import glob
import os

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

import ex4

IMG_SIZE = 100


class ImageDataset(Dataset):
    def __init__(self, image_dir):
        self.image_files = sorted(glob.glob(os.path.join(image_dir, "**", "*.jpg"), recursive=True))
        # Mean and std arrays could also be defined as class attributes
        # self.norm_mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
        # self.norm_std = np.array([0.229, 0.224, 0.225], dtype=np.float32)

    def __getitem__(self, index):
        # Open image file, convert to numpy array and scale to [0, 1]
        target_image = Image.open(self.image_files[index])
        # image = np.array(Image.open(self.image_files[index]), dtype=np.float32) / 255
        resize_transforms = transforms.Compose([
            transforms.Resize(size=IMG_SIZE),
            transforms.CenterCrop(size=(IMG_SIZE, IMG_SIZE)),
        ])
        target_image = resize_transforms(target_image)
        target_image = preprocess(target_image)

        # calculate image with black grid
        doomed_image = ex4.ex4(target_image, (5, 5), (4, 4))

        # convert image to grayscale
        # target_image = rgb2gray(target_image)  # todo look if gray image makes sense

        return doomed_image[0], np.transpose(target_image, (2, 0, 1))

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


def preprocess(input: np.array) -> np.array:
    # normalize image from 0-1
    target_image = np.array(input, dtype=np.float64) / 255.0

    # Perform normalization for each channel
    # image = (image - self.norm_mean) / self.norm_std

    return target_image


# postprecess should be the inverese function of preprocess!
def postprocess(input: np.array) -> np.array:
    target_image = (input * 255.0).astype(np.uint8)
    return target_image


def get_image_loader(path: str):
    image_dataset = ImageDataset(path)
    totlen = len(image_dataset)

    test_set_size = .001
    trains, tests = torch.utils.data.dataset.random_split(image_dataset, lengths=(totlen - int(totlen * test_set_size),
                                                                                  int(totlen * test_set_size)),
                                                          generator=torch.Generator().manual_seed(42))

    train_loader = DataLoader(
        trains,
        shuffle=True,  # shuffle the order of our samples
        batch_size=5,  # stack 4 samples to a minibatch
        num_workers=4  # no background workers (see comment below)
    )

    test_loader = DataLoader(
        tests,
        shuffle=True,  # shuffle the order of our samples
        batch_size=1,  # stack 4 samples to a minibatch
        num_workers=0  # no background workers (see comment below)
    )

    return train_loader, test_loader


def rgb2gray(rgb_array: np.ndarray):
    r, g, b = rgb_array[:, :, 0], rgb_array[:, :, 1], rgb_array[:, :, 2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
    return gray