import os, random, time, copy
from skimage import io, transform
import json
import numpy as np
from subprocess import check_output
import numpy as np
import os.path as path
import scipy.io as sio
from scipy import misc
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
import pickle
import skimage.transform 
import csv
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler 
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
from collections import namedtuple


class Cityscapes(Dataset):
    """`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset.

    Args:
        root (string): Root directory of dataset where directory ``leftImg8bit``
            and ``gtFine`` or ``gtCoarse`` are located.
        split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="gtFine"
            otherwise ``train``, ``train_extra`` or ``val``
        mode (string, optional): The quality mode to use, ``gtFine`` or ``gtCoarse``
        target_type (string or list, optional): Type of target to use, ``instance``, ``semantic``, ``polygon``
            or ``color``. Can also be a list to output a tuple with all specified target types.
        transform (callable, optional): A function/transform that takes in a PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        transforms (callable, optional): A function/transform that takes input sample and its target as entry
            and returns a transformed version.

    Examples:

        Get semantic segmentation target

        .. code-block:: python

            dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                                 target_type='semantic')

            img, smnt = dataset[0]

        Get multiple targets

        .. code-block:: python

            dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                                 target_type=['instance', 'color', 'polygon'])

            img, (inst, col, poly) = dataset[0]

        Validate on the "coarse" set

        .. code-block:: python

            dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse',
                                 target_type='semantic')

            img, smnt = dataset[0]
    """

    # Based on https://github.com/mcordts/cityscapesScripts
    CityscapesClass = namedtuple('CityscapesClass', 
                                 ['name', 'id', 'train_id', 'category', 'category_id',
                                  'has_instances', 'ignore_in_eval', 'color'])

    classes = [
        CityscapesClass('unlabeled', 0, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('ego vehicle', 1, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('rectification border', 2, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('out of roi', 3, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('static', 4, 255, 'void', 0, False, True, (0, 0, 0)),
        CityscapesClass('dynamic', 5, 255, 'void', 0, False, True, (111, 74, 0)),
        CityscapesClass('ground', 6, 255, 'void', 0, False, True, (81, 0, 81)),
        CityscapesClass('road', 7, 0, 'flat', 1, False, False, (128, 64, 128)),
        CityscapesClass('sidewalk', 8, 1, 'flat', 1, False, False, (244, 35, 232)),
        CityscapesClass('parking', 9, 255, 'flat', 1, False, True, (250, 170, 160)),
        CityscapesClass('rail track', 10, 255, 'flat', 1, False, True, (230, 150, 140)),
        CityscapesClass('building', 11, 2, 'construction', 2, False, False, (70, 70, 70)),
        CityscapesClass('wall', 12, 3, 'construction', 2, False, False, (102, 102, 156)),
        CityscapesClass('fence', 13, 4, 'construction', 2, False, False, (190, 153, 153)),
        CityscapesClass('guard rail', 14, 255, 'construction', 2, False, True, (180, 165, 180)),
        CityscapesClass('bridge', 15, 255, 'construction', 2, False, True, (150, 100, 100)),
        CityscapesClass('tunnel', 16, 255, 'construction', 2, False, True, (150, 120, 90)),
        CityscapesClass('pole', 17, 5, 'object', 3, False, False, (153, 153, 153)),
        CityscapesClass('polegroup', 18, 255, 'object', 3, False, True, (153, 153, 153)),
        CityscapesClass('traffic light', 19, 6, 'object', 3, False, False, (250, 170, 30)),
        CityscapesClass('traffic sign', 20, 7, 'object', 3, False, False, (220, 220, 0)),
        CityscapesClass('vegetation', 21, 8, 'nature', 4, False, False, (107, 142, 35)),
        CityscapesClass('terrain', 22, 9, 'nature', 4, False, False, (152, 251, 152)),
        CityscapesClass('sky', 23, 10, 'sky', 5, False, False, (70, 130, 180)),
        CityscapesClass('person', 24, 11, 'human', 6, True, False, (220, 20, 60)),
        CityscapesClass('rider', 25, 12, 'human', 6, True, False, (255, 0, 0)),
        CityscapesClass('car', 26, 13, 'vehicle', 7, True, False, (0, 0, 142)),
        CityscapesClass('truck', 27, 14, 'vehicle', 7, True, False, (0, 0, 70)),
        CityscapesClass('bus', 28, 15, 'vehicle', 7, True, False, (0, 60, 100)),
        CityscapesClass('caravan', 29, 255, 'vehicle', 7, True, True, (0, 0, 90)),
        CityscapesClass('trailer', 30, 255, 'vehicle', 7, True, True, (0, 0, 110)),
        CityscapesClass('train', 31, 16, 'vehicle', 7, True, False, (0, 80, 100)),
        CityscapesClass('motorcycle', 32, 17, 'vehicle', 7, True, False, (0, 0, 230)),
        CityscapesClass('bicycle', 33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
        CityscapesClass('license plate', -1, -1, 'vehicle', 7, False, True, (0, 0, 142)),
    ]

