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()

from dataloader_2d import *

from dataloader_3d import *

train_path = '/beegfs/ark576/new_knee_data/train'

val_path = '/beegfs/ark576/new_knee_data/val'

test_path = '/beegfs/ark576/new_knee_data/test'

train_file_names = sorted(pickle.load(open(train_path + '/train_file_names.p','rb')))

val_file_names = sorted(pickle.load(open(val_path + '/val_file_names.p','rb')))

test_file_names = sorted(pickle.load(open(test_path + '/test_file_names.p','rb')))

transformed_dataset = {'train': KneeMRIDataset(train_path,train_file_names, train_data= True, flipping=False, normalize= True),

                       'validate': KneeMRIDataset(val_path,val_file_names, normalize= True),

                       'test': KneeMRIDataset(test_path,test_file_names, normalize= True)

                                          }

dataloader = {x: DataLoader(transformed_dataset[x], batch_size=5,

                        shuffle=True, num_workers=0) for x in ['train', 'validate','test']}

data_sizes ={x: len(transformed_dataset[x]) for x in ['train', 'validate','test']}

def plot_hist(hist_dict,hist_type,chart_type = 'semi-log'):

    if chart_type == 'log-log':

        plt.loglog(range(len(hist_dict['train'])),hist_dict['train'], label='Train ' + hist_type)

        plt.loglog(range(len(hist_dict['validate'])),hist_dict['validate'], label = 'Validation ' + hist_type)

    if chart_type == 'semi-log':

        plt.semilogy(range(len(hist_dict['train'])),hist_dict['train'], label='Train ' + hist_type)

        plt.semilogy(range(len(hist_dict['validate'])),hist_dict['validate'], label = 'Validation ' + hist_type)

    plt.xlabel('Epochs')

    plt.ylabel(hist_type)

    plt.legend()

    plt.show()

def dice_loss(true,scores, epsilon = 1e-4,p = 2):

    preds = F.softmax(scores)

    N, C, sh1, sh2 = true.size()

    true = true.view(N, C, -1)

    preds = preds.view(N, C, -1)

    wts = torch.sum(true, dim = 2) + epsilon

    wts = 1/torch.pow(wts,p)

    wts = torch.clamp(wts,0,0.1)

    wts[wts == 0.1] = 0

    wts = wts/(torch.sum(wts,dim = 1)[:,None])

    prod = torch.sum(true*preds,dim = 2)

    sum_tnp = torch.sum(true + preds, dim = 2)

    num = torch.sum(wts * prod, dim = 1)

    denom = torch.sum(wts * sum_tnp, dim = 1) + epsilon

    loss = 1 - 2*(num/denom)

    return torch.mean(loss)

def dice_loss_2(true,scores, epsilon = 1e-4,p = 2):

    preds = F.softmax(scores)

    N, C, sh1, sh2 = true.size()

    true = true.view(N, C, -1)

    preds = preds.view(N, C, -1)

    wts = torch.sum(true, dim = 2) + epsilon

    wts = 1/torch.pow(wts,p)

    wts = torch.clamp(wts,0,0.1)

    wts[wts == 0.1] = 1e-6

    wts[:,-1] = 1e-15

    wts = wts/(torch.sum(wts,dim = 1)[:,None])

    prod = torch.sum(true*preds,dim = 2)

    sum_tnp = torch.sum(true + preds, dim = 2)

    num = torch.sum(wts * prod, dim = 1)

    denom = torch.sum(wts * sum_tnp, dim = 1) + epsilon

    loss = 1 - 2*(num/denom)

    return torch.mean(loss)

def segments(seg_1, seg_2, seg_3):

    seg_tot = seg_1 + seg_2 + seg_3

    seg_none = (seg_tot == 0).type(torch.FloatTensor)

    seg_all = torch.cat((seg_1.unsqueeze(1),seg_2.unsqueeze(1),seg_3.unsqueeze(1),seg_none.unsqueeze(1)), dim = 1)

    return seg_all

seg_sum = torch.zeros(3)

for i, data in enumerate(dataloader['train']):

    input, segF, segP, segT,_ = data

    seg_sum[0] += torch.sum(segF)

    seg_sum[1] += torch.sum(segP)

    seg_sum[2] += torch.sum(segT)

mean_s_sum = seg_sum/i

def dice_loss_3(true,scores, epsilon = 1e-4,p = 2, mean=mean_s_sum):

    preds = F.softmax(scores)

