
-
-This document describes the implementation of 16-bit grayscale TIFF support for YOLO object detection. The system properly loads 16-bit TIFF images, normalizes them to float32 [0-1], and handles them appropriately for both **inference** and **training** **without uint8 conversion** to preserve the full dynamic range and avoid data loss.
-
-## Key Features
-
-✅ Reads 16-bit or float32 images using tifffile
-✅ Converts to float32 [0-1] (NO uint8 conversion)
-✅ Replicates grayscale → RGB (3 channels)
-✅ **Inference**: Passes numpy arrays directly to YOLO (no file I/O)
-✅ **Training**: On-the-fly float32 conversion (NO disk caching)
-✅ Uses Ultralytics YOLOv8/v11 models
-✅ Works with segmentation models
-✅ No data loss, no double normalization, no silent clipping
-
-## Changes Made
-
-### 1. Dependencies ([`requirements.txt`](../requirements.txt:14))
-- Added `tifffile>=2023.0.0` for reliable 16-bit TIFF loading
-
-### 2. Image Loading ([`src/utils/image.py`](../src/utils/image.py))
-
-#### Enhanced TIFF Loading
-- Modified [`Image._load()`](../src/utils/image.py:87) to use `tifffile` for `.tif` and `.tiff` files
-- Preserves original 16-bit data type during loading
-- Properly handles both grayscale and multi-channel TIFF files
-
-#### New Normalization Method
-Added [`Image.to_normalized_float32()`](../src/utils/image.py:280) method that:
-- Converts image data to `float32`
-- Properly scales values to [0, 1] range:
-  - **16-bit images**: divides by 65535 (full dynamic range)
-  - 8-bit images: divides by 255
-  - Float images: clips to [0, 1]
-- Handles various data types automatically
-
-### 3. YOLO Preprocessing ([`src/model/yolo_wrapper.py`](../src/model/yolo_wrapper.py))
-
-Enhanced [`YOLOWrapper._prepare_source()`](../src/model/yolo_wrapper.py:231) to:
-1. Detect 16-bit TIFF files automatically
-2. Load and normalize to float32 [0-1] using the new method
-3. Replicate grayscale to RGB (3 channels)
-4. **Return numpy array directly** (NO file saving, NO uint8 conversion)
-5. Pass float32 array directly to YOLO for inference
-
-## Processing Pipeline
-
-### For Inference (predict)
-
-For 16-bit TIFF files during inference:
-
-1. **Load**: File loaded using `tifffile` → preserves 16-bit uint16 data
-2. **Normalize**: Convert to float32 and scale to [0, 1]
-   ```python
-   float_data = uint16_data.astype(np.float32) / 65535.0
-   ```
-3. **RGB Conversion**: Replicate grayscale to 3 channels
-   ```python
-   rgb_float = np.stack([float_data] * 3, axis=-1)
-   ```
-4. **Pass to YOLO**: Return float32 array directly (no uint8, no file I/O)
-5. **Inference**: YOLO processes the float32 [0-1] RGB array
-
-### For Training (train)
-
-Training now uses a custom dataset loader with on-the-fly conversion (NO disk caching):
-
-1. **Custom Dataset**: Uses `Float32Dataset` class that extends Ultralytics' `YOLODataset`
-2. **Load On-The-Fly**: Each image is loaded and converted during training:
-   - Detect 16-bit TIFF files automatically
-   - Load with `tifffile` (preserves uint16)
-   - Convert to float32 [0-1] in memory
-   - Replicate to 3 channels (RGB)
-3. **No Disk Cache**: Conversion happens in memory, no files written
-4. **Train**: YOLO trains on float32 [0-1] RGB arrays directly
-
-See [`src/utils/train_ultralytics_float.py`](../src/utils/train_ultralytics_float.py) for implementation.
-
-### No Data Loss!
-
-Unlike approaches that convert to uint8 (256 levels), this implementation:
-- Preserves full 16-bit dynamic range (65536 levels)
-- Maintains precision with float32 representation
-- For inference: passes data directly without file conversions
-- For training: uses float32 TIFFs (not uint8 PNGs)
-
-## Usage
-
-### Basic Image Loading
-
-```python
-from src.utils.image import Image
-
-# Load a 16-bit TIFF file
-img = Image("path/to/16bit_image.tif")
-
-# Get normalized float32 data [0-1]
-normalized = img.to_normalized_float32()  # Shape: (H, W), dtype: float32
-
-# Original data is preserved
-original = img.data  # Still uint16
-```
-
-### YOLO Inference
-
-The preprocessing is automatic - just use YOLO as normal:
-
-```python
-from src.model.yolo_wrapper import YOLOWrapper
-
-# Initialize model
-yolo = YOLOWrapper("yolov8s-seg.pt")
-yolo.load_model()
-
-# Perform inference on 16-bit TIFF
-# The image will be automatically normalized and passed as float32 [0-1]
-detections = yolo.predict("path/to/16bit_image.tif", conf=0.25)
-```
-
-### With InferenceEngine
-
-```python
-from src.model.inference import InferenceEngine
-from src.database.db_manager import DatabaseManager
-
-# Setup
-db = DatabaseManager("database.db")
-engine = InferenceEngine("model.pt", db, model_id=1)
-
-# Detect objects in 16-bit TIFF
-result = engine.detect_single(
-    image_path="path/to/16bit_image.tif",
-    relative_path="images/16bit_image.tif",
-    conf=0.25
-)
-```
-
-## Testing
-
-Three test scripts are provided:
-
-### 1. Image Loading Test
-```bash
-./venv/bin/python tests/test_16bit_tiff_loading.py
-```
-
-Tests:
-- Loading 16-bit TIFF files with tifffile
-- Normalization to float32 [0-1]
-- Data type and value range verification
-
-### 2. Float32 Passthrough Test (Most Important!)
-```bash
-./venv/bin/python tests/test_yolo_16bit_float32.py
-```
-
-Tests:
-- YOLO preprocessing returns numpy array (not file path)
-- Data is float32 [0-1] (not uint8)
-- No quantization to 256 levels (proves no uint8 conversion)
-- Sample output:
-  ```
-  ✓ SUCCESS: Prepared source is a numpy array (float32 passthrough)
-    Shape: (200, 200, 3)
-    Dtype: float32
-    Min value: 0.000000
-    Max value: 1.000000
-    Unique values: 399
-  
-  ✓ SUCCESS: Data has 399 unique values (> 256)
-    This confirms NO uint8 quantization occurred!
-  ```
-
-### 3. Legacy Test (Shows Old Behavior)
-```bash
-./venv/bin/python tests/test_yolo_16bit_preprocessing.py
-```
-
-This test shows the old behavior (uint8 conversion) - kept for comparison.
-
-## Benefits
-
-1. **No Data Loss**: Preserves full 16-bit dynamic range (65536 levels vs 256)
-2. **High Precision**: Float32 maintains fine-grained intensity differences
-3. **Automatic Processing**: No manual preprocessing needed
-4. **YOLO Compatible**: Ultralytics YOLO accepts float32 [0-1] arrays
-5. **Performance**: No intermediate file I/O for 16-bit TIFFs
-6. **Backwards Compatible**: Regular images (8-bit PNG, JPEG, etc.) still work as before
-
-## Technical Notes
-
-### Float32 vs uint8
-
-**With uint8 conversion (OLD - BAD):**
-- 16-bit (65536 levels) → uint8 (256 levels) = **99.6% data loss!**
-- Fine intensity differences are lost
-- Quantization artifacts
-
-**With float32 [0-1] (NEW - GOOD):**
-- 16-bit (65536 levels) → float32 (continuous) = **No data loss**
-- Full dynamic range preserved
-- Smooth gradients maintained
-
-### Memory Considerations
-
-For a 2048×2048 single-channel image:
-
-| Format | Memory | Disk Space | Notes |
-|--------|--------|------------|-------|
-| Original 16-bit | 8 MB | ~8 MB | uint16 grayscale TIFF |
-| Float32 grayscale | 16 MB | - | Intermediate |
-| Float32 3-channel | 48 MB | ~48 MB | Training cache |
-| uint8 RGB (old) | 12 MB | ~12 MB | OLD approach with data loss |
-
-The float32 approach uses ~3× more memory than uint8 during training but preserves **all information**.
-
-**No Disk Cache**: The new on-the-fly approach eliminates the need for cached datasets on disk.
-
-### Why Direct Numpy Array?
-
-Passing numpy arrays directly to YOLO (instead of saving to file):
-
-1. **Faster**: No disk I/O overhead
-2. **No Quantization**: Avoids PNG/JPEG quantization
-3. **Memory Efficient**: Single copy in memory
-4. **Cleaner**: No temp file management
-
-Ultralytics YOLO supports various input types:
-- File paths (str): `"image.jpg"`
-- Numpy arrays: `np.ndarray` ← **we use this**
-- PIL Images: `PIL.Image`
-- Torch tensors: `torch.Tensor`
-
-## Training with Float32 Dataset Loader
-
-The system now includes a custom dataset loader for 16-bit TIFF training:
-
-```python
-from src.utils.train_ultralytics_float import train_with_float32_loader
-
-# Train with on-the-fly float32 conversion
-results = train_with_float32_loader(
-    model_path="yolov8s-seg.pt",
-    data_yaml="data/my_dataset/data.yaml",
-    epochs=100,
-    batch=16,
-    imgsz=640,
-)
-```
-
-The `Float32Dataset` class automatically:
-- Detects 16-bit TIFF files
-- Loads with `tifffile` (not PIL/cv2)
-- Converts to float32 [0-1] on-the-fly
-- Replicates to 3 channels
-- Integrates seamlessly with Ultralytics training pipeline
-
-This is used automatically by the training tab in the GUI.
-
-## Installation
-
-Install the updated dependencies:
-
-```bash
-./venv/bin/pip install -r requirements.txt
-```
-
-Or install tifffile directly:
-
-```bash
-./venv/bin/pip install tifffile>=2023.0.0
-```
-
-## Example Test Output
-
-```
-=== Testing Float32 Passthrough (NO uint8) ===
-Created test 16-bit TIFF: /tmp/tmpdt5hm0ab.tif
-  Shape: (200, 200)
-  Dtype: uint16
-  Min value: 0
-  Max value: 65535
-
-Preprocessing result:
-  Prepared source type: <class 'numpy.ndarray'>
-
-✓ SUCCESS: Prepared source is a numpy array (float32 passthrough)
-  Shape: (200, 200, 3)
-  Dtype: float32
-  Min value: 0.000000
-  Max value: 1.000000
-  Mean value: 0.499992
-  Unique values: 399
-
-✓ SUCCESS: Data has 399 unique values (> 256)
-  This confirms NO uint8 quantization occurred!
-
-✓ All float32 passthrough tests passed!

docs/TRAINING_16BIT_TIFF.md

@@ -1,269 +0,0 @@
-# Training YOLO with 16-bit TIFF Datasets
-
-## Quick Start
-
-If your dataset contains 16-bit grayscale TIFF files, the training tab will automatically:
-
-1. Detect 16-bit TIFF images in your dataset
-2. Convert them to float32 [0-1] RGB **on-the-fly** during training
-3. Train without any disk caching (memory-efficient)
-
-**No manual intervention or disk space needed!**
-
-## Why Float32 On-The-Fly Conversion?
-
-### The Problem
-
-YOLO's training expects:
-- 3-channel images (RGB)
-- Images loaded from disk by the dataloader
-
-16-bit grayscale TIFFs are:
-- 1-channel (grayscale)
-- Need to be converted to RGB format
-
-### The Solution
-
-**NEW APPROACH (Current)**: On-the-fly float32 conversion
-- Load 16-bit TIFF with `tifffile` (not PIL/cv2)
-- Convert uint16 [0-65535] → float32 [0-1] in memory
-- Replicate grayscale to 3 channels
-- Pass directly to YOLO training pipeline
-- **No disk caching required!**
-
-**OLD APPROACH (Deprecated)**: Disk caching
-- Created 16-bit RGB PNG cache files on disk
-- Required ~2x dataset size in disk space
-- Slower first training run
-
-## How It Works
-
-### Custom Dataset Loader
-
-The system uses a custom `Float32Dataset` class that extends Ultralytics' `YOLODataset`:
-
-```python
-from src.utils.train_ultralytics_float import Float32Dataset
-
-# This dataset loader:
-# 1. Intercepts image loading
-# 2. Detects 16-bit TIFFs
-# 3. Converts to float32 [0-1] RGB on-the-fly
-# 4. Passes to training pipeline
-```
-
-### Conversion Process
-
-For each 16-bit grayscale TIFF during training:
-
-```
-1. Load with tifffile → uint16 [0, 65535]
-2. Convert to float32 → img.astype(float32) / 65535.0
-3. Replicate to RGB → np.stack([img] * 3, axis=-1)
-4. Result: float32 [0, 1] RGB array, shape (H, W, 3)
-```
-
-### Memory vs Disk
-
-| Aspect | On-the-fly (NEW) | Disk Cache (OLD) |
-|--------|------------------|------------------|
-| Disk Space | Dataset size only | ~2× dataset size |
-| First Training | Fast | Slow (creates cache) |
-| Subsequent Training | Fast | Fast |
-| Data Loss | None | None |
-| Setup Required | None | Cache creation |
-
-## Data Preservation
-
-### Float32 Precision
-
-16-bit TIFF: 65,536 levels (0-65535)
-Float32: ~7 decimal digits precision
-
-**Conversion accuracy:**
-```python
-Original: 32768 (uint16, middle intensity)
-Float32: 32768 / 65535 = 0.50000763 (exact)
-```
-
-Full 16-bit precision is preserved in float32 representation.
-
-### Comparison to uint8
-
-| Approach | Precision Loss | Recommended |
-|----------|----------------|-------------|
-| **float32 [0-1]** | None | ✓ YES |
-| uint16 RGB | None | ✓ YES (but disk-heavy) |
-| uint8 | 99.6% data loss | ✗ NO |
-
-**Why NO uint8:**
-```
-Original values:     32768, 32769, 32770 (distinct)
-Converted to uint8:  128,   128,   128   (collapsed!)
-```
-
-Multiple 16-bit values collapse to the same uint8 value.
-
-## Training Tab Behavior
-
-When you click "Start Training" with a 16-bit TIFF dataset:
-
-```
-[01:23:45] Exported 150 annotations across 50 image(s).
-[01:23:45] Using Float32 on-the-fly loader for 16-bit TIFF support (no disk caching)
-[01:23:45] Starting training run 'my_model_v1' using yolov8s-seg.pt
-[01:23:46] Using Float32Dataset loader for 16-bit TIFF support
-```
-
-Every training run uses the same approach - fast and efficient!
-
-## Inference vs Training
-
-| Operation | Input | Processing | Output to YOLO |
-|-----------|-------|------------|----------------|
-| **Inference** | 16-bit TIFF file | Load → float32 [0-1] → 3ch | numpy array (float32) |
-| **Training** | 16-bit TIFF dataset | Load on-the-fly → float32 [0-1] → 3ch | numpy array (float32) |
-
-Both preserve full 16-bit precision using float32 representation.
-
-## Technical Details
-
-### Custom Dataset Class
-
-Located in `src/utils/train_ultralytics_float.py`:
-
-```python
-class Float32Dataset(YOLODataset):
-    """
-    Extends Ultralytics YOLODataset to handle 16-bit TIFFs.
-    
-    Key methods:
-    - load_image(): Intercepts image loading
-    - Detects .tif/.tiff with dtype == uint16
-    - Converts: uint16 → float32 [0-1] → RGB (3-channel)
-    """
-```
-
-### Integration with YOLO
-
-The `YOLOWrapper.train()` method automatically uses the custom loader:
-
-```python
-# In src/model/yolo_wrapper.py
-def train(self, data_yaml, use_float32_loader=True, **kwargs):
-    if use_float32_loader:
-        # Use custom Float32Dataset
-        return train_with_float32_loader(...)
-    else:
-        # Standard YOLO training
-        return self.model.train(...)
-```
-
-### No PIL or cv2 for 16-bit
-
-16-bit TIFF loading uses `tifffile` directly:
-- PIL: Can load 16-bit but converts during processing
-- cv2: Limited 16-bit TIFF support
-- tifffile: Native 16-bit support, numpy output
-
-## Advantages Over Disk Caching
-
-### 1. No Disk Space Required
-```
-Dataset: 1000 images × 12 MB = 12 GB
-Old cache: Additional 24 GB (16-bit RGB PNGs)
-New approach: 0 GB additional (on-the-fly)
-```
-
-### 2. Faster Setup
-```
-Old: First training requires cache creation (minutes)
-New: Start training immediately (seconds)
-```
-
-### 3. Always In Sync
-```
-Old: Cache could become stale if images change
-New: Always loads current version from disk
-```
-
-### 4. Simpler Workflow
-```
-Old: Manage cache directory, cleanup, etc.
-New: Just point to dataset and train
-```
-
-## Troubleshooting
-
-### Error: "expected input to have 3 channels, but got 1"
-
-This shouldn't happen with the new Float32Dataset, but if it does:
-
-1. Check that `use_float32_loader=True` in training call
-2. Verify `Float32Dataset` is being used (check logs)
-3. Ensure `tifffile` is installed: `pip install tifffile`
-
-### Memory Usage
-
-On-the-fly conversion uses memory during training:
-- Image loaded: ~24 MB (2048×2048 uint16)
-- Converted float32 RGB: ~48 MB (temporary)
-- Released after augmentation pipeline
-
-**Mitigation:**
-- Reduce batch size if OOM errors occur
-- Images are processed one at a time during loading
-- Only active batch kept in memory
-
-### Slow Training
-
-If training seems slow:
-- Check disk I/O (slow disk can bottleneck loading)
-- Verify images aren't being re-converted each epoch (should cache after first load)
-- Monitor CPU usage during loading
-
-## Migration from Old Approach
-
-If you have existing cached datasets:
-
-```bash
-# Old cache location (safe to delete)
-rm -rf data/datasets/_float32_cache/
-
-# The new approach doesn't use this directory
-```
-
-Your original dataset structure remains unchanged:
-```
-data/my_dataset/
-├── train/
-│   ├── images/  (original 16-bit TIFFs)
-│   └── labels/
-├── val/
-│   ├── images/
-│   └── labels/
-└── data.yaml
-```
-
-Just point to the same `data.yaml` and train!
-
-## Performance Comparison
-
-| Metric | Old (Disk Cache) | New (On-the-fly) |
-|--------|------------------|------------------|
-| First training setup | 5-10 min | 0 sec |
-| Disk space overhead | 100% | 0% |
-| Training speed | Fast | Fast |
-| Subsequent runs | Fast | Fast |
-| Data accuracy | 16-bit preserved | 16-bit preserved |
-
-## Summary
-
-✓ **On-the-fly conversion**: Load and convert during training  
-✓ **No disk caching**: Zero additional disk space  
-✓ **Full precision**: Float32 preserves 16-bit dynamic range  
-✓ **No PIL/cv2**: Direct tifffile loading  
-✓ **Automatic**: Works transparently with training tab  
-✓ **Fast**: Efficient memory-based conversion  
-
-The new approach is simpler, faster to set up, and requires no disk space overhead!

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"

requirements.txt

@@ -11,7 +11,6 @@ pyqtgraph>=0.13.0
 opencv-python>=4.8.0
 Pillow>=10.0.0
 numpy>=1.24.0
-tifffile>=2023.0.0
 
 # Database
 sqlalchemy>=2.0.0

scripts/README_FLOAT32_TRAINING.md

@@ -1,179 +0,0 @@
-# Standalone Float32 Training Script for 16-bit TIFFs
-
-## Overview
-
-This standalone script (`train_float32_standalone.py`) trains YOLO models on 16-bit grayscale TIFF datasets with **no data loss**.
-
-- Loads 16-bit TIFFs with `tifffile` (not PIL/cv2)
-- Converts to float32 [0-1] on-the-fly (preserves full 16-bit precision)
-- Replicates grayscale → 3-channel RGB in memory
-- **No disk caching required**
-- Uses custom PyTorch Dataset + training loop
-
-## Quick Start
-
-```bash
-# Activate virtual environment
-source venv/bin/activate
-
-# Train on your 16-bit TIFF dataset
-python scripts/train_float32_standalone.py \
-    --data data/my_dataset/data.yaml \
-    --weights yolov8s-seg.pt \
-    --epochs 100 \
-    --batch 16 \
-    --imgsz 640 \
-    --lr 0.0001 \
-    --save-dir runs/my_training \
-    --device cuda
-```
-
-## Arguments
-
-| Argument | Required | Default | Description |
-|----------|----------|---------|-------------|
-| `--data` | Yes | - | Path to YOLO data.yaml file |
-| `--weights` | No | yolov8s-seg.pt | Pretrained model weights |
-| `--epochs` | No | 100 | Number of training epochs |
-| `--batch` | No | 16 | Batch size |
-| `--imgsz` | No | 640 | Input image size |
-| `--lr` | No | 0.0001 | Learning rate |
-| `--save-dir` | No | runs/train | Directory to save checkpoints |
-| `--device` | No | cuda/cpu | Training device (auto-detected) |
-
-## Dataset Format
-
-Your data.yaml should follow standard YOLO format:
-
-```yaml
-path: /path/to/dataset
-train: train/images
-val: val/images
-test: test/images  # optional
-
-names:
-  0: class1
-  1: class2
-
-nc: 2
-```
-
-Directory structure:
-```
-dataset/
-├── train/
-│   ├── images/
-│   │   ├── img1.tif  (16-bit grayscale TIFF)
-│   │   └── img2.tif
-│   └── labels/
-│       ├── img1.txt  (YOLO format)
-│       └── img2.txt
-├── val/
-│   ├── images/
-│   └── labels/
-└── data.yaml
-```
-
-## Output
-
-The script saves:
-- `epoch{N}.pt`: Checkpoint after each epoch
-- `best.pt`: Best model weights (lowest loss)
-- Training logs to console
-
-## Features
-
-✅ **16-bit precision preserved**: Float32 [0-1] maintains full dynamic range  
-✅ **No disk caching**: Conversion happens in memory  
-✅ **No PIL/cv2**: Direct tifffile loading  
-✅ **Variable-length labels**: Handles segmentation polygons  
-✅ **Checkpoint saving**: Resume training if interrupted  
-✅ **Best model tracking**: Automatically saves best weights  
-
-## Example
-
-Train a segmentation model on microscopy data:
-
-```bash
-python scripts/train_float32_standalone.py \
-    --data data/microscopy/data.yaml \
-    --weights yolov11s-seg.pt \
-    --epochs 150 \
-    --batch 8 \
-    --imgsz 1024 \
-    --lr 0.0003 \
-    --save-dir data/models/microscopy_v1
-```
-
-## Troubleshooting
-
-### Out of Memory (OOM)
-Reduce batch size:
-```bash
---batch 4
-```
-
-### Slow Loading
-Reduce num_workers (edit script line 208):
-```python
-num_workers=2  # instead of 4
-```
-
-### Different Image Sizes
-The script expects all images to have the same dimensions. For variable sizes:
-1. Implement letterbox/resize in dataset's `_read_image()`
-2. Or preprocess images to same size
-
-### Loss Computation Errors
-If you see "Cannot determine loss", the script may need adjustment for your Ultralytics version. Check:
-```python
-# In train() function, the preds format may vary
-# Current script assumes: preds is tuple with loss OR dict with 'loss' key
-```
-
-## vs GUI Training
-
-| Feature | Standalone Script | GUI Training Tab |
-|---------|------------------|------------------|
-| Float32 conversion | ✓ Yes | ✓ Yes (automatic) |
-| Disk caching | ✗ None | ✗ None |
-| Progress UI | ✗ Console only | ✓ Visual progress bar |
-| Dataset selection | Manual CLI args | ✓ GUI browsing |
-| Multi-stage training | Manual runs | ✓ Built-in |
-| Use case | Advanced users | General users |
-
-## Technical Details
-
-### Data Loading Pipeline
-
-```
-16-bit TIFF file
-    ↓ (tifffile.imread)
-uint16 [0-65535]
-    ↓ (/ 65535.0)
-float32 [0-1]
-    ↓ (replicate channels)
-float32 RGB (H,W,3) [0-1]
-    ↓ (permute to C,H,W)
-torch.Tensor (3,H,W) float32
-    ↓ (DataLoader stack)
-Batch (B,3,H,W) float32
-    ↓
-YOLO Model
-```
-
-### Precision Comparison
-
-| Method | Unique Values | Data Loss |
-|--------|---------------|-----------|
-| **float32 [0-1]** | ~65,536 | None ✓ |
-| uint16 RGB | 65,536 | None ✓ |
-| uint8 | 256 | 99.6% ✗ |
-
-Example: Pixel value 32,768 (middle intensity)
-- Float32: 32768 / 65535.0 = 0.50000763 (exact)
-- uint8: 32768 → 128 → many values collapse!
-
-## License
-
-Same as main project.