    def __init__(self, root='/home/skong2/restore/dataset/Cityscapes',
                 newsize=(256, 256),
                 split='train',
                 mode='fine',
                 trainnum=10,
                 target_type='semantic',
                 transform=None,
                 target_transform=None,
                 transforms=None):
        
        #super(Cityscapes, self).__init__(root, transforms, transform, target_transform)
        self.newsize = newsize
        self.trainnum = trainnum
        self.flagResize = True
        if newsize[0]<0 or newsize[1]<0:
            self.flagResize = False
            
        self.root = root
        self.mode = 'gtFine' if mode == 'fine' else 'gtCoarse'
        self.images_dir = os.path.join(self.root, 'leftImg8bit', split)
        self.targets_dir = os.path.join(self.root, self.mode, split)
        self.target_type = target_type
        self.split = split
        self.images = []
        self.targets = []
        if self.split=='test':
            self.split = 'val'            
        self.transform = transform
        self.transforms = transforms
        self.target_transform = target_transform
        
        #verify_str_arg(mode, "mode", ("fine", "coarse"))
        if mode == "fine":
            valid_modes = ("train", "test", "val")
        else:
            valid_modes = ("train", "train_extra", "val")
            
        msg = ("Unknown value '{}' for argument split if mode is '{}'. "
               "Valid values are {{{}}}.")
        
        #msg = msg.format(split, mode, iterable_to_str(valid_modes))
        #verify_str_arg(split, "split", valid_modes, msg)

        if not isinstance(target_type, list):
            self.target_type = [target_type]
        
        #[verify_str_arg(value, "target_type", ("instance", "semantic", "polygon", "color"))  for value in self.target_type]

        if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir):
            if split == 'train_extra':
                image_dir_zip = os.path.join(self.root, 'leftImg8bit{}'.format('_trainextra.zip'))
            else:
                image_dir_zip = os.path.join(self.root, 'leftImg8bit{}'.format('_trainvaltest.zip'))

            if self.mode == 'gtFine':
                target_dir_zip = os.path.join(self.root, '{}{}'.format(self.mode, '_trainvaltest.zip'))
            elif self.mode == 'gtCoarse':
                target_dir_zip = os.path.join(self.root, '{}{}'.format(self.mode, '.zip'))

            if os.path.isfile(image_dir_zip) and os.path.isfile(target_dir_zip):
                extract_archive(from_path=image_dir_zip, to_path=self.root)
                extract_archive(from_path=target_dir_zip, to_path=self.root)
            else:
                raise RuntimeError('Dataset not found or incomplete. Please make sure all required folders for the'
                                   ' specified "split" and "mode" are inside the "root" directory')

