data_augmentation.py

# Copyright 2025 Robotics Group of the University of León (ULE)

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import random
from pathlib import Path
import numpy as np
from PIL import Image, ImageEnhance, ImageOps, ImageFilter
from tqdm import tqdm


def load_image(image_path):
    """Load an image from the file system.
    
    Args:
        image_path: Path to the image file.
        
    Returns:
        Loaded PIL Image object.
    """
    return Image.open(image_path)


def save_image(image, output_path):
    """Save the image to the specified path.
    
    Args:
        image: PIL Image object to save.
        output_path: Destination path for the image.
    """
    image.save(output_path)


def flip_horizontal(image):
    """Flip the image horizontally.
    
    Args:
        image: Input PIL Image.
        
    Returns:
        Horizontally flipped PIL Image.
    """
    return ImageOps.mirror(image)


def rotate(image, angle):
    """Rotate the image by the specified angle.
    
    Args:
        image: Input PIL Image.
        angle: Rotation angle in degrees (positive = counter-clockwise).
        
    Returns:
        Rotated PIL Image.
    """
    return image.rotate(angle, expand=False)


def adjust_brightness(image, factor):
    """Adjust the brightness of the image.
    
    Args:
        image: Input PIL Image.
        factor: Brightness adjustment factor (1.0 = original, <1.0 darker, >1.0 brighter).
        
    Returns:
        Brightness-adjusted PIL Image.
    """
    enhancer = ImageEnhance.Brightness(image)
    return enhancer.enhance(factor)


def adjust_contrast(image, factor):
    """Adjust the contrast of the image.
    
    Args:
        image: Input PIL Image.
        factor: Contrast adjustment factor (1.0 = original, <1.0 less contrast, >1.0 more contrast).
        
    Returns:
        Contrast-adjusted PIL Image.
    """
    enhancer = ImageEnhance.Contrast(image)
    return enhancer.enhance(factor)


def adjust_saturation(image, factor):
    """Adjust the saturation of the image.
    
    Args:
        image: Input PIL Image.
        factor: Saturation adjustment factor (1.0 = original, 0.0 = grayscale, >1.0 more saturated).
        
    Returns:
        Saturation-adjusted PIL Image.
    """
    enhancer = ImageEnhance.Color(image)
    return enhancer.enhance(factor)


def add_noise(image, intensity=0.05):
    """Add random noise to the image.
    
    Args:
        image: Input PIL Image.
        intensity: Noise intensity factor (0.0-1.0). Defaults to 0.05.
        
    Returns:
        PIL Image with added noise.
    """
    img_array = np.array(image).copy()
    if len(img_array.shape) == 3:
        h, w, c = img_array.shape
        noise = np.random.randint(-int(intensity * 255), int(intensity * 255), (h, w, c))
        img_array = np.clip(img_array + noise, 0, 255).astype(np.uint8)
    else:
        h, w = img_array.shape
        noise = np.random.randint(-int(intensity * 255), int(intensity * 255), (h, w))
        img_array = np.clip(img_array + noise, 0, 255).astype(np.uint8)
    return Image.fromarray(img_array)


def apply_blur(image, radius=2):
    """Apply Gaussian blur to the image.
    
    Args:
        image: Input PIL Image.
        radius: Blur radius in pixels. Defaults to 2.
        
    Returns:
        Blurred PIL Image.
    """
    return image.filter(ImageFilter.GaussianBlur(radius=radius))

def transform_yolo_label(label_line, technique_name):
    """Transform YOLO format label coordinates based on the augmentation technique.
    
    Args:
        label_line: A line from a YOLO label file (class_id center_x center_y width height).
        technique_name: The augmentation technique applied.
        
    Returns:
        Transformed label line in YOLO format.
    """
    parts = label_line.strip().split()
    if len(parts) < 5:
        return label_line
    
    class_id = parts[0]
    x_center = float(parts[1])
    y_center = float(parts[2])
    width = float(parts[3])
    height = float(parts[4])
    
    if technique_name == "flip_horizontal":
        x_center = 1.0 - x_center
    
    return f"{class_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}"


def process_yolo_label(original_label_path, new_label_path, technique_name):
    """Process a YOLO format label file, applying transformations to the coordinates.
    
    Args:
        original_label_path: Path to the original label file.
        new_label_path: Path for the new transformed label file.
        technique_name: Name of the augmentation technique applied.
        
