""" Document Localization using Recursive CNN
 Maintainer : Khurram Javed
 Email : kjaved@ualberta.ca """

import imgaug.augmenters as iaa
import csv
import logging
import os
import xml.etree.ElementTree as ET

import numpy as np
from torchvision import transforms

import utils.utils as utils

# To incdude a new Dataset, inherit from Dataset and add all the Dataset specific parameters here.
# Goal : Remove any data specific parameters from the rest of the code

logger = logging.getLogger("iCARL")


class Dataset:
    """
    Base class to reprenent a Dataset
    """

    def __init__(self, name):
        self.name = name
        self.data = []
        self.labels = []


def getTransformsByImgaug():
    return iaa.Sequential(
        [
            iaa.Resize(32),
            # Add blur
            iaa.Sometimes(
                0.05,
                iaa.OneOf(
                    [
                        iaa.GaussianBlur(
                            (0, 3.0)
                        ),  # blur images with a sigma between 0 and 3.0
                        iaa.AverageBlur(
                            k=(2, 11)
                        ),  # blur image using local means with kernel sizes between 2 and 7
                        iaa.MedianBlur(
                            k=(3, 11)
                        ),  # blur image using local medians with kernel sizes between 2 and 7
                        iaa.MotionBlur(k=15, angle=[-45, 45]),
                    ]
                ),
            ),
            # Add color
            iaa.Sometimes(
                0.05,
                iaa.OneOf(
                    [
                        iaa.WithHueAndSaturation(iaa.WithChannels(0, iaa.Add((0, 50)))),
                        iaa.AddToBrightness((-30, 30)),
                        iaa.MultiplyBrightness((0.5, 1.5)),
                        iaa.AddToHueAndSaturation((-50, 50), per_channel=True),
                        iaa.Grayscale(alpha=(0.0, 1.0)),
                        iaa.ChangeColorTemperature((1100, 10000)),
                        iaa.KMeansColorQuantization(),
                    ]
                ),
            ),
            # Add wether
            iaa.Sometimes(
                0.05,
                iaa.OneOf(
                    [
                        iaa.Clouds(),
                        iaa.Fog(),
                        iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05)),
                        iaa.Rain(speed=(0.1, 0.3)),
                    ]
                ),
            ),
            # Add contrast
            iaa.Sometimes(
                0.05,
                iaa.OneOf(
                    [
                        iaa.GammaContrast((0.5, 2.0)),
                        iaa.GammaContrast((0.5, 2.0), per_channel=True),
                        iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
                        iaa.SigmoidContrast(
                            gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True
                        ),
                        iaa.LogContrast(gain=(0.6, 1.4)),
                        iaa.LogContrast(gain=(0.6, 1.4), per_channel=True),
                        iaa.LinearContrast((0.4, 1.6)),
                        iaa.LinearContrast((0.4, 1.6), per_channel=True),
                        iaa.AllChannelsCLAHE(),
                        iaa.AllChannelsCLAHE(clip_limit=(1, 10)),
                        iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True),
                        iaa.Alpha((0.0, 1.0), iaa.HistogramEqualization()),
                        iaa.Alpha((0.0, 1.0), iaa.AllChannelsHistogramEqualization()),
                        iaa.AllChannelsHistogramEqualization(),
                    ]
                ),
            )
        ]
    ).augment_image


class SmartDoc(Dataset):
    """
    Class to include MNIST specific details
    """

    def __init__(self, directory="data"):
        super().__init__("smartdoc")
        self.data = []
        self.labels = []
        for d in directory:
            self.directory = d
            self.train_transform = transforms.Compose(
                [
                    getTransformsByImgaug(),
                    #     transforms.Resize([32, 32]),
                    #    transforms.ColorJitter(1.5, 1.5, 0.9, 0.5),
                    transforms.ToTensor(),
                ]
            )

            self.test_transform = transforms.Compose(
                [
                    iaa.Sequential(
                        [
                            iaa.Resize(32),
                        ]
                    ).augment_image,
                    transforms.ToTensor(),
                ]
            )

            logger.info("Pass train/test data paths here")