    N, C, sh1, sh2 = true.size()

    true = true.view(N, C, -1)

    preds = preds.view(N, C, -1)

    wts = torch.sum(true, dim = 2) + epsilon

    mean = 1/torch.pow(mean,p)

    wts[:,:-1] = mean[None].repeat(N,1)

    wts[:,-1] = 0

    wts = wts/(torch.sum(wts,dim = 1)[:,None])

    prod = torch.sum(true*preds,dim = 2)

    sum_tnp = torch.sum(true + preds, dim = 2)

    num = torch.sum(wts * prod, dim = 1)

    denom = torch.sum(wts * sum_tnp, dim = 1) + epsilon

    loss = 1 - 2*(num/denom)

    return torch.mean(loss)

def predict(scores,smooth = False,filter_size = 3):

    preds = F.softmax(scores)

    pred_class = (torch.max(preds, dim = 1)[1])

    class_0_pred_seg = (pred_class == 0).type(torch.cuda.FloatTensor)

    class_1_pred_seg = (pred_class == 1).type(torch.cuda.FloatTensor)

    class_2_pred_seg = (pred_class == 2).type(torch.cuda.FloatTensor)

    if smooth:

        class_0_pred_seg = F.avg_pool2d(class_0_pred_seg,filter_size,1,int((filter_size-1)/2))>0.5

        class_1_pred_seg = F.avg_pool2d(class_1_pred_seg,filter_size,1,int((filter_size-1)/2))>0.5

        class_2_pred_seg = F.avg_pool2d(class_2_pred_seg,filter_size,1,int((filter_size-1)/2))>0.5

    return class_0_pred_seg.data.type(torch.cuda.FloatTensor), class_1_pred_seg.data.type(torch.cuda.FloatTensor)\

, class_2_pred_seg.data.type(torch.cuda.FloatTensor)

def dice_score(true,scores,smooth = False,filter_size = 3, epsilon = 1e-7):

    N ,C, sh1, sh2 = true.size()

    true = true.view(N,C,-1)

    class_0_pred_seg,class_1_pred_seg,class_2_pred_seg = predict(scores, smooth = smooth,filter_size = filter_size)

    class_0_pred_seg = class_0_pred_seg.view(N,-1)

    class_1_pred_seg = class_1_pred_seg.view(N,-1)

    class_2_pred_seg = class_2_pred_seg.view(N,-1)

    true = true.data.type(torch.cuda.FloatTensor)

    def numerator(truth,pred, idx):

        return(torch.sum(truth[:,idx,:] * pred,dim = 1)) + epsilon/2

    def denominator(truth,pred,idx):

        return(torch.sum(truth[:,idx,:]+pred,dim = 1)) + epsilon

    dice_score_class_0 = torch.mean(2*(numerator(true,class_0_pred_seg,0))/(denominator(true,class_0_pred_seg,0)))

    dice_score_class_1 = torch.mean(2*(numerator(true,class_1_pred_seg,1))/(denominator(true,class_1_pred_seg,1)))

    dice_score_class_2 = torch.mean(2*(numerator(true,class_2_pred_seg,2))/(denominator(true,class_2_pred_seg,2)))

    return (dice_score_class_0,dice_score_class_1, dice_score_class_2)

def entropy_loss(true,scores,mean = mean_s_sum,epsilon = 1e-4, p=2):

    N,C,sh1,sh2 = true.size()

    wts = Variable(torch.zeros(4).cuda()) + epsilon

    mean = 1/torch.pow(mean,p)

    wts[:-1] = mean

    wts[-1] = 1e-9

    wts = wts/(torch.sum(wts))

    log_prob = F.log_softmax(scores)

    prod = (log_prob*true).view(N,C,-1)

    prod_t = torch.transpose(prod,1,2)

    loss = -torch.mean(prod_t*wts)

    return loss

def image_to_mask(img, femur, patellar, tibia,femur_pr,patellar_pr,tibia_pr,cm = None):

    masked_1 = np.ma.masked_where(femur == 0, femur)

    masked_2 = np.ma.masked_where(patellar == 0,patellar)

    masked_3 = np.ma.masked_where(tibia == 0, tibia)

    masked_1_pr = np.ma.masked_where(femur_pr == 0, femur_pr)

    masked_2_pr = np.ma.masked_where(patellar_pr == 0,patellar_pr)