scripts/train_float32_standalone.py

@@ -1,349 +0,0 @@
-#!/usr/bin/env python3
-"""
-Standalone training script for YOLO with 16-bit TIFF float32 support.
-
-This script trains YOLO models on 16-bit grayscale TIFF datasets without data loss.
-Converts images to float32 [0-1] on-the-fly using tifffile (no PIL/cv2).
-
-Usage:
-    python scripts/train_float32_standalone.py \\
-        --data path/to/data.yaml \\
-        --weights yolov8s-seg.pt \\
-        --epochs 100 \\
-        --batch 16 \\
-        --imgsz 640
-
-Based on the custom dataset approach to avoid Ultralytics' channel conversion issues.
-"""
-
-import argparse
-import os
-import sys
-import time
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn as nn
-import tifffile
-import yaml
-from torch.utils.data import Dataset, DataLoader
-from ultralytics import YOLO
-
-# Add project root to path
-project_root = Path(__file__).parent.parent
-sys.path.insert(0, str(project_root))
-
-from src.utils.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-# ===================== Dataset =====================
-
-
-class Float32YOLODataset(Dataset):
-    """PyTorch dataset for 16-bit TIFF images with float32 conversion."""
-
-    def __init__(self, images_dir, labels_dir, img_size=640):
-        self.images_dir = Path(images_dir)
-        self.labels_dir = Path(labels_dir)
-        self.img_size = img_size
-
-        # Find images
-        extensions = {".tif", ".tiff", ".png", ".jpg", ".jpeg", ".bmp"}
-        self.paths = sorted(
-            [
-                p
-                for p in self.images_dir.rglob("*")
-                if p.is_file() and p.suffix.lower() in extensions
-            ]
-        )
-
-        if not self.paths:
-            raise ValueError(f"No images found in {images_dir}")
-
-        logger.info(f"Dataset: {len(self.paths)} images from {images_dir}")
-
-    def __len__(self):
-        return len(self.paths)
-
-    def _read_image(self, path: Path) -> np.ndarray:
-        """Load image as float32 [0-1] RGB."""
-        # Load with tifffile
-        img = tifffile.imread(str(path))
-
-        # Convert to float32
-        img = img.astype(np.float32)
-
-        # Normalize 16-bit→[0,1]
-        if img.max() > 1.5:
-            img = img / 65535.0
-
-        img = np.clip(img, 0.0, 1.0)
-
-        # Grayscale→RGB
-        if img.ndim == 2:
-            img = np.repeat(img[..., None], 3, axis=2)
-        elif img.ndim == 3 and img.shape[2] == 1:
-            img = np.repeat(img, 3, axis=2)
-
-        return img  # float32 (H,W,3) [0,1]
-
-    def _parse_label(self, path: Path) -> np.ndarray:
-        """Parse YOLO label with variable-length  rows."""
-        if not path.exists():
-            return np.zeros((0, 5), dtype=np.float32)
-
-        labels = []
-        with open(path, "r") as f:
-            for line in f:
-                vals = line.strip().split()
-                if len(vals) >= 5:
-                    labels.append([float(v) for v in vals])
-
-        return (
-            np.array(labels, dtype=np.float32)
-            if labels
-            else np.zeros((0, 5), dtype=np.float32)
-        )
-
-    def __getitem__(self, idx):
-        img_path = self.paths[idx]
-        label_path = self.labels_dir / f"{img_path.stem}.txt"
-
-        # Load & convert to tensor (C,H,W)
-        img = self._read_image(img_path)
-        img_t = torch.from_numpy(img).permute(2, 0, 1).contiguous()
-
-        # Load labels
-        labels = self._parse_label(label_path)
-
-        return img_t, labels, str(img_path.name)
-
-
-# ===================== Collate =====================
-
-
-def collate_fn(batch):
-    """Stack images, keep labels as list."""
-    imgs = torch.stack([b[0] for b in batch], dim=0)
-    labels = [b[1] for b in batch]
-    names = [b[2] for b in batch]
-    return imgs, labels, names
-
-
-# ===================== Training =====================
-
-
-def get_pytorch_model(ul_model):
-    """Extract PyTorch model and loss from Ultralytics wrapper."""
-    pt_model = None
-    loss_fn = None
-
-    # Try common patterns
-    if hasattr(ul_model, "model"):
-        pt_model = ul_model.model
-    if pt_model and hasattr(pt_model, "model"):
-        pt_model = pt_model.model
-
-    # Find loss
-    if pt_model and hasattr(pt_model, "loss"):
-        loss_fn = pt_model.loss
-    elif pt_model and hasattr(pt_model, "compute_loss"):
-        loss_fn = pt_model.compute_loss
-
-    if pt_model is None:
-        raise RuntimeError("Could not extract PyTorch model")
-
-    return pt_model, loss_fn
-
-
-def train(args):
-    """Main training function."""
-    device = args.device
-    logger.info(f"Device: {device}")
-
-    # Parse data.yaml
-    with open(args.data, "r") as f:
-        data_config = yaml.safe_load(f)
-
-    dataset_root = Path(data_config.get("path", Path(args.data).parent))
-    train_img = dataset_root / data_config.get("train", "train/images")
-    val_img = dataset_root / data_config.get("val", "val/images")
-    train_lbl = train_img.parent / "labels"
-    val_lbl = val_img.parent / "labels"
-
-    # Load model
-    logger.info(f"Loading {args.weights}")
-    ul_model = YOLO(args.weights)
-    pt_model, loss_fn = get_pytorch_model(ul_model)
-
-    # Configure model args
-    from types import SimpleNamespace
-
-    if not hasattr(pt_model, "args"):
-        pt_model.args = SimpleNamespace()
-    if isinstance(pt_model.args, dict):
-        pt_model.args = SimpleNamespace(**pt_model.args)
-
-    # Set segmentation loss args
-    pt_model.args.overlap_mask = getattr(pt_model.args, "overlap_mask", True)
-    pt_model.args.mask_ratio = getattr(pt_model.args, "mask_ratio", 4)
-    pt_model.args.task = "segment"
-
-    pt_model.to(device)
-    pt_model.train()
-
-    # Create datasets
-    train_ds = Float32YOLODataset(str(train_img), str(train_lbl), args.imgsz)
-    val_ds = Float32YOLODataset(str(val_img), str(val_lbl), args.imgsz)
-
-    train_loader = DataLoader(
-        train_ds,
-        batch_size=args.batch,
-        shuffle=True,
-        num_workers=4,
-        pin_memory=(device == "cuda"),
-        collate_fn=collate_fn,
-    )
-    val_loader = DataLoader(
-        val_ds,
-        batch_size=args.batch,
-        shuffle=False,
-        num_workers=2,
-        pin_memory=(device == "cuda"),
-        collate_fn=collate_fn,
-    )
-
-    # Optimizer
-    optimizer = torch.optim.AdamW(pt_model.parameters(), lr=args.lr)
-
-    # Training loop
-    os.makedirs(args.save_dir, exist_ok=True)
-    best_loss = float("inf")
-
-    for epoch in range(args.epochs):
-        t0 = time.time()
-        running_loss = 0.0
-        num_batches = 0
-
-        for imgs, labels_list, names in train_loader:
-            imgs = imgs.to(device)
-            optimizer.zero_grad()
-            num_batches += 1
-
-            # Forward (simple approach - just use preds)
-            preds = pt_model(imgs)
-
-            # Try to compute loss
-            # Simplest fallback: if preds is tuple/list, assume last element is loss
-            if isinstance(preds, (tuple, list)):
-                # Often YOLO forward returns (preds, loss) in training mode
-                if (
-                    len(preds) >= 2
-                    and isinstance(preds[-1], dict)
-                    and "loss" in preds[-1]
-                ):
-                    loss = preds[-1]["loss"]
-                elif len(preds) >= 2 and isinstance(preds[-1], torch.Tensor):
-                    loss = preds[-1]
-                else:
-                    # Manually compute using loss_fn if available
-                    if loss_fn:
-                        # This may fail - see logs
-                        try:
-                            loss_out = loss_fn(preds, labels_list)
-                            loss = (
-                                loss_out[0]
-                                if isinstance(loss_out, (tuple, list))
-                                else loss_out
-                            )
-                        except Exception as e:
-                            logger.error(f"Loss computation failed: {e}")
-                            logger.error(
-                                "Consider using Ultralytics .train() or check model/loss compatibility"
-                            )
-                            raise
-                    else:
-                        raise RuntimeError("Cannot determine loss from model output")
-            elif isinstance(preds, dict) and "loss" in preds:
-                loss = preds["loss"]
-            else:
-                raise RuntimeError(f"Unexpected preds format: {type(preds)}")
-
-            # Backward
-            loss.backward()
-            optimizer.step()
-
-            running_loss += loss.item()
-
-            if (num_batches % 10) == 0:
-                logger.info(
-                    f"Epoch {epoch+1} Batch {num_batches} Loss: {loss.item():.4f}"
-                )
-
-        epoch_loss = running_loss / max(1, num_batches)
-        epoch_time = time.time() - t0
-        logger.info(
-            f"Epoch {epoch+1}/{args.epochs} - Loss: {epoch_loss:.4f}, Time: {epoch_time:.1f}s"
-        )
-
-        # Save checkpoint
-        ckpt = Path(args.save_dir) / f"epoch{epoch+1}.pt"
-        torch.save(
-            {
-                "epoch": epoch + 1,
-                "model_state_dict": pt_model.state_dict(),
-                "optimizer_state_dict": optimizer.state_dict(),
-                "loss": epoch_loss,
-            },
-            ckpt,
-        )
-
-        # Save best
-        if epoch_loss < best_loss:
-            best_loss = epoch_loss
-            best_ckpt = Path(args.save_dir) / "best.pt"
-            torch.save(pt_model.state_dict(), best_ckpt)
-            logger.info(f"New best: {best_ckpt}")
-
-    logger.info("Training complete")
-
-
-# ===================== Main =====================
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Train YOLO on 16-bit TIFF with float32"
-    )
-    parser.add_argument("--data", type=str, required=True, help="Path to data.yaml")
-    parser.add_argument(
-        "--weights", type=str, default="yolov8s-seg.pt", help="Pretrained weights"
-    )
-    parser.add_argument("--epochs", type=int, default=100, help="Number of epochs")
-    parser.add_argument("--batch", type=int, default=16, help="Batch size")
-    parser.add_argument("--imgsz", type=int, default=640, help="Image size")
-    parser.add_argument("--lr", type=float, default=1e-4, help="Learning rate")
-    parser.add_argument(
-        "--save-dir", type=str, default="runs/train", help="Save directory"
-    )
-    parser.add_argument(
-        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
-    )
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    logger.info("=" * 70)
-    logger.info("Float32 16-bit TIFF Training - Standalone Script")
-    logger.info("=" * 70)
-    logger.info(f"Data: {args.data}")
-    logger.info(f"Weights: {args.weights}")
-    logger.info(f"Epochs: {args.epochs}, Batch: {args.batch}, ImgSz: {args.imgsz}")
-    logger.info(f"LR: {args.lr}, Device: {args.device}")
-    logger.info("=" * 70)
-
-    train(args)

src/database/db_manager.py

@@ -60,9 +60,7 @@ class DatabaseManager:
         cursor = conn.cursor()
 
         # Check if annotations table exists
-        cursor.execute(
+        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='annotations'")
-            "SELECT name FROM sqlite_master WHERE type='table' AND name='annotations'"
-        )
         if not cursor.fetchone():
             # Table doesn't exist yet, no migration needed
             return
@@ -203,6 +201,28 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def delete_model(self, model_id: int) -> bool:
+        """Delete a model from the database.
+
+        Note: detections referencing this model are deleted automatically via
+        the `detections.model_id` foreign key (ON DELETE CASCADE).
+
+        Args:
+            model_id: ID of the model to delete.
+
+        Returns:
+            True if a model row was deleted, False otherwise.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM models WHERE id = ?", (model_id,))
+            conn.commit()
+            return cursor.rowcount > 0
+        finally:
+            conn.close()
+
     # ==================== Image Operations ====================
 
     def add_image(
@@ -242,9 +262,7 @@ class DatabaseManager:
             return cursor.lastrowid
         except sqlite3.IntegrityError:
             # Image already exists, return its ID
-            cursor.execute(
+            cursor.execute("SELECT id FROM images WHERE relative_path = ?", (relative_path,))
-                "SELECT id FROM images WHERE relative_path = ?", (relative_path,)
-            )
             row = cursor.fetchone()
             return row["id"] if row else None
         finally:
@@ -255,17 +273,13 @@ class DatabaseManager:
         conn = self.get_connection()
         try:
             cursor = conn.cursor()
-            cursor.execute(
+            cursor.execute("SELECT * FROM images WHERE relative_path = ?", (relative_path,))
-                "SELECT * FROM images WHERE relative_path = ?", (relative_path,)
-            )
             row = cursor.fetchone()
             return dict(row) if row else None
         finally:
             conn.close()
 
-    def get_or_create_image(
+    def get_or_create_image(self, relative_path: str, filename: str, width: int, height: int) -> int:
-        self, relative_path: str, filename: str, width: int, height: int
-    ) -> int:
         """Get existing image or create new one."""
         existing = self.get_image_by_path(relative_path)
         if existing:
@@ -355,16 +369,8 @@ class DatabaseManager:
                         bbox[2],
                         bbox[3],
                         det["confidence"],
-                        (
+                        (json.dumps(det.get("segmentation_mask")) if det.get("segmentation_mask") else None),
-                            json.dumps(det.get("segmentation_mask"))
+                        (json.dumps(det.get("metadata")) if det.get("metadata") else None),
-                            if det.get("segmentation_mask")
-                            else None
-                        ),
-                        (
-                            json.dumps(det.get("metadata"))
-                            if det.get("metadata")
-                            else None
-                        ),
                     ),
                 )
             conn.commit()
@@ -409,12 +415,13 @@ class DatabaseManager:
             if filters:
                 conditions = []
                 for key, value in filters.items():
-                    if (
+                    if key.startswith("d.") or key.startswith("i.") or key.startswith("m."):
-                        key.startswith("d.")
+                        if "like" in value.lower():
-                        or key.startswith("i.")
+                            conditions.append(f"{key} LIKE ?")
-                        or key.startswith("m.")
+                            params.append(value.split(" ")[1])
-                    ):
+                        else:
                             conditions.append(f"{key} = ?")
+                            params.append(value)
                     else:
                         conditions.append(f"d.{key} = ?")
                         params.append(value)
@@ -442,18 +449,14 @@ class DatabaseManager:
         finally:
             conn.close()
 
-    def get_detections_for_image(
+    def get_detections_for_image(self, image_id: int, model_id: Optional[int] = None) -> List[Dict]:
-        self, image_id: int, model_id: Optional[int] = None
-    ) -> List[Dict]:
         """Get all detections for a specific image."""
         filters = {"image_id": image_id}
         if model_id:
             filters["model_id"] = model_id
         return self.get_detections(filters)
 
-    def delete_detections_for_image(
+    def delete_detections_for_image(self, image_id: int, model_id: Optional[int] = None) -> int:
-        self, image_id: int, model_id: Optional[int] = None
-    ) -> int:
         """Delete detections tied to a specific image and optional model."""
         conn = self.get_connection()
         try:
@@ -481,6 +484,22 @@ class DatabaseManager:
         finally:
             conn.close()
 
+    def delete_all_detections(self) -> int:
+        """Delete all detections from the database.
+
+        Returns:
+            Number of rows deleted.
+        """
+
+        conn = self.get_connection()
+        try:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM detections")
+            conn.commit()
+            return cursor.rowcount
+        finally:
+            conn.close()
+
     # ==================== Statistics Operations ====================
 
     def get_detection_statistics(
@@ -524,9 +543,7 @@ class DatabaseManager:
             """,
                 params,
             )
-            class_counts = {
+            class_counts = {row["class_name"]: row["count"] for row in cursor.fetchall()}
-                row["class_name"]: row["count"] for row in cursor.fetchall()
-            }
 
             # Average confidence
             cursor.execute(
@@ -583,9 +600,7 @@ class DatabaseManager:
 
     # ==================== Export Operations ====================
 
-    def export_detections_to_csv(
+    def export_detections_to_csv(self, output_path: str, filters: Optional[Dict] = None) -> bool:
-        self, output_path: str, filters: Optional[Dict] = None
-    ) -> bool:
         """Export detections to CSV file."""
         try:
             detections = self.get_detections(filters)
@@ -614,9 +629,7 @@ class DatabaseManager:
                 for det in detections:
                     row = {k: det[k] for k in fieldnames if k in det}
                     # Convert segmentation mask list to JSON string for CSV
-                    if row.get("segmentation_mask") and isinstance(
+                    if row.get("segmentation_mask") and isinstance(row["segmentation_mask"], list):
-                        row["segmentation_mask"], list
-                    ):
                         row["segmentation_mask"] = json.dumps(row["segmentation_mask"])
                     writer.writerow(row)
 
@@ -625,9 +638,7 @@ class DatabaseManager:
             print(f"Error exporting to CSV: {e}")
             return False
 
-    def export_detections_to_json(
+    def export_detections_to_json(self, output_path: str, filters: Optional[Dict] = None) -> bool:
-        self, output_path: str, filters: Optional[Dict] = None
-    ) -> bool:
         """Export detections to JSON file."""
         try:
             detections = self.get_detections(filters)
@@ -647,6 +658,75 @@ class DatabaseManager:
 
     # ==================== Annotation Operations ====================
 
+    def get_annotated_images_summary(
+        self,
+        name_filter: Optional[str] = None,
+        order_by: str = "filename",
+        order_dir: str = "ASC",
+        limit: Optional[int] = None,
+        offset: int = 0,
+    ) -> List[Dict]:
+        """Return images that have at least one manual annotation.
+
+        Args:
+            name_filter: Optional substring filter applied to filename/relative_path.
+            order_by: One of: 'filename', 'relative_path', 'annotation_count', 'added_at'.
+            order_dir: 'ASC' or 'DESC'.
+            limit: Optional max number of rows.
+            offset: Pagination offset.
+
+        Returns:
+            List of dicts: {id, relative_path, filename, added_at, annotation_count}
+        """
+
+        allowed_order_by = {
+            "filename": "i.filename",
+            "relative_path": "i.relative_path",
+            "annotation_count": "annotation_count",
+            "added_at": "i.added_at",
+        }
+        order_expr = allowed_order_by.get(order_by, "i.filename")
+        dir_norm = str(order_dir).upper().strip()
+        if dir_norm not in {"ASC", "DESC"}:
+            dir_norm = "ASC"
+
+        conn = self.get_connection()
+        try:
+            params: List[Any] = []
+            where_sql = ""
+            if name_filter:
+                # Case-insensitive substring search.
+                token = f"%{name_filter}%"
+                where_sql = "WHERE (i.filename LIKE ? OR i.relative_path LIKE ?)"
+                params.extend([token, token])
+
+            limit_sql = ""
+            if limit is not None:
+                limit_sql = " LIMIT ? OFFSET ?"
+                params.extend([int(limit), int(offset)])
+
+            query = f"""
+                SELECT
+                    i.id,
+                    i.relative_path,
+                    i.filename,
+                    i.added_at,
+                    COUNT(a.id) AS annotation_count
+                FROM images i
+                JOIN annotations a ON a.image_id = i.id
+                {where_sql}
+                GROUP BY i.id
+                HAVING annotation_count > 0
+                ORDER BY {order_expr} {dir_norm}
+                {limit_sql}
+            """
+
+            cursor = conn.cursor()
+            cursor.execute(query, params)
+            return [dict(row) for row in cursor.fetchall()]
+        finally:
+            conn.close()
+
     def add_annotation(
         self,
         image_id: int,
@@ -785,17 +865,13 @@ class DatabaseManager:
         conn = self.get_connection()
         try:
             cursor = conn.cursor()
-            cursor.execute(
+            cursor.execute("SELECT * FROM object_classes WHERE class_name = ?", (class_name,))
-                "SELECT * FROM object_classes WHERE class_name = ?", (class_name,)
-            )
             row = cursor.fetchone()
             return dict(row) if row else None
         finally:
             conn.close()
 
-    def add_object_class(
+    def add_object_class(self, class_name: str, color: str, description: Optional[str] = None) -> int:
-        self, class_name: str, color: str, description: Optional[str] = None
-    ) -> int:
         """
         Add a new object class.
 
@@ -928,8 +1004,7 @@ class DatabaseManager:
             if not split_map[required]:
                 raise ValueError(
                     "Unable to determine %s image directory under %s. Provide it "
-                    "explicitly via the 'splits' argument."
+                    "explicitly via the 'splits' argument." % (required, dataset_root_path)
-                    % (required, dataset_root_path)
                 )
 
         yaml_splits: Dict[str, str] = {}
@@ -955,11 +1030,7 @@ class DatabaseManager:
         if yaml_splits.get("test"):
             payload["test"] = yaml_splits["test"]
 
-        output_path_obj = (
+        output_path_obj = Path(output_path).expanduser() if output_path else dataset_root_path / "data.yaml"
-            Path(output_path).expanduser()
-            if output_path
-            else dataset_root_path / "data.yaml"
-        )
         output_path_obj.parent.mkdir(parents=True, exist_ok=True)
 
         with open(output_path_obj, "w", encoding="utf-8") as handle:
@@ -1019,15 +1090,9 @@ class DatabaseManager:
         for split_name, options in patterns.items():
             for relative in options:
                 candidate = (dataset_root / relative).resolve()
-                if (
+                if candidate.exists() and candidate.is_dir() and self._directory_has_images(candidate):
-                    candidate.exists()
-                    and candidate.is_dir()
-                    and self._directory_has_images(candidate)
-                ):
                     try:
-                        inferred[split_name] = candidate.relative_to(
+                        inferred[split_name] = candidate.relative_to(dataset_root).as_posix()
-                            dataset_root
-                        ).as_posix()
                     except ValueError:
                         inferred[split_name] = candidate.as_posix()
                     break

src/database/schema.sql

@@ -55,10 +55,7 @@ CREATE TABLE IF NOT EXISTS object_classes (
 
 -- Insert default object classes
 INSERT OR IGNORE INTO object_classes (class_name, color, description) VALUES
-    ('cell', '#FF0000', 'Cell object'),
+    ('terminal', '#FFFF00', 'Axion terminal');
-    ('nucleus', '#00FF00', 'Cell nucleus'),
-    ('mitochondria', '#0000FF', 'Mitochondria'),
-    ('vesicle', '#FFFF00', 'Vesicle');
 
 -- Annotations table: stores manual annotations
 CREATE TABLE IF NOT EXISTS annotations (

src/gui/main_window.py

@@ -1,6 +1,7 @@
-"""
+"""Main window for the microscopy object detection application."""
-Main window for the microscopy object detection application.
+
-"""
+import shutil
+from pathlib import Path
 
 from PySide6.QtWidgets import (
     QMainWindow,
@@ -20,6 +21,7 @@ from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
 from src.utils.logger import get_logger
 from src.gui.dialogs.config_dialog import ConfigDialog
+from src.gui.dialogs.delete_model_dialog import DeleteModelDialog
 from src.gui.tabs.detection_tab import DetectionTab
 from src.gui.tabs.training_tab import TrainingTab
 from src.gui.tabs.validation_tab import ValidationTab
@@ -91,6 +93,12 @@ class MainWindow(QMainWindow):
         db_stats_action.triggered.connect(self._show_database_stats)
         tools_menu.addAction(db_stats_action)
 
+        tools_menu.addSeparator()
+
+        delete_model_action = QAction("Delete &Model…", self)
+        delete_model_action.triggered.connect(self._show_delete_model_dialog)
+        tools_menu.addAction(delete_model_action)
+
         # Help menu
         help_menu = menubar.addMenu("&Help")
 
@@ -117,10 +125,10 @@ class MainWindow(QMainWindow):
 
             # Add tabs to widget
             self.tab_widget.addTab(self.detection_tab, "Detection")
+            self.tab_widget.addTab(self.results_tab, "Results")
+            self.tab_widget.addTab(self.annotation_tab, "Annotation")
             self.tab_widget.addTab(self.training_tab, "Training")
             self.tab_widget.addTab(self.validation_tab, "Validation")
-            self.tab_widget.addTab(self.results_tab, "Results")
-            self.tab_widget.addTab(self.annotation_tab, "Annotation (Future)")
 
             # Connect tab change signal
             self.tab_widget.currentChanged.connect(self._on_tab_changed)
@@ -152,9 +160,7 @@ class MainWindow(QMainWindow):
         """Center window on screen."""
         screen = self.screen().geometry()
         size = self.geometry()
-        self.move(
+        self.move((screen.width() - size.width()) // 2, (screen.height() - size.height()) // 2)
-            (screen.width() - size.width()) // 2, (screen.height() - size.height()) // 2
-        )
 
     def _restore_window_state(self):
         """Restore window geometry from settings or center window."""
@@ -193,6 +199,10 @@ class MainWindow(QMainWindow):
                 self.training_tab.refresh()
             if hasattr(self, "results_tab"):
                 self.results_tab.refresh()
+            if hasattr(self, "annotation_tab"):
+                self.annotation_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
         except Exception as e:
             logger.error(f"Error applying settings: {e}")
 
@@ -209,6 +219,14 @@ class MainWindow(QMainWindow):
         logger.debug(f"Switched to tab: {tab_name}")
         self._update_status(f"Viewing: {tab_name}")
 
+        # Ensure the Annotation tab always shows up-to-date DB-backed lists.
+        try:
+            current_widget = self.tab_widget.widget(index)
+            if hasattr(self, "annotation_tab") and current_widget is self.annotation_tab:
+                self.annotation_tab.refresh()
+        except Exception as exc:
+            logger.debug(f"Failed to refresh annotation tab on selection: {exc}")
+
     def _show_database_stats(self):
         """Show database statistics dialog."""
         try:
@@ -231,10 +249,230 @@ class MainWindow(QMainWindow):
 
         except Exception as e:
             logger.error(f"Error getting database stats: {e}")
-            QMessageBox.warning(
+            QMessageBox.warning(self, "Error", f"Failed to get database statistics:\n{str(e)}")
-                self, "Error", f"Failed to get database statistics:\n{str(e)}"
+
+    def _show_delete_model_dialog(self) -> None:
+        """Open the model deletion dialog."""
+        dialog = DeleteModelDialog(self.db_manager, self)
+        if not dialog.exec():
+            return
+
+        model_ids = dialog.selected_model_ids
+        if not model_ids:
+            return
+
+        self._delete_models(model_ids)
+
+    def _delete_models(self, model_ids: list[int]) -> None:
+        """Delete one or more models from the database and remove artifacts from disk."""
+
+        deleted_count = 0
+        removed_paths: list[str] = []
+        remove_errors: list[str] = []
+
+        for model_id in model_ids:
+            model = None
+            try:
+                model = self.db_manager.get_model_by_id(int(model_id))
+            except Exception as exc:
+                logger.error(f"Failed to load model {model_id} before deletion: {exc}")
+
+            if not model:
+                remove_errors.append(f"Model id {model_id} not found in database.")
+                continue
+
+            try:
+                deleted = self.db_manager.delete_model(int(model_id))
+            except Exception as exc:
+                logger.error(f"Failed to delete model {model_id}: {exc}")
+                remove_errors.append(f"Failed to delete model id {model_id} from DB: {exc}")
+                continue
+
+            if not deleted:
+                remove_errors.append(f"Model id {model_id} was not deleted (already removed?).")
+                continue
+
+            deleted_count += 1
+            removed, errors = self._delete_model_artifacts_from_disk(model)
+            removed_paths.extend(removed)
+            remove_errors.extend(errors)
+
+        # Refresh tabs to reflect the deletion(s).
+        try:
+            if hasattr(self, "detection_tab"):
+                self.detection_tab.refresh()
+            if hasattr(self, "results_tab"):
+                self.results_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
+            if hasattr(self, "training_tab"):
+                self.training_tab.refresh()
+        except Exception as exc:
+            logger.warning(f"Failed to refresh tabs after model deletion: {exc}")
+
+        details: list[str] = []
+        if removed_paths:
+            details.append("Removed from disk:\n" + "\n".join(removed_paths))
+        if remove_errors:
+            details.append("\nDisk cleanup warnings:\n" + "\n".join(remove_errors))
+
+        QMessageBox.information(
+            self,
+            "Delete Model",
+            f"Deleted {deleted_count} model(s) from database." + ("\n\n" + "\n".join(details) if details else ""),
         )
 
+    def _delete_model(self, model_id: int) -> None:
+        """Delete a model from the database and remove its artifacts from disk."""
+
+        model = None
+        try:
+            model = self.db_manager.get_model_by_id(model_id)
+        except Exception as exc:
+            logger.error(f"Failed to load model {model_id} before deletion: {exc}")
+
+        if not model:
+            QMessageBox.warning(self, "Delete Model", "Selected model was not found in the database.")
+            return
+
+        model_path = str(model.get("model_path") or "")
+
+        try:
+            deleted = self.db_manager.delete_model(model_id)
+        except Exception as exc:
+            logger.error(f"Failed to delete model {model_id}: {exc}")
+            QMessageBox.critical(self, "Delete Model", f"Failed to delete model from database:\n{exc}")
+            return
+
+        if not deleted:
+            QMessageBox.warning(self, "Delete Model", "No model was deleted (it may have already been removed).")
+            return
+
+        removed_paths, remove_errors = self._delete_model_artifacts_from_disk(model)
+
+        # Refresh tabs to reflect the deletion.
+        try:
+            if hasattr(self, "detection_tab"):
+                self.detection_tab.refresh()
+            if hasattr(self, "results_tab"):
+                self.results_tab.refresh()
+            if hasattr(self, "validation_tab"):
+                self.validation_tab.refresh()
+            if hasattr(self, "training_tab"):
+                self.training_tab.refresh()
+        except Exception as exc:
+            logger.warning(f"Failed to refresh tabs after model deletion: {exc}")
+
+        details = []
+        if model_path:
+            details.append(f"Deleted model record for: {model_path}")
+        if removed_paths:
+            details.append("\nRemoved from disk:\n" + "\n".join(removed_paths))
+        if remove_errors:
+            details.append("\nDisk cleanup warnings:\n" + "\n".join(remove_errors))
+
+        QMessageBox.information(
+            self,
+            "Delete Model",
+            "Model deleted from database." + ("\n\n" + "\n".join(details) if details else ""),
+        )
+
+    def _delete_model_artifacts_from_disk(self, model: dict) -> tuple[list[str], list[str]]:
+        """Best-effort removal of model artifacts on disk.
+
+        Strategy:
+        - Remove run directories inferred from:
+          - model.model_path (…/<run>/weights/*.pt => <run>)
+          - training_params.stage_results[].results.save_dir
+          but only if they are under the configured models directory.
+        - If the weights file itself exists and is outside the models directory, delete only the file.
+
+        Returns:
+            (removed_paths, errors)
+        """
+
+        removed: list[str] = []
+        errors: list[str] = []
+
+        models_root = Path(self.config_manager.get_models_directory() or "data/models").expanduser()
+        try:
+            models_root_resolved = models_root.resolve()
+        except Exception:
+            models_root_resolved = models_root
+
+        inferred_dirs: list[Path] = []
+
+        # 1) From model_path
+        model_path_value = model.get("model_path")
+        if model_path_value:
+            try:
+                p = Path(str(model_path_value)).expanduser()
+                p_resolved = p.resolve() if p.exists() else p
+                if p_resolved.is_file():
+                    if p_resolved.parent.name == "weights" and p_resolved.parent.parent.exists():
+                        inferred_dirs.append(p_resolved.parent.parent)
+                    elif p_resolved.parent.exists():
+                        inferred_dirs.append(p_resolved.parent)
+            except Exception:
+                pass
+
+        # 2) From training_params.stage_results[].results.save_dir
+        training_params = model.get("training_params") or {}
+        if isinstance(training_params, dict):
+            stage_results = training_params.get("stage_results")
+            if isinstance(stage_results, list):
+                for stage in stage_results:
+                    results = (stage or {}).get("results")
+                    save_dir = (results or {}).get("save_dir") if isinstance(results, dict) else None
+                    if not save_dir:
+                        continue
+                    try:
+                        d = Path(str(save_dir)).expanduser()
+                        if d.exists() and d.is_dir():
+                            inferred_dirs.append(d)
+                    except Exception:
+                        continue
+
+        # Deduplicate inferred_dirs
+        unique_dirs: list[Path] = []
+        seen: set[str] = set()
+        for d in inferred_dirs:
+            try:
+                key = str(d.resolve())
+            except Exception:
+                key = str(d)
+            if key in seen:
+                continue
+            seen.add(key)
+            unique_dirs.append(d)
+
+        # Delete directories under models_root
+        for d in unique_dirs:
+            try:
+                d_resolved = d.resolve()
+            except Exception:
+                d_resolved = d
+            try:
+                if d_resolved.exists() and d_resolved.is_dir() and d_resolved.is_relative_to(models_root_resolved):
+                    shutil.rmtree(d_resolved)
+                    removed.append(str(d_resolved))
+            except Exception as exc:
+                errors.append(f"Failed to remove directory {d_resolved}: {exc}")
+
+        # If nothing matched (e.g., model_path outside models_root), delete just the file.
+        if model_path_value:
+            try:
+                p = Path(str(model_path_value)).expanduser()
+                if p.exists() and p.is_file():
+                    p_resolved = p.resolve()
+                    if not p_resolved.is_relative_to(models_root_resolved):
+                        p_resolved.unlink()
+                        removed.append(str(p_resolved))
+            except Exception as exc:
+                errors.append(f"Failed to remove model file {model_path_value}: {exc}")
+
+        return removed, errors
+
     def _show_about(self):
         """Show about dialog."""
         about_text = """
@@ -301,6 +539,11 @@ class MainWindow(QMainWindow):
             if hasattr(self, "training_tab"):
                 self.training_tab.shutdown()
             if hasattr(self, "annotation_tab"):
+                # Best-effort refresh so DB-backed UI state is consistent at shutdown.
+                try:
+                    self.annotation_tab.refresh()
+                except Exception:
+                    pass
                 self.annotation_tab.save_state()
 
             logger.info("Application closing")

src/gui/tabs/annotation_tab.py

@@ -13,6 +13,11 @@ from PySide6.QtWidgets import (
     QFileDialog,
     QMessageBox,
     QSplitter,
+    QLineEdit,
+    QTableWidget,
+    QTableWidgetItem,
+    QHeaderView,
+    QAbstractItemView,
 )
 from PySide6.QtCore import Qt, QSettings
 from pathlib import Path
@@ -29,9 +34,7 @@ logger = get_logger(__name__)
 class AnnotationTab(QWidget):
     """Annotation tab for manual image annotation."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
@@ -52,6 +55,32 @@ class AnnotationTab(QWidget):
         self.main_splitter = QSplitter(Qt.Horizontal)
         self.main_splitter.setHandleWidth(10)
 