        for city in os.listdir(self.images_dir):
            img_dir = os.path.join(self.images_dir, city)
            target_dir = os.path.join(self.targets_dir, city)
            for file_name in os.listdir(img_dir):
                target_types = []
                for t in self.target_type:
                    target_name = '{}_{}'.format(file_name.split('_leftImg8bit')[0],
                                                 self._get_target_suffix(self.mode, t))
                    target_types.append(os.path.join(target_dir, target_name))

                self.images.append(os.path.join(img_dir, file_name))
                self.targets.append(target_types)
                
        if self.split=='train' and self.trainnum>0:
            self.images = self.images[:self.trainnum]
            self.targets = self.targets[:self.trainnum]
        elif self.split=='train' and self.trainnum<-5:
            self.images = self.images[self.trainnum:]
            self.targets = self.targets[self.trainnum:]
        else:
            self.trainnum = -1

            
    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is a tuple of all target types if target_type is a list with more
            than one item. Otherwise target is a json object if target_type="polygon", else the image segmentation.
        """

        image = Image.open(self.images[index]).convert('RGB')
        #b, g, r = image.split()
        #image = Image.merge("RGB", (r, g, b))
        
        if self.flagResize:
            image = image.resize(self.newsize, resample=Image.BILINEAR)
        #print(self.targets[index], self.target_type)
        
        targets = []
        for i, t in enumerate(self.target_type):
            if t == 'polygon':
                target = self._load_json(self.targets[index][i])
            else:
                target = Image.open(self.targets[index][i])

                if self.flagResize:
                    target = target.resize(self.newsize, resample=Image.NEAREST)

            targets.append(target)

        target = tuple(targets) if len(targets) > 1 else targets[0]

        image = self.transform(image)
        #print('image', type(image), image.shape)
        
        target = np.asarray(target).astype(np.float32)
        target = torch.from_numpy(target)
        #target = self.target_transform(target)
        #print('target', type(target), target.shape)
        
        if self.transforms is not None:
            image, target = self.transforms(image, target)
        
        return image, target

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

    def extra_repr(self):
        lines = ["Split: {split}", "Mode: {mode}", "Type: {target_type}"]
        return '\n'.join(lines).format(**self.__dict__)

    def _load_json(self, path):
        with open(path, 'r') as file:
            data = json.load(file)
        return data

    def _get_target_suffix(self, mode, target_type):
        if target_type == 'instance':
            return '{}_instanceIds.png'.format(mode)
        elif target_type == 'semantic':
            return '{}_labelIds.png'.format(mode)
        elif target_type == 'color':
            return '{}_color.png'.format(mode)
        else:
            return '{}_polygons.json'.format(mode)
        
        
        
        
        
'''

Label = namedtuple( 'Label' , [

    'name'        , # The identifier of this label, e.g. 'car', 'person', ... .
                    # We use them to uniquely name a class

    'id'          , # An integer ID that is associated with this label.
                    # The IDs are used to represent the label in ground truth images
                    # An ID of -1 means that this label does not have an ID and thus
                    # is ignored when creating ground truth images (e.g. license plate).
                    # Do not modify these IDs, since exactly these IDs are expected by the
                    # evaluation server.

    'trainId'     , # Feel free to modify these IDs as suitable for your method. Then create
                    # ground truth images with train IDs, using the tools provided in the
                    # 'preparation' folder. However, make sure to validate or submit results
                    # to our evaluation server using the regular IDs above!
                    # For trainIds, multiple labels might have the same ID. Then, these labels
                    # are mapped to the same class in the ground truth images. For the inverse
                    # mapping, we use the label that is defined first in the list below.
                    # For example, mapping all void-type classes to the same ID in training,
                    # might make sense for some approaches.
                    # Max value is 255!

    'category'    , # The name of the category that this label belongs to

    'categoryId'  , # The ID of this category. Used to create ground truth images
                    # on category level.

