# Copyright 2020 MONAI Consortium

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import os

import sys

from glob import glob

import tempfile

import numpy as np

import matplotlib.pyplot as plt

import nibabel as nib

import torch

from torch.utils.data import DataLoader

from init import Options

import monai

from monai.data import ArrayDataset, GridPatchDataset, create_test_image_3d

from monai.transforms import (Compose, LoadImaged, AddChanneld, Transpose, Resized, CropForegroundd, CastToTyped,RandGaussianSmoothd,

                              ScaleIntensityd, ToTensord, RandSpatialCropd, Rand3DElasticd, RandAffined, SpatialPadd,

    Spacingd, Orientationd, RandZoomd, ThresholdIntensityd, RandShiftIntensityd, RandGaussianNoised, BorderPadd,RandAdjustContrastd, NormalizeIntensityd,RandFlipd, ScaleIntensityRanged)

class IndexTracker(object):

    def __init__(self, ax, X):

        self.ax = ax

        ax.set_title('use scroll wheel to navigate images')

        self.X = X

        rows, cols, self.slices = X.shape

        self.ind = self.slices//2

        self.im = ax.imshow(self.X[:, :, self.ind],cmap= 'gray')

        self.update()

    def onscroll(self, event):

        print("%s %s" % (event.button, event.step))

        if event.button == 'up':

            self.ind = (self.ind + 1) % self.slices

        else:

            self.ind = (self.ind - 1) % self.slices

        self.update()

    def update(self):

        self.im.set_data(self.X[:, :, self.ind])

        self.ax.set_ylabel('slice %s' % self.ind)

        self.im.axes.figure.canvas.draw()

def plot3d(image):

    original=image

    original = np.rot90(original, k=-1)

    fig, ax = plt.subplots(1, 1)

    tracker = IndexTracker(ax, original)

    fig.canvas.mpl_connect('scroll_event', tracker.onscroll)

    plt.show()

if __name__ == "__main__":

    opt = Options().parse()

    train_images = sorted(glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))

    train_segs = sorted(glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))

    data_dicts = [{'image': image_name, 'label': label_name}

                  for image_name, label_name in zip(train_images, train_segs)]

    monai_transforms = [

        LoadImaged(keys=['image', 'label']),

        AddChanneld(keys=['image', 'label']),

        # Orientationd(keys=["image", "label"], axcodes="RAS"),

        # ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),

        # ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),

        CropForegroundd(keys=['image', 'label'], source_key='image', start_coord_key='foreground_start_coord',

                        end_coord_key='foreground_end_coord', ),  # crop CropForeground

        NormalizeIntensityd(keys=['image']),

        ScaleIntensityd(keys=['image']),

        # Spacingd(keys=['image', 'label'], pixdim=opt.resolution, mode=('bilinear', 'nearest')),

        SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'),

        RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),

        ToTensord(keys=['image', 'label','foreground_start_coord', 'foreground_end_coord'],)

    ]

    transform = Compose(monai_transforms)

    check_ds = monai.data.Dataset(data=data_dicts, transform=transform)

    loader = DataLoader(check_ds, batch_size=1, shuffle=True, num_workers=0, pin_memory=torch.cuda.is_available())

    check_data = monai.utils.misc.first(loader)

    im, seg, coord1, coord2 = (check_data['image'][0], check_data['label'][0],check_data['foreground_start_coord'][0],

                      check_data['foreground_end_coord'][0])

    print(im.shape, seg.shape, coord1, coord2)

    vol = im[0].numpy()

    mask = seg[0].numpy()

    print(vol.shape, mask.shape)

    plot3d(vol)

    plot3d(mask)