+        # { Left-most pane: annotated images list
+        annotated_group = QGroupBox("Annotated Images")
+        annotated_layout = QVBoxLayout()
+
+        filter_row = QHBoxLayout()
+        filter_row.addWidget(QLabel("Filter:"))
+        self.annotated_filter_edit = QLineEdit()
+        self.annotated_filter_edit.setPlaceholderText("Type to filter by image name…")
+        self.annotated_filter_edit.textChanged.connect(self._refresh_annotated_images_list)
+        filter_row.addWidget(self.annotated_filter_edit, 1)
+        annotated_layout.addLayout(filter_row)
+
+        self.annotated_images_table = QTableWidget(0, 2)
+        self.annotated_images_table.setHorizontalHeaderLabels(["Image", "Annotations"])
+        self.annotated_images_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
+        self.annotated_images_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)
+        self.annotated_images_table.setSelectionBehavior(QAbstractItemView.SelectRows)
+        self.annotated_images_table.setSelectionMode(QAbstractItemView.SingleSelection)
+        self.annotated_images_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
+        self.annotated_images_table.setSortingEnabled(True)
+        self.annotated_images_table.itemSelectionChanged.connect(self._on_annotated_image_selected)
+        annotated_layout.addWidget(self.annotated_images_table, 1)
+
+        annotated_group.setLayout(annotated_layout)
+        # }
+
         # { Left splitter for image display and zoom info
         self.left_splitter = QSplitter(Qt.Vertical)
         self.left_splitter.setHandleWidth(10)
@@ -62,6 +91,9 @@ class AnnotationTab(QWidget):
 
         # Use the AnnotationCanvasWidget
         self.annotation_canvas = AnnotationCanvasWidget()
+        # Auto-zoom so newly loaded images fill the available canvas viewport.
+        # (Matches the behavior used in ResultsTab.)
+        self.annotation_canvas.set_auto_fit_to_view(True)
         self.annotation_canvas.zoom_changed.connect(self._on_zoom_changed)
         self.annotation_canvas.annotation_drawn.connect(self._on_annotation_drawn)
         # Selection of existing polylines (when tool is not in drawing mode)
@@ -72,9 +104,7 @@ class AnnotationTab(QWidget):
         self.left_splitter.addWidget(canvas_group)
 
         # Controls info
-        controls_info = QLabel(
+        controls_info = QLabel("Zoom: Mouse wheel or +/- keys | Drawing: Enable pen and drag mouse")
-            "Zoom: Mouse wheel or +/- keys | Drawing: Enable pen and drag mouse"
-        )
         controls_info.setStyleSheet("QLabel { color: #888; font-style: italic; }")
         self.left_splitter.addWidget(controls_info)
         # }
@@ -85,36 +115,20 @@ class AnnotationTab(QWidget):
 
         # Annotation tools section
         self.annotation_tools = AnnotationToolsWidget(self.db_manager)
-        self.annotation_tools.polyline_enabled_changed.connect(
+        self.annotation_tools.polyline_enabled_changed.connect(self.annotation_canvas.set_polyline_enabled)
-            self.annotation_canvas.set_polyline_enabled
+        self.annotation_tools.polyline_pen_color_changed.connect(self.annotation_canvas.set_polyline_pen_color)
-        )
+        self.annotation_tools.polyline_pen_width_changed.connect(self.annotation_canvas.set_polyline_pen_width)
-        self.annotation_tools.polyline_pen_color_changed.connect(
-            self.annotation_canvas.set_polyline_pen_color
-        )
-        self.annotation_tools.polyline_pen_width_changed.connect(
-            self.annotation_canvas.set_polyline_pen_width
-        )
         # Show / hide bounding boxes
-        self.annotation_tools.show_bboxes_changed.connect(
+        self.annotation_tools.show_bboxes_changed.connect(self.annotation_canvas.set_show_bboxes)
-            self.annotation_canvas.set_show_bboxes
-        )
         # RDP simplification controls
-        self.annotation_tools.simplify_on_finish_changed.connect(
+        self.annotation_tools.simplify_on_finish_changed.connect(self._on_simplify_on_finish_changed)
-            self._on_simplify_on_finish_changed
+        self.annotation_tools.simplify_epsilon_changed.connect(self._on_simplify_epsilon_changed)
-        )
-        self.annotation_tools.simplify_epsilon_changed.connect(
-            self._on_simplify_epsilon_changed
-        )
         # Class selection and class-color changes
         self.annotation_tools.class_selected.connect(self._on_class_selected)
         self.annotation_tools.class_color_changed.connect(self._on_class_color_changed)
-        self.annotation_tools.clear_annotations_requested.connect(
+        self.annotation_tools.clear_annotations_requested.connect(self._on_clear_annotations)
-            self._on_clear_annotations
-        )
         # Delete selected annotation on canvas
-        self.annotation_tools.delete_selected_annotation_requested.connect(
+        self.annotation_tools.delete_selected_annotation_requested.connect(self._on_delete_selected_annotation)
-            self._on_delete_selected_annotation
-        )
         self.right_splitter.addWidget(self.annotation_tools)
 
         # Image loading section
@@ -137,12 +151,13 @@ class AnnotationTab(QWidget):
         self.right_splitter.addWidget(load_group)
         # }
 
-        # Add both splitters to the main horizontal splitter
+        # Add list + both splitters to the main horizontal splitter
+        self.main_splitter.addWidget(annotated_group)
         self.main_splitter.addWidget(self.left_splitter)
         self.main_splitter.addWidget(self.right_splitter)
 
-        # Set initial sizes: 75% for left (image), 25% for right (controls)
+        # Set initial sizes: list (left), canvas (middle), controls (right)
-        self.main_splitter.setSizes([750, 250])
+        self.main_splitter.setSizes([320, 650, 280])
 
         layout.addWidget(self.main_splitter)
         self.setLayout(layout)
@@ -150,6 +165,9 @@ class AnnotationTab(QWidget):
         # Restore splitter positions from settings
         self._restore_state()
 
+        # Populate list on startup.
+        self._refresh_annotated_images_list()
+
     def _load_image(self):
         """Load and display an image file."""
         # Get last opened directory from QSettings
@@ -180,12 +198,24 @@ class AnnotationTab(QWidget):
             self.current_image_path = file_path
 
             # Store the directory for next time
-            settings.setValue(
+            settings.setValue("annotation_tab/last_directory", str(Path(file_path).parent))
-                "annotation_tab/last_directory", str(Path(file_path).parent)
-            )
 
             # Get or create image in database
-            relative_path = str(Path(file_path).name)  # Simplified for now
+            repo_root = self.config_manager.get_image_repository_path()
+            relative_path: str
+            try:
+                if repo_root:
+                    repo_root_path = Path(repo_root).expanduser().resolve()
+                    file_resolved = Path(file_path).expanduser().resolve()
+                    if file_resolved.is_relative_to(repo_root_path):
+                        relative_path = file_resolved.relative_to(repo_root_path).as_posix()
+                    else:
+                        # Fallback: store filename only to avoid leaking absolute paths.
+                        relative_path = file_resolved.name
+                else:
+                    relative_path = str(Path(file_path).name)
+            except Exception:
+                relative_path = str(Path(file_path).name)
             self.current_image_id = self.db_manager.get_or_create_image(
                 relative_path,
                 Path(file_path).name,
@@ -199,6 +229,9 @@ class AnnotationTab(QWidget):
             # Load and display any existing annotations for this image
             self._load_annotations_for_current_image()
 
+            # Update annotated images list (newly annotated image added/selected).
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
             # Update info label
             self._update_image_info()
 
@@ -206,9 +239,7 @@ class AnnotationTab(QWidget):
 
         except ImageLoadError as e:
             logger.error(f"Failed to load image: {e}")
-            QMessageBox.critical(
+            QMessageBox.critical(self, "Error Loading Image", f"Failed to load image:\n{str(e)}")
-                self, "Error Loading Image", f"Failed to load image:\n{str(e)}"
-            )
         except Exception as e:
             logger.error(f"Unexpected error loading image: {e}")
             QMessageBox.critical(self, "Error", f"Unexpected error:\n{str(e)}")
@@ -296,6 +327,9 @@ class AnnotationTab(QWidget):
             # Reload annotations from DB and redraw (respecting current class filter)
             self._load_annotations_for_current_image()
 
+            # Update list counts.
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
         except Exception as e:
             logger.error(f"Failed to save annotation: {e}")
             QMessageBox.critical(self, "Error", f"Failed to save annotation:\n{str(e)}")
@@ -340,9 +374,7 @@ class AnnotationTab(QWidget):
         if not self.current_image_id:
             return
 
-        logger.debug(
+        logger.debug(f"Class color changed; reloading annotations for image ID {self.current_image_id}")
-            f"Class color changed; reloading annotations for image ID {self.current_image_id}"
-        )
         self._load_annotations_for_current_image()
 
     def _on_class_selected(self, class_data):
@@ -355,9 +387,7 @@ class AnnotationTab(QWidget):
         if class_data:
             logger.debug(f"Object class selected: {class_data['class_name']}")
         else:
-            logger.debug(
+            logger.debug('No class selected ("-- Select Class --"), showing all annotations')
-                'No class selected ("-- Select Class --"), showing all annotations'
-            )
 
         # Changing the class filter invalidates any previous selection
         self.selected_annotation_ids = []
@@ -390,9 +420,7 @@ class AnnotationTab(QWidget):
             question = "Are you sure you want to delete the selected annotation?"
             title = "Delete Annotation"
         else:
-            question = (
+            question = f"Are you sure you want to delete the {count} selected annotations?"
-                f"Are you sure you want to delete the {count} selected annotations?"
-            )
             title = "Delete Annotations"
 
         reply = QMessageBox.question(
@@ -420,13 +448,11 @@ class AnnotationTab(QWidget):
                 QMessageBox.warning(
                     self,
                     "Partial Failure",
-                    "Some annotations could not be deleted:\n"
+                    "Some annotations could not be deleted:\n" + ", ".join(str(a) for a in failed_ids),
-                    + ", ".join(str(a) for a in failed_ids),
                 )
             else:
                 logger.info(
-                    f"Deleted {count} annotation(s): "
+                    f"Deleted {count} annotation(s): " + ", ".join(str(a) for a in self.selected_annotation_ids)
-                    + ", ".join(str(a) for a in self.selected_annotation_ids)
                 )
 
             # Clear selection and reload annotations for the current image from DB
@@ -434,6 +460,9 @@ class AnnotationTab(QWidget):
             self.annotation_tools.set_has_selected_annotation(False)
             self._load_annotations_for_current_image()
 
+            # Update list counts.
+            self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
         except Exception as e:
             logger.error(f"Failed to delete annotations: {e}")
             QMessageBox.critical(
@@ -456,17 +485,13 @@ class AnnotationTab(QWidget):
             return
 
         try:
-            self.current_annotations = self.db_manager.get_annotations_for_image(
+            self.current_annotations = self.db_manager.get_annotations_for_image(self.current_image_id)
-                self.current_image_id
-            )
             # New annotations loaded; reset any selection
             self.selected_annotation_ids = []
             self.annotation_tools.set_has_selected_annotation(False)
             self._redraw_annotations_for_current_filter()
         except Exception as e:
-            logger.error(
+            logger.error(f"Failed to load annotations for image {self.current_image_id}: {e}")
-                f"Failed to load annotations for image {self.current_image_id}: {e}"
-            )
             QMessageBox.critical(
                 self,
                 "Error",
@@ -490,10 +515,7 @@ class AnnotationTab(QWidget):
         drawn_count = 0
         for ann in self.current_annotations:
             # Filter by class if one is selected
-            if (
+            if selected_class_id is not None and ann.get("class_id") != selected_class_id:
-                selected_class_id is not None
-                and ann.get("class_id") != selected_class_id
-            ):
                 continue
 
             if ann.get("segmentation_mask"):
@@ -545,22 +567,176 @@ class AnnotationTab(QWidget):
         settings = QSettings("microscopy_app", "object_detection")
 
         # Save main splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/main_splitter_state", self.main_splitter.saveState())
-            "annotation_tab/main_splitter_state", self.main_splitter.saveState()
-        )
 
         # Save left splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/left_splitter_state", self.left_splitter.saveState())
-            "annotation_tab/left_splitter_state", self.left_splitter.saveState()
-        )
 
         # Save right splitter state
-        settings.setValue(
+        settings.setValue("annotation_tab/right_splitter_state", self.right_splitter.saveState())
-            "annotation_tab/right_splitter_state", self.right_splitter.saveState()
-        )
 
         logger.debug("Saved annotation tab splitter states")
 
     def refresh(self):
         """Refresh the tab."""
+        self._refresh_annotated_images_list(select_image_id=self.current_image_id)
+
+    # ==================== Annotated images list ====================
+
+    def _refresh_annotated_images_list(self, select_image_id: int | None = None) -> None:
+        """Reload annotated-images list from the database."""
+        if not hasattr(self, "annotated_images_table"):
+            return
+
+        # Preserve selection if possible
+        desired_id = select_image_id if select_image_id is not None else self.current_image_id
+
+        name_filter = ""
+        if hasattr(self, "annotated_filter_edit"):
+            name_filter = self.annotated_filter_edit.text().strip()
+
+        try:
+            rows = self.db_manager.get_annotated_images_summary(name_filter=name_filter)
+        except Exception as exc:
+            logger.error(f"Failed to load annotated images summary: {exc}")
+            rows = []
+
+        sorting_enabled = self.annotated_images_table.isSortingEnabled()
+        self.annotated_images_table.setSortingEnabled(False)
+        self.annotated_images_table.blockSignals(True)
+        try:
+            self.annotated_images_table.setRowCount(len(rows))
+            for r, entry in enumerate(rows):
+                image_name = str(entry.get("filename") or "")
+                count = int(entry.get("annotation_count") or 0)
+                rel_path = str(entry.get("relative_path") or "")
+
+                name_item = QTableWidgetItem(image_name)
+                # Tooltip shows full path of the image (best-effort: repository_root + relative_path)
+                full_path = rel_path
+                repo_root = self.config_manager.get_image_repository_path()
+                if repo_root and rel_path and not Path(rel_path).is_absolute():
+                    try:
+                        full_path = str((Path(repo_root) / rel_path).resolve())
+                    except Exception:
+                        full_path = str(Path(repo_root) / rel_path)
+                name_item.setToolTip(full_path)
+                name_item.setData(Qt.UserRole, int(entry.get("id")))
+                name_item.setData(Qt.UserRole + 1, rel_path)
+
+                count_item = QTableWidgetItem()
+                # Use EditRole to ensure numeric sorting.
+                count_item.setData(Qt.EditRole, count)
+                count_item.setData(Qt.UserRole, int(entry.get("id")))
+                count_item.setData(Qt.UserRole + 1, rel_path)
+
+                self.annotated_images_table.setItem(r, 0, name_item)
+                self.annotated_images_table.setItem(r, 1, count_item)
+
+            # Re-select desired row
+            if desired_id is not None:
+                for r in range(self.annotated_images_table.rowCount()):
+                    item = self.annotated_images_table.item(r, 0)
+                    if item and item.data(Qt.UserRole) == desired_id:
+                        self.annotated_images_table.selectRow(r)
+                        break
+        finally:
+            self.annotated_images_table.blockSignals(False)
+            self.annotated_images_table.setSortingEnabled(sorting_enabled)
+
+    def _on_annotated_image_selected(self) -> None:
+        """When user clicks an item in the list, load that image in the annotation canvas."""
+        selected = self.annotated_images_table.selectedItems()
+        if not selected:
+            return
+
+        # Row selection -> take the first column item
+        row = self.annotated_images_table.currentRow()
+        item = self.annotated_images_table.item(row, 0)
+        if not item:
+            return
+
+        image_id = item.data(Qt.UserRole)
+        rel_path = item.data(Qt.UserRole + 1) or ""
+        if not image_id:
+            return
+
+        image_path = self._resolve_image_path_for_relative_path(rel_path)
+        if not image_path:
+            QMessageBox.warning(
+                self,
+                "Image Not Found",
+                "Unable to locate image on disk for:\n"
+                f"{rel_path}\n\n"
+                "Tip: set Settings → Image repository path to the folder containing your images.",
+            )
+            return
+
+        try:
+            self.current_image = Image(image_path)
+            self.current_image_path = image_path
+            self.current_image_id = int(image_id)
+            self.annotation_canvas.load_image(self.current_image)
+            self._load_annotations_for_current_image()
+            self._update_image_info()
+        except ImageLoadError as exc:
+            logger.error(f"Failed to load image '{image_path}': {exc}")
+            QMessageBox.critical(self, "Error Loading Image", f"Failed to load image:\n{exc}")
+        except Exception as exc:
+            logger.error(f"Unexpected error loading image '{image_path}': {exc}")
+            QMessageBox.critical(self, "Error", f"Unexpected error:\n{exc}")
+
+    def _resolve_image_path_for_relative_path(self, relative_path: str) -> str | None:
+        """Best-effort conversion from a DB relative_path to an on-disk file path."""
+
+        rel = (relative_path or "").strip()
+        if not rel:
+            return None
+
+        candidates: list[Path] = []
+
+        # 1) Repository root + relative
+        repo_root = (self.config_manager.get_image_repository_path() or "").strip()
+        if repo_root:
+            candidates.append(Path(repo_root) / rel)
+
+        # 2) If the DB path is absolute, try it directly.
+        candidates.append(Path(rel))
+
+        # 3) Try the directory of the currently loaded image (helps when DB stores only filenames)
+        if self.current_image_path:
+            try:
+                candidates.append(Path(self.current_image_path).expanduser().resolve().parent / Path(rel).name)
+            except Exception:
                 pass
+
+        # 4) Try the last directory used by the annotation file picker
+        try:
+            settings = QSettings("microscopy_app", "object_detection")
+            last_dir = settings.value("annotation_tab/last_directory", None)
+            if last_dir:
+                candidates.append(Path(str(last_dir)) / Path(rel).name)
+        except Exception:
+            pass
+
+        for p in candidates:
+            try:
+                expanded = p.expanduser()
+                if expanded.exists() and expanded.is_file():
+                    return str(expanded.resolve())
+            except Exception:
+                continue
+
+        # 5) Fallback: search by filename within repository root.
+        filename = Path(rel).name
+        if repo_root and filename:
+            root = Path(repo_root).expanduser()
+            try:
+                if root.exists():
+                    for match in root.rglob(filename):
+                        if match.is_file():
+                            return str(match.resolve())
+            except Exception as exc:
+                logger.debug(f"Search for {filename} under {root} failed: {exc}")
+
+        return None

src/gui/tabs/results_tab.py

@@ -3,7 +3,7 @@ Results tab for browsing stored detections and visualizing overlays.
 """
 
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from PySide6.QtWidgets import (
     QWidget,
@@ -35,9 +35,7 @@ logger = get_logger(__name__)
 class ResultsTab(QWidget):
     """Results tab showing detection history and preview overlays."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
@@ -67,28 +65,32 @@ class ResultsTab(QWidget):
         self.refresh_btn = QPushButton("Refresh")
         self.refresh_btn.clicked.connect(self.refresh)
         controls_layout.addWidget(self.refresh_btn)
+
+        self.delete_all_btn = QPushButton("Delete All Detections")
+        self.delete_all_btn.setToolTip(
+            "Permanently delete ALL detections from the database.\n" "This cannot be undone."
+        )
+        self.delete_all_btn.clicked.connect(self._delete_all_detections)
+        controls_layout.addWidget(self.delete_all_btn)
+
+        self.export_labels_btn = QPushButton("Export Labels")
+        self.export_labels_btn.setToolTip(
+            "Export YOLO .txt labels for the selected image/model run.\n"
+            "Output path is inferred from the image path (images/ -> labels/)."
+        )
+        self.export_labels_btn.clicked.connect(self._export_labels_for_current_selection)
+        controls_layout.addWidget(self.export_labels_btn)
+
         controls_layout.addStretch()
         left_layout.addLayout(controls_layout)
 
         self.results_table = QTableWidget(0, 5)
-        self.results_table.setHorizontalHeaderLabels(
+        self.results_table.setHorizontalHeaderLabels(["Image", "Model", "Detections", "Classes", "Last Updated"])
-            ["Image", "Model", "Detections", "Classes", "Last Updated"]
+        self.results_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
-        )
+        self.results_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.Stretch)
-        self.results_table.horizontalHeader().setSectionResizeMode(
+        self.results_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeToContents)
-            0, QHeaderView.Stretch
+        self.results_table.horizontalHeader().setSectionResizeMode(3, QHeaderView.Stretch)
-        )
+        self.results_table.horizontalHeader().setSectionResizeMode(4, QHeaderView.ResizeToContents)
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            1, QHeaderView.Stretch
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            2, QHeaderView.ResizeToContents
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            3, QHeaderView.Stretch
-        )
-        self.results_table.horizontalHeader().setSectionResizeMode(
-            4, QHeaderView.ResizeToContents
-        )
         self.results_table.setSelectionBehavior(QAbstractItemView.SelectRows)
         self.results_table.setSelectionMode(QAbstractItemView.SingleSelection)
         self.results_table.setEditTriggers(QAbstractItemView.NoEditTriggers)
@@ -106,6 +108,8 @@ class ResultsTab(QWidget):
         preview_layout = QVBoxLayout()
 
         self.preview_canvas = AnnotationCanvasWidget()
+        # Auto-zoom so newly loaded images fill the available preview viewport.
+        self.preview_canvas.set_auto_fit_to_view(True)
         self.preview_canvas.set_polyline_enabled(False)
         self.preview_canvas.set_show_bboxes(True)
         preview_layout.addWidget(self.preview_canvas)
@@ -119,9 +123,7 @@ class ResultsTab(QWidget):
         self.show_bboxes_checkbox.stateChanged.connect(self._toggle_bboxes)
         self.show_confidence_checkbox = QCheckBox("Show Confidence")
         self.show_confidence_checkbox.setChecked(False)
-        self.show_confidence_checkbox.stateChanged.connect(
+        self.show_confidence_checkbox.stateChanged.connect(self._apply_detection_overlays)
-            self._apply_detection_overlays
-        )
         toggles_layout.addWidget(self.show_masks_checkbox)
         toggles_layout.addWidget(self.show_bboxes_checkbox)
         toggles_layout.addWidget(self.show_confidence_checkbox)
@@ -144,6 +146,41 @@ class ResultsTab(QWidget):
         layout.addWidget(splitter)
         self.setLayout(layout)
 