    'hasInstances', # Whether this label distinguishes between single instances or not

    'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
                    # during evaluations or not

    'color'       , # The color of this label
    ] )
    
    
    
labels = [
    #       name                     id    trainId   category            catId     hasInstances   ignoreInEval   color
    Label(  'unlabeled'            ,  0 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'ego vehicle'          ,  1 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'rectification border' ,  2 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'out of roi'           ,  3 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'static'               ,  4 ,      255 , 'void'            , 0       , False        , True         , (  0,  0,  0) ),
    Label(  'dynamic'              ,  5 ,      255 , 'void'            , 0       , False        , True         , (111, 74,  0) ),
    Label(  'ground'               ,  6 ,      255 , 'void'            , 0       , False        , True         , ( 81,  0, 81) ),
    Label(  'road'                 ,  7 ,        0 , 'flat'            , 1       , False        , False        , (128, 64,128) ),
    Label(  'sidewalk'             ,  8 ,        1 , 'flat'            , 1       , False        , False        , (244, 35,232) ),
    Label(  'parking'              ,  9 ,      255 , 'flat'            , 1       , False        , True         , (250,170,160) ),
    Label(  'rail track'           , 10 ,      255 , 'flat'            , 1       , False        , True         , (230,150,140) ),
    Label(  'building'             , 11 ,        2 , 'construction'    , 2       , False        , False        , ( 70, 70, 70) ),
    Label(  'wall'                 , 12 ,        3 , 'construction'    , 2       , False        , False        , (102,102,156) ),
    Label(  'fence'                , 13 ,        4 , 'construction'    , 2       , False        , False        , (190,153,153) ),
    Label(  'guard rail'           , 14 ,      255 , 'construction'    , 2       , False        , True         , (180,165,180) ),
    Label(  'bridge'               , 15 ,      255 , 'construction'    , 2       , False        , True         , (150,100,100) ),
    Label(  'tunnel'               , 16 ,      255 , 'construction'    , 2       , False        , True         , (150,120, 90) ),
    Label(  'pole'                 , 17 ,        5 , 'object'          , 3       , False        , False        , (153,153,153) ),
    Label(  'polegroup'            , 18 ,      255 , 'object'          , 3       , False        , True         , (153,153,153) ),
    Label(  'traffic light'        , 19 ,        6 , 'object'          , 3       , False        , False        , (250,170, 30) ),
    Label(  'traffic sign'         , 20 ,        7 , 'object'          , 3       , False        , False        , (220,220,  0) ),
    Label(  'vegetation'           , 21 ,        8 , 'nature'          , 4       , False        , False        , (107,142, 35) ),
    Label(  'terrain'              , 22 ,        9 , 'nature'          , 4       , False        , False        , (152,251,152) ),
    Label(  'sky'                  , 23 ,       10 , 'sky'             , 5       , False        , False        , ( 70,130,180) ),
    Label(  'person'               , 24 ,       11 , 'human'           , 6       , True         , False        , (220, 20, 60) ),
    Label(  'rider'                , 25 ,       12 , 'human'           , 6       , True         , False        , (255,  0,  0) ),
    Label(  'car'                  , 26 ,       13 , 'vehicle'         , 7       , True         , False        , (  0,  0,142) ),
    Label(  'truck'                , 27 ,       14 , 'vehicle'         , 7       , True         , False        , (  0,  0, 70) ),
    Label(  'bus'                  , 28 ,       15 , 'vehicle'         , 7       , True         , False        , (  0, 60,100) ),
    Label(  'caravan'              , 29 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0, 90) ),
    Label(  'trailer'              , 30 ,      255 , 'vehicle'         , 7       , True         , True         , (  0,  0,110) ),
    Label(  'train'                , 31 ,       16 , 'vehicle'         , 7       , True         , False        , (  0, 80,100) ),
    Label(  'motorcycle'           , 32 ,       17 , 'vehicle'         , 7       , True         , False        , (  0,  0,230) ),
    Label(  'bicycle'              , 33 ,       18 , 'vehicle'         , 7       , True         , False        , (119, 11, 32) ),
    Label(  'license plate'        , -1 ,       -1 , 'vehicle'         , 7       , False        , True         , (  0,  0,142) ),
]


'''        