from PIL import Image
import cv2
import numpy as np
from torch.utils.data import Dataset
from torchvision import transforms

import albumentations as A
from albumentations.pytorch import ToTensorV2

class TorchvisionDataset(Dataset):
    def __init__(self, file_paths, labels, transform=None):
        self.file_paths = file_paths
        self.labels = labels
        self.transform = transform

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        label = self.labels[idx]
        file_path = self.file_paths[idx]

        # Read an image with PIL
        image = Image.open(file_path)
        if self.transform:
            image = self.transform(image)
        return image, label


torchvision_transform = transforms.Compose([
    transforms.Resize((256, 256)), 
    transforms.RandomCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    )
])


torchvision_dataset = TorchvisionDataset(
    file_paths=['./images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg'],
    labels=[1, 2, 3],
    transform=torchvision_transform,
)

class AlbumentationsDataset(Dataset):
    """__init__ and __len__ functions are the same as in TorchvisionDataset"""
    def __init__(self, file_paths, labels, transform=None):
        self.file_paths = file_paths
        self.labels = labels
        self.transform = transform

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        label = self.labels[idx]
        file_path = self.file_paths[idx]

        # Read an image with OpenCV
        image = cv2.imread(file_path)

        # By default OpenCV uses BGR color space for color images,
        # so we need to convert the image to RGB color space.
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        if self.transform:
            augmented = self.transform(image=image)
            image = augmented['image']
        return image, label


albumentations_transform = A.Compose([
    A.Resize(256, 256), 
    A.RandomCrop(224, 224),
    A.HorizontalFlip(),
    A.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    ),
    ToTensorV2()
])


albumentations_dataset = AlbumentationsDataset(
    file_paths=['./images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg'],
    labels=[1, 2, 3],
    transform=albumentations_transform,
)

class AlbumentationsPilDataset(Dataset):
    """__init__ and __len__ functions are the same as in TorchvisionDataset"""
    def __init__(self, file_paths, labels, transform=None):
        self.file_paths = file_paths
        self.labels = labels
        self.transform = transform

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        label = self.labels[idx]
        file_path = self.file_paths[idx]

        image = Image.open(file_path)

        if self.transform:
            # Convert PIL image to numpy array
            image_np = np.array(image)
            # Apply transformations
            augmented = self.transform(image=image_np)
            # Convert numpy array to PIL Image
            image = Image.fromarray(augmented['image'])
        return image, label


albumentations_pil_transform = A.Compose([
    A.Resize(256, 256), 
    A.RandomCrop(224, 224),
    A.HorizontalFlip(),
])


# Note that this dataset will output PIL images and not numpy arrays nor PyTorch tensors
albumentations_pil_dataset = AlbumentationsPilDataset(
    file_paths=['./images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg'],
    labels=[1, 2, 3],
    transform=albumentations_pil_transform,
)