saw_mill_knot_detection/train_model.py

#!/usr/bin/env python3
"""
Training script for wood knot detection models.

Supports: RF-DETR, RT-DETR, YOLOv6, YOLOX
All models are MIT/Apache 2.0 licensed - free for commercial use.

Usage:
    python train_model.py --framework rtdetr --dataset dataset_prepared --output runs/training
"""

import argparse
import json
import shutil
import subprocess
from pathlib import Path
from typing import Any


def prepare_training_dataset(
    images_dir: Path,
    annotations_file: Path,
    output_dir: Path,
    train_split: float = 0.8,
    valid_split: float = 0.1
) -> str:
    """Prepare dataset in RF-DETR format (train/valid/test splits)."""
    output_dir.mkdir(parents=True, exist_ok=True)

    # Load annotations
    with annotations_file.open() as f:
        annotations = json.load(f)

    # Get all image files
    image_files = []
    for img_name in annotations.keys():
        img_path = images_dir / img_name
        if img_path.exists():
            image_files.append(img_name)

    if not image_files:
        return "❌ No annotated images found"

    # Shuffle and split
    import random
    random.seed(42)
    random.shuffle(image_files)

    n_total = len(image_files)
    n_train = int(n_total * train_split)
    n_valid = int(n_total * valid_split)
    n_test = n_total - n_train - n_valid

    splits = {
        'train': image_files[:n_train],
        'valid': image_files[n_train:n_train + n_valid],
        'test': image_files[n_train + n_valid:]
    }

    # Create directories
    for split in ['train', 'valid', 'test']:
        (output_dir / split / 'images').mkdir(parents=True, exist_ok=True)
        (output_dir / split / 'labels').mkdir(parents=True, exist_ok=True)

    # Copy images and create labels
    for split, img_names in splits.items():
        for img_name in img_names:
            # Copy image
            src_img = images_dir / img_name
            dst_img = output_dir / split / 'images' / img_name
            shutil.copy2(src_img, dst_img)

            # Convert annotations to YOLO format
            boxes = annotations.get(img_name, [])
            if boxes:
                img_width, img_height = get_image_size(src_img)
                yolo_lines = []
                for box in boxes:
                    if isinstance(box, dict) and "bbox" in box:
                        x1, y1, x2, y2 = box["bbox"]
                    else:
                        x1, y1, x2, y2 = box
                    # Convert to YOLO format (normalized center x, center y, width, height)
                    x_center = (x1 + x2) / 2 / img_width
                    y_center = (y1 + y2) / 2 / img_height
                    width = (x2 - x1) / img_width
                    height = (y2 - y1) / img_height
                    yolo_lines.append(f"0 {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}")

                # Save label file
                label_file = output_dir / split / 'labels' / img_name.replace('.jpg', '.txt').replace('.png', '.txt')
                with label_file.open('w') as f:
                    f.write('\n'.join(yolo_lines))

    # Create data.yaml for YOLO models
    data_yaml = f"""train: {output_dir}/train/images
val: {output_dir}/valid/images
test: {output_dir}/test/images

nc: 1
names: ['knot']
"""

    with (output_dir / 'data.yaml').open('w') as f:
        f.write(data_yaml)

    return f"✓ Dataset prepared: {n_train} train, {n_valid} valid, {n_test} test images"


def get_image_size(img_path: Path) -> tuple[int, int]:
    """Get image dimensions."""
    from PIL import Image
    with Image.open(img_path) as img:
        return img.size


def train_rfdetr(dataset_dir: Path, output_dir: Path, model_size: str, epochs: int, batch_size: int, lr: float):
    """Train RF-DETR model."""
    output_dir.mkdir(parents=True, exist_ok=True)

    # Import and run training
    try:
        from train_rfdetr import main as train_main
        import sys

        # Set up arguments
        sys.argv = [
            'train_rfdetr.py',
            '--data', str(dataset_dir / 'data.yaml'),
            '--output', str(output_dir),
            '--model', model_size,
            '--epochs', str(epochs),
            '--batch', str(batch_size),
            '--lr', str(lr)
        ]

        train_main()
        return "✓ RF-DETR training completed"

    except Exception as e:
        return f"❌ RF-DETR training failed: {e}"


def train_rtdetr(dataset_dir: Path, output_dir: Path, model_size: str, epochs: int, batch_size: int, lr: float):
    """Train RT-DETR model."""
    output_dir.mkdir(parents=True, exist_ok=True)

