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+# 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 passes them directly to YOLO **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)  
+✅ Passes numpy arrays directly to YOLO (no file I/O)  
+✅ 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 16-bit TIFF files:
+
+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
+
+### No Data Loss!
+
+Unlike the previous approach that converted to uint8 (256 levels), the new implementation:
+- Preserves full 16-bit dynamic range (65536 levels)
+- Maintains precision with float32 representation
+- Passes data directly without intermediate file conversions
+
+## 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 | Notes |
+|--------|--------|-------|
+| Original 16-bit | 8 MB | uint16 grayscale |
+| Float32 grayscale | 16 MB | Intermediate |
+| Float32 RGB | 48 MB | Final (3 channels) |
+| uint8 RGB (old) | 12 MB | OLD approach with data loss |
+
+The float32 approach uses ~4× more memory than uint8 but preserves **all information**.
+
+### 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`
+
+## For Training with Custom Dataset
+
+If you need to train YOLO on 16-bit TIFF images, you should create a custom dataset loader similar to the example provided by the user:
+
+```python
+import torch
+import numpy as np
+import tifffile as tiff
+from pathlib import Path
+
+class FloatYoloSegDataset(torch.utils.data.Dataset):
+    def __init__(self, img_dir, label_dir, img_size=640):
+        self.img_paths = sorted(Path(img_dir).glob('*'))
+        self.label_dir = Path(label_dir)
+        self.img_size = img_size
+
+    def __len__(self):
+        return len(self.img_paths)
+
+    def __getitem__(self, idx):
+        img_path = self.img_paths[idx]
+        
+        # Load 16-bit TIFF
+        img = tiff.imread(img_path)
+        
+        # Convert to float32 [0-1]
+        img = img.astype(np.float32)
+        if img.max() > 1.5:  # Assume 16-bit if max > 1.5
+            img /= 65535.0
+        
+        # Grayscale → RGB
+        if img.ndim == 2:
+            img = np.repeat(img[..., None], 3, axis=2)
+        
+        # HWC → CHW for PyTorch
+        img = torch.from_numpy(img).permute(2, 0, 1).contiguous()
+        
+        # Load labels...
+        # (implementation depends on your label format)
+        
+        return img, labels
+```
+
+Then use this dataset with Ultralytics training API or custom training loop.
+
+## 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!