+    def _delete_all_detections(self):
+        """Delete all detections from the database after user confirmation."""
+        confirm = QMessageBox.warning(
+            self,
+            "Delete All Detections",
+            "This will permanently delete ALL detections from the database.\n\n"
+            "This action cannot be undone.\n\n"
+            "Do you want to continue?",
+            QMessageBox.Yes | QMessageBox.No,
+            QMessageBox.No,
+        )
+
+        if confirm != QMessageBox.Yes:
+            return
+
+        try:
+            deleted = self.db_manager.delete_all_detections()
+        except Exception as exc:
+            logger.error(f"Failed to delete all detections: {exc}")
+            QMessageBox.critical(
+                self,
+                "Error",
+                f"Failed to delete detections:\n{exc}",
+            )
+            return
+
+        QMessageBox.information(
+            self,
+            "Delete All Detections",
+            f"Deleted {deleted} detection(s) from the database.",
+        )
+
+        # Reset UI state.
+        self.refresh()
+
     def refresh(self):
         """Refresh the detection list and preview."""
         self._load_detection_summary()
@@ -153,6 +190,8 @@ class ResultsTab(QWidget):
         self.current_detections = []
         self.preview_canvas.clear()
         self.summary_label.setText("Select a detection result to preview.")
+        if hasattr(self, "export_labels_btn"):
+            self.export_labels_btn.setEnabled(False)
 
     def _load_detection_summary(self):
         """Load latest detection summaries grouped by image + model."""
@@ -169,8 +208,7 @@ class ResultsTab(QWidget):
                         "image_id": det["image_id"],
                         "model_id": det["model_id"],
                         "image_path": det.get("image_path"),
-                        "image_filename": det.get("image_filename")
+                        "image_filename": det.get("image_filename") or det.get("image_path"),
-                        or det.get("image_path"),
                         "model_name": det.get("model_name", ""),
                         "model_version": det.get("model_version", ""),
                         "last_detected": det.get("detected_at"),
@@ -183,8 +221,7 @@ class ResultsTab(QWidget):
 
                 entry["count"] += 1
                 if det.get("detected_at") and (
-                    not entry.get("last_detected")
+                    not entry.get("last_detected") or str(det.get("detected_at")) > str(entry.get("last_detected"))
-                    or str(det.get("detected_at")) > str(entry.get("last_detected"))
                 ):
                     entry["last_detected"] = det.get("detected_at")
                 if det.get("class_name"):
@@ -214,9 +251,7 @@ class ResultsTab(QWidget):
 
         for row, entry in enumerate(self.detection_summary):
             model_label = f"{entry['model_name']} {entry['model_version']}".strip()
-            class_list = (
+            class_list = ", ".join(sorted(entry["classes"])) if entry["classes"] else "-"
-                ", ".join(sorted(entry["classes"])) if entry["classes"] else "-"
-            )
 
             items = [
                 QTableWidgetItem(entry.get("image_filename", "")),
@@ -276,6 +311,231 @@ class ResultsTab(QWidget):
         self._load_detections_for_selection(entry)
         self._apply_detection_overlays()
         self._update_summary_label(entry)
+        if hasattr(self, "export_labels_btn"):
+            self.export_labels_btn.setEnabled(True)
+
+    def _export_labels_for_current_selection(self):
+        """Export YOLO label file(s) for the currently selected image/model."""
+        if not self.current_selection:
+            QMessageBox.information(self, "Export Labels", "Select a detection result first.")
+            return
+
+        entry = self.current_selection
+
+        image_path_str = self._resolve_image_path(entry)
+        if not image_path_str:
+            QMessageBox.warning(
+                self,
+                "Export Labels",
+                "Unable to locate the image file for this detection; cannot infer labels path.",
+            )
+            return
+
+        # Ensure we have the detections for the selection.
+        if not self.current_detections:
+            self._load_detections_for_selection(entry)
+
+        if not self.current_detections:
+            QMessageBox.information(
+                self,
+                "Export Labels",
+                "No detections found for this image/model selection.",
+            )
+            return
+
+        image_path = Path(image_path_str)
+        try:
+            label_path = self._infer_yolo_label_path(image_path)
+        except Exception as exc:
+            logger.error(f"Failed to infer label path for {image_path}: {exc}")
+            QMessageBox.critical(
+                self,
+                "Export Labels",
+                f"Failed to infer export path for labels:\n{exc}",
+            )
+            return
+
+        class_map = self._build_detection_class_index_map(self.current_detections)
+        if not class_map:
+            QMessageBox.warning(
+                self,
+                "Export Labels",
+                "Unable to build class->index mapping (missing class names).",
+            )
+            return
+
+        lines_written = 0
+        skipped = 0
+        label_path.parent.mkdir(parents=True, exist_ok=True)
+        try:
+            with open(label_path, "w", encoding="utf-8") as handle:
+                print("writing to", label_path)
+                for det in self.current_detections:
+                    yolo_line = self._format_detection_as_yolo_line(det, class_map)
+                    if not yolo_line:
+                        skipped += 1
+                        continue
+                    handle.write(yolo_line + "\n")
+                    lines_written += 1
+        except OSError as exc:
+            logger.error(f"Failed to write labels file {label_path}: {exc}")
+            QMessageBox.critical(
+                self,
+                "Export Labels",
+                f"Failed to write label file:\n{label_path}\n\n{exc}",
+            )
+            return
+
+        return
+        # Optional: write a classes.txt next to the labels root to make the mapping discoverable.
+        # This is not required by Ultralytics (data.yaml usually holds class names), but helps reuse.
+        try:
+            classes_txt = label_path.parent.parent / "classes.txt"
+            classes_txt.parent.mkdir(parents=True, exist_ok=True)
+            inv = {idx: name for name, idx in class_map.items()}
+            with open(classes_txt, "w", encoding="utf-8") as handle:
+                for idx in range(len(inv)):
+                    handle.write(f"{inv[idx]}\n")
+        except Exception:
+            # Non-fatal
+            pass
+
+        QMessageBox.information(
+            self,
+            "Export Labels",
+            f"Exported {lines_written} label line(s) to:\n{label_path}\n\nSkipped {skipped} invalid detection(s).",
+        )
+
+    def _infer_yolo_label_path(self, image_path: Path) -> Path:
+        """Infer a YOLO label path from an image path.
+
+        If the image lives under an `images/` directory (anywhere in the path), we mirror the
+        subpath under a sibling `labels/` directory at the same level.
+
+        Example:
+            /dataset/train/images/sub/img.jpg -> /dataset/train/labels/sub/img.txt
+        """
+
+        resolved = image_path.expanduser().resolve()
+
+        # Find the nearest ancestor directory named 'images'
+        images_dir: Optional[Path] = None
+        for parent in [resolved.parent, *resolved.parents]:
+            if parent.name.lower() == "images":
+                images_dir = parent
+                break
+
+        if images_dir is not None:
+            rel = resolved.relative_to(images_dir)
+            labels_dir = images_dir.parent / "labels"
+            return (labels_dir / rel).with_suffix(".txt")
+
+        # Fallback: create a local sibling labels folder next to the image.
+        return (resolved.parent / "labels" / resolved.name).with_suffix(".txt")
+
+    def _build_detection_class_index_map(self, detections: List[Dict]) -> Dict[str, int]:
+        """Build a stable class_name -> YOLO class index mapping.
+
+        Preference order:
+        1) Database object_classes table (alphabetical class_name order)
+        2) Fallback to class_name values present in the detections (alphabetical)
+        """
+
+        names: List[str] = []
+        try:
+            db_classes = self.db_manager.get_object_classes() or []
+            names = [str(row.get("class_name")) for row in db_classes if row.get("class_name")]
+        except Exception:
+            names = []
+
+        if not names:
+            observed = sorted({str(det.get("class_name")) for det in detections if det.get("class_name")})
+            names = list(observed)
+
+        return {name: idx for idx, name in enumerate(names)}
+
+    def _format_detection_as_yolo_line(self, det: Dict, class_map: Dict[str, int]) -> Optional[str]:
+        """Convert a detection row to a YOLO label line.
+
+        - If segmentation_mask is present, exports segmentation polygon format:
+            class x1 y1 x2 y2 ...
+          (normalized coordinates)
+        - Otherwise exports bbox format:
+            class x_center y_center width height
+          (normalized coordinates)
+        """
+
+        class_name = det.get("class_name")
+        if not class_name or class_name not in class_map:
+            return None
+        class_idx = class_map[class_name]
+
+        mask = det.get("segmentation_mask")
+        polygon = self._convert_segmentation_mask_to_polygon(mask)
+        if polygon:
+            coords = " ".join(f"{value:.6f}" for value in polygon)
+            return f"{class_idx} {coords}".strip()
+
+        bbox = self._convert_bbox_to_yolo_xywh(det)
+        if bbox is None:
+            return None
+        x_center, y_center, width, height = bbox
+        return f"{class_idx} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}"
+
+    def _convert_bbox_to_yolo_xywh(self, det: Dict) -> Optional[Tuple[float, float, float, float]]:
+        """Convert stored xyxy (normalized) bbox to YOLO xywh (normalized)."""
+
+        x_min = det.get("x_min")
+        y_min = det.get("y_min")
+        x_max = det.get("x_max")
+        y_max = det.get("y_max")
+        if any(v is None for v in (x_min, y_min, x_max, y_max)):
+            return None
+
+        try:
+            x_min_f = self._clamp01(float(x_min))
+            y_min_f = self._clamp01(float(y_min))
+            x_max_f = self._clamp01(float(x_max))
+            y_max_f = self._clamp01(float(y_max))
+        except (TypeError, ValueError):
+            return None
+
+        width = max(0.0, x_max_f - x_min_f)
+        height = max(0.0, y_max_f - y_min_f)
+        if width <= 0.0 or height <= 0.0:
+            return None
+
+        x_center = x_min_f + width / 2.0
+        y_center = y_min_f + height / 2.0
+        return x_center, y_center, width, height
+
+    def _convert_segmentation_mask_to_polygon(self, mask_data) -> List[float]:
+        """Convert stored segmentation_mask [[x,y], ...] to YOLO polygon coords [x1,y1,...]."""
+
+        if not isinstance(mask_data, list):
+            return []
+
+        coords: List[float] = []
+        for point in mask_data:
+            if not isinstance(point, (list, tuple)) or len(point) < 2:
+                continue
+            try:
+                x = self._clamp01(float(point[0]))
+                y = self._clamp01(float(point[1]))
+            except (TypeError, ValueError):
+                continue
+            coords.extend([x, y])
+
+        # Need at least 3 points => 6 values.
+        return coords if len(coords) >= 6 else []
+
+    @staticmethod
+    def _clamp01(value: float) -> float:
+        if value < 0.0:
+            return 0.0
+        if value > 1.0:
+            return 1.0
+        return value
 
     def _load_detections_for_selection(self, entry: Dict):
         """Load detection records for the selected image/model pair."""

src/gui/tabs/training_tab.py

@@ -3,12 +3,14 @@ Training tab for the microscopy object detection application.
 Handles model training with YOLO.
 """
 
+import hashlib
+import shutil
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
-import numpy as np
 import yaml
+import numpy as np
 from PySide6.QtCore import Qt, QThread, Signal
 from PySide6.QtWidgets import (
     QWidget,
@@ -90,10 +92,7 @@ class TrainingWorker(QThread):
                 },
             }
         ]
-        computed_total = sum(
+        computed_total = sum(max(0, int((stage.get("params") or {}).get("epochs", 0))) for stage in self.stage_plan)
-            max(0, int((stage.get("params") or {}).get("epochs", 0)))
-            for stage in self.stage_plan
-        )
         self.total_epochs = total_epochs if total_epochs else computed_total or epochs
         self._stop_requested = False
 
@@ -200,9 +199,7 @@ class TrainingWorker(QThread):
 class TrainingTab(QWidget):
     """Training tab for model training."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
@@ -336,18 +333,14 @@ class TrainingTab(QWidget):
         self.model_version_edit = QLineEdit("v1")
         form_layout.addRow("Version:", self.model_version_edit)
 
-        default_base_model = self.config_manager.get(
+        default_base_model = self.config_manager.get("models.default_base_model", "yolov8s-seg.pt")
-            "models.default_base_model", "yolov8s-seg.pt"
-        )
         base_model_choices = self.config_manager.get("models.base_model_choices", [])
 
         self.base_model_combo = QComboBox()
         self.base_model_combo.addItem("Custom path…", "")
         for choice in base_model_choices:
             self.base_model_combo.addItem(choice, choice)
-        self.base_model_combo.currentIndexChanged.connect(
+        self.base_model_combo.currentIndexChanged.connect(self._on_base_model_preset_changed)
-            self._on_base_model_preset_changed
-        )
         form_layout.addRow("Base Model Preset:", self.base_model_combo)
 
         base_model_layout = QHBoxLayout()
@@ -433,12 +426,8 @@ class TrainingTab(QWidget):
         group_layout = QVBoxLayout()
 
         self.two_stage_checkbox = QCheckBox("Enable staged head-only + full fine-tune")
-        two_stage_defaults = (
+        two_stage_defaults = training_defaults.get("two_stage", {}) if training_defaults else {}
-            training_defaults.get("two_stage", {}) if training_defaults else {}
+        self.two_stage_checkbox.setChecked(bool(two_stage_defaults.get("enabled", False)))
-        )
-        self.two_stage_checkbox.setChecked(
-            bool(two_stage_defaults.get("enabled", False))
-        )
         self.two_stage_checkbox.toggled.connect(self._on_two_stage_toggled)
         group_layout.addWidget(self.two_stage_checkbox)
 
@@ -500,9 +489,7 @@ class TrainingTab(QWidget):
         stage2_group.setLayout(stage2_form)
         controls_layout.addWidget(stage2_group)
 
-        helper_label = QLabel(
+        helper_label = QLabel("When enabled, staged hyperparameters override the global epochs/patience/lr.")
-            "When enabled, staged hyperparameters override the global epochs/patience/lr."
-        )
         helper_label.setWordWrap(True)
         controls_layout.addWidget(helper_label)
 
@@ -547,9 +534,7 @@ class TrainingTab(QWidget):
             if normalized == preset_value:
                 target_index = idx
                 break
-            if normalized.endswith(f"/{preset_value}") or normalized.endswith(
+            if normalized.endswith(f"/{preset_value}") or normalized.endswith(f"\\{preset_value}"):
-                f"\\{preset_value}"
-            ):
                 target_index = idx
                 break
         self.base_model_combo.blockSignals(True)
@@ -637,9 +622,7 @@ class TrainingTab(QWidget):
 
     def _browse_dataset(self):
         """Open a file dialog to manually select data.yaml."""
-        start_dir = self.config_manager.get(
+        start_dir = self.config_manager.get("training.last_dataset_dir", "data/datasets")
-            "training.last_dataset_dir", "data/datasets"
-        )
         start_path = Path(start_dir).expanduser()
         if not start_path.exists():
             start_path = Path.cwd()
@@ -675,9 +658,7 @@ class TrainingTab(QWidget):
             return
         except Exception as exc:
             logger.exception("Unexpected error while generating data.yaml")
-            self._display_dataset_error(
+            self._display_dataset_error("Unexpected error while generating data.yaml. Check logs for details.")
-                "Unexpected error while generating data.yaml. Check logs for details."
-            )
             QMessageBox.critical(
                 self,
                 "data.yaml Generation Failed",
@@ -754,13 +735,9 @@ class TrainingTab(QWidget):
         self.selected_dataset = info
 
         self.dataset_root_label.setText(info["root"])  # type: ignore[arg-type]
-        self.train_count_label.setText(
+        self.train_count_label.setText(self._format_split_info(info["splits"].get("train")))
-            self._format_split_info(info["splits"].get("train"))
-        )
         self.val_count_label.setText(self._format_split_info(info["splits"].get("val")))
-        self.test_count_label.setText(
+        self.test_count_label.setText(self._format_split_info(info["splits"].get("test")))
-            self._format_split_info(info["splits"].get("test"))
-        )
         self.num_classes_label.setText(str(info["num_classes"]))
         class_names = ", ".join(info["class_names"]) or "–"
         self.class_names_label.setText(class_names)
@@ -814,18 +791,12 @@ class TrainingTab(QWidget):
                 if split_path.exists():
                     split_info["count"] = self._count_images(split_path)
                     if split_info["count"] == 0:
-                        warnings.append(
+                        warnings.append(f"No images found for {split_name} split at {split_path}")
-                            f"No images found for {split_name} split at {split_path}"
-                        )
                 else:
-                    warnings.append(
+                    warnings.append(f"{split_name.capitalize()} path does not exist: {split_path}")
-                        f"{split_name.capitalize()} path does not exist: {split_path}"
-                    )
             else:
                 if split_name in ("train", "val"):
-                    warnings.append(
+                    warnings.append(f"{split_name.capitalize()} split missing in data.yaml")
-                        f"{split_name.capitalize()} split missing in data.yaml"
-                    )
             splits[split_name] = split_info
 
         names_list = self._normalize_class_names(data.get("names"))
@@ -843,9 +814,7 @@ class TrainingTab(QWidget):
         if not names_list and nc_value:
             names_list = [f"class_{idx}" for idx in range(int(nc_value))]
         elif nc_value and len(names_list) not in (0, int(nc_value)):
-            warnings.append(
+            warnings.append(f"Number of class names ({len(names_list)}) does not match nc={nc_value}")
-                f"Number of class names ({len(names_list)}) does not match nc={nc_value}"
-            )
 
         dataset_name = data.get("name") or base_path.name
 
@@ -897,16 +866,12 @@ class TrainingTab(QWidget):
 
         class_index_map = self._build_class_index_map(dataset_info)
         if not class_index_map:
-            self._append_training_log(
+            self._append_training_log("Skipping label export: dataset classes do not match database entries.")
-                "Skipping label export: dataset classes do not match database entries."
-            )
             return
 
         dataset_root_str = dataset_info.get("root")
         dataset_yaml_path = dataset_info.get("yaml_path")
-        dataset_yaml = (
+        dataset_yaml = Path(dataset_yaml_path).expanduser() if dataset_yaml_path else None
-            Path(dataset_yaml_path).expanduser() if dataset_yaml_path else None
-        )
         dataset_root: Optional[Path]
         if dataset_root_str:
             dataset_root = Path(dataset_root_str).resolve()
@@ -940,12 +905,17 @@ class TrainingTab(QWidget):
             if stats["registered_images"]:
                 message += f" {stats['registered_images']} image(s) had database-backed annotations."
             if stats["missing_records"]:
-                message += f" {stats['missing_records']} image(s) had no database entry; empty label files were written."
+                message += (
+                    f" {stats['missing_records']} image(s) had no database entry; empty label files were written."
+                )
             split_messages.append(message)
 
         for msg in split_messages:
             self._append_training_log(msg)
 
+        if dataset_yaml:
+            self._clear_rgb_cache_for_dataset(dataset_yaml)
+
     def _export_labels_for_split(
         self,
         split_name: str,
@@ -969,9 +939,7 @@ class TrainingTab(QWidget):
                 continue
 
             processed_images += 1
-            label_path = (labels_dir / image_file.relative_to(images_dir)).with_suffix(
+            label_path = (labels_dir / image_file.relative_to(images_dir)).with_suffix(".txt")
-                ".txt"
-            )
             label_path.parent.mkdir(parents=True, exist_ok=True)
 
             found, annotation_entries = self._fetch_annotations_for_image(
@@ -987,25 +955,23 @@ class TrainingTab(QWidget):
                 for entry in annotation_entries:
                     polygon = entry.get("polygon") or []
                     if polygon:
+                        print(image_file, polygon[:4], polygon[-2:], entry.get("bbox"))
+                        # coords = " ".join(f"{value:.6f}" for value in entry.get("bbox"))
+                        # coords += " "
                         coords = " ".join(f"{value:.6f}" for value in polygon)
                         handle.write(f"{entry['class_idx']} {coords}\n")
                         annotations_written += 1
                     elif entry.get("bbox"):
                         x_center, y_center, width, height = entry["bbox"]
-                        handle.write(
+                        handle.write(f"{entry['class_idx']} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n")
-                            f"{entry['class_idx']} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}\n"
-                        )
                         annotations_written += 1
-
             total_annotations += annotations_written
 
         cache_reset_root = labels_dir.parent
         self._invalidate_split_cache(cache_reset_root)
 
         if processed_images == 0:
-            self._append_training_log(
+            self._append_training_log(f"[{split_name}] No images found to export labels for.")
-                f"[{split_name}] No images found to export labels for."
-            )
             return None
 
         return {
@@ -1131,6 +1097,10 @@ class TrainingTab(QWidget):
                 xs.append(x_val)
                 ys.append(y_val)
 
+            if any(np.abs(np.array(coords[:2]) - np.array(coords[-2:])) < 1e-5):
+                print("Closing polygon")
+                coords.extend(coords[:2])
+
             if len(coords) < 6:
                 continue
 
@@ -1143,6 +1113,11 @@ class TrainingTab(QWidget):
                     + abs((min(ys) if ys else 0.0) - y_min)
                     + abs((max(ys) if ys else 0.0) - y_max)
                 )
+                width = max(0.0, x_max - x_min)
+                height = max(0.0, y_max - y_min)
+                x_center = x_min + width / 2.0
+                y_center = y_min + height / 2.0
+                score = (x_center, y_center, width, height)
 
             candidates.append((score, coords))
 
@@ -1160,6 +1135,35 @@ class TrainingTab(QWidget):
             return 1.0
         return value
 
+    def _prepare_dataset_for_training(self, dataset_yaml: Path, dataset_info: Optional[Dict[str, Any]] = None) -> Path:
+        dataset_info = dataset_info or (
+            self.selected_dataset
+            if self.selected_dataset and self.selected_dataset.get("yaml_path") == str(dataset_yaml)
+            else self._parse_dataset_yaml(dataset_yaml)
+        )
+
+        train_split = dataset_info.get("splits", {}).get("train") or {}
+        images_path_str = train_split.get("path")
+        if not images_path_str:
+            return dataset_yaml
+
+        images_path = Path(images_path_str)
+        if not images_path.exists():
+            return dataset_yaml
+
+        if not self._dataset_requires_rgb_conversion(images_path):
+            return dataset_yaml
+
+        cache_root = self._get_rgb_cache_root(dataset_yaml)
+        rgb_yaml = cache_root / "data.yaml"
+        if rgb_yaml.exists():
+            self._append_training_log(f"Detected grayscale dataset; reusing RGB cache at {cache_root}")
+            return rgb_yaml
+
+        self._append_training_log(f"Detected grayscale dataset; creating RGB cache at {cache_root}")
+        self._build_rgb_dataset(cache_root, dataset_info)
+        return rgb_yaml
+
     def _compose_stage_plan(self, params: Dict[str, Any]) -> List[Dict[str, Any]]:
         two_stage = params.get("two_stage") or {}
         base_stage = {
@@ -1244,6 +1248,113 @@ class TrainingTab(QWidget):
                 f"    • {stage_label}: epochs={epochs}, lr0={lr0}, patience={patience}, freeze={freeze}"
             )
 
+    def _get_rgb_cache_root(self, dataset_yaml: Path) -> Path:
+        cache_base = Path("data/datasets/_rgb_cache")
+        cache_base.mkdir(parents=True, exist_ok=True)
+        key = hashlib.md5(str(dataset_yaml.parent.resolve()).encode()).hexdigest()[:8]
+        return cache_base / f"{dataset_yaml.parent.name}_{key}"
+
+    def _clear_rgb_cache_for_dataset(self, dataset_yaml: Path):
+        cache_root = self._get_rgb_cache_root(dataset_yaml)
+        if cache_root.exists():
+            try:
+                shutil.rmtree(cache_root)
+                logger.debug(f"Removed RGB cache at {cache_root}")
+            except OSError as exc:
+                logger.warning(f"Failed to remove RGB cache {cache_root}: {exc}")
+
+    def _dataset_requires_rgb_conversion(self, images_dir: Path) -> bool:
+        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"
+        except Exception as exc:
+            logger.warning(f"Failed to inspect image {sample_image}: {exc}")
+            return False
+
+    def _find_first_image(self, directory: Path) -> Optional[Path]:
+        if not directory.exists():
+            return None
+        for path in directory.rglob("*"):
+            if path.is_file() and path.suffix.lower() in self.allowed_extensions:
+                return path
+        return None
+
+    def _build_rgb_dataset(self, cache_root: Path, dataset_info: Dict[str, Any]):
+        if cache_root.exists():
+            shutil.rmtree(cache_root)
+        cache_root.mkdir(parents=True, exist_ok=True)
+
+        splits = dataset_info.get("splits", {})
+        for split_name in ("train", "val", "test"):
+            split_entry = splits.get(split_name)
+            if not split_entry:
+                continue
+            images_src = Path(split_entry.get("path", ""))
+            if not images_src.exists():
+                continue
+            images_dst = cache_root / split_name / "images"
+            self._convert_images_to_rgb(images_src, images_dst)
+
+            labels_src = self._infer_labels_dir(images_src)
+            if labels_src.exists():
+                labels_dst = cache_root / split_name / "labels"
+                self._copy_labels(labels_src, labels_dst)
+
+        class_names = dataset_info.get("class_names") or []
+        names_map = {idx: name for idx, name in enumerate(class_names)}
+        num_classes = dataset_info.get("num_classes") or len(class_names)
+
+        yaml_payload: Dict[str, Any] = {
+            "path": cache_root.as_posix(),
+            "names": names_map,
+            "nc": num_classes,
+        }
+
+        for split_name in ("train", "val", "test"):
+            images_dir = cache_root / split_name / "images"
+            if images_dir.exists():
+                yaml_payload[split_name] = f"{split_name}/images"
+
+        with open(cache_root / "data.yaml", "w", encoding="utf-8") as handle:
+            yaml.safe_dump(yaml_payload, handle, sort_keys=False)
+
+    def _convert_images_to_rgb(self, src_dir: Path, dst_dir: Path):
+        for src in src_dir.rglob("*"):
+            if not src.is_file() or src.suffix.lower() not in self.allowed_extensions:
+                continue
+            relative = src.relative_to(src_dir)
+            dst = dst_dir / relative
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            try:
+                img_obj = Image(src)
+                pil_img = img_obj.pil_image
+                if len(pil_img.getbands()) == 1:
+                    rgb_img = img_obj.convert_grayscale_to_rgb_preserve_range()
+                else:
+                    rgb_img = pil_img.convert("RGB")
+                rgb_img.save(dst)
+            except Exception as exc:
+                logger.warning(f"Failed to convert {src} to RGB: {exc}")
+
+    def _copy_labels(self, labels_src: Path, labels_dst: Path):
+        label_files = list(labels_src.rglob("*.txt"))
+        for label_file in label_files:
+            relative = label_file.relative_to(labels_src)
+            dst = labels_dst / relative
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copy2(label_file, dst)
+
     def _infer_labels_dir(self, images_dir: Path) -> Path:
         return images_dir.parent / "labels"
 
@@ -1316,31 +1427,26 @@ class TrainingTab(QWidget):
 
         dataset_path = Path(dataset_yaml).expanduser()
         if not dataset_path.exists():
-            QMessageBox.warning(
+            QMessageBox.warning(self, "Invalid Dataset", "Selected data.yaml file does not exist.")
-                self, "Invalid Dataset", "Selected data.yaml file does not exist."
-            )
             return
 
         dataset_info = (
             self.selected_dataset
-            if self.selected_dataset
+            if self.selected_dataset and self.selected_dataset.get("yaml_path") == str(dataset_path)
-            and self.selected_dataset.get("yaml_path") == str(dataset_path)
             else self._parse_dataset_yaml(dataset_path)
         )
 
         self.training_log.clear()
         self._export_labels_from_database(dataset_info)
 
-        self._append_training_log(
+        dataset_to_use = self._prepare_dataset_for_training(dataset_path, dataset_info)
-            "Using Float32 on-the-fly loader for 16-bit TIFF support (no disk caching)"
+        if dataset_to_use != dataset_path:
-        )
+            self._append_training_log(f"Using RGB-converted dataset at {dataset_to_use.parent}")
 
         params = self._collect_training_params()
         stage_plan = self._compose_stage_plan(params)
         params["stage_plan"] = stage_plan
-        total_planned_epochs = (
+        total_planned_epochs = self._calculate_total_stage_epochs(stage_plan) or params["epochs"]
-            self._calculate_total_stage_epochs(stage_plan) or params["epochs"]
-        )
         params["total_planned_epochs"] = total_planned_epochs
         self._active_training_params = params
         self._training_cancelled = False
@@ -1349,9 +1455,7 @@ class TrainingTab(QWidget):
             self._append_training_log("Two-stage fine-tuning schedule:")
             self._log_stage_plan(stage_plan)
 
-        self._append_training_log(
+        self._append_training_log(f"Starting training run '{params['run_name']}' using {params['base_model']}")
-            f"Starting training run '{params['run_name']}' using {params['base_model']}"
-        )
 
         self.training_progress_bar.setVisible(True)
         self.training_progress_bar.setMaximum(max(1, total_planned_epochs))
@@ -1359,7 +1463,7 @@ class TrainingTab(QWidget):
         self._set_training_state(True)
 
         self.training_worker = TrainingWorker(
-            data_yaml=dataset_path.as_posix(),
+            data_yaml=dataset_to_use.as_posix(),
             base_model=params["base_model"],
             epochs=params["epochs"],
             batch=params["batch"],
@@ -1379,9 +1483,7 @@ class TrainingTab(QWidget):
     def _stop_training(self):
         if self.training_worker and self.training_worker.isRunning():
             self._training_cancelled = True
-            self._append_training_log(
+            self._append_training_log("Stop requested. Waiting for the current epoch to finish...")
-                "Stop requested. Waiting for the current epoch to finish..."
-            )
             self.training_worker.stop()
             self.stop_training_button.setEnabled(False)
 
@@ -1417,9 +1519,7 @@ class TrainingTab(QWidget):
 
         if worker.isRunning():
             if not worker.wait(wait_timeout_ms):
-                logger.warning(
+                logger.warning("Training worker did not finish within %sms", wait_timeout_ms)
-                    "Training worker did not finish within %sms", wait_timeout_ms
-                )
 
         worker.deleteLater()
 
@@ -1436,16 +1536,12 @@ class TrainingTab(QWidget):
         self._set_training_state(False)
         self.training_progress_bar.setVisible(False)
 
-    def _on_training_progress(
+    def _on_training_progress(self, current_epoch: int, total_epochs: int, metrics: Dict[str, Any]):
-        self, current_epoch: int, total_epochs: int, metrics: Dict[str, Any]
-    ):
         self.training_progress_bar.setMaximum(total_epochs)
         self.training_progress_bar.setValue(current_epoch)
         parts = [f"Epoch {current_epoch}/{total_epochs}"]
         if metrics:
-            metric_text = ", ".join(
+            metric_text = ", ".join(f"{key}: {value:.4f}" for key, value in metrics.items())
-                f"{key}: {value:.4f}" for key, value in metrics.items()
-            )
             parts.append(metric_text)
         self._append_training_log(" | ".join(parts))
 
@@ -1472,9 +1568,7 @@ class TrainingTab(QWidget):
                 f"Model trained but not registered: {exc}",
             )
         else:
-            QMessageBox.information(
+            QMessageBox.information(self, "Training Complete", "Training finished successfully.")
-                self, "Training Complete", "Training finished successfully."
-            )
 
     def _on_training_error(self, message: str):
         self._cleanup_training_worker()
@@ -1520,9 +1614,7 @@ class TrainingTab(QWidget):
             metrics=results.get("metrics"),
         )
 
-        self._append_training_log(
+        self._append_training_log(f"Registered model '{params['model_name']}' (ID {model_id}) at {model_path}")
-            f"Registered model '{params['model_name']}' (ID {model_id}) at {model_path}"
-        )
         self._active_training_params = None
 
     def _set_training_state(self, is_training: bool):
@@ -1565,9 +1657,7 @@ class TrainingTab(QWidget):
 
     def _browse_save_dir(self):
         start_path = self.save_dir_edit.text().strip() or "data/models"
-        directory = QFileDialog.getExistingDirectory(
+        directory = QFileDialog.getExistingDirectory(self, "Select Save Directory", start_path)
-            self, "Select Save Directory", start_path
-        )
         if directory:
             self.save_dir_edit.setText(directory)
 

src/gui/tabs/validation_tab.py

@@ -2,45 +2,554 @@
 Validation tab for the microscopy object detection application.
 """
 
-from PySide6.QtWidgets import QWidget, QVBoxLayout, QLabel, QGroupBox
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+from PySide6.QtCore import Qt, QSize
+from PySide6.QtGui import QPainter, QPixmap
+from PySide6.QtWidgets import (
+    QWidget,
+    QVBoxLayout,
+    QLabel,
+    QGroupBox,
+    QHBoxLayout,
+    QPushButton,
+    QComboBox,
+    QFormLayout,
+    QScrollArea,
+    QGridLayout,
+    QFrame,
+    QTableWidget,
+    QTableWidgetItem,
+    QHeaderView,
+    QSplitter,
+    QListWidget,
+    QListWidgetItem,
+    QAbstractItemView,
+    QGraphicsView,
+    QGraphicsScene,
+    QGraphicsPixmapItem,
+)
 
 from src.database.db_manager import DatabaseManager
 from src.utils.config_manager import ConfigManager
+from src.utils.logger import get_logger
+
+
+logger = get_logger(__name__)
+
+
+@dataclass(frozen=True)
+class _PlotItem:
+    label: str
+    path: Path
+
+
+class _ZoomableImageView(QGraphicsView):
+    """Zoomable image viewer.
+
+    - Mouse wheel: zoom in/out
+    - Left mouse drag: pan (ScrollHandDrag)
+    """
+
+    def __init__(self, parent: Optional[QWidget] = None):
+        super().__init__(parent)
+        self._scene = QGraphicsScene(self)
+        self.setScene(self._scene)
+        self._pixmap_item = QGraphicsPixmapItem()
+        self._scene.addItem(self._pixmap_item)
+
+        # QGraphicsView render hints are QPainter.RenderHints.
+        self.setRenderHints(self.renderHints() | QPainter.RenderHint.SmoothPixmapTransform)
+        self.setDragMode(QGraphicsView.DragMode.ScrollHandDrag)
+        self.setTransformationAnchor(QGraphicsView.ViewportAnchor.AnchorUnderMouse)
+        self.setResizeAnchor(QGraphicsView.ViewportAnchor.AnchorUnderMouse)
+
+        self._has_pixmap = False
+
+    def clear(self) -> None:
+        self._pixmap_item.setPixmap(QPixmap())
+        self._scene.setSceneRect(0, 0, 1, 1)
+        self.resetTransform()
+        self._has_pixmap = False
+
+    def set_pixmap(self, pixmap: QPixmap, *, fit: bool = True) -> None:
+        self._pixmap_item.setPixmap(pixmap)
+        self._scene.setSceneRect(pixmap.rect())
+        self._has_pixmap = not pixmap.isNull()
+        self.resetTransform()
+        if fit and self._has_pixmap:
+            self.fitInView(self._pixmap_item, Qt.AspectRatioMode.KeepAspectRatio)
+
+    def wheelEvent(self, event) -> None:  # type: ignore[override]
+        if not self._has_pixmap:
+            return
+        zoom_in_factor = 1.25
+        zoom_out_factor = 1.0 / zoom_in_factor
+        factor = zoom_in_factor if event.angleDelta().y() > 0 else zoom_out_factor
+        self.scale(factor, factor)
 
 
 class ValidationTab(QWidget):
-    """Validation tab placeholder."""
+    """Validation tab that shows stored validation metrics + plots for a selected model."""
 
