object_detector_benchmark/transformer_benchmark/augmentations.py

from datasets import load_from_disk
import albumentations as A
import numpy as np
from collections import defaultdict
import os
from PIL import Image, ImageDraw
from functools import partial
from custom_parser import parse_args


# augmentation probabilities need to be hyperparameters
def augment_data_point(batch, args):
    assert len(batch["image_id"]) == len(batch["image"]) == len(batch["annotations"])
    transform = A.Compose([
        A.RandomRotate90(p=args.rotate90),
        A.HorizontalFlip(p=args.horizontal_flip),
        A.RandomBrightnessContrast(p=args.brightness_contrast),
        A.ElasticTransform(
            alpha=args.elastic_alpha, 
            sigma=args.elastic_sigma, 
            p=args.elastic_transform
        ),
        A.Resize(640, 640)
    ], bbox_params=A.BboxParams(
        format="coco",
        label_fields=["category_ids"],
    ))
    
    new_batch = defaultdict(list)
    for id, image, annotations in zip(batch["image_id"], batch["image"], batch["annotations"]):
        image_np = np.array(image)
        bboxes = [ann["bbox"] for ann in annotations]
        category_ids = [ann["category_id"] for ann in annotations]
    
        transformed = transform(image=image_np, bboxes=bboxes, category_ids=category_ids)
        transformed_image = Image.fromarray(transformed["image"])
    
        transformed_annotations = []
        for ann, new_bbox in zip(annotations, transformed["bboxes"]):
            new_ann = ann.copy()
            new_ann["bbox"] = new_bbox
            transformed_annotations.append(new_ann)
        new_batch["image_id"].append(id)
        new_batch["image"].append(transformed_image)
        new_batch["annotations"].append(transformed_annotations)
    
    return new_batch



def validation_transform(batch):
    assert len(batch["image_id"]) == len(batch["image"]) == len(batch["annotations"])
    transform = A.Compose([
        A.Resize(640, 640)
    ], bbox_params=A.BboxParams(
        format="coco",
        label_fields=["category_ids"],
    ))
    
    new_batch = defaultdict(list)
    for id, image, annotations in zip(batch["image_id"], batch["image"], batch["annotations"]):
        image_np = np.array(image)
        bboxes = [ann["bbox"] for ann in annotations]
        category_ids = [ann["category_id"] for ann in annotations]
    
        transformed = transform(image=image_np, bboxes=bboxes, category_ids=category_ids)
        transformed_image = Image.fromarray(transformed["image"])
    
        transformed_annotations = []
        for ann, new_bbox in zip(annotations, transformed["bboxes"]):
            new_ann = ann.copy()
            new_ann["bbox"] = new_bbox
            transformed_annotations.append(new_ann)
        new_batch["image_id"].append(id)
        new_batch["image"].append(transformed_image)
        new_batch["annotations"].append(transformed_annotations)
    
    return new_batch


def save_sample(dataset, idx, filename, save_dir="augmented_samples"):
    os.makedirs(save_dir, exist_ok=True)
    sample = dataset[idx]  # Fetch sample (augmented or original)
    image = sample["image"]
    bboxes = [ann["bbox"] for ann in sample["annotations"]]

    # Convert image to RGB (if not already)
    image = image.convert("RGB")

    # Draw bounding boxes
    draw = ImageDraw.Draw(image)
    for bbox in bboxes:
        x, y, w, h = bbox
        draw.rectangle([x, y, x + w, y + h], outline="red", width=3)

    # Save the image
    image_path = os.path.join(save_dir, filename)
    image.save(image_path)



def main():
    args = parse_args()
    dataset = load_from_disk(args.dataset_path)

    train_dataset = dataset["train"]
    augmented_train_dataset = train_dataset.with_transform(lambda batch: augment_data_point(batch, args))
    valid_dataset = dataset["val"].with_transform(validation_transform)
    # print(train_dataset[0])
    for i in range(5):  # Adjust index as needed
        save_sample(augmented_train_dataset, i, f"augmented_sample_{i}.png")
        save_sample(train_dataset, i, f"original_sample_{i}.png")
        save_sample(valid_dataset, i, f"validation_image_{i}.png")


if __name__ == "__main__":
    main()