import torch

import torch.nn as nn

from torch.autograd import Variable

import torch.optim as optim

import torchvision

from torchvision import datasets, models

from torchvision import transforms as T

from torch.utils.data import DataLoader, Dataset

import numpy as np

import matplotlib.pyplot as plt

import os

import time

import pandas as pd

from skimage import io, transform

import matplotlib.image as mpimg

from PIL import Image

from sklearn.metrics import roc_auc_score

import torch.nn.functional as F

import scipy

import random

import pickle

import scipy.io as sio

import itertools

from scipy.ndimage.interpolation import shift

import copy

import warnings

warnings.filterwarnings("ignore")

plt.ion()

def count_parameters(model):

    return sum(p.numel() for p in model.parameters() if p.requires_grad)

class KneeMRIDataset(Dataset):

    '''Knee MRI Dataset'''

    def __init__(self, root_dir, label, train_data = False, flipping = True, rotation = True, translation = True,

                normalize = False):

        self.root_dir = root_dir

        self.label = label

        self.flipping = flipping

        self.rotation = rotation

        self.translation = translation

        self.train_data = train_data

        self.normalize = normalize

    def __len__(self):

        return len(self.label)

    def __getitem__(self,idx):

        variable_path_name = os.path.join(self.root_dir, self.label[idx])

        variables = pickle.load(open(variable_path_name,'rb'))

        segment_T = variables['SegmentationT'].astype(float)

        segment_F = variables['SegmentationF'].astype(float)

        segment_P = variables['SegmentationP'].astype(float)

        md = variables['MDnr']

        image = variables['NUFnr']

        fa = variables['FAnr']

        images = []

        flip = random.random() > 0.5

        angle = random.uniform(-4,4)

        dx = np.round(random.uniform(-7,7))

        dy = np.round(random.uniform(-7,7))

        for i in range(7):

            im = image[:,:,i]

            if self.train_data:

                if self.flipping and flip:

                    im = np.fliplr(im)

                if self.rotation:

                    im = transform.rotate(im, angle, order = 0)

                if self.translation:

                    im = shift(im,(dx,dy), order = 0)

            im = torch.from_numpy(im).type(torch.DoubleTensor)

            images.append(im.unsqueeze(0))

        if self.train_data:

            if self.flipping and flip:

                segment_T = np.fliplr(segment_T)

                segment_F = np.fliplr(segment_F)

                segment_P = np.fliplr(segment_P)

                md = np.fliplr(md)

                fa = np.fliplr(fa)

            if self.rotation:

                segment_T = transform.rotate(segment_T,angle, order = 0)

                segment_F = transform.rotate(segment_F,angle, order = 0)

                segment_P = transform.rotate(segment_P,angle, order = 0)

                md = transform.rotate(md,angle, order = 0)

                fa = transform.rotate(fa,angle, order = 0)

            if self.translation:

                segment_T = shift(segment_T,(dx,dy), order = 0)

                segment_F = shift(segment_F,(dx,dy), order = 0)

                segment_P = shift(segment_P,(dx,dy), order = 0)

                md = shift(md,(dx,dy), order = 0)

                fa = shift(fa,(dx,dy), order = 0)

        fa = torch.from_numpy(fa).type(torch.DoubleTensor)

        md = torch.from_numpy(md).type(torch.DoubleTensor)

        images.append(fa.unsqueeze(0))

        images.append(md.unsqueeze(0))

        image_all = torch.cat(images, dim = 0)

        segment_F = torch.from_numpy(segment_F)

        segment_T = torch.from_numpy(segment_T)

        segment_P = torch.from_numpy(segment_P)

        if self.normalize:

#             max_image, min_image, max_fa, min_fa, max_md, min_md = pickle.load(open('normalizing_values','rb'))

            max_image, min_image = torch.max(image_all[:7,:,:]), torch.min(image_all[:7,:,:])

            max_fa, min_fa = torch.max(image_all[7,:,:]), torch.min(image_all[7,:,:])

            max_md, min_md = torch.max(image_all[8,:,:]), torch.min(image_all[8,:,:])

            image_all[:7,:,:] = (image_all[:7,:,:] - min_image)/(max_image - min_image)

            image_all[7,:,:] = (image_all[7,:,:] - min_fa)/(max_fa - min_fa)

            image_all[-1,:,:] = (image_all[-1,:,:] - min_md)/(max_md - min_md)

        return (image_all.type(torch.FloatTensor), segment_F.type(torch.FloatTensor), 

                segment_P.type(torch.FloatTensor), segment_T.type(torch.FloatTensor),self.label[idx])