-    def __init__(
+    def __init__(self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None):
-        self, db_manager: DatabaseManager, config_manager: ConfigManager, parent=None
-    ):
         super().__init__(parent)
         self.db_manager = db_manager
         self.config_manager = config_manager
 
+        self._models: List[Dict[str, Any]] = []
+        self._selected_model_id: Optional[int] = None
+        self._plot_widgets: List[QWidget] = []
+        self._plot_items: List[_PlotItem] = []
+
         self._setup_ui()
+        self.refresh()
 
     def _setup_ui(self):
         """Setup user interface."""
-        layout = QVBoxLayout()
+        layout = QVBoxLayout(self)
 
-        group = QGroupBox("Validation")
+        # ===== Header controls =====
-        group_layout = QVBoxLayout()
+        header = QGroupBox("Validation")
-        label = QLabel(
+        header_layout = QVBoxLayout()
-            "Validation functionality will be implemented here.\n\n"
+        header_row = QHBoxLayout()
-            "Features:\n"
-            "- Model validation\n"
-            "- Metrics visualization\n"
-            "- Confusion matrix\n"
-            "- Precision-Recall curves"
-        )
-        group_layout.addWidget(label)
-        group.setLayout(group_layout)
 
-        layout.addWidget(group)
+        header_row.addWidget(QLabel("Select model:"))
-        layout.addStretch()
+
-        self.setLayout(layout)
+        self.model_combo = QComboBox()
+        self.model_combo.setMinimumWidth(420)
+        self.model_combo.currentIndexChanged.connect(self._on_model_selected)
+        header_row.addWidget(self.model_combo, 1)
+
+        self.refresh_btn = QPushButton("Refresh")
+        self.refresh_btn.clicked.connect(self.refresh)
+        header_row.addWidget(self.refresh_btn)
+        header_row.addStretch()
+
+        header_layout.addLayout(header_row)
+        self.header_status = QLabel("No models loaded.")
+        self.header_status.setWordWrap(True)
+        header_layout.addWidget(self.header_status)
+        header.setLayout(header_layout)
+        layout.addWidget(header)
+
+        # ===== Metrics =====
+        metrics_group = QGroupBox("Validation Metrics")
+        metrics_layout = QVBoxLayout()
+
+        self.metrics_form = QFormLayout()
+        self.metric_labels: Dict[str, QLabel] = {}
+        for key in ("mAP50", "mAP50-95", "precision", "recall", "fitness"):
+            value_label = QLabel("–")
+            value_label.setTextInteractionFlags(Qt.TextSelectableByMouse)
+            self.metric_labels[key] = value_label
+            self.metrics_form.addRow(f"{key}:", value_label)
+        metrics_layout.addLayout(self.metrics_form)
+
+        self.per_class_table = QTableWidget(0, 3)
+        self.per_class_table.setHorizontalHeaderLabels(["Class", "AP", "AP50"])
+        self.per_class_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)
+        self.per_class_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)
+        self.per_class_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeToContents)
+        self.per_class_table.setEditTriggers(QTableWidget.NoEditTriggers)
+        self.per_class_table.setMinimumHeight(160)
+        metrics_layout.addWidget(QLabel("Per-class metrics (if available):"))
+        metrics_layout.addWidget(self.per_class_table)
+
+        metrics_group.setLayout(metrics_layout)
+        layout.addWidget(metrics_group)
+
+        # ===== Plots =====
+        plots_group = QGroupBox("Validation Plots")
+        plots_layout = QVBoxLayout()
+
+        self.plots_status = QLabel("Select a model to see validation plots.")
+        self.plots_status.setWordWrap(True)
+        plots_layout.addWidget(self.plots_status)
+
+        self.plots_splitter = QSplitter(Qt.Orientation.Horizontal)
+
+        # Left: selected image viewer
+        left_widget = QWidget()
+        left_layout = QVBoxLayout(left_widget)
+        left_layout.setContentsMargins(0, 0, 0, 0)
+
+        self.selected_plot_title = QLabel("No image selected.")
+        self.selected_plot_title.setWordWrap(True)
+        self.selected_plot_title.setTextInteractionFlags(Qt.TextSelectableByMouse)
+        left_layout.addWidget(self.selected_plot_title)
+
+        self.plot_view = _ZoomableImageView()
+        self.plot_view.setMinimumHeight(360)
+        left_layout.addWidget(self.plot_view, 1)
+
+        self.selected_plot_path = QLabel("")
+        self.selected_plot_path.setWordWrap(True)
+        self.selected_plot_path.setStyleSheet("color: #888;")
+        self.selected_plot_path.setTextInteractionFlags(Qt.TextSelectableByMouse)
+        left_layout.addWidget(self.selected_plot_path)
+
+        # Right: scrollable list
+        right_widget = QWidget()
+        right_layout = QVBoxLayout(right_widget)
+        right_layout.setContentsMargins(0, 0, 0, 0)
+        right_layout.addWidget(QLabel("Images:"))
+
+        self.plots_list = QListWidget()
+        self.plots_list.setSelectionMode(QAbstractItemView.SelectionMode.SingleSelection)
+        self.plots_list.setIconSize(QSize(160, 160))
+        self.plots_list.itemSelectionChanged.connect(self._on_plot_item_selected)
+        right_layout.addWidget(self.plots_list, 1)
+
+        self.plots_splitter.addWidget(left_widget)
+        self.plots_splitter.addWidget(right_widget)
+        self.plots_splitter.setStretchFactor(0, 3)
+        self.plots_splitter.setStretchFactor(1, 1)
+        plots_layout.addWidget(self.plots_splitter, 1)
+
+        plots_group.setLayout(plots_layout)
+        layout.addWidget(plots_group, 1)
+
+        layout.addStretch(0)
+
+        self._clear_metrics()
+        self._clear_plots()
+
+    # ==================== Public API ====================
 
     def refresh(self):
         """Refresh the tab."""
+        self._load_models()
+        self._populate_model_combo()
+        self._restore_or_select_default_model()
+
+    # ==================== Internal: models ====================
+
+    def _load_models(self) -> None:
+        try:
+            self._models = self.db_manager.get_models() or []
+        except Exception as exc:
+            logger.error("Failed to load models: %s", exc)
+            self._models = []
+
+    def _populate_model_combo(self) -> None:
+        self.model_combo.blockSignals(True)
+        self.model_combo.clear()
+        self.model_combo.addItem("Select a model…", None)
+
+        for model in self._models:
+            model_id = model.get("id")
+            name = (model.get("model_name") or "").strip()
+            version = (model.get("model_version") or "").strip()
+            created_at = model.get("created_at")
+            label = f"{name} {version}".strip()
+            if created_at:
+                label = f"{label}  ({created_at})"
+            self.model_combo.addItem(label, model_id)
+
+        self.model_combo.blockSignals(False)
+
+        if self._models:
+            self.header_status.setText(f"Loaded {len(self._models)} model(s).")
+        else:
+            self.header_status.setText("No models found. Train a model first.")
+
+    def _restore_or_select_default_model(self) -> None:
+        if not self._models:
+            self._selected_model_id = None
+            self._clear_metrics()
+            self._clear_plots()
+            return
+
+        # Keep selection if still present.
+        if self._selected_model_id is not None:
+            for idx in range(1, self.model_combo.count()):
+                if self.model_combo.itemData(idx) == self._selected_model_id:
+                    self.model_combo.setCurrentIndex(idx)
+                    return
+
+        # Otherwise select the newest model (top of get_models ORDER BY created_at DESC).
+        first_model_id = self.model_combo.itemData(1) if self.model_combo.count() > 1 else None
+        if first_model_id is not None:
+            self.model_combo.setCurrentIndex(1)
+
+    def _on_model_selected(self, index: int) -> None:
+        model_id = self.model_combo.itemData(index)
+        if not model_id:
+            self._selected_model_id = None
+            self._clear_metrics()
+            self._clear_plots()
+            self.plots_status.setText("Select a model to see validation plots.")
+            return
+
+        self._selected_model_id = int(model_id)
+        model = self._get_model_by_id(self._selected_model_id)
+        if not model:
+            self._clear_metrics()
+            self._clear_plots()
+            self.plots_status.setText("Selected model not found.")
+            return
+
+        self._render_metrics(model)
+        self._render_plots(model)
+
+    def _get_model_by_id(self, model_id: int) -> Optional[Dict[str, Any]]:
+        for model in self._models:
+            if model.get("id") == model_id:
+                return model
+        try:
+            return self.db_manager.get_model_by_id(model_id)
+        except Exception:
+            return None
+
+    # ==================== Internal: metrics ====================
+
+    def _clear_metrics(self) -> None:
+        for label in self.metric_labels.values():
+            label.setText("–")
+        self.per_class_table.setRowCount(0)
+
+    def _render_metrics(self, model: Dict[str, Any]) -> None:
+        self._clear_metrics()
+
+        metrics: Dict[str, Any] = model.get("metrics") or {}
+        # Training tab stores metrics under results['metrics'] in training results payload.
+        if isinstance(metrics, dict) and "metrics" in metrics and isinstance(metrics.get("metrics"), dict):
+            metrics = metrics.get("metrics") or {}
+
+        def set_metric(key: str, value: Any) -> None:
+            if key not in self.metric_labels:
+                return
+            if value is None:
+                self.metric_labels[key].setText("–")
+                return
+            try:
+                self.metric_labels[key].setText(f"{float(value):.4f}")
+            except Exception:
+                self.metric_labels[key].setText(str(value))
+
+        set_metric("mAP50", metrics.get("mAP50"))
+        set_metric("mAP50-95", metrics.get("mAP50-95") or metrics.get("mAP50_95") or metrics.get("mAP50-95"))
+        set_metric("precision", metrics.get("precision"))
+        set_metric("recall", metrics.get("recall"))
+        set_metric("fitness", metrics.get("fitness"))
+
+        # Optional per-class metrics
+        class_metrics = metrics.get("class_metrics") if isinstance(metrics, dict) else None
+        if isinstance(class_metrics, dict) and class_metrics:
+            items = sorted(class_metrics.items(), key=lambda kv: str(kv[0]))
+            self.per_class_table.setRowCount(len(items))
+            for row, (cls_name, cls_stats) in enumerate(items):
+                ap = (cls_stats or {}).get("ap")
+                ap50 = (cls_stats or {}).get("ap50")
+                self.per_class_table.setItem(row, 0, QTableWidgetItem(str(cls_name)))
+                self.per_class_table.setItem(row, 1, QTableWidgetItem(self._format_float(ap)))
+                self.per_class_table.setItem(row, 2, QTableWidgetItem(self._format_float(ap50)))
+        else:
+            self.per_class_table.setRowCount(0)
+
+    @staticmethod
+    def _format_float(value: Any) -> str:
+        if value is None:
+            return "–"
+        try:
+            return f"{float(value):.4f}"
+        except Exception:
+            return str(value)
+
+    # ==================== Internal: plots ====================
+
+    def _clear_plots(self) -> None:
+        # Remove legacy grid widgets (from the initial implementation).
+        for widget in self._plot_widgets:
+            widget.setParent(None)
+            widget.deleteLater()
+        self._plot_widgets = []
+
+        self._plot_items = []
+
+        if hasattr(self, "plots_list"):
+            self.plots_list.blockSignals(True)
+            self.plots_list.clear()
+            self.plots_list.blockSignals(False)
+
+        if hasattr(self, "plot_view"):
+            self.plot_view.clear()
+        if hasattr(self, "selected_plot_title"):
+            self.selected_plot_title.setText("No image selected.")
+        if hasattr(self, "selected_plot_path"):
+            self.selected_plot_path.setText("")
+
+    def _render_plots(self, model: Dict[str, Any]) -> None:
+        self._clear_plots()
+
+        plot_dirs = self._infer_run_directories(model)
+        plot_items = self._discover_plot_items(plot_dirs)
+
+        if not plot_items:
+            dirs_text = "\n".join(str(p) for p in plot_dirs if p)
+            self.plots_status.setText(
+                "No validation plot images found for this model.\n\n"
+                "Searched directories:\n" + (dirs_text or "(none)")
+            )
+            return
+
+        self._plot_items = list(plot_items)
+        self.plots_status.setText(f"Found {len(plot_items)} plot image(s). Select one to view/zoom.")
+
+        self.plots_list.blockSignals(True)
+        self.plots_list.clear()
+        for idx, item in enumerate(self._plot_items):
+            qitem = QListWidgetItem(item.label)
+            qitem.setData(Qt.ItemDataRole.UserRole, idx)
+
+            pix = QPixmap(str(item.path))
+            if not pix.isNull():
+                thumb = pix.scaled(
+                    self.plots_list.iconSize(),
+                    Qt.AspectRatioMode.KeepAspectRatio,
+                    Qt.TransformationMode.SmoothTransformation,
+                )
+                qitem.setIcon(thumb)
+            self.plots_list.addItem(qitem)
+        self.plots_list.blockSignals(False)
+
+        if self.plots_list.count() > 0:
+            self.plots_list.setCurrentRow(0)
+
+    def _on_plot_item_selected(self) -> None:
+        if not self._plot_items:
+            return
+
+        selected = self.plots_list.selectedItems()
+        if not selected:
+            return
+
+        idx = selected[0].data(Qt.ItemDataRole.UserRole)
+        try:
+            idx_int = int(idx)
+        except Exception:
+            return
+        if idx_int < 0 or idx_int >= len(self._plot_items):
+            return
+
+        plot = self._plot_items[idx_int]
+        self.selected_plot_title.setText(plot.label)
+        self.selected_plot_path.setText(str(plot.path))
+
+        pix = QPixmap(str(plot.path))
+        if pix.isNull():
+            self.plot_view.clear()
+            return
+        self.plot_view.set_pixmap(pix, fit=True)
+
+    def _infer_run_directories(self, model: Dict[str, Any]) -> List[Path]:
+        dirs: List[Path] = []
+
+        # 1) Infer from model_path: .../<run>/weights/best.pt -> <run>
+        model_path = model.get("model_path")
+        if model_path:
+            try:
+                p = Path(str(model_path)).expanduser()
+                if p.name.lower().endswith(".pt"):
+                    # If it lives under weights/, use parent.parent.
+                    if p.parent.name == "weights" and p.parent.parent.exists():
+                        dirs.append(p.parent.parent)
+                    elif p.parent.exists():
+                        dirs.append(p.parent)
+            except Exception:
                 pass
+
+        # 2) Look at training_params.stage_results[].results.save_dir
+        training_params = model.get("training_params") or {}
+        stage_results = None
+        if isinstance(training_params, dict):
+            stage_results = training_params.get("stage_results")
+        if isinstance(stage_results, list):
+            for stage in stage_results:
+                results = (stage or {}).get("results")
+                save_dir = (results or {}).get("save_dir") if isinstance(results, dict) else None
+                if save_dir:
+                    try:
+                        save_path = Path(str(save_dir)).expanduser()
+                        if save_path.exists():
+                            dirs.append(save_path)
+                    except Exception:
+                        continue
+
+        # Deduplicate while preserving order.
+        unique: List[Path] = []
+        seen: set[str] = set()
+        for d in dirs:
+            try:
+                resolved = str(d.resolve())
+            except Exception:
+                resolved = str(d)
+            if resolved not in seen and d.exists() and d.is_dir():
+                seen.add(resolved)
+                unique.append(d)
+        return unique
+
+    def _discover_plot_items(self, directories: Sequence[Path]) -> List[_PlotItem]:
+        # Prefer canonical Ultralytics filenames first, then fall back to any png/jpg.
+        preferred_names = [
+            "results.png",
+            "results.jpg",
+            "confusion_matrix.png",
+            "confusion_matrix_normalized.png",
+            "labels.jpg",
+            "labels.png",
+            "BoxPR_curve.png",
+            "BoxP_curve.png",
+            "BoxR_curve.png",
+            "BoxF1_curve.png",
+            "MaskPR_curve.png",
+            "MaskP_curve.png",
+            "MaskR_curve.png",
+            "MaskF1_curve.png",
+            "val_batch0_pred.jpg",
+            "val_batch0_labels.jpg",
+        ]
+
+        found: List[_PlotItem] = []
+        seen: set[str] = set()
+
+        for d in directories:
+            # 1) Preferred
+            for name in preferred_names:
+                p = d / name
+                if p.exists() and p.is_file():
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{name} (from {d.name})", path=p))
+
+            # 2) Curated globs
+            for pattern in ("train_batch*.jpg", "val_batch*.jpg", "*curve*.png"):
+                for p in sorted(d.glob(pattern)):
+                    if not p.is_file():
+                        continue
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{p.name} (from {d.name})", path=p))
+
+            # 3) Fallback: any top-level png/jpg (excluding weights dir contents)
+            for ext in ("*.png", "*.jpg", "*.jpeg", "*.webp"):
+                for p in sorted(d.glob(ext)):
+                    if not p.is_file():
+                        continue
+                    key = str(p)
+                    if key in seen:
+                        continue
+                    seen.add(key)
+                    found.append(_PlotItem(label=f"{p.name} (from {d.name})", path=p))
+
+        # Keep list bounded to avoid UI overload for huge runs.
+        return found[:60]

src/gui/widgets/annotation_canvas_widget.py

@@ -18,7 +18,7 @@ from PySide6.QtGui import (
     QPaintEvent,
     QPolygonF,
 )
-from PySide6.QtCore import Qt, QEvent, Signal, QPoint, QPointF, QRect
+from PySide6.QtCore import Qt, QEvent, Signal, QPoint, QPointF, QRect, QTimer
 from typing import Any, Dict, List, Optional, Tuple
 
 from src.utils.image import Image, ImageLoadError
@@ -79,9 +79,7 @@ def rdp(points: List[Tuple[float, float]], epsilon: float) -> List[Tuple[float,
     return [start, end]
 
 
-def simplify_polyline(
+def simplify_polyline(points: List[Tuple[float, float]], epsilon: float) -> List[Tuple[float, float]]:
-    points: List[Tuple[float, float]], epsilon: float
-) -> List[Tuple[float, float]]:
     """
     Simplify a polyline with RDP while preserving closure semantics.
 
@@ -145,6 +143,10 @@ class AnnotationCanvasWidget(QWidget):
         self.zoom_step = 0.1
         self.zoom_wheel_step = 0.15
 
+        # Auto-fit behavior (opt-in): when enabled, newly loaded images (and resizes)
+        # will scale to fill the available viewport while preserving aspect ratio.
+        self._auto_fit_to_view: bool = False
+
         # Drawing / interaction state
         self.is_drawing = False
         self.polyline_enabled = False
@@ -175,6 +177,35 @@ class AnnotationCanvasWidget(QWidget):
 
         self._setup_ui()
 
+    def set_auto_fit_to_view(self, enabled: bool):
+        """Enable/disable automatic zoom-to-fit behavior."""
+        self._auto_fit_to_view = bool(enabled)
+        if self._auto_fit_to_view and self.original_pixmap is not None:
+            QTimer.singleShot(0, self.fit_to_view)
+
+    def fit_to_view(self, padding_px: int = 6):
+        """Zoom the image so it fits the scroll area's viewport (aspect preserved)."""
+        if self.original_pixmap is None:
+            return
+
+        viewport = self.scroll_area.viewport().size()
+        available_w = max(1, int(viewport.width()) - int(padding_px))
+        available_h = max(1, int(viewport.height()) - int(padding_px))
+
+        img_w = max(1, int(self.original_pixmap.width()))
+        img_h = max(1, int(self.original_pixmap.height()))
+
+        scale_w = available_w / img_w
+        scale_h = available_h / img_h
+        new_scale = min(scale_w, scale_h)
+        new_scale = max(self.zoom_min, min(self.zoom_max, float(new_scale)))
+
+        if abs(new_scale - self.zoom_scale) < 1e-4:
+            return
+
+        self.zoom_scale = new_scale
+        self._apply_zoom()
+
     def _setup_ui(self):
         """Setup user interface."""
         layout = QVBoxLayout()
@@ -187,9 +218,7 @@ class AnnotationCanvasWidget(QWidget):
 
         self.canvas_label = QLabel("No image loaded")
         self.canvas_label.setAlignment(Qt.AlignCenter)
-        self.canvas_label.setStyleSheet(
+        self.canvas_label.setStyleSheet("QLabel { background-color: #2b2b2b; color: #888; }")
-            "QLabel { background-color: #2b2b2b; color: #888; }"
-        )
         self.canvas_label.setScaledContents(False)
         self.canvas_label.setMouseTracking(True)
 
@@ -212,9 +241,18 @@ class AnnotationCanvasWidget(QWidget):
         self.zoom_scale = 1.0
         self.clear_annotations()
         self._display_image()
-        logger.debug(
+
-            f"Loaded image into annotation canvas: {image.width}x{image.height}"
+        # Defer fit-to-view until the widget has a valid viewport size.
-        )
+        if self._auto_fit_to_view:
+            QTimer.singleShot(0, self.fit_to_view)
+
+        logger.debug(f"Loaded image into annotation canvas: {image.width}x{image.height}")
+
+    def resizeEvent(self, event):
+        """Optionally keep the image fitted when the widget is resized."""
+        super().resizeEvent(event)
+        if self._auto_fit_to_view and self.original_pixmap is not None:
+            QTimer.singleShot(0, self.fit_to_view)
 
     def clear(self):
         """Clear the displayed image and all annotations."""
@@ -250,12 +288,10 @@ class AnnotationCanvasWidget(QWidget):
             # Get image data in a format compatible with Qt
             if self.current_image.channels in (3, 4):
                 image_data = self.current_image.get_rgb()
-                height, width = image_data.shape[:2]
             else:
-                image_data = self.current_image.get_grayscale()
+                image_data = self.current_image.get_qt_rgb()
-                height, width = image_data.shape
 
-            image_data = np.ascontiguousarray(image_data)
+            height, width = image_data.shape[:2]
             bytes_per_line = image_data.strides[0]
 
             qimage = QImage(
@@ -263,7 +299,7 @@ class AnnotationCanvasWidget(QWidget):
                 width,
                 height,
                 bytes_per_line,
-                self.current_image.qtimage_format,
+                QImage.Format_RGBX32FPx4,  # self.current_image.qtimage_format,
             ).copy()  # Copy so Qt owns the buffer even after numpy array goes out of scope
 
             self.original_pixmap = QPixmap.fromImage(qimage)
@@ -291,22 +327,14 @@ class AnnotationCanvasWidget(QWidget):
             scaled_width,
             scaled_height,
             Qt.KeepAspectRatio,
-            (
+            (Qt.SmoothTransformation if self.zoom_scale >= 1.0 else Qt.FastTransformation),
-                Qt.SmoothTransformation
-                if self.zoom_scale >= 1.0
-                else Qt.FastTransformation
-            ),
         )
 
         scaled_annotations = self.annotation_pixmap.scaled(
             scaled_width,
             scaled_height,
             Qt.KeepAspectRatio,
-            (
+            (Qt.SmoothTransformation if self.zoom_scale >= 1.0 else Qt.FastTransformation),
-                Qt.SmoothTransformation
-                if self.zoom_scale >= 1.0
-                else Qt.FastTransformation
-            ),
         )
 
         # Composite image and annotations
@@ -392,16 +420,11 @@ class AnnotationCanvasWidget(QWidget):
         y = (pos.y() - offset_y) / self.zoom_scale
 
         # Check bounds
-        if (
+        if 0 <= x < self.original_pixmap.width() and 0 <= y < self.original_pixmap.height():
-            0 <= x < self.original_pixmap.width()
-            and 0 <= y < self.original_pixmap.height()
-        ):
             return (int(x), int(y))
         return None
 
-    def _find_polyline_at(
+    def _find_polyline_at(self, img_x: float, img_y: float, threshold_px: float = 5.0) -> Optional[int]:
-        self, img_x: float, img_y: float, threshold_px: float = 5.0
-    ) -> Optional[int]:
         """
         Find index of polyline whose geometry is within threshold_px of (img_x, img_y).
         Returns the index in self.polylines, or None if none is close enough.
@@ -423,9 +446,7 @@ class AnnotationCanvasWidget(QWidget):
 
             # Precise distance to all segments
             for (x1, y1), (x2, y2) in zip(polyline[:-1], polyline[1:]):
-                d = perpendicular_distance(
+                d = perpendicular_distance((img_x, img_y), (float(x1), float(y1)), (float(x2), float(y2)))
-                    (img_x, img_y), (float(x1), float(y1)), (float(x2), float(y2))
-                )
                 if d < best_dist:
                     best_dist = d
                     best_index = idx
@@ -626,11 +647,7 @@ class AnnotationCanvasWidget(QWidget):
 
     def mouseMoveEvent(self, event: QMouseEvent):
         """Handle mouse move events for drawing."""
-        if (
+        if not self.is_drawing or not self.polyline_enabled or self.annotation_pixmap is None:
-            not self.is_drawing
-            or not self.polyline_enabled
-            or self.annotation_pixmap is None
-        ):
             super().mouseMoveEvent(event)
             return
 
@@ -690,15 +707,10 @@ class AnnotationCanvasWidget(QWidget):
 
             if len(simplified) >= 2:
                 # Store polyline and redraw all annotations
-                self._add_polyline(
+                self._add_polyline(simplified, self.polyline_pen_color, self.polyline_pen_width)
-                    simplified, self.polyline_pen_color, self.polyline_pen_width
-                )
 
                 # Convert to normalized coordinates for metadata + signal
-                normalized_stroke = [
+                normalized_stroke = [self._image_to_normalized_coords(int(x), int(y)) for (x, y) in simplified]
-                    self._image_to_normalized_coords(int(x), int(y))
-                    for (x, y) in simplified
-                ]
                 self.all_strokes.append(
                     {
                         "points": normalized_stroke,
@@ -711,8 +723,7 @@ class AnnotationCanvasWidget(QWidget):
                 # Emit signal with normalized coordinates
                 self.annotation_drawn.emit(normalized_stroke)
                 logger.debug(
-                    f"Completed stroke with {len(simplified)} points "
+                    f"Completed stroke with {len(simplified)} points " f"(normalized len={len(normalized_stroke)})"
-                    f"(normalized len={len(normalized_stroke)})"
                 )
 
         self.current_stroke = []
@@ -752,9 +763,7 @@ class AnnotationCanvasWidget(QWidget):
 