    try:
        from train_rtdetr import main as train_main
        import sys

        sys.argv = [
            'train_rtdetr.py',
            '--data', str(dataset_dir / 'data.yaml'),
            '--output', str(output_dir),
            '--model', model_size,
            '--epochs', str(epochs),
            '--batch', str(batch_size),
            '--lr', str(lr)
        ]

        train_main()
        return "✓ RT-DETR training completed"

    except Exception as e:
        return f"❌ RT-DETR training failed: {e}"


def train_yolov6(dataset_dir: Path, output_dir: Path, model_size: str, epochs: int, batch_size: int, lr: float):
    """Train YOLOv6 model."""
    output_dir.mkdir(parents=True, exist_ok=True)

    try:
        from train_yolov6 import main as train_main
        import sys

        sys.argv = [
            'train_yolov6.py',
            '--data', str(dataset_dir / 'data.yaml'),
            '--output', str(output_dir),
            '--model', model_size,
            '--epochs', str(epochs),
            '--batch', str(batch_size),
            '--lr', str(lr)
        ]

        train_main()
        return "✓ YOLOv6 training completed"

    except Exception as e:
        return f"❌ YOLOv6 training failed: {e}"


def train_yolox(dataset_dir: Path, output_dir: Path, model_size: str, epochs: int, batch_size: int, lr: float):
    """Train YOLOX model."""
    output_dir.mkdir(parents=True, exist_ok=True)

    try:
        from train_yolox import main as train_main
        import sys

        sys.argv = [
            'train_yolox.py',
            '--data', str(dataset_dir / 'data.yaml'),
            '--output', str(output_dir),
            '--model', model_size,
            '--epochs', str(epochs),
            '--batch', str(batch_size),
            '--lr', str(lr)
        ]

        train_main()
        return "✓ YOLOX training completed"

    except Exception as e:
        return f"❌ YOLOX training failed: {e}"


def main():
    parser = argparse.ArgumentParser(description="Train object detection models for wood knot detection")
    parser.add_argument(
        '--framework',
        choices=['rf-detr', 'rt-detr', 'yolov6', 'yolox'],
        default='rt-detr',
        help='Model framework to train'
    )
    parser.add_argument(
        '--dataset',
        type=Path,
        required=True,
        help='Path to prepared dataset directory'
    )
    parser.add_argument(
        '--output',
        type=Path,
        default=Path('runs/training'),
        help='Output directory for trained model'
    )
    parser.add_argument(
        '--model-size',
        choices=['nano', 'small', 'medium', 'base'],
        default='small',
        help='Model size/variant'
    )
    parser.add_argument(
        '--epochs',
        type=int,
        default=20,
        help='Number of training epochs'
    )
    parser.add_argument(
        '--batch-size',
        type=int,
        default=4,
        help='Batch size for training'
    )
    parser.add_argument(
        '--lr',
        type=float,
        default=1e-4,
        help='Learning rate'
    )
    parser.add_argument(
        '--prepare-dataset',
        action='store_true',
        help='Prepare dataset from annotations first'
    )
    parser.add_argument(
        '--images-dir',
        type=Path,
        default=Path('IMAGE'),
        help='Images directory (for --prepare-dataset)'
    )
    parser.add_argument(
        '--annotations',
        type=Path,
        default=Path('annotations.json'),
        help='Annotations file (for --prepare-dataset)'
    )

    args = parser.parse_args()

    if args.prepare_dataset:
        print("Preparing dataset...")
        result = prepare_training_dataset(
            args.images_dir,
            args.annotations,
            args.dataset
        )
        print(result)
        if "❌" in result:
            return

    print(f"Training {args.framework.upper()} model...")
    print(f"Dataset: {args.dataset}")
    print(f"Output: {args.output}")
    print(f"Model size: {args.model_size}")
    print(f"Epochs: {args.epochs}")
    print(f"Batch size: {args.batch_size}")
    print(f"Learning rate: {args.lr}")

    # Train based on framework
    if args.framework == 'rf-detr':
        result = train_rfdetr(args.dataset, args.output, args.model_size, args.epochs, args.batch_size, args.lr)
    elif args.framework == 'rt-detr':
        result = train_rtdetr(args.dataset, args.output, args.model_size, args.epochs, args.batch_size, args.lr)
    elif args.framework == 'yolov6':
        result = train_yolov6(args.dataset, args.output, args.model_size, args.epochs, args.batch_size, args.lr)
    elif args.framework == 'yolox':
        result = train_yolox(args.dataset, args.output, args.model_size, args.epochs, args.batch_size, args.lr)

    print(result)


if __name__ == "__main__":
    main()</content>
<parameter name="filePath">/home/dillon/_code/saw_mill_knot_detection/train_model.py