object-segmentation/docs/16BIT_TIFF_SUPPORT.md

# 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!