             # Store polyline as [y_norm, x_norm] to match DB convention and
             # the expectations of draw_saved_polyline().
-            normalized_polyline = [
+            normalized_polyline = [[y / img_height, x / img_width] for (x, y) in polyline]
-                [y / img_height, x / img_width] for (x, y) in polyline
-            ]
 
             logger.debug(
                 f"Polyline {idx}: {len(polyline)} points, "
@@ -774,7 +783,7 @@ class AnnotationCanvasWidget(QWidget):
         self,
         polyline: List[List[float]],
         color: str,
-        width: int = 3,
+        width: int = 1,
         annotation_id: Optional[int] = None,
     ):
         """
@@ -812,17 +821,13 @@ class AnnotationCanvasWidget(QWidget):
 
         # Store and redraw using common pipeline
         pen_color = QColor(color)
-        pen_color.setAlpha(128)  # Add semi-transparency
+        pen_color.setAlpha(255)  # Add semi-transparency
         self._add_polyline(img_coords, pen_color, width, annotation_id=annotation_id)
 
         # Store in all_strokes for consistency (uses normalized coordinates)
-        self.all_strokes.append(
+        self.all_strokes.append({"points": polyline, "color": color, "alpha": 255, "width": width})
-            {"points": polyline, "color": color, "alpha": 128, "width": width}
-        )
 
-        logger.debug(
+        logger.debug(f"Drew saved polyline with {len(polyline)} points in color {color}")
-            f"Drew saved polyline with {len(polyline)} points in color {color}"
-        )
 
     def draw_saved_bbox(
         self,
@@ -846,9 +851,7 @@ class AnnotationCanvasWidget(QWidget):
             return
 
         if len(bbox) != 4:
-            logger.warning(
+            logger.warning(f"Invalid bounding box format: expected 4 values, got {len(bbox)}")
-                f"Invalid bounding box format: expected 4 values, got {len(bbox)}"
-            )
             return
 
         # Convert normalized coordinates to image coordinates (for logging/debug)
@@ -869,15 +872,11 @@ class AnnotationCanvasWidget(QWidget):
         # in _redraw_annotations() together with all polylines.
         pen_color = QColor(color)
         pen_color.setAlpha(128)  # Add semi-transparency
-        self.bboxes.append(
+        self.bboxes.append([float(x_min_norm), float(y_min_norm), float(x_max_norm), float(y_max_norm)])
-            [float(x_min_norm), float(y_min_norm), float(x_max_norm), float(y_max_norm)]
-        )
         self.bbox_meta.append({"color": pen_color, "width": int(width), "label": label})
 
         # Store in all_strokes for consistency
-        self.all_strokes.append(
+        self.all_strokes.append({"bbox": bbox, "color": color, "alpha": 128, "width": width, "label": label})
-            {"bbox": bbox, "color": color, "alpha": 128, "width": width, "label": label}
-        )
 
         # Redraw overlay (polylines + all bounding boxes)
         self._redraw_annotations()

src/model/yolo_wrapper.py

@@ -1,18 +1,21 @@
-"""
+"""YOLO model wrapper for the microscopy object detection application.
-YOLO model wrapper for the microscopy object detection application.
+
-Provides a clean interface to YOLOv8 for training, validation, and inference.
+Notes on 16-bit TIFF support:
+- Ultralytics training defaults assume 8-bit images and normalize by dividing by 255.
+- This project can patch Ultralytics at runtime to decode TIFFs via `tifffile` and
+  normalize `uint16` correctly.
+
+See [`apply_ultralytics_16bit_tiff_patches()`](src/utils/ultralytics_16bit_patch.py:1).
 """
 
-from ultralytics import YOLO
 from pathlib import Path
 from typing import Optional, List, Dict, Callable, Any
 import torch
 import tempfile
 import os
-import numpy as np
+from src.utils.image import Image
-from src.utils.image import Image, convert_grayscale_to_rgb_preserve_range
 from src.utils.logger import get_logger
-from src.utils.train_ultralytics_float import train_with_float32_loader
+from src.utils.ultralytics_16bit_patch import apply_ultralytics_16bit_tiff_patches
 
 
 logger = get_logger(__name__)
@@ -33,6 +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.
@@ -42,6 +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")
@@ -61,11 +70,10 @@ class YOLOWrapper:
         name: str = "custom_model",
         resume: bool = False,
         callbacks: Optional[Dict[str, Callable]] = None,
-        use_float32_loader: bool = True,
         **kwargs,
     ) -> Dict[str, Any]:
         """
-        Train the YOLO model with optional float32 loader for 16-bit TIFFs.
+        Train the YOLO model.
 
         Args:
             data_yaml: Path to data.yaml configuration file
@@ -77,43 +85,30 @@ class YOLOWrapper:
             name: Name for the training run
             resume: Resume training from last checkpoint
             callbacks: Optional Ultralytics callback dictionary
-            use_float32_loader: Use custom Float32Dataset for 16-bit TIFFs (default: True)
             **kwargs: Additional training arguments
 
         Returns:
             Dictionary with training results
         """
-        if 1:
-            logger.info(f"Starting training: {name}")
-            logger.info(
-                f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}"
-            )
-
-            # Check if dataset has 16-bit TIFFs and use float32 loader
-            if use_float32_loader:
-                logger.info("Using Float32Dataset loader for 16-bit TIFF support")
-                return train_with_float32_loader(
-                    model_path=self.model_path,
-                    data_yaml=data_yaml,
-                    epochs=epochs,
-                    imgsz=imgsz,
-                    batch=batch,
-                    patience=patience,
-                    save_dir=save_dir,
-                    name=name,
-                    callbacks=callbacks,
-                    device=self.device,
-                    resume=resume,
-                    **kwargs,
-                )
-            else:
-                # Standard training (old behavior)
         if self.model is None:
             if not self.load_model():
-                        raise RuntimeError(
+                raise RuntimeError(f"Failed to load model from {self.model_path}")
-                            f"Failed to load model from {self.model_path}"
-                        )
 
+        try:
+            logger.info(f"Starting training: {name}")
+            logger.info(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,
                 epochs=epochs,
@@ -130,9 +125,9 @@ class YOLOWrapper:
             logger.info("Training completed successfully")
             return self._format_training_results(results)
 
-        # except Exception as e:
+        except Exception as e:
-        #     logger.error(f"Error during training: {e}")
+            logger.error(f"Error during training: {e}")
-        #     raise
+            raise
 
     def validate(self, data_yaml: str, split: str = "val", **kwargs) -> Dict[str, Any]:
         """
@@ -152,9 +147,7 @@ class YOLOWrapper:
 
         try:
             logger.info(f"Starting validation on {split} split")
-            results = self.model.val(
+            results = self.model.val(data=data_yaml, split=split, device=self.device, **kwargs)
-                data=data_yaml, split=split, device=self.device, **kwargs
-            )
 
             logger.info("Validation completed successfully")
             return self._format_validation_results(results)
@@ -193,17 +186,18 @@ class YOLOWrapper:
                 raise RuntimeError(f"Failed to load model from {self.model_path}")
 
         prepared_source, cleanup_path = self._prepare_source(source)
-
+        imgsz = 1088
         try:
-            logger.info(f"Running inference on {source}")
+            logger.info(f"Running inference on {source} -> prepared_source {prepared_source}")
             results = self.model.predict(
-                source=prepared_source,
+                source=source,
                 conf=conf,
                 iou=iou,
                 save=save,
                 save_txt=save_txt,
                 save_conf=save_conf,
                 device=self.device,
+                imgsz=imgsz,
                 **kwargs,
             )
 
@@ -215,20 +209,13 @@ class YOLOWrapper:
             logger.error(f"Error during inference: {e}")
             raise
         finally:
-            # Clean up temporary files (only for non-16-bit images)
+            if 0:  # cleanup_path:
-            # 16-bit TIFFs return numpy arrays directly, so cleanup_path is None
-            if cleanup_path:
                 try:
                     os.remove(cleanup_path)
-                    logger.debug(f"Cleaned up temporary file: {cleanup_path}")
                 except OSError as cleanup_error:
-                    logger.warning(
+                    logger.warning(f"Failed to delete temporary RGB image {cleanup_path}: {cleanup_error}")
-                        f"Failed to delete temporary file {cleanup_path}: {cleanup_error}"
-                    )
 
-    def export(
+    def export(self, format: str = "onnx", output_path: Optional[str] = None, **kwargs) -> str:
-        self, format: str = "onnx", output_path: Optional[str] = None, **kwargs
-    ) -> str:
         """
         Export model to different format.
 
@@ -255,14 +242,7 @@ class YOLOWrapper:
             raise
 
     def _prepare_source(self, source):
-        """Convert single-channel images to RGB for inference.
+        """Convert single-channel images to RGB temporarily for inference."""
-
-        For 16-bit TIFF files, this will:
-        1. Load using tifffile
-        2. Normalize to float32 [0-1] (NO uint8 conversion to avoid data loss)
-        3. Replicate grayscale → RGB (3 channels)
-        4. Pass directly as numpy array to YOLO
-        """
         cleanup_path = None
 
         if isinstance(source, (str, Path)):
@@ -270,60 +250,13 @@ class YOLOWrapper:
             if source_path.is_file():
                 try:
                     img_obj = Image(source_path)
-
-                    # Check if it's a 16-bit TIFF file
-                    is_16bit_tiff = (
-                        source_path.suffix.lower() in [".tif", ".tiff"]
-                        and img_obj.dtype == np.uint16
-                    )
-
-                    if is_16bit_tiff:
-                        # Process 16-bit TIFF: normalize to float32 [0-1]
-                        # NO uint8 conversion - pass float32 directly to avoid data loss
-                        normalized_float = img_obj.to_normalized_float32()
-
-                        # Convert grayscale to RGB by replicating channels
-                        if len(normalized_float.shape) == 2:
-                            # Grayscale: H,W → H,W,3
-                            rgb_float = np.stack([normalized_float] * 3, axis=-1)
-                        elif (
-                            len(normalized_float.shape) == 3
-                            and normalized_float.shape[2] == 1
-                        ):
-                            # Grayscale with channel dim: H,W,1 → H,W,3
-                            rgb_float = np.repeat(normalized_float, 3, axis=2)
-                        else:
-                            # Already multi-channel
-                            rgb_float = normalized_float
-
-                        # Ensure contiguous array and float32
-                        rgb_float = np.ascontiguousarray(rgb_float, dtype=np.float32)
-
-                        logger.info(
-                            f"Loaded 16-bit TIFF {source_path} as float32 [0-1] RGB "
-                            f"(shape: {rgb_float.shape}, dtype: {rgb_float.dtype}, "
-                            f"range: [{rgb_float.min():.4f}, {rgb_float.max():.4f}])"
-                        )
-
-                        # Return numpy array directly - YOLO can handle it
-                        return rgb_float, cleanup_path
-                    else:
-                        # Standard processing for other images
-                        pil_img = img_obj.pil_image
-                        if len(pil_img.getbands()) == 1:
-                            rgb_img = convert_grayscale_to_rgb_preserve_range(pil_img)
-                        else:
-                            rgb_img = pil_img.convert("RGB")
-
                     suffix = source_path.suffix or ".png"
                     tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
                     tmp_path = tmp.name
                     tmp.close()
-                        rgb_img.save(tmp_path)
+                    img_obj.save(tmp_path)
                     cleanup_path = tmp_path
-                        logger.info(
+                    logger.info(f"Converted image {source_path} to RGB for inference at {tmp_path}")
-                            f"Converted image {source_path} to RGB for inference at {tmp_path}"
-                        )
                     return tmp_path, cleanup_path
                 except Exception as convert_error:
                     logger.warning(
@@ -336,9 +269,7 @@ class YOLOWrapper:
         """Format training results into dictionary."""
         try:
             # Get the results dict
-            results_dict = (
+            results_dict = results.results_dict if hasattr(results, "results_dict") else {}
-                results.results_dict if hasattr(results, "results_dict") else {}
-            )
 
             formatted = {
                 "success": True,
@@ -371,9 +302,7 @@ class YOLOWrapper:
                 "mAP50-95": float(box_metrics.map),
                 "precision": float(box_metrics.mp),
                 "recall": float(box_metrics.mr),
-                "fitness": (
+                "fitness": (float(results.fitness) if hasattr(results, "fitness") else 0.0),
-                    float(results.fitness) if hasattr(results, "fitness") else 0.0
-                ),
             }
 
             # Add per-class metrics if available
@@ -383,11 +312,7 @@ class YOLOWrapper:
                     if idx < len(box_metrics.ap):
                         class_metrics[name] = {
                             "ap": float(box_metrics.ap[idx]),
-                            "ap50": (
+                            "ap50": (float(box_metrics.ap50[idx]) if hasattr(box_metrics, "ap50") else 0.0),
-                                float(box_metrics.ap50[idx])
-                                if hasattr(box_metrics, "ap50")
-                                else 0.0
-                            ),
                         }
                 formatted["class_metrics"] = class_metrics
 
@@ -420,21 +345,15 @@ class YOLOWrapper:
                         "class_id": int(boxes.cls[i]),
                         "class_name": result.names[int(boxes.cls[i])],
                         "confidence": float(boxes.conf[i]),
-                        "bbox_normalized": [
+                        "bbox_normalized": [float(v) for v in xyxyn],  # [x_min, y_min, x_max, y_max]
-                            float(v) for v in xyxyn
+                        "bbox_absolute": [float(v) for v in boxes.xyxy[i].cpu().numpy()],  # Absolute pixels
-                        ],  # [x_min, y_min, x_max, y_max]
-                        "bbox_absolute": [
-                            float(v) for v in boxes.xyxy[i].cpu().numpy()
-                        ],  # Absolute pixels
                     }
 
                     # Extract segmentation mask if available
                     if has_masks:
                         try:
                             # Get the mask for this detection
-                            mask_data = result.masks.xy[
+                            mask_data = result.masks.xy[i]  # Polygon coordinates in absolute pixels
-                                i
-                            ]  # Polygon coordinates in absolute pixels
 
                             # Convert to normalized coordinates
                             if len(mask_data) > 0:
@@ -447,9 +366,7 @@ class YOLOWrapper:
                             else:
                                 detection["segmentation_mask"] = None
                         except Exception as mask_error:
-                            logger.warning(
+                            logger.warning(f"Error extracting mask for detection {i}: {mask_error}")
-                                f"Error extracting mask for detection {i}: {mask_error}"
-                            )
                             detection["segmentation_mask"] = None
                     else:
                         detection["segmentation_mask"] = None
@@ -463,9 +380,7 @@ class YOLOWrapper:
             return []
 
     @staticmethod
-    def convert_bbox_format(
+    def convert_bbox_format(bbox: List[float], format_from: str = "xywh", format_to: str = "xyxy") -> List[float]:
-        bbox: List[float], format_from: str = "xywh", format_to: str = "xyxy"
-    ) -> List[float]:
         """
         Convert bounding box between formats.
 

src/utils/config_manager.py

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

src/utils/create_mask_from_detection.py

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

src/utils/image.py

@@ -6,17 +6,55 @@ import cv2
 import numpy as np
 from pathlib import Path
 from typing import Optional, Tuple, Union
-from PIL import Image as PILImage
-import tifffile
 
 from src.utils.logger import get_logger
 from src.utils.file_utils import validate_file_path, is_image_file
 
 from PySide6.QtGui import QImage
 
+from tifffile import imread, imwrite
+
 logger = get_logger(__name__)
 
 
+def get_pseudo_rgb(arr: np.ndarray, gamma: float = 0.5) -> np.ndarray:
+    """
+    Convert a grayscale image to a pseudo-RGB image using a gamma correction.
+
+    Args:
+        arr: Input grayscale image as numpy array
+
+    Returns:
+        Pseudo-RGB image as numpy array
+    """
+    if arr.ndim != 2:
+        raise ValueError("Input array must be a grayscale image with shape (H, W)")
+
+    a1 = arr.copy().astype(np.float32)
+    a1 -= np.percentile(a1, 2)
+    a1[a1 < 0] = 0
+    p999 = np.percentile(a1, 99.9)
+    a1[a1 > p999] = p999
+    a1 /= a1.max()
+
+    if 1:
+        a2 = a1.copy()
+        a2 = a2**gamma
+        a2 /= a2.max()
+
+        a3 = a1.copy()
+        p9999 = np.percentile(a3, 99.99)
+        a3[a3 > p9999] = p9999
+        a3 /= a3.max()
+
+    # return np.stack([a1, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    # return np.stack([a2, np.zeros(a1.shape), np.zeros(a1.shape)], axis=0)
+    out = np.stack([a1, a2, a3], axis=0)
+    # print(any(np.isnan(out).flatten()))
+
+    return out
+
+
 class ImageLoadError(Exception):
     """Exception raised when an image cannot be loaded."""
 
@@ -55,7 +93,6 @@ class Image:
         """
         self.path = Path(image_path)
         self._data: Optional[np.ndarray] = None
-        self._pil_image: Optional[PILImage.Image] = None
         self._width: int = 0
         self._height: int = 0
         self._channels: int = 0
@@ -81,75 +118,34 @@ class Image:
         if not is_image_file(str(self.path), self.SUPPORTED_EXTENSIONS):
             ext = self.path.suffix.lower()
             raise ImageLoadError(
-                f"Unsupported image format: {ext}. "
+                f"Unsupported image format: {ext}. " f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
-                f"Supported formats: {', '.join(self.SUPPORTED_EXTENSIONS)}"
             )
 
         try:
-            # Check if it's a TIFF file - use tifffile for better support
             if self.path.suffix.lower() in [".tif", ".tiff"]:
-                self._data = tifffile.imread(str(self.path))
+                self._data = imread(str(self.path))
-
-                if self._data is None:
-                    raise ImageLoadError(
-                        f"Failed to load TIFF with tifffile: {self.path}"
-                    )
-
-                # Extract metadata
-                self._height, self._width = (
-                    self._data.shape[:2]
-                    if len(self._data.shape) >= 2
-                    else (self._data.shape[0], 1)
-                )
-                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
-                if self._channels == 1:
-                    # Grayscale
-                    self._pil_image = PILImage.fromarray(self._data)
             else:
-                    # Multi-channel (RGB or RGBA)
+                # raise NotImplementedError("RGB is not implemented")
-                    self._pil_image = PILImage.fromarray(self._data)
+                # Load with OpenCV (returns BGR format)
-
-                logger.info(
-                    f"Successfully loaded TIFF image: {self.path.name} "
-                    f"({self._width}x{self._height}, {self._channels} channels, "
-                    f"dtype={self._dtype}, {self._format.upper()})"
-                )
-            else:
-                # Load with OpenCV (returns BGR format) for non-TIFF images
                 self._data = cv2.imread(str(self.path), cv2.IMREAD_UNCHANGED)
 
             if self._data is None:
-                    raise ImageLoadError(
+                raise ImageLoadError(f"Failed to load image with OpenCV: {self.path}")
-                        f"Failed to load image with OpenCV: {self.path}"
-                    )
 
             # Extract metadata
+            # print(self._data.shape)
+            if len(self._data.shape) == 2:
                 self._height, self._width = self._data.shape[:2]
-                self._channels = (
+                self._channels = 1
-                    self._data.shape[2] if len(self._data.shape) == 3 else 1
+            else:
-                )
+                self._height, self._width = self._data.shape[1:]
+                self._channels = self._data.shape[0]
+            # self._channels = self._data.shape[2] if len(self._data.shape) == 3 else 1
             self._format = self.path.suffix.lower().lstrip(".")
             self._size_bytes = self.path.stat().st_size
             self._dtype = self._data.dtype
 
-                # Load PIL version for compatibility (convert BGR to RGB)
+            if 0:
-                if self._channels == 3:
-                    rgb_data = cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
-                    self._pil_image = PILImage.fromarray(rgb_data)
-                elif self._channels == 4:
-                    rgba_data = cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
-                    self._pil_image = PILImage.fromarray(rgba_data)
-                else:
-                    # Grayscale
-                    self._pil_image = PILImage.fromarray(self._data)
-
                 logger.info(
                     f"Successfully loaded image: {self.path.name} "
                     f"({self._width}x{self._height}, {self._channels} channels, "
@@ -172,18 +168,6 @@ class Image:
             raise ImageLoadError("Image data not available")
         return self._data
 
-    @property
-    def pil_image(self) -> PILImage.Image:
-        """
-        Get image data as PIL Image (RGB or grayscale).
-
-        Returns:
-            PIL Image object
-        """
-        if self._pil_image is None:
-            raise ImageLoadError("PIL image not available")
-        return self._pil_image
-
     @property
     def width(self) -> int:
         """Get image width in pixels."""
@@ -228,6 +212,7 @@ class Image:
     @property
     def dtype(self) -> np.dtype:
         """Get the data type of the image array."""
+
         if self._dtype is None:
             raise ImageLoadError("Image dtype not available")
         return self._dtype
@@ -247,8 +232,10 @@ class Image:
         elif self._channels == 1:
             if self._dtype == np.uint16:
                 return QImage.Format_Grayscale16
-            else:
+            elif self._dtype == np.uint8:
                 return QImage.Format_Grayscale8
+            elif self._dtype == np.float32:
+                return QImage.Format_BGR30
         else:
             raise ImageLoadError(f"Unsupported number of channels: {self._channels}")
 
@@ -259,12 +246,36 @@ class Image:
         Returns:
             Image data in RGB format as numpy array
         """
-        if self._channels == 3:
+        if self.channels == 1:
-            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB)
+            img = get_pseudo_rgb(self.data)
+            self._dtype = img.dtype
+            return img, True
+
+        elif self._channels == 3:
+            return cv2.cvtColor(self._data, cv2.COLOR_BGR2RGB), False
         elif self._channels == 4:
-            return cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA)
+            return cv2.cvtColor(self._data, cv2.COLOR_BGRA2RGBA), False
+
         else:
-            return self._data
+            raise NotImplementedError
+
+        # else:
+        #     return self._data
+
+    def get_qt_rgb(self) -> np.ascontiguousarray:
+        # we keep data as (C, H, W)
+        _img, pseudo = self.get_rgb()
+
+        if pseudo:
+            img = np.zeros((self.height, self.width, 4), dtype=np.float32)
+            img[..., 0] = _img[0]  # R gradient
+            img[..., 1] = _img[1]  # G gradient
+            img[..., 2] = _img[2]  # B constant
+            img[..., 3] = 1.0  # A = 1.0 (opaque)
+
+            return np.ascontiguousarray(img)
+        else:
+            return np.ascontiguousarray(_img)
 
     def get_grayscale(self) -> np.ndarray:
         """
@@ -318,49 +329,26 @@ class Image:
         """
         return self._channels >= 3
 
-    def to_normalized_float32(self) -> np.ndarray:
+    def save(self, path: Union[str, Path], pseudo_rgb: bool = True) -> None:
-        """
-        Convert image data to normalized float32 in range [0, 1].
 
-        For 16-bit images, this properly scales the full dynamic range.
+        if self.channels == 1:
-        For 8-bit images, divides by 255.
+            if pseudo_rgb:
-        Already float images are clipped to [0, 1].
+                img = get_pseudo_rgb(self.data)
-
+                print("Image.save", img.shape)
-        Returns:
-            Normalized image data as float32 numpy array [0, 1]
-        """
-        data = self._data.astype(np.float32)
-
-        if self._dtype == np.uint16:
-            # 16-bit: normalize by max value (65535)
-            data = data / 65535.0
-        elif self._dtype == np.uint8:
-            # 8-bit: normalize by 255
-            data = data / 255.0
-        elif np.issubdtype(self._dtype, np.floating):
-            # Already float, just clip to [0, 1]
-            data = np.clip(data, 0.0, 1.0)
             else:
-            # Other integer types: use dtype info
+                img = np.repeat(self.data, 3, axis=2)
-            if np.issubdtype(self._dtype, np.integer):
-                max_val = np.iinfo(self._dtype).max
-                data = data / float(max_val)
-            else:
-                # Unknown type: attempt min-max normalization
-                min_val = data.min()
-                max_val = data.max()
-                if max_val > min_val:
-                    data = (data - min_val) / (max_val - min_val)
-                else:
-                    data = np.zeros_like(data)
 
-        return np.clip(data, 0.0, 1.0)
+        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)"
         )
@@ -370,38 +358,13 @@ class Image:
         return self.__repr__()
 
 
-def convert_grayscale_to_rgb_preserve_range(
+if __name__ == "__main__":
-    pil_image: PILImage.Image,
+    import argparse
-) -> PILImage.Image:
-    """Convert a single-channel PIL image to RGB while preserving dynamic range.
 