    masked_3_pr = np.ma.masked_where(tibia_pr == 0, tibia_pr)

    masked_cm = np.ma.masked_where(cm ==-1000,cm)

    x = 3

    plt.figure(figsize=(20,10))

    plt.subplot(1,x,1)

    plt.imshow(img, 'gray', interpolation='none')

    plt.subplot(1,x,2)

    plt.imshow(img, 'gray', interpolation='none')

    if np.sum(femur) != 0:

        plt.imshow(masked_1, 'spring', interpolation='none', alpha=0.9)

    if np.sum(patellar) != 0:

        plt.imshow(masked_2, 'coolwarm_r', interpolation='none', alpha=0.9)

    if np.sum(tibia) != 0:

        plt.imshow(masked_3, 'Wistia', interpolation='none', alpha=0.9)

    plt.subplot(1,x,3)

    plt.imshow(img, 'gray', interpolation='none')

    if np.sum(femur_pr) != 0:

        plt.imshow(masked_1_pr, 'spring', interpolation='none', alpha=0.9)

    if np.sum(patellar_pr) != 0:

        plt.imshow(masked_2_pr, 'coolwarm_r', interpolation='none', alpha=0.9)

    if np.sum(tibia_pr) != 0:

        plt.imshow(masked_3_pr, 'Wistia', interpolation='none', alpha=0.9)

    plt.show()

    if cm is not None:

        plt.figure(figsize=(20,20))

        plt.imshow(masked_cm,'coolwarm_r')

        plt.colorbar()

        plt.show()

def generate_noise(true):

    return Variable((2*torch.rand(true.size())-1)*0.1).cuda()

def save_segmentations_2d(model,prediction_models, dataloader,data_sizes,batch_size,phase,model_name,\

                          num_samples = 7, smooth = False, filter_size = 3):

    y_preds = []

    name_list = []

    num_samples = num_samples

    if phase == 'train':

        path = '/beegfs/ark576/Knee Cartilage Data/Train Data/'

    if phase == 'validate':

        path = '/beegfs/ark576/Knee Cartilage Data/Validation Data/'

    if phase == 'test':

        path = '/beegfs/ark576/Knee Cartilage Data/Test Data/'

    for i in prediction_models:

        for param in i.parameters():

            param.requires_grad = False

    for i,data in enumerate(dataloader[phase]):

        input, segF,segP, segT,variable_name = data

        input = Variable(input).cuda()

        input_pp = []

        for j in prediction_models:

            output = j(input)

            preds_m = predict_pp(output)

            input_pp.append(preds_m)

        input_pp = torch.cat(input_pp,dim = 1)

        output_pp = model(input_pp)

        preds = predict(output_pp,smooth = smooth, filter_size=filter_size)

        preds = torch.cat((preds[0][:,None],preds[1][:,None],preds[2][:,None]),dim = 1)

        y_preds.append(preds.cpu().numpy())

        name_list.append(variable_name)

    list_of_names = list(itertools.chain(*name_list))

    y_preds = np.concatenate(y_preds).astype(np.uint8)

    for i in range(num_samples):

        name = list_of_names[i*15][:-3]

        pred_segment = y_preds[i*15:(i+1)*15]

        file_name = path + name

        variable = sio.loadmat(file_name)

        temp_variable = {}

        temp_variable['MDnr'] = variable['MDnr']

        preds_all = pred_segment[[0,1,7,8,9,10,11,12,13,14,2,3,4,5,6],:]

        temp_variable['Predicted_segment_F'] = np.transpose(preds_all[:,0,:,:],(1,2,0))

        temp_variable['Predicted_segment_P'] = np.transpose(preds_all[:,1,:,:],(1,2,0))

        temp_variable['Predicted_segment_T'] = np.transpose(preds_all[:,2,:,:],(1,2,0))

        save_path = '/beegfs/ark576/knee-segments/predictions/'+ model_name +'/'+phase+'/'

        sio.savemat(save_path+name,temp_variable,appendmat=False, do_compression=True)

def save_segmentations_2d_prob(model,prediction_models, dataloader,data_sizes,batch_size,phase,model_name,\