    Returns:
        True if processing was successful, False otherwise.
    """
    if not original_label_path.exists():
        return False
    
    with open(original_label_path, "r") as infile:
        lines = infile.readlines()
    
    new_lines = []
    for line in lines:
        if line.strip():
            if technique_name in ["adjust_brightness", "adjust_contrast", 
                                  "adjust_saturation", "add_noise", "apply_blur"]:
                new_lines.append(line.strip())
            else:
                transformed_line = transform_yolo_label(line, technique_name)
                new_lines.append(transformed_line)
    
    os.makedirs(new_label_path.parent, exist_ok=True)
    
    with open(new_label_path, "w") as outfile:
        outfile.write("\n".join(new_lines))
    
    return True

def augment_yolo_dataset(base_dir, augmentations_per_image=3):
    """Apply data augmentation to images in a YOLO dataset.
    
    For each image in the dataset, this function applies a specified number of random
    augmentation techniques and transforms the corresponding YOLO label files to maintain
    annotation accuracy.
    
    Args:
        base_dir: Base directory of the YOLO dataset (should contain images/ and labels/).
        augmentations_per_image: Number of random augmentations per image. Defaults to 3.
    """
    images_dir = os.path.join(base_dir, "images")
    labels_dir = os.path.join(base_dir, "labels")
    
    if not os.path.exists(images_dir):
        print(f"Error: Images directory not found at {images_dir}")
        return
    
    if not os.path.exists(labels_dir):
        print(f"Error: Labels directory not found at {labels_dir}")
        return
    
    image_extensions = [".jpg", ".jpeg", ".png", ".bmp"]
    image_files = []
    
    for ext in image_extensions:
        image_files.extend(list(Path(images_dir).glob(f"*{ext}")))
    
    print(f"Found {len(image_files)} images in {images_dir}")
    
    augmentation_techniques = [
        (flip_horizontal, {}, "flip_horizontal"),
        (rotate, {"angle": lambda: random.randint(-15, 15)}, "rotate"),
        (adjust_brightness, {"factor": lambda: random.uniform(0.8, 1.2)}, "adjust_brightness"),
        (adjust_contrast, {"factor": lambda: random.uniform(0.8, 1.2)}, "adjust_contrast"),
        (adjust_saturation, {"factor": lambda: random.uniform(0.8, 1.2)}, "adjust_saturation"),
        (add_noise, {"intensity": lambda: random.uniform(0.01, 0.05)}, "add_noise"),
        (apply_blur, {"radius": lambda: random.uniform(0.5, 1.5)}, "apply_blur")
    ]
    
    total_augmented = 0
    labels_processed = 0
    
    for img_path in tqdm(image_files, desc="Augmenting images"):
        try:
            original_image = load_image(img_path)
            
            rel_path = img_path.relative_to(images_dir)
            label_path = Path(labels_dir) / rel_path.with_suffix(".txt")
            
            if not label_path.exists():
                continue
            
            for i in range(augmentations_per_image):
                technique, params, technique_name = random.choice(augmentation_techniques)
                
                resolved_params = {k: v() if callable(v) else v for k, v in params.items()}
                
                augmented_image = original_image.copy()
                augmented_image = technique(augmented_image, **resolved_params)
                
                filename = img_path.stem
                extension = img_path.suffix
                augmented_filename = f"{filename}_aug_{technique_name}_{i + 1}{extension}"
                augmented_path = img_path.parent / augmented_filename
                save_image(augmented_image, augmented_path)
                
                augmented_label_path = label_path.parent / f"{filename}_aug_{technique_name}_{i + 1}.txt"
                if process_yolo_label(label_path, augmented_label_path, technique_name):
                    labels_processed += 1
                
                total_augmented += 1
                
        except Exception as e:
            tqdm.write(f"Error processing {img_path.name}: {str(e)}")
            continue
    
    print(f"Created {total_augmented} augmented images")
    print(f"Processed {labels_processed} YOLO label files")
    
    train_txt_path = os.path.join(base_dir, "train.txt")
    if os.path.exists(train_txt_path):
        all_images = []
        for ext in image_extensions:
            all_images.extend([str(p.relative_to(base_dir)) for p in Path(images_dir).glob(f"*{ext}")])
        
        with open(train_txt_path, "w") as f:
            f.write("\n".join(all_images))
        print(f"Updated {train_txt_path} with {len(all_images)} image paths")

def main():
    """Main function to execute dataset augmentation.
    
    Configures the base directory and number of augmentations, then runs the
    augmentation pipeline on the YOLO dataset.
    """
    train_dir = "/home/pcrn/datainbrief/beet_augmented/train"
    augmentations_per_image = 3
    
    print(f"Starting augmentation on YOLO dataset in {train_dir}")
    augment_yolo_dataset(train_dir, augmentations_per_image)


if __name__ == "__main__":
    main()