-    Args:
+    parser = argparse.ArgumentParser()
-        pil_image: Single-channel PIL image (e.g., 16-bit grayscale).
+    parser.add_argument("--path", type=str, required=True)
+    args = parser.parse_args()
 
-    Returns:
+    img = Image(args.path)
-        PIL Image in RGB mode with intensities normalized to 0-255.
+    img.save(args.path + "test.tif")
-    """
+    print(img)
-
-    if pil_image.mode == "RGB":
-        return pil_image
-
-    grayscale = np.array(pil_image)
-    if grayscale.ndim == 3:
-        grayscale = grayscale[:, :, 0]
-
-    original_dtype = grayscale.dtype
-    grayscale = grayscale.astype(np.float32)
-
-    if grayscale.size == 0:
-        return PILImage.new("RGB", pil_image.size, color=(0, 0, 0))
-
-    if np.issubdtype(original_dtype, np.integer):
-        denom = float(max(np.iinfo(original_dtype).max, 1))
-    else:
-        max_val = float(grayscale.max())
-        denom = max(max_val, 1.0)
-
-    grayscale = np.clip(grayscale / denom, 0.0, 1.0)
-    grayscale_u8 = (grayscale * 255.0).round().astype(np.uint8)
-    rgb_arr = np.repeat(grayscale_u8[:, :, None], 3, axis=2)
-    return PILImage.fromarray(rgb_arr, mode="RGB")

src/utils/image_converters.py

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

src/utils/image_splitter.py

@@ -0,0 +1,368 @@
+import numpy as np
+
+from pathlib import Path
+from tifffile import imread, imwrite
+from shapely.geometry import LineString
+from copy import deepcopy
+from scipy.ndimage import zoom
+
+
+# debug
+from src.utils.image import Image
+from show_yolo_seg import draw_annotations
+
+import pylab as plt
+import cv2
+
+
+class Label:
+    def __init__(self, yolo_annotation: str):
+        class_id, bbox, polygon = self.parse_yolo_annotation(yolo_annotation)
+        self.class_id = class_id
+        self.bbox = bbox
+        self.polygon = polygon
+
+    def parse_yolo_annotation(self, yolo_annotation: str):
+        class_id, *coords = yolo_annotation.split()
+        class_id = int(class_id)
+        bbox = np.array(coords[:4], dtype=np.float32)
+        polygon = np.array(coords[4:], dtype=np.float32).reshape(-1, 2) if len(coords) > 4 else None
+        if not any(np.isclose(polygon[0], polygon[-1])):
+            polygon = np.vstack([polygon, polygon[0]])
+        return class_id, bbox, polygon
+
+    def offset_label(
+        self,
+        img_w,
+        img_h,
+        distance: float = 1.0,
+        cap_style: int = 2,
+        join_style: int = 2,
+    ):
+        if self.polygon is None:
+            self.bbox = np.array(
+                [
+                    self.bbox[0] - distance if self.bbox[0] - distance > 0 else 0,
+                    self.bbox[1] - distance if self.bbox[1] - distance > 0 else 0,
+                    self.bbox[2] + distance if self.bbox[2] + distance < 1 else 1,
+                    self.bbox[3] + distance if self.bbox[3] + distance < 1 else 1,
+                ],
+                dtype=np.float32,
+            )
+            return self.bbox
+
+        def coords_are_normalized(coords):
+            # If every coordinate is between 0 and 1 (inclusive-ish), assume normalized
+            print(coords)
+            # if not coords:
+            #     return False
+            return all(max(coords.flatten)) <= 1.001
+
+        def poly_to_pts(coords, img_w, img_h):
+            # coords: [x1 y1 x2 y2 ...] either normalized or absolute
+            # if coords_are_normalized(coords):
+            coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(coords))]
+            pts = np.array(coords, dtype=np.int32).reshape(-1, 2)
+            return pts
+
+        pts = poly_to_pts(self.polygon, img_w, img_h)
+        line = LineString(pts)
+        # Buffer distance in pixels
+        buffered = line.buffer(distance=distance, cap_style=cap_style, join_style=join_style)
+        self.polygon = np.array(buffered.exterior.coords, dtype=np.float32) / (img_w, img_h)
+        xmn, ymn = self.polygon.min(axis=0)
+        xmx, ymx = self.polygon.max(axis=0)
+        xc = (xmn + xmx) / 2
+        yc = (ymn + ymx) / 2
+        bw = xmx - xmn
+        bh = ymx - ymn
+        self.bbox = np.array([xc, yc, bw, bh], dtype=np.float32)
+
+        return self.bbox, self.polygon
+
+    def translate(self, x, y, scale_x, scale_y):
+        self.bbox[0] -= x
+        self.bbox[0] *= scale_x
+        self.bbox[1] -= y
+        self.bbox[1] *= scale_y
+        self.bbox[2] *= scale_x
+        self.bbox[3] *= scale_y
+        if self.polygon is not None:
+            self.polygon[:, 0] -= x
+            self.polygon[:, 0] *= scale_x
+            self.polygon[:, 1] -= y
+            self.polygon[:, 1] *= scale_y
+
+    def in_range(self, hrange, wrange):
+        xc, yc, h, w = self.bbox
+        x1 = xc - w / 2
+        y1 = yc - h / 2
+        x2 = xc + w / 2
+        y2 = yc + h / 2
+        truth_val = (
+            xc >= wrange[0]
+            and x1 <= wrange[1]
+            and x2 >= wrange[0]
+            and x2 <= wrange[1]
+            and y1 >= hrange[0]
+            and y1 <= hrange[1]
+            and y2 >= hrange[0]
+            and y2 <= hrange[1]
+        )
+
+        print(x1, x2, wrange, y1, y2, hrange, truth_val)
+        return truth_val
+
+    def to_string(self, bbox: list = None, polygon: list = None):
+        coords = ""
+        if bbox is None:
+            bbox = self.bbox
+            # coords += " ".join([f"{x:.6f}" for x in self.bbox])
+        if polygon is None:
+            polygon = self.polygon
+        if self.polygon is not None:
+            coords += " " + " ".join([f"{x:.6f} {y:.6f}" for x, y in self.polygon])
+        return f"{self.class_id} {coords}"
+
+    def __str__(self):
+        return f"Class: {self.class_id}, BBox: {self.bbox}, Polygon: {self.polygon}"
+
+
+class YoloLabelReader:
+    def __init__(self, label_path: Path):
+        self.label_path = label_path
+        self.labels = self._read_labels()
+
+    def _read_labels(self):
+        with open(self.label_path, "r") as f:
+            labels = [Label(line) for line in f.readlines()]
+
+        return labels
+
+    def get_labels(self, hrange, wrange):
+        """hrange and wrange are tuples of (start, end) normalized to [0, 1]"""
+        labels = []
+        # print(hrange, wrange)
+        for lbl in self.labels:
+            # print(lbl)
+            if lbl.in_range(hrange, wrange):
+                labels.append(lbl)
+        return labels if len(labels) > 0 else None
+
+    def __get_item__(self, index):
+        return self.labels[index]
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __iter__(self):
+        return iter(self.labels)
+
+
+class ImageSplitter:
+    def __init__(self, image_path: Path, label_path: Path):
+        self.image = imread(image_path)
+        self.image_path = image_path
+        self.label_path = label_path
+        if not label_path.exists():
+            print(f"Label file {label_path} not found")
+            self.labels = None
+        else:
+            self.labels = YoloLabelReader(label_path)
+
+    def split_into_tiles(self, patch_size: tuple = (2, 2)):
+        """Split image into patches of size patch_size"""
+        hstep, wstep = (
+            self.image.shape[0] // patch_size[0],
+            self.image.shape[1] // patch_size[1],
+        )
+        h, w = self.image.shape[:2]
+
+        for i in range(patch_size[0]):
+            for j in range(patch_size[1]):
+                metadata = {
+                    "image_path": str(self.image_path),
+                    "label_path": str(self.label_path),
+                    "tile_section": f"{i}, {j}",
+                    "tile_size": f"{hstep}, {wstep}",
+                    "patch_size": f"{patch_size[0]}, {patch_size[1]}",
+                }
+                tile_reference = f"i{i}j{j}"
+                hrange = (i * hstep / h, (i + 1) * hstep / h)
+                wrange = (j * wstep / w, (j + 1) * wstep / w)
+                tile = self.image[i * hstep : (i + 1) * hstep, j * wstep : (j + 1) * wstep]
+
+                labels = None
+                if self.labels is not None:
+                    labels = deepcopy(self.labels.get_labels(hrange, wrange))
+                    print(id(labels))
+
+                    if labels is not None:
+                        print(hrange[0], wrange[0])
+                        for l in labels:
+                            print(l.bbox)
+                        [l.translate(wrange[0], hrange[0], 2, 2) for l in labels]
+                        print("translated")
+                        for l in labels:
+                            print(l.bbox)
+
+                # print(labels)
+                yield tile_reference, tile, labels, metadata
+
+    def split_respective_to_label(self, padding: int = 67):
+        if self.labels is None:
+            raise ValueError("No labels found. Only images having labels can be split.")
+
+        for i, label in enumerate(self.labels):
+            tile_reference = f"_lbl-{i+1:02d}"
+            # print(label.bbox)
+            metadata = {"image_path": str(self.image_path), "label_path": str(self.label_path), "label_index": str(i)}
+
+            xc_norm, yc_norm, h_norm, w_norm = label.bbox  # normalized coords
+            xc, yc, h, w = [
+                int(np.round(f))
+                for f in [
+                    xc_norm * self.image.shape[1],
+                    yc_norm * self.image.shape[0],
+                    h_norm * self.image.shape[0],
+                    w_norm * self.image.shape[1],
+                ]
+            ]  # image coords
+
+            # print("img coords:", xc, yc, h, w)
+            pad_xneg = padding + 1  # int(w / 2) + padding
+            pad_xpos = padding  # int(w / 2) + padding
+            pad_yneg = padding + 1  # int(h / 2) + padding
+            pad_ypos = padding  # int(h / 2) + padding
+            if xc - pad_xneg < 0:
+                pad_xneg = xc
+            if pad_xpos + xc > self.image.shape[1]:
+                pad_xpos = self.image.shape[1] - xc
+            if yc - pad_yneg < 0:
+                pad_yneg = yc
+            if pad_ypos + yc > self.image.shape[0]:
+                pad_ypos = self.image.shape[0] - yc
+
+            # print("pads:", pad_xneg, pad_xpos, pad_yneg, pad_ypos)
+
+            tile = self.image[
+                yc - pad_yneg : yc + pad_ypos,
+                xc - pad_xneg : xc + pad_xpos,
+            ]
+            ny, nx = tile.shape
+            x_offset = pad_xneg
+            y_offset = pad_yneg
+
+            # print("tile shape:", tile.shape)
+
+            yolo_annotation = f"{label.class_id} "  # {x_offset/nx} {y_offset/ny} {h_norm} {w_norm} "
+            yolo_annotation += " ".join(
+                [
+                    f"{(x*self.image.shape[1]-(xc - x_offset))/nx:.6f} {(y*self.image.shape[0]-(yc-y_offset))/ny:.6f}"
+                    for x, y in label.polygon
+                ]
+            )
+            print(yolo_annotation)
+            new_label = Label(yolo_annotation=yolo_annotation)
+
+            yield tile_reference, tile, [new_label], metadata
+
+
+def main(args):
+
+    if args.output:
+        args.output.mkdir(exist_ok=True, parents=True)
+        (args.output / "images").mkdir(exist_ok=True)
+        (args.output / "images-zoomed").mkdir(exist_ok=True)
+        (args.output / "labels").mkdir(exist_ok=True)
+
+    for image_path in (args.input / "images").glob("*.tif"):
+        data = ImageSplitter(
+            image_path=image_path,
+            label_path=(args.input / "labels" / image_path.stem).with_suffix(".txt"),
+        )
+
+        if args.split_around_label:
+            data = data.split_respective_to_label(padding=args.padding)
+        else:
+            data = data.split_into_tiles(patch_size=args.patch_size)
+
+        for tile_reference, tile, labels, metadata in data:
+            print()
+            print(tile_reference, tile.shape, labels, metadata)  # len(labels) if labels else None)
+
+            # { debug
+            debug = False
+            if debug:
+                plt.figure(figsize=(10, 10 * tile.shape[0] / tile.shape[1]))
+                if labels is None:
+                    plt.imshow(tile, cmap="gray")
+                    plt.axis("off")
+                    plt.title(f"{image_path.name} ({tile_reference})")
+                    plt.show()
+                    continue
+
+                print(labels[0].bbox)
+                # Draw annotations
+                out = draw_annotations(
+                    cv2.cvtColor((tile / tile.max() * 255).astype(np.uint8), cv2.COLOR_GRAY2BGR),
+                    [l.to_string() for l in labels],
+                    alpha=0.1,
+                )
+
+                # Convert BGR -> RGB for matplotlib display
+                out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+                plt.imshow(out_rgb)
+                plt.axis("off")
+                plt.title(f"{image_path.name} ({tile_reference})")
+                plt.show()
+            # } debug
+
+            if args.output:
+                # imwrite(args.output / "images" / f"{image_path.stem}_{tile_reference}.tif", tile, metadata=metadata)
+                scale = 5
+                tile_zoomed = zoom(tile, zoom=scale)
+                metadata["scale"] = scale
+                imwrite(
+                    args.output / "images" / f"{image_path.stem}_{tile_reference}.tif",
+                    tile_zoomed,
+                    metadata=metadata,
+                    imagej=True,
+                )
+
+                if labels is not None:
+                    with open(args.output / "labels" / f"{image_path.stem}_{tile_reference}.txt", "w") as f:
+                        for label in labels:
+                            # label.offset_label(tile.shape[1], tile.shape[0])
+                            f.write(label.to_string() + "\n")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--input", type=Path)
+    parser.add_argument("-o", "--output", type=Path)
+    parser.add_argument(
+        "-p",
+        "--patch-size",
+        nargs=2,
+        type=int,
+        default=[2, 2],
+        help="Number of patches along height and width, rows and columns, respectively",
+    )
+    parser.add_argument(
+        "-sal",
+        "--split-around-label",
+        action="store_true",
+        help="If enabled, the image will be split around the label and for each label, a separate image will be created.",
+    )
+    parser.add_argument(
+        "--padding",
+        type=int,
+        default=67,
+        help="Padding around the label when splitting around the label.",
+    )
+    args = parser.parse_args()
+
+    main(args)

src/utils/show_yolo_seg.py

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

src/utils/train_ultralytics_float.py

@@ -1,561 +0,0 @@
-"""
-Custom YOLO training with on-the-fly float32 conversion for 16-bit grayscale images.
-
-This module provides a custom dataset class and training function that:
-1. Load 16-bit TIFF images directly with tifffile (no PIL/cv2)
-2. Convert to float32 [0-1] on-the-fly (no data loss)
-3. Replicate grayscale to 3-channel RGB in memory
-4. Use custom training loop to bypass Ultralytics' dataset infrastructure
-5. No disk caching required
-"""
-
-import numpy as np
-import tifffile
-import torch
-from torch.utils.data import Dataset, DataLoader
-from pathlib import Path
-from typing import Optional, Dict, Any, List, Tuple
-from ultralytics import YOLO
-import yaml
-import time
-
-from src.utils.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-class Float32YOLODataset(Dataset):
-    """
-    Custom PyTorch dataset for YOLO that loads 16-bit grayscale TIFFs as float32 RGB.
-
-    This dataset:
-    - Loads with tifffile (not PIL/cv2)
-    - Converts uint16 → float32 [0-1] (preserves full dynamic range)
-    - Replicates grayscale to 3 channels
-    - Returns torch tensors in (C, H, W) format
-    """
-
-    def __init__(self, images_dir: str, labels_dir: str, img_size: int = 640):
-        """
-        Initialize dataset.
-
-        Args:
-            images_dir: Directory containing images
-            labels_dir: Directory containing YOLO label files (.txt)
-            img_size: Target image size (for reference, actual resizing done by model)
-        """
-        self.images_dir = Path(images_dir)
-        self.labels_dir = Path(labels_dir)
-        self.img_size = img_size
-
-        # Find all image files
-        extensions = {".tif", ".tiff", ".png", ".jpg", ".jpeg", ".bmp"}
-        self.image_paths = sorted(
-            [
-                p
-                for p in self.images_dir.rglob("*")
-                if p.is_file() and p.suffix.lower() in extensions
-            ]
-        )
-
-        if not self.image_paths:
-            raise ValueError(f"No images found in {images_dir}")
-
-        logger.info(
-            f"Float32YOLODataset initialized with {len(self.image_paths)} images from {images_dir}"
-        )
-
-    def __len__(self):
-        return len(self.image_paths)
-
-    def _read_image(self, img_path: Path) -> np.ndarray:
-        """
-        Read image and convert to float32 [0-1] RGB.
-
-        Returns:
-            numpy array, shape (H, W, 3), dtype float32, range [0, 1]
-        """
-        # Load image with tifffile
-        img = tifffile.imread(str(img_path))
-
-        # Convert to float32
-        img = img.astype(np.float32)
-
-        # Normalize if 16-bit (values > 1.5 indicates uint16)
-        if img.max() > 1.5:
-            img = img / 65535.0
-
-        # Ensure [0, 1] range
-        img = np.clip(img, 0.0, 1.0)
-
-        # Convert grayscale to RGB
-        if img.ndim == 2:
-            # H,W → H,W,3
-            img = np.repeat(img[..., None], 3, axis=2)
-        elif img.ndim == 3 and img.shape[2] == 1:
-            # H,W,1 → H,W,3
-            img = np.repeat(img, 3, axis=2)
-
-        return img  # float32 (H, W, 3) in [0, 1]
-
-    def _parse_label(self, label_path: Path) -> List[np.ndarray]:
-        """
-        Parse YOLO label file with variable-length rows (segmentation polygons).
-
-        Returns:
-            List of numpy arrays, one per annotation
-        """
-        if not label_path.exists():
-            return []
-
-        labels = []
-        try:
-            with open(label_path, "r") as f:
-                for line in f:
-                    line = line.strip()
-                    if not line:
-                        continue
-                    # Parse space-separated values
-                    values = line.split()
-                    if len(values) >= 5:  # At minimum: class_id x y w h
-                        labels.append(
-                            np.array([float(v) for v in values], dtype=np.float32)
-                        )
-        except Exception as e:
-            logger.warning(f"Error parsing label {label_path}: {e}")
-            return []
-
-        return labels
-
-    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, List[np.ndarray], str]:
-        """
-        Get a single training sample.
-
-        Returns:
-            Tuple of (image_tensor, labels, filename)
-            - image_tensor: shape (3, H, W), dtype float32, range [0, 1]
-            - labels: list of numpy arrays with YOLO format labels (variable length for segmentation)
-            - filename: image filename
-        """
-        img_path = self.image_paths[idx]
-        label_path = self.labels_dir / f"{img_path.stem}.txt"
-
-        # Load image as float32 RGB
-        img = self._read_image(img_path)
-
-        # Convert to tensor: (H, W, 3) → (3, H, W)
-        img_tensor = torch.from_numpy(img).permute(2, 0, 1).contiguous()
-
-        # Load labels (list of variable-length arrays for segmentation)
-        labels = self._parse_label(label_path)
-
-        return img_tensor, labels, img_path.name
-
-
-def collate_fn(
-    batch: List[Tuple[torch.Tensor, List[np.ndarray], str]],
-) -> Tuple[torch.Tensor, List[List[np.ndarray]], List[str]]:
-    """
-    Collate function for DataLoader.
-
-    Args:
-        batch: List of (img_tensor, labels_list, filename) tuples
-            where labels_list is a list of variable-length numpy arrays
-
-    Returns:
-        Tuple of (stacked_images, list_of_labels_lists, list_of_filenames)
-    """
-    imgs = [b[0] for b in batch]
-    labels = [b[1] for b in batch]  # Each element is a list of arrays
-    names = [b[2] for b in batch]
-
-    # Stack images - requires same H,W
-    # For different sizes, implement letterbox/resize in dataset
-    imgs_batch = torch.stack(imgs, dim=0)
-
-    return imgs_batch, labels, names
-
-
-def train_with_float32_loader(
-    model_path: str,
-    data_yaml: str,
-    epochs: int = 100,
-    imgsz: int = 640,
-    batch: int = 16,
-    patience: int = 50,
-    save_dir: str = "data/models",
-    name: str = "custom_model",
-    callbacks: Optional[Dict] = None,
-    **kwargs,
-) -> Dict[str, Any]:
-    """
-    Train YOLO model with custom Float32 dataset for 16-bit TIFF support.
-
-    Uses a custom training loop to bypass Ultralytics' dataset pipeline,
-    avoiding channel conversion issues.
-
-    Args:
-        model_path: Path to base model weights (.pt file)
-        data_yaml: Path to dataset YAML configuration
-        epochs: Number of training epochs
-        imgsz: Input image size
-        batch: Batch size
-        patience: Early stopping patience
-        save_dir: Directory to save trained model
-        name: Name for the training run
-        callbacks: Optional callback dictionary (for progress reporting)
-        **kwargs: Additional training arguments (lr0, freeze, device, etc.)
-
-    Returns:
-        Dict with training results including model paths and metrics
-    """
-    try:
-        logger.info(f"Starting Float32 custom training: {name}")
-        logger.info(
-            f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}"
-        )
-
-        # Parse data.yaml to get dataset paths
-        with open(data_yaml, "r") as f:
-            data_config = yaml.safe_load(f)
-
-        dataset_root = Path(data_config.get("path", Path(data_yaml).parent))
-        train_images = dataset_root / data_config.get("train", "train/images")
-        val_images = dataset_root / data_config.get("val", "val/images")
-
-        # Infer label directories
-        train_labels = train_images.parent / "labels"
-        val_labels = val_images.parent / "labels"
-
-        logger.info(f"Train images: {train_images}")
-        logger.info(f"Train labels: {train_labels}")
-        logger.info(f"Val images: {val_images}")
-        logger.info(f"Val labels: {val_labels}")
-
-        # Create datasets
-        train_dataset = Float32YOLODataset(
-            str(train_images), str(train_labels), img_size=imgsz
-        )
-        val_dataset = Float32YOLODataset(
-            str(val_images), str(val_labels), img_size=imgsz
-        )
-
-        # Create data loaders
-        train_loader = DataLoader(
-            train_dataset,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=4,
-            pin_memory=True,
-            collate_fn=collate_fn,
-        )
-
-        val_loader = DataLoader(
-            val_dataset,
-            batch_size=batch,
-            shuffle=False,
-            num_workers=2,
-            pin_memory=True,
-            collate_fn=collate_fn,
-        )
-
-        # Load model
-        logger.info(f"Loading model from {model_path}")
-        ul_model = YOLO(model_path)
-
-        # Get PyTorch model
-        pt_model, loss_fn = _get_pytorch_model(ul_model)
-
-        # Setup device
-        device = kwargs.get("device", "cuda" if torch.cuda.is_available() else "cpu")
-
-        # Configure model args for loss function
-        from types import SimpleNamespace
-
-        # Required args for segmentation loss
-        required_args = {
-            "overlap_mask": True,
-            "mask_ratio": 4,
-            "task": "segment",
-            "single_cls": False,
-            "box": 7.5,
-            "cls": 0.5,
-            "dfl": 1.5,
-        }
-
-        if not hasattr(pt_model, "args"):
-            # No args - create SimpleNamespace
-            pt_model.args = SimpleNamespace(**required_args)
-        elif isinstance(pt_model.args, dict):
-            # Args is dict - MUST convert to SimpleNamespace for attribute access
-            # The loss function uses model.args.overlap_mask (attribute access)
-            merged = {**pt_model.args, **required_args}
-            pt_model.args = SimpleNamespace(**merged)
-            logger.info(
-                "Converted model.args from dict to SimpleNamespace for loss function compatibility"
-            )
-        else:
-            # Args is SimpleNamespace or other - set attributes
-            for key, value in required_args.items():
-                if not hasattr(pt_model.args, key):
-                    setattr(pt_model.args, key, value)
-
-        pt_model.to(device)
-        pt_model.train()
-
-        logger.info(f"Training on device: {device}")
-        logger.info(f"PyTorch model type: {type(pt_model)}")
-        logger.info(f"Model args configured for segmentation loss")
-
-        # Setup optimizer
-        lr0 = kwargs.get("lr0", 0.01)
-        optimizer = torch.optim.AdamW(pt_model.parameters(), lr=lr0)
-
-        # Training loop
-        save_path = Path(save_dir) / name
-        save_path.mkdir(parents=True, exist_ok=True)
-        weights_dir = save_path / "weights"
-        weights_dir.mkdir(exist_ok=True)
-
-        best_loss = float("inf")
-        patience_counter = 0
-
-        for epoch in range(epochs):
-            epoch_start = time.time()
-            running_loss = 0.0
-            num_batches = 0
-
-            logger.info(f"Epoch {epoch+1}/{epochs} starting...")
-
-            for batch_idx, (imgs, labels_list, names) in enumerate(train_loader):
-                imgs = imgs.to(device)  # (B, 3, H, W) float32
-
-                optimizer.zero_grad()
-
-                # Forward pass
-                try:
-                    preds = pt_model(imgs)
-                except Exception as e:
-                    # Try with labels
-                    preds = pt_model(imgs, labels_list)
-
-                # Compute loss
-                # For Ultralytics models, the easiest approach is to construct a batch dict
-                # and call the model in training mode which returns preds + loss
-                batch_dict = {
-                    "img": imgs,  # Already on device
-                    "batch_idx": (
-                        torch.cat(
-                            [
-                                torch.full((len(lab),), i, dtype=torch.long)
-                                for i, lab in enumerate(labels_list)
-                            ]
-                        ).to(device)
-                        if any(len(lab) > 0 for lab in labels_list)
-                        else torch.tensor([], dtype=torch.long, device=device)
-                    ),
-                    "cls": (
-                        torch.cat(
-                            [
-                                torch.from_numpy(lab[:, 0:1])
-                                for lab in labels_list
-                                if len(lab) > 0
-                            ]
-                        ).to(device)
-                        if any(len(lab) > 0 for lab in labels_list)
-                        else torch.tensor([], dtype=torch.float32, device=device)
-                    ),
-                    "bboxes": (
-                        torch.cat(
-                            [
-                                torch.from_numpy(lab[:, 1:5])
-                                for lab in labels_list
-                                if len(lab) > 0
-                            ]
-                        ).to(device)
-                        if any(len(lab) > 0 for lab in labels_list)
-                        else torch.tensor([], dtype=torch.float32, device=device)
-                    ),
-                    "ori_shape": (imgs.shape[2], imgs.shape[3]),  # H, W
-                    "resized_shape": (imgs.shape[2], imgs.shape[3]),
-                }
-
-                # Add masks if segmentation labels exist
-                if any(len(lab) > 5 for lab in labels_list if len(lab) > 0):
-                    masks = []
-                    for lab in labels_list:
-                        if len(lab) > 0 and lab.shape[1] > 5:
-                            # Has segmentation points
-                            masks.append(torch.from_numpy(lab[:, 5:]))
-                    if masks:
-                        batch_dict["masks"] = masks
-
-                # Call model loss (it will compute loss internally)
-                try:
-                    loss_output = pt_model.loss(batch_dict, preds)
-                    if isinstance(loss_output, (tuple, list)):
-                        loss = loss_output[0]
-                    else:
-                        loss = loss_output
-                except Exception as e:
-                    logger.error(f"Model loss computation failed: {e}")
-                    # Last resort: maybe preds is already a dict with 'loss'
-                    if isinstance(preds, dict) and "loss" in preds:
-                        loss = preds["loss"]
-                    else:
-                        raise RuntimeError(f"Cannot compute loss: {e}")
-
-                # Backward pass
-                loss.backward()
-                optimizer.step()
-
-                running_loss += loss.item()
-                num_batches += 1
-
-                # Report progress via callback
-                if callbacks and "on_fit_epoch_end" in callbacks:
-                    # Create a mock trainer object for callback
-                    class MockTrainer:
-                        def __init__(self, epoch):
-                            self.epoch = epoch
-                            self.loss_items = [loss.item()]
-
-                    callbacks["on_fit_epoch_end"](MockTrainer(epoch))
-
-            epoch_loss = running_loss / max(1, num_batches)
-            epoch_time = time.time() - epoch_start
-
-            logger.info(
-                f"Epoch {epoch+1}/{epochs} completed. Avg Loss: {epoch_loss:.4f}, Time: {epoch_time:.1f}s"
-            )
-
-            # Save checkpoint
-            ckpt_path = weights_dir / f"epoch{epoch+1}.pt"
-            torch.save(
-                {
-                    "epoch": epoch + 1,
-                    "model_state_dict": pt_model.state_dict(),
-                    "optimizer_state_dict": optimizer.state_dict(),
-                    "loss": epoch_loss,
-                },
-                ckpt_path,
-            )
-
-            # Save as last.pt
-            last_path = weights_dir / "last.pt"
-            torch.save(pt_model.state_dict(), last_path)
-
-            # Check for best model
-            if epoch_loss < best_loss:
-                best_loss = epoch_loss
-                patience_counter = 0
-                best_path = weights_dir / "best.pt"
-                torch.save(pt_model.state_dict(), best_path)
-                logger.info(f"New best model saved: {best_path}")
-            else:
-                patience_counter += 1
-
-            # Early stopping
-            if patience_counter >= patience:
-                logger.info(f"Early stopping triggered after {epoch+1} epochs")
-                break
-
-        logger.info("Training completed successfully")
-
-        # Format results
-        return {
-            "success": True,
-            "final_epoch": epoch + 1,
-            "metrics": {
-                "final_loss": epoch_loss,
-                "best_loss": best_loss,
-            },
-            "best_model_path": str(weights_dir / "best.pt"),
-            "last_model_path": str(weights_dir / "last.pt"),
-            "save_dir": str(save_path),
-        }
-
-    except Exception as e:
-        logger.error(f"Error during Float32 training: {e}")
-        import traceback
-
-        logger.error(traceback.format_exc())
-        raise
-
-
-def _get_pytorch_model(ul_model: YOLO) -> Tuple[torch.nn.Module, Optional[callable]]:
-    """
-    Extract PyTorch model and loss function from Ultralytics YOLO wrapper.
-
-    Args:
-        ul_model: Ultralytics YOLO model wrapper
-
-    Returns:
-        Tuple of (pytorch_model, loss_function)
-    """
-    # Try to get the underlying PyTorch model
-    candidates = []
-
-    # Direct model attribute
-    if hasattr(ul_model, "model"):
-        candidates.append(ul_model.model)
-
-    # Sometimes nested
-    if hasattr(ul_model, "model") and hasattr(ul_model.model, "model"):
-        candidates.append(ul_model.model.model)
-
-    # The wrapper itself
-    if isinstance(ul_model, torch.nn.Module):
-        candidates.append(ul_model)
-
-    # Find a valid model
-    pt_model = None
-    loss_fn = None
-
-    for candidate in candidates:
-        if candidate is None or not isinstance(candidate, torch.nn.Module):
-            continue
-
-        pt_model = candidate
-
-        # Try to find loss function
-        if hasattr(candidate, "loss") and callable(getattr(candidate, "loss")):
-            loss_fn = getattr(candidate, "loss")
-        elif hasattr(candidate, "compute_loss") and callable(
-            getattr(candidate, "compute_loss")
-        ):
-            loss_fn = getattr(candidate, "compute_loss")
-
-        break
-
-    if pt_model is None:
-        raise RuntimeError("Could not extract PyTorch model from Ultralytics wrapper")
-
-    logger.info(f"Extracted PyTorch model: {type(pt_model)}")
-    logger.info(
-        f"Loss function: {type(loss_fn) if loss_fn else 'None (will attempt fallback)'}"
-    )
-
-    return pt_model, loss_fn
-
-
-# Compatibility function (kept for backwards compatibility)
-def train_float32(model: YOLO, data_yaml: str, **train_kwargs) -> Any:
-    """
-    Train YOLO model with Float32YOLODataset (alternative API).
-
-    Args:
-        model: Initialized YOLO model instance
-        data_yaml: Path to dataset YAML
-        **train_kwargs: Training parameters
-
-    Returns:
-        Training results dict
-    """
-    return train_with_float32_loader(
-        model_path=(
-            model.model_path if hasattr(model, "model_path") else "yolov8s-seg.pt"
-        ),
-        data_yaml=data_yaml,
-        **train_kwargs,
-    )

src/utils/ultralytics_16bit_patch.py

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

tests/show_yolo_seg.py

@@ -17,6 +17,9 @@ 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):
@@ -52,36 +55,55 @@ def yolo_bbox_to_xyxy(coords, img_w, img_h):
 
 def poly_to_pts(coords, img_w, img_h):
     # coords: [x1 y1 x2 y2 ...] either normalized or absolute
-    if coords_are_normalized(coords):
+    if coords_are_normalized(coords[4:]):
-        coords = [
+        coords = [coords[i] * (img_w if i % 2 == 0 else img_h) for i in range(len(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 tuple(int(x) for x in np.array([random.randint(0, 255) for _ in range(3)]))
+    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 cls, coords in labels:
+    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:
-            pts = poly_to_pts(coords, w, h)
             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=2)
+            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),
@@ -92,9 +114,7 @@ def draw_annotations(img, labels, alpha=0.4, draw_bbox_for_poly=True):
                     2,
                     cv2.LINE_AA,
                 )
-            if draw_bbox_for_poly:
+
-                x, y, w_box, h_box = cv2.boundingRect(pts)
-                cv2.rectangle(img, (x, y), (x + w_box, y + h_box), color, 1)
         # YOLO bbox case (4 coords)
         elif len(coords) == 4:
             x1, y1, x2, y2 = yolo_bbox_to_xyxy(coords, w, h)
@@ -133,21 +153,21 @@ def load_labels_file(label_path):
 
 
 def main():
-    parser = argparse.ArgumentParser(
+    parser = argparse.ArgumentParser(description="Show YOLO segmentation / polygon annotations")
-        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("--labels", type=str, help="Path to YOLO label file (polygons)")
-    parser.add_argument(
+    parser.add_argument("--alpha", type=float, default=0.4, help="Polygon fill alpha (0..1)")
-        "--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")
-    )
-    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)
@@ -156,7 +176,9 @@ def main():
         print("Label file not found:", lbl_path)
         sys.exit(1)
 