                          num_samples = 7, smooth = False, filter_size = 3):

    y_preds = []

    name_list = []

    num_samples = num_samples

    if phase == 'train':

        path = '/beegfs/ark576/Knee Cartilage Data/Train Data/'

    if phase == 'validate':

        path = '/beegfs/ark576/Knee Cartilage Data/Validation Data/'

    if phase == 'test':

        path = '/beegfs/ark576/Knee Cartilage Data/Test Data/'

    for i in prediction_models:

        for param in i.parameters():

            param.requires_grad = False

    for i,data in enumerate(dataloader[phase]):

        input, segF,segP, segT,variable_name = data

        input = Variable(input).cuda()

        input_pp = []

        for j in prediction_models:

            output = j(input)

            preds_m = predict_pp(output)

            input_pp.append(preds_m)

        input_pp = torch.cat(input_pp,dim = 1)

        output_pp = model(input_pp)

        preds = F.softmax(output_pp)

        y_preds.append(preds.data.cpu().numpy())

        name_list.append(variable_name)

    list_of_names = list(itertools.chain(*name_list))

    y_preds = np.concatenate(y_preds)

    for i in range(num_samples):

        name = list_of_names[i*15][:-3]

        pred_segment = y_preds[i*15:(i+1)*15]

        file_name = path + name

        variable = sio.loadmat(file_name)

        temp_variable = {}

        temp_variable['NUFnr'] = variable['NUFnr']

        temp_variable['GT_F'] = variable['SegmentationF']

        temp_variable['GT_P'] = variable['SegmentationP']

        temp_variable['GT_T'] = variable['SegmentationT']

        preds_all = pred_segment[[0,1,7,8,9,10,11,12,13,14,2,3,4,5,6],:]

        temp_variable['Predicted_prob'] = preds_all

        save_path = '/beegfs/ark576/knee-segments/predictions/'+ model_name +'/'+phase+'/'

        sio.savemat(save_path+name+'_prob',temp_variable,appendmat=False, do_compression=True)

from sklearn.metrics import confusion_matrix

def dice_score_image(pred,true,epsilon = 1e-5):

    num = 2*np.sum(pred*true) + epsilon

    pred_norm = np.sum(pred)

    true_norm = np.sum(true)

    if pred_norm == 0 or true_norm == 0:

        return None

    else:

        denom = pred_norm + true_norm + epsilon

        return num/denom

def save_segmentations_3D(model, dataloader,data_sizes,batch_size,phase,model_name, num_samples = 7):

    y_preds = []

    name_list = []

    num_samples = num_samples

    if phase == 'train':

        path = '/beegfs/ark576/Knee Cartilage Data/Train Data/'

    if phase == 'validate':

        path = '/beegfs/ark576/Knee Cartilage Data/Validation Data/'

    if phase == 'test':

        path = '/beegfs/ark576/Knee Cartilage Data/Test Data/'

    for data in dataloader[phase]:

        input, segments, variable_name = data

        input = Variable(input).cuda()

        output = model(input)

        output_reshaped = torch.transpose(output,2,1).contiguous().view(-1,4,256,256)

        preds = predict(output_reshaped)

        preds = torch.cat((preds[0][:,None],preds[1][:,None],preds[2][:,None]),dim = 1)

        y_preds.append(preds.cpu().numpy())

        name_list.append(variable_name)

    list_of_names = list(itertools.chain(*name_list))

    y_preds = np.concatenate(y_preds).astype(np.uint8)

    for i in range(num_samples):

        name = list_of_names[i]

        preds_all = y_preds[i*15:(i+1)*15]

        file_name = path + name

        variable = sio.loadmat(file_name)

        temp_variable = {}

        temp_variable['MDnr'] = variable['MDnr']

        temp_variable['Predicted_segment_F'] = np.transpose(preds_all[:,0,:,:],(1,2,0))

        temp_variable['Predicted_segment_T'] = np.transpose(preds_all[:,1,:,:],(1,2,0))

        temp_variable['Predicted_segment_P'] = np.transpose(preds_all[:,2,:,:],(1,2,0))

        save_path = '/beegfs/ark576/knee-segments/predictions/'+ model_name +'/'+phase+'/'

        sio.savemat(save_path+name,temp_variable,appendmat=False, do_compression=True)

def make_certainity_maps(scores):

    probs = F.softmax(scores)

    pred_prob,idx = (torch.max(probs, dim = 1))

    pred_prob_c = torch.clamp(pred_prob,0.00001,0.999999)

    ret_value = torch.log(pred_prob_c) - torch.log((1-pred_prob_c))

    ret_value[idx==3]=-1000

    return ret_value