import collections
import json
import os
import os.path
import re
from collections import namedtuple
from glob import glob
import numpy as np
import torch

import matplotlib.pyplot as plt
from PIL import Image
from torch.utils import data

#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------

# a label and all meta information
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
    ] )


#--------------------------------------------------------------------------------
# A list of all labels
#--------------------------------------------------------------------------------

# Please adapt the train IDs as appropriate for you approach.
# Note that you might want to ignore labels with ID 255 during training.
# Further note that the current train IDs are only a suggestion. You can use whatever you like.
# Make sure to provide your results using the original IDs and not the training IDs.
# Note that many IDs are ignored in evaluation and thus you never need to predict these!

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] ),
]

def get_id2color():
    id2color = {label.trainId : label.color for label in labels}
    id2color[19] = [0, 0, 0]
    return id2color

def get_trainId2Id():
    return {label.trainId : label.id for label in labels if label.ignoreInEval == False}


def get_data_path(name):
    js = open('config.json').read()
    data = json.loads(js)
    return data[name]['data_path']


class CityscapesLoader2(data.Dataset):
    def __init__(self, root, split="training", img_size=None, transforms=None, augmentation=1, return_name = False):
        self.root = root
        self.split = split
        self.n_classes = 19
        self.augmentation = augmentation
        self.return_name = return_name
        if img_size:
            self.img_size = img_size if isinstance(img_size, tuple) else (img_size, img_size)
        else:
            self.img_size = None
        self.files = collections.defaultdict(list)
        self.labels = collections.defaultdict(list)
        self.transforms = transforms
        self.trainId2color = {label.trainId : label.color for label in labels}

        for split in ["train", "test", "val"]:
            file_list = glob(root + 'leftImg8bit/' + split + '/**/*.png', recursive=True)
            self.files[split] = file_list
            label_list = {
                os.path.basename(path): re.sub('_leftImg8bit.', '_gtFine_labelTrainIds.', re.sub('leftImg8bit/', 'gtFine/', path))
                for path in file_list}
            self.labels[split] = label_list

    def __len__(self):
        return self.augmentation * len(self.files[self.split])

    def __getitem__(self, index):
        img_name = self.files[self.split][int(index / self.augmentation)]
        img_path = img_name
        try:
            img = Image.open(img_path).convert('RGB')
        except Exception:
            print("Image truncated: ", img_path)
            return self.__getitem__(index+1)

        if self.split not in ["testing", "test"]:
            lbl_path = self.labels[self.split][os.path.basename(img_path)]
            lbl = Image.open(lbl_path)
        else:
            lbl=None

        if self.img_size:
            img = img.resize(self.img_size, resample=Image.BILINEAR)
            lbl = lbl.resize(self.img_size, resample=Image.NEAREST)

        if self.transforms:
            img, lbl = self.transforms(img, lbl)

        if self.split not in ["testing","test"]:
            if self.return_name:
                return os.path.basename(img_path), img, lbl
            else:
                return img, lbl
        else:
            if self.return_name:
                return os.path.basename(img_path), img
            else:
                return img

    def encode_segmap(self, mask):
        background_color = np.array([255, 0, 0])
        gt_bg = np.all(mask == background_color, axis=2)
        gt_bg = gt_bg.reshape(*gt_bg.shape, 1)
        gt_image = np.concatenate((gt_bg, np.invert(gt_bg)), axis=2)
        return gt_bg

    def get_pascal_labels(self):
        return self.trainId2color

    def decode_segmap(self, temp, plot=False):
        label_colours = self.get_pascal_labels()
        r = temp.copy()
        g = temp.copy()
        b = temp.copy()
        for l in range(0, self.n_classes):
            r[temp == l] = label_colours[l, 0]
            g[temp == l] = label_colours[l, 1]
            b[temp == l] = label_colours[l, 2]
        rgb = np.zeros((temp.shape[0], temp.shape[1], 3))
        rgb[:, :, 0] = r
        rgb[:, :, 1] = g
        rgb[:, :, 2] = b
        if plot:
            plt.imshow(rgb)
            plt.show()
        else:
            return rgb