-    img = cv2.imread(str(img_path), cv2.IMREAD_COLOR)
+    # 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)
@@ -165,15 +187,42 @@ def main():
     if not labels:
         print("No labels parsed from", lbl_path)
         # continue and just show image
-    out = draw_annotations(
+    out = draw_annotations(img.copy(), labels, alpha=args.alpha, draw_bbox_for_poly=(not args.no_bbox))
-        img.copy(), labels, alpha=args.alpha, draw_bbox_for_poly=(not args.no_bbox)
+
-    )
+    out_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
+    if 0:
+        plt.imshow(out_rgb.transpose(1, 0, 2))
+    else:
+        plt.imshow(out_rgb)
+
+    for label in labels:
+        lclass, coords = label
+        # print(lclass, coords)
+        bbox = coords[:4]
+        # print("bbox", bbox)
+        bbox = np.array(bbox) * np.array([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        yc, xc, h, w = bbox
+        # print("bbox", bbox)
+
+        # polyline = np.array(coords[4:]).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+        polyline = np.array(coords).reshape(-1, 2) * np.array([img.shape[1], img.shape[0]])
+        # print("pl", coords[4:])
+        # print("pl", polyline)
 
         # Convert BGR -> RGB for matplotlib display
-    out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
+        # out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
-    plt.figure(figsize=(10, 10 * out.shape[0] / out.shape[1]))
+        # out_rgb = Image()
-    plt.imshow(out_rgb)
+        plt.plot(polyline[:, 0], polyline[:, 1], "y", linewidth=2)
-    plt.axis("off")
+        if 0:
+            plt.plot(
+                [yc - h / 2, yc - h / 2, yc + h / 2, yc + h / 2, yc - h / 2],
+                [xc - w / 2, xc + w / 2, xc + w / 2, xc - w / 2, xc - w / 2],
+                "r",
+                linewidth=2,
+            )
+
+    # plt.axis("off")
     plt.title(f"{img_path.name} ({lbl_path.name})")
     plt.show()
 

tests/test_16bit_tiff_loading.py

@@ -1,109 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for 16-bit TIFF loading and normalization.
-"""
-
-import numpy as np
-import tifffile
-from pathlib import Path
-import tempfile
-import sys
-import os
-
-# Add parent directory to path to import modules
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from src.utils.image import Image
-
-
-def create_test_16bit_tiff(output_path: str) -> str:
-    """Create a test 16-bit grayscale TIFF file.
-
-    Args:
-        output_path: Path where to save the test TIFF
-
-    Returns:
-        Path to the created TIFF file
-    """
-    # Create a 16-bit grayscale test image (100x100)
-    # With values ranging from 0 to 65535 (full 16-bit range)
-    height, width = 100, 100
-
-    # Create a gradient pattern
-    test_data = np.zeros((height, width), dtype=np.uint16)
-    for i in range(height):
-        for j in range(width):
-            # Create a diagonal gradient
-            test_data[i, j] = int((i + j) / (height + width - 2) * 65535)
-
-    # Save as TIFF
-    tifffile.imwrite(output_path, test_data)
-    print(f"Created test 16-bit TIFF: {output_path}")
-    print(f"  Shape: {test_data.shape}")
-    print(f"  Dtype: {test_data.dtype}")
-    print(f"  Min value: {test_data.min()}")
-    print(f"  Max value: {test_data.max()}")
-
-    return output_path
-
-
-def test_image_loading():
-    """Test loading 16-bit TIFF with the Image class."""
-    print("\n=== Testing Image Loading ===")
-
-    # Create temporary test file
-    with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmp:
-        test_path = tmp.name
-
-    try:
-        # Create test image
-        create_test_16bit_tiff(test_path)
-
-        # Load with Image class
-        print("\nLoading with Image class...")
-        img = Image(test_path)
-
-        print(f"Successfully loaded image:")
-        print(f"  Width: {img.width}")
-        print(f"  Height: {img.height}")
-        print(f"  Channels: {img.channels}")
-        print(f"  Dtype: {img.dtype}")
-        print(f"  Format: {img.format}")
-
-        # Test normalization
-        print("\nTesting normalization to float32 [0-1]...")
-        normalized = img.to_normalized_float32()
-
-        print(f"Normalized image:")
-        print(f"  Shape: {normalized.shape}")
-        print(f"  Dtype: {normalized.dtype}")
-        print(f"  Min value: {normalized.min():.6f}")
-        print(f"  Max value: {normalized.max():.6f}")
-        print(f"  Mean value: {normalized.mean():.6f}")
-
-        # Verify normalization
-        assert normalized.dtype == np.float32, "Dtype should be float32"
-        assert (
-            0.0 <= normalized.min() <= normalized.max() <= 1.0
-        ), "Values should be in [0, 1]"
-
-        print("\n✓ All tests passed!")
-        return True
-
-    except Exception as e:
-        print(f"\n✗ Test failed with error: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return False
-
-    finally:
-        # Cleanup
-        if os.path.exists(test_path):
-            os.remove(test_path)
-            print(f"\nCleaned up test file: {test_path}")
-
-
-if __name__ == "__main__":
-    success = test_image_loading()
-    sys.exit(0 if success else 1)

tests/test_float32_training_loader.py

@@ -1,211 +0,0 @@
-"""
-Test script for Float32 on-the-fly loading for 16-bit TIFFs.
-
-This test verifies that:
-1. Float32YOLODataset can load 16-bit TIFF files
-2. Images are converted to float32 [0-1] in memory
-3. Grayscale is replicated to 3 channels (RGB)
-4. No disk caching is used
-5. Full 16-bit precision is preserved
-"""
-
-import tempfile
-import numpy as np
-import tifffile
-from pathlib import Path
-import yaml
-
-
-def create_test_dataset():
-    """Create a minimal test dataset with 16-bit TIFF images."""
-    temp_dir = Path(tempfile.mkdtemp())
-    dataset_dir = temp_dir / "test_dataset"
-
-    # Create directory structure
-    train_images = dataset_dir / "train" / "images"
-    train_labels = dataset_dir / "train" / "labels"
-    train_images.mkdir(parents=True, exist_ok=True)
-    train_labels.mkdir(parents=True, exist_ok=True)
-
-    # Create a 16-bit TIFF test image
-    img_16bit = np.random.randint(0, 65536, (100, 100), dtype=np.uint16)
-    img_path = train_images / "test_image.tif"
-    tifffile.imwrite(str(img_path), img_16bit)
-
-    # Create a dummy label file
-    label_path = train_labels / "test_image.txt"
-    with open(label_path, "w") as f:
-        f.write("0 0.5 0.5 0.2 0.2\n")  # class_id x_center y_center width height
-
-    # Create data.yaml
-    data_yaml = {
-        "path": str(dataset_dir),
-        "train": "train/images",
-        "val": "train/images",  # Use same for val in test
-        "names": {0: "object"},
-        "nc": 1,
-    }
-    yaml_path = dataset_dir / "data.yaml"
-    with open(yaml_path, "w") as f:
-        yaml.safe_dump(data_yaml, f)
-
-    print(f"✓ Created test dataset at: {dataset_dir}")
-    print(f"  - Image: {img_path} (shape={img_16bit.shape}, dtype={img_16bit.dtype})")
-    print(f"  - Min value: {img_16bit.min()}, Max value: {img_16bit.max()}")
-    print(f"  - data.yaml: {yaml_path}")
-
-    return dataset_dir, img_path, img_16bit
-
-
-def test_float32_dataset():
-    """Test the Float32YOLODataset class directly."""
-    print("\n=== Testing Float32YOLODataset ===\n")
-
-    try:
-        from src.utils.train_ultralytics_float import Float32YOLODataset
-
-        print("✓ Successfully imported Float32YOLODataset")
-    except ImportError as e:
-        print(f"✗ Failed to import Float32YOLODataset: {e}")
-        return False
-
-    # Create test dataset
-    dataset_dir, img_path, original_img = create_test_dataset()
-
-    try:
-        # Initialize the dataset
-        print("\nInitializing Float32YOLODataset...")
-        dataset = Float32YOLODataset(
-            images_dir=str(dataset_dir / "train" / "images"),
-            labels_dir=str(dataset_dir / "train" / "labels"),
-            img_size=640,
-        )
-        print(f"✓ Float32YOLODataset initialized with {len(dataset)} images")
-
-        # Get an item
-        if len(dataset) > 0:
-            print("\nGetting first item...")
-            img_tensor, labels, filename = dataset[0]
-
-            print(f"✓ Item retrieved successfully")
-            print(f"  - Image tensor shape: {img_tensor.shape}")
-            print(f"  - Image tensor dtype: {img_tensor.dtype}")
-            print(f"  - Value range: [{img_tensor.min():.6f}, {img_tensor.max():.6f}]")
-            print(f"  - Filename: {filename}")
-            print(f"  - Labels: {len(labels)} annotations")
-            if labels:
-                print(
-                    f"  - First label shape: {labels[0].shape if len(labels) > 0 else 'N/A'}"
-                )
-
-            # Verify it's float32
-            if img_tensor.dtype == torch.float32:
-                print("✓ Correct dtype: float32")
-            else:
-                print(f"✗ Wrong dtype: {img_tensor.dtype} (expected float32)")
-                return False
-
-            # Verify it's 3-channel in correct format (C, H, W)
-            if len(img_tensor.shape) == 3 and img_tensor.shape[0] == 3:
-                print(
-                    f"✓ Correct format: (C, H, W) = {img_tensor.shape} with 3 channels"
-                )
-            else:
-                print(f"✗ Wrong shape: {img_tensor.shape} (expected (3, H, W))")
-                return False
-
-            # Verify it's in [0, 1] range
-            if 0.0 <= img_tensor.min() and img_tensor.max() <= 1.0:
-                print("✓ Values in correct range: [0, 1]")
-            else:
-                print(
-                    f"✗ Values out of range: [{img_tensor.min()}, {img_tensor.max()}]"
-                )
-                return False
-
-            # Verify precision (should have many unique values)
-            unique_values = len(torch.unique(img_tensor))
-            print(f"  - Unique values: {unique_values}")
-            if unique_values > 256:
-                print(f"✓ High precision maintained ({unique_values} > 256 levels)")
-            else:
-                print(f"⚠ Low precision: only {unique_values} unique values")
-
-            print("\n✓ All Float32YOLODataset tests passed!")
-            return True
-        else:
-            print("✗ No items in dataset")
-            return False
-
-    except Exception as e:
-        print(f"✗ Error during testing: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return False
-
-
-def test_integration():
-    """Test integration with train_with_float32_loader."""
-    print("\n=== Testing Integration with train_with_float32_loader ===\n")
-
-    # Create test dataset
-    dataset_dir, img_path, original_img = create_test_dataset()
-    data_yaml = dataset_dir / "data.yaml"
-
-    print(f"\nTest dataset ready at: {data_yaml}")
-    print("\nTo test full training, run:")
-    print(f"  from src.utils.train_ultralytics_float import train_with_float32_loader")
-    print(f"  results = train_with_float32_loader(")
-    print(f"      model_path='yolov8n-seg.pt',")
-    print(f"      data_yaml='{data_yaml}',")
-    print(f"      epochs=1,")
-    print(f"      batch=1,")
-    print(f"      imgsz=640")
-    print(f"  )")
-    print("\nThis will use custom training loop with Float32YOLODataset")
-
-    return True
-
-
-def main():
-    """Run all tests."""
-    import torch  # Import here to ensure torch is available
-
-    print("=" * 70)
-    print("Float32 Training Loader Test Suite")
-    print("=" * 70)
-
-    results = []
-
-    # Test 1: Float32YOLODataset
-    results.append(("Float32YOLODataset", test_float32_dataset()))
-
-    # Test 2: Integration check
-    results.append(("Integration Check", test_integration()))
-
-    # Summary
-    print("\n" + "=" * 70)
-    print("Test Summary")
-    print("=" * 70)
-    for test_name, passed in results:
-        status = "✓ PASSED" if passed else "✗ FAILED"
-        print(f"{status}: {test_name}")
-
-    all_passed = all(passed for _, passed in results)
-    print("=" * 70)
-    if all_passed:
-        print("✓ All tests passed!")
-    else:
-        print("✗ Some tests failed")
-    print("=" * 70)
-
-    return all_passed
-
-
-if __name__ == "__main__":
-    import sys
-    import torch  # Make torch available
-
-    success = main()
-    sys.exit(0 if success else 1)

tests/test_training_dataset_prep.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for training dataset preparation with 16-bit TIFFs.
-"""
-
-import numpy as np
-import tifffile
-from pathlib import Path
-import tempfile
-import sys
-import os
-import shutil
-
-# Add parent directory to path to import modules
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from src.utils.image import Image
-
-
-def test_float32_3ch_conversion():
-    """Test conversion of 16-bit TIFF to 16-bit RGB PNG."""
-    print("\n=== Testing 16-bit RGB PNG Conversion ===")
-
-    # Create temporary directory structure
-    with tempfile.TemporaryDirectory() as tmpdir:
-        tmpdir = Path(tmpdir)
-        src_dir = tmpdir / "original"
-        dst_dir = tmpdir / "converted"
-        src_dir.mkdir()
-        dst_dir.mkdir()
-
-        # Create test 16-bit TIFF
-        test_data = np.zeros((100, 100), dtype=np.uint16)
-        for i in range(100):
-            for j in range(100):
-                test_data[i, j] = int((i + j) / 198 * 65535)
-
-        test_file = src_dir / "test_16bit.tif"
-        tifffile.imwrite(test_file, test_data)
-        print(f"Created test 16-bit TIFF: {test_file}")
-        print(f"  Shape: {test_data.shape}")
-        print(f"  Dtype: {test_data.dtype}")
-        print(f"  Range: [{test_data.min()}, {test_data.max()}]")
-
-        # Simulate the conversion process (matching training_tab.py)
-        print("\nConverting to 16-bit RGB PNG using PIL merge...")
-        img_obj = Image(test_file)
-        from PIL import Image as PILImage
-
-        # Get uint16 data
-        uint16_data = img_obj.data
-
-        # Use PIL's merge method with 'I;16' channels (proper way for 16-bit RGB)
-        if len(uint16_data.shape) == 2:
-            # Grayscale - replicate to RGB
-            r_img = PILImage.fromarray(uint16_data, mode="I;16")
-            g_img = PILImage.fromarray(uint16_data, mode="I;16")
-            b_img = PILImage.fromarray(uint16_data, mode="I;16")
-        else:
-            r_img = PILImage.fromarray(uint16_data[:, :, 0], mode="I;16")
-            g_img = PILImage.fromarray(
-                (
-                    uint16_data[:, :, 1]
-                    if uint16_data.shape[2] > 1
-                    else uint16_data[:, :, 0]
-                ),
-                mode="I;16",
-            )
-            b_img = PILImage.fromarray(
-                (
-                    uint16_data[:, :, 2]
-                    if uint16_data.shape[2] > 2
-                    else uint16_data[:, :, 0]
-                ),
-                mode="I;16",
-            )
-
-        # Merge channels into RGB
-        rgb_img = PILImage.merge("RGB", (r_img, g_img, b_img))
-
-        # Save as PNG
-        output_file = dst_dir / "test_16bit_rgb.png"
-        rgb_img.save(output_file)
-        print(f"Saved 16-bit RGB PNG: {output_file}")
-        print(f"  PIL mode after merge: {rgb_img.mode}")
-
-        # Verify the output - Load with OpenCV (as YOLO does)
-        import cv2
-
-        loaded = cv2.imread(str(output_file), cv2.IMREAD_UNCHANGED)
-        print(f"\nVerifying output (loaded with OpenCV):")
-        print(f"  Shape: {loaded.shape}")
-        print(f"  Dtype: {loaded.dtype}")
-        print(f"  Channels: {loaded.shape[2] if len(loaded.shape) == 3 else 1}")
-        print(f"  Range: [{loaded.min()}, {loaded.max()}]")
-        print(f"  Unique values: {len(np.unique(loaded[:,:,0]))}")
-
-        # Assertions
-        assert loaded.dtype == np.uint16, f"Expected uint16, got {loaded.dtype}"
-        assert loaded.shape[2] == 3, f"Expected 3 channels, got {loaded.shape[2]}"
-        assert (
-            loaded.min() >= 0 and loaded.max() <= 65535
-        ), f"Expected [0,65535] range, got [{loaded.min()}, {loaded.max()}]"
-
-        # Verify all channels are identical (replicated grayscale)
-        assert np.array_equal(
-            loaded[:, :, 0], loaded[:, :, 1]
-        ), "Channel 0 and 1 should be identical"
-        assert np.array_equal(
-            loaded[:, :, 0], loaded[:, :, 2]
-        ), "Channel 0 and 2 should be identical"
-
-        # Verify no data loss
-        unique_vals = len(np.unique(loaded[:, :, 0]))
-        print(f"\n  Precision check:")
-        print(f"    Unique values in channel: {unique_vals}")
-        print(f"    Source unique values: {len(np.unique(test_data))}")
-
-        assert unique_vals == len(
-            np.unique(test_data)
-        ), f"Expected {len(np.unique(test_data))} unique values, got {unique_vals}"
-
-        print("\n✓ All conversion tests passed!")
-        print("  - uint16 dtype preserved")
-        print("  - 3 channels created")
-        print("  - Range [0-65535] maintained")
-        print("  - No precision loss from conversion")
-        print("  - Channels properly replicated")
-
-        return True
-
-
-if __name__ == "__main__":
-    try:
-        success = test_float32_3ch_conversion()
-        sys.exit(0 if success else 1)
-    except Exception as e:
-        print(f"\n✗ Test failed with error: {e}")
-        import traceback
-
-        traceback.print_exc()
-        sys.exit(1)

tests/test_yolo_16bit_float32.py

@@ -1,150 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for YOLO preprocessing of 16-bit TIFF images with float32 passthrough.
-Verifies that no uint8 conversion occurs and data is preserved.
-"""
-
-import numpy as np
-import tifffile
-from pathlib import Path
-import tempfile
-import sys
-import os
-
-# Add parent directory to path to import modules
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from src.model.yolo_wrapper import YOLOWrapper
-
-
-def create_test_16bit_tiff(output_path: str) -> str:
-    """Create a test 16-bit grayscale TIFF file.
-
-    Args:
-        output_path: Path where to save the test TIFF
-
-    Returns:
-        Path to the created TIFF file
-    """
-    # Create a 16-bit grayscale test image (200x200)
-    # With specific values to test precision preservation
-    height, width = 200, 200
-
-    # Create a gradient pattern with the full 16-bit range
-    test_data = np.zeros((height, width), dtype=np.uint16)
-    for i in range(height):
-        for j in range(width):
-            # Create a diagonal gradient using full 16-bit range
-            test_data[i, j] = int((i + j) / (height + width - 2) * 65535)
-
-    # Save as TIFF
-    tifffile.imwrite(output_path, test_data)
-    print(f"Created test 16-bit TIFF: {output_path}")
-    print(f"  Shape: {test_data.shape}")
-    print(f"  Dtype: {test_data.dtype}")
-    print(f"  Min value: {test_data.min()}")
-    print(f"  Max value: {test_data.max()}")
-    print(
-        f"  Sample values: {test_data[50, 50]}, {test_data[100, 100]}, {test_data[150, 150]}"
-    )
-
-    return output_path
-
-
-def test_float32_passthrough():
-    """Test that 16-bit TIFF preprocessing passes float32 directly without uint8 conversion."""
-    print("\n=== Testing Float32 Passthrough (NO uint8) ===")
-
-    # Create temporary test file
-    with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmp:
-        test_path = tmp.name
-
-    try:
-        # Create test image
-        create_test_16bit_tiff(test_path)
-
-        # Create YOLOWrapper instance
-        print("\nTesting YOLOWrapper._prepare_source() for float32 passthrough...")
-        wrapper = YOLOWrapper()
-
-        # Call _prepare_source to preprocess the image
-        prepared_source, cleanup_path = wrapper._prepare_source(test_path)
-
-        print(f"\nPreprocessing result:")
-        print(f"  Original path: {test_path}")
-        print(f"  Prepared source type: {type(prepared_source)}")
-
-        # Verify it returns a numpy array (not a file path)
-        if isinstance(prepared_source, np.ndarray):
-            print(
-                f"\n✓ SUCCESS: Prepared source is a numpy array (float32 passthrough)"
-            )
-            print(f"  Shape: {prepared_source.shape}")
-            print(f"  Dtype: {prepared_source.dtype}")
-            print(f"  Min value: {prepared_source.min():.6f}")
-            print(f"  Max value: {prepared_source.max():.6f}")
-            print(f"  Mean value: {prepared_source.mean():.6f}")
-
-            # Verify it's float32 in [0, 1] range
-            assert (
-                prepared_source.dtype == np.float32
-            ), f"Expected float32, got {prepared_source.dtype}"
-            assert (
-                0.0 <= prepared_source.min() <= prepared_source.max() <= 1.0
-            ), f"Expected values in [0, 1], got [{prepared_source.min()}, {prepared_source.max()}]"
-
-            # Verify it has 3 channels (RGB)
-            assert (
-                prepared_source.shape[2] == 3
-            ), f"Expected 3 channels (RGB), got {prepared_source.shape[2]}"
-
-            # Verify no quantization to 256 levels (would happen with uint8 conversion)
-            unique_values = len(np.unique(prepared_source))
-            print(f"  Unique values: {unique_values}")
-
-            # With float32, we should have much more than 256 unique values
-            if unique_values > 256:
-                print(f"\n✓ SUCCESS: Data has {unique_values} unique values (> 256)")
-                print(f"  This confirms NO uint8 quantization occurred!")
-            else:
-                print(f"\n✗ WARNING: Data has only {unique_values} unique values")
-                print(f"  This might indicate uint8 quantization happened")
-
-            # Sample some values to show precision
-            print(f"\n  Sample normalized values:")
-            print(f"    [50, 50]: {prepared_source[50, 50, 0]:.8f}")
-            print(f"    [100, 100]: {prepared_source[100, 100, 0]:.8f}")
-            print(f"    [150, 150]: {prepared_source[150, 150, 0]:.8f}")
-
-            # No cleanup needed since we returned array directly
-            assert (
-                cleanup_path is None
-            ), "Cleanup path should be None for float32 pass through"
-
-            print("\n✓ All float32 passthrough tests passed!")
-            return True
-
-        else:
-            print(f"\n✗ FAILED: Prepared source is a file path: {prepared_source}")
-            print(f"  This means data was saved to disk, not passed as float32 array")
-            if cleanup_path and os.path.exists(cleanup_path):
-                os.remove(cleanup_path)
-            return False
-
-    except Exception as e:
-        print(f"\n✗ Test failed with error: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return False
-
-    finally:
-        # Cleanup
-        if os.path.exists(test_path):
-            os.remove(test_path)
-            print(f"\nCleaned up test file: {test_path}")
-
-
-if __name__ == "__main__":
-    success = test_float32_passthrough()
-    sys.exit(0 if success else 1)

tests/test_yolo_16bit_preprocessing.py

@@ -1,126 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for YOLO preprocessing of 16-bit TIFF images.
-"""
-
-import numpy as np
-import tifffile
-from pathlib import Path
-import tempfile
-import sys
-import os
-
-# Add parent directory to path to import modules
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from src.model.yolo_wrapper import YOLOWrapper
-from src.utils.image import Image
-from PIL import Image as PILImage
-
-
-def create_test_16bit_tiff(output_path: str) -> str:
-    """Create a test 16-bit grayscale TIFF file.
-
-    Args:
-        output_path: Path where to save the test TIFF
-
-    Returns:
-        Path to the created TIFF file
-    """
-    # Create a 16-bit grayscale test image (200x200)
-    # With values ranging from 0 to 65535 (full 16-bit range)
-    height, width = 200, 200
-
-    # Create a gradient pattern
-    test_data = np.zeros((height, width), dtype=np.uint16)
-    for i in range(height):
-        for j in range(width):
-            # Create a diagonal gradient
-            test_data[i, j] = int((i + j) / (height + width - 2) * 65535)
-
-    # Save as TIFF
-    tifffile.imwrite(output_path, test_data)
-    print(f"Created test 16-bit TIFF: {output_path}")
-    print(f"  Shape: {test_data.shape}")
-    print(f"  Dtype: {test_data.dtype}")
-    print(f"  Min value: {test_data.min()}")
-    print(f"  Max value: {test_data.max()}")
-
-    return output_path
-
-
-def test_yolo_preprocessing():
-    """Test YOLO preprocessing of 16-bit TIFF images."""
-    print("\n=== Testing YOLO Preprocessing of 16-bit TIFF ===")
-
-    # Create temporary test file
-    with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmp:
-        test_path = tmp.name
-
-    try:
-        # Create test image
-        create_test_16bit_tiff(test_path)
-
-        # Create YOLOWrapper instance (no actual model loading needed for this test)
-        print("\nTesting YOLOWrapper._prepare_source()...")
-        wrapper = YOLOWrapper()
-
-        # Call _prepare_source to preprocess the image
-        prepared_path, cleanup_path = wrapper._prepare_source(test_path)
-
-        print(f"\nPreprocessing complete:")
-        print(f"  Original path: {test_path}")
-        print(f"  Prepared path: {prepared_path}")
-        print(f"  Cleanup path: {cleanup_path}")
-
-        # Verify the prepared image exists
-        assert os.path.exists(prepared_path), "Prepared image should exist"
-
-        # Load the prepared image and verify it's uint8 RGB
-        prepared_img = PILImage.open(prepared_path)
-        print(f"\nPrepared image properties:")
-        print(f"  Mode: {prepared_img.mode}")
-        print(f"  Size: {prepared_img.size}")
-        print(f"  Format: {prepared_img.format}")
-
-        # Convert to numpy to check values
-        img_array = np.array(prepared_img)
-        print(f"  Shape: {img_array.shape}")
-        print(f"  Dtype: {img_array.dtype}")
-        print(f"  Min value: {img_array.min()}")
-        print(f"  Max value: {img_array.max()}")
-        print(f"  Mean value: {img_array.mean():.2f}")
-
-        # Verify it's RGB uint8
-        assert prepared_img.mode == "RGB", "Prepared image should be RGB"
-        assert img_array.dtype == np.uint8, "Prepared image should be uint8"
-        assert img_array.shape[2] == 3, "Prepared image should have 3 channels"
-        assert (
-            0 <= img_array.min() <= img_array.max() <= 255
-        ), "Values should be in [0, 255]"
-
-        # Cleanup prepared file if needed
-        if cleanup_path and os.path.exists(cleanup_path):
-            os.remove(cleanup_path)
-            print(f"\nCleaned up prepared image: {cleanup_path}")
-
-        print("\n✓ All YOLO preprocessing tests passed!")
-        return True
-
-    except Exception as e:
-        print(f"\n✗ Test failed with error: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return False
-
-    finally:
-        # Cleanup
-        if os.path.exists(test_path):
-            os.remove(test_path)
-            print(f"Cleaned up test file: {test_path}")
-
-
-if __name__ == "__main__":
-    success = test_yolo_preprocessing()
-    sys.exit(0 if success else 1)