import os
import cv2
from glob import glob
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from albumentations import HorizontalFlip, VerticalFlip, Rotate


def create_dir(path):
    """
    Create a directory.
    """
    if not os.path.exists(path):
        os.makedirs(path)


def load_data(path, split=0.2):
    """
    Load the images and masks
    """
    images = sorted(glob(f"{path}/*/image/*.png"))
    masks = sorted(glob(f"{path}/*/mask/*.png"))

    """ Split the data """
    split_size = int(len(images) * split)
    train_x, valid_x = train_test_split(images, test_size=split_size, random_state=42)
    train_y, valid_y = train_test_split(masks, test_size=split_size, random_state=42)

    return (train_x, train_y), (valid_x, valid_y)


def augment_data(images, masks, save_path, augment=True):
    """
    Performing data augmentation.
    """
    IMG_HEIGHT = 512
    IMG_WIDTH = 512

    for idx, (x, y) in tqdm(enumerate(zip(images, masks)), total=len(images)):
        """Extracting the directory and image name"""
        directory_name = x.split("/")[-3]
        name = directory_name + "_" + x.split("/")[-1].split(".")[0]

        """ Read the image and mask """
        x = cv2.imread(x, cv2.IMREAD_COLOR)
        y = cv2.imread(y, cv2.IMREAD_COLOR)

        if augment == True:
            aug = HorizontalFlip(p=1.0)  # Applying Horizontal Flip 100%
            augmented = aug(image=x, mask=y)
            x1 = augmented["image"]
            y1 = augmented["mask"]

            aug = VerticalFlip(p=1)  # Applying Vertical Flip 100%
            augmented = aug(image=x, mask=y)
            x2 = augmented["image"]
            y2 = augmented["mask"]

            aug = Rotate(limit=45, p=1.0)  # Applying Rotation till 45 degress
            augmented = aug(image=x, mask=y)
            x3 = augmented["image"]
            y3 = augmented["mask"]

            X = [x, x1, x2, x3]
            Y = [y, y1, y2, y3]

        else:
            X = [x]
            Y = [y]

        idx = 0
        for i, m in zip(X, Y):
            i = cv2.resize(i, (IMG_WIDTH, IMG_HEIGHT))
            m = cv2.resize(m, (IMG_WIDTH, IMG_HEIGHT))
            m = m / 255.0
            m = (m > 0.5) * 255

            if len(X) == 1:
                tmp_image_name = f"{name}.jpg"
                tmp_mask_name = f"{name}.jpg"
            else:
                tmp_image_name = f"{name}_{idx}.jpg"
                tmp_mask_name = f"{name}_{idx}.jpg"

            image_path = os.path.join(save_path, "image/", tmp_image_name)
            mask_path = os.path.join(save_path, "mask/", tmp_mask_name)

            cv2.imwrite(image_path, i)
            cv2.imwrite(mask_path, m)

            idx += 1


if __name__ == "__main__":
    """
    Load the dataset
    """
    dataset_path = os.path.join("data", "train")
    (train_x, train_y), (valid_x, valid_y) = load_data(dataset_path, split=0.2)
    print("Train: ", len(train_x))
    print("Valid: ", len(valid_x))

    """ Create the directories """
    create_dir("new_data/train/image/")
    create_dir("new_data/train/mask/")
    create_dir("new_data/valid/image/")
    create_dir("new_data/valid/mask/")

    """ Augment the data """
    augment_data(
        train_x, train_y, "new_data/train/", augment=True
    )  # Applying Data Augmentation for training data
    augment_data(valid_x, valid_y, "new_data/valid/", augment=False)