import glob
import nibabel as nib
import numpy as np
import random
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import re



'''
Prepares CustomDatasets for training, validating, and testing CNN
'''
def prepare_datasets(mri_dir, val_split=0.2, seed=50):

    rndm = random.Random(seed)

    raw_data = glob.glob(mri_dir + "*")

    AD_list = []
    NL_list = []

    print("--- DATA INFO ---")
    print("Amount of images: " + str(len(raw_data)))

    # TODO Check that image is in CSV?
    for image in raw_data:
        if "NL" in image:
            NL_list.append(image)
        elif "AD" in image:
            AD_list.append(image)

    print("Total AD: " + str(len(AD_list)))
    print("Total NL: " + str(len(NL_list)))

    rndm.shuffle(AD_list)
    rndm.shuffle(NL_list)

    train_list, val_list, test_list = get_train_val_test(AD_list, NL_list, val_split)

    rndm.shuffle(train_list)
    rndm.shuffle(val_list)
    rndm.shuffle(test_list)

    print(f"DATA INITIALIZATION")
    print(f"Training size: {len(train_list)}")
    print(f"Validation size: {len(val_list)}")
    print(f"Test size: {len(test_list)}")


    # # TRANSFORM
    # transform = transforms.Compose([
    #     transforms.Grayscale(num_output_channels=1)
    # ])

    train_dataset = CustomDataset(train_list)
    val_dataset = CustomDataset(val_list)
    test_dataset = CustomDataset(test_list)

    return train_dataset, val_dataset, test_dataset

    # TODO  Normalize data? Later add / Extract clinical data? Which data?


def prepare_predict(mri_dir, IDs):

    raw_data = glob.glob(mri_dir + "*")

    image_list = []

    # Gets all images and prepares them for Dataset
    for ID in IDs:
        pattern = re.compile(ID)
        matches = [item for item in raw_data if pattern.search(item)]
        if (len(matches) != 1): print("No image found, or more than one")
        for match in matches:
            if "NL" in match: image_list.append((match, 0))
            if "AD" in match: image_list.append((match, 1))

    return CustomDataset(image_list)


'''
Returns train_list, val_list and test_list in format [(image, id), ...] each
'''
def get_train_val_test(AD_list, NL_list, val_split):

    train_list, val_list, test_list = [], [], []

    num_test_ad = int(len(AD_list) * val_split)
    num_test_nl = int(len(NL_list) * val_split)

    num_val_ad = int((len(AD_list) - num_test_ad) * val_split)
    num_val_nl = int((len(NL_list) - num_test_nl) * val_split)

    # Sets up ADs
    for image in AD_list[0:num_val_ad]:
        val_list.append((image, 1))

    for image in AD_list[num_val_ad:num_test_ad]:
        test_list.append((image, 1))

    for image in AD_list[num_test_ad:]:
        train_list.append((image, 1))

    # Sets up NLs
    for image in NL_list[0:num_val_nl]:
        val_list.append((image, 0))

    for image in NL_list[num_val_nl:num_test_nl]:
        test_list.append((image, 0))

    for image in NL_list[num_test_nl:]:
        train_list.append((image, 0))

    return train_list, val_list, test_list


class CustomDataset(Dataset):
    def __init__(self, list):
        self.data = list        # DATA IS A LIST WITH TUPLES (image_dir, class_id)

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

    def __getitem__(self, idx):     # RETURNS TUPLE WITH IMAGE AND CLASS_ID, BASED ON INDEX IDX
        mri_path, class_id = self.data[idx]
        mri = nib.load(mri_path)
        image = np.asarray(mri.dataobj)
        mri_data = np.asarray(np.expand_dims(image, axis=0))

        # mri_data = mri.get_fdata()
        # mri_array = np.array(mri)
        # mri_tensor = torch.from_numpy(mri_array)
        # class_id = torch.tensor([class_id]) TODO return tensor or just id (0, 1)??

        return mri_data, class_id