            self.classes_list = {}

            file_names = []
            print(self.directory, "gt.csv")
            with open(os.path.join(self.directory, "gt.csv"), "r") as csvfile:
                spamreader = csv.reader(
                    csvfile, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL
                )
                import ast

                for row in spamreader:
                    file_names.append(row[0])
                    self.data.append(os.path.join(self.directory, row[0]))
                    test = row[1].replace("array", "")
                    self.labels.append((ast.literal_eval(test)))
        self.labels = np.array(self.labels)

        self.labels = np.reshape(self.labels, (-1, 8))
        logger.debug("Ground Truth Shape: %s", str(self.labels.shape))
        logger.debug("Data shape %s", str(len(self.data)))

        self.myData = [self.data, self.labels]


class SmartDocDirectories(Dataset):
    """
    Class to include MNIST specific details
    """

    def __init__(self, directory="data"):
        super().__init__("smartdoc")
        self.data = []
        self.labels = []

        for folder in os.listdir(directory):
            if os.path.isdir(directory + "/" + folder):
                for file in os.listdir(directory + "/" + folder):
                    images_dir = directory + "/" + folder + "/" + file
                    if os.path.isdir(images_dir):

                        list_gt = []
                        tree = ET.parse(images_dir + "/" + file + ".gt")
                        root = tree.getroot()
                        for a in root.iter("frame"):
                            list_gt.append(a)

                        im_no = 0
                        for image in os.listdir(images_dir):
                            if image.endswith(".jpg"):
                                # print(im_no)
                                im_no += 1

                                # Now we have opened the file and GT. Write code to create multiple files and scale gt
                                list_of_points = {}

                                # img = cv2.imread(images_dir + "/" + image)
                                self.data.append(os.path.join(images_dir, image))

                                for point in list_gt[int(float(image[0:-4])) - 1].iter(
                                    "point"
                                ):
                                    myDict = point.attrib

                                    list_of_points[myDict["name"]] = (
                                        int(float(myDict["x"])),
                                        int(float(myDict["y"])),
                                    )

                                ground_truth = np.asarray(
                                    (
                                        list_of_points["tl"],
                                        list_of_points["tr"],
                                        list_of_points["br"],
                                        list_of_points["bl"],
                                    )
                                )
                                ground_truth = utils.sort_gt(ground_truth)
                                self.labels.append(ground_truth)

        self.labels = np.array(self.labels)

        self.labels = np.reshape(self.labels, (-1, 8))
        logger.debug("Ground Truth Shape: %s", str(self.labels.shape))
        logger.debug("Data shape %s", str(len(self.data)))

        self.myData = []
        for a in range(len(self.data)):
            self.myData.append([self.data[a], self.labels[a]])


class SelfCollectedDataset(Dataset):
    """
    Class to include MNIST specific details
    """

    def __init__(self, directory="data"):
        super().__init__("smartdoc")
        self.data = []
        self.labels = []

        for image in os.listdir(directory):
            # print (image)
            if image.endswith("jpg") or image.endswith("JPG"):
                if os.path.isfile(os.path.join(directory, image + ".csv")):
                    with open(os.path.join(directory, image + ".csv"), "r") as csvfile:
                        spamwriter = csv.reader(
                            csvfile,
                            delimiter=" ",
                            quotechar="|",
                            quoting=csv.QUOTE_MINIMAL,
                        )

                        img_path = os.path.join(directory, image)

                        gt = []
                        for row in spamwriter:
                            gt.append(row)
                        gt = np.array(gt).astype(np.float32)
                        ground_truth = utils.sort_gt(gt)
                        self.labels.append(ground_truth)
                        self.data.append(img_path)

        self.labels = np.array(self.labels)

        self.labels = np.reshape(self.labels, (-1, 8))
        logger.debug("Ground Truth Shape: %s", str(self.labels.shape))
        logger.debug("Data shape %s", str(len(self.data)))

        self.myData = []
        for a in range(len(self.data)):
            self.myData.append([self.data[a], self.labels[a]])


class SmartDocCorner(Dataset):
    """
    Class to include MNIST specific details
    """

    def __init__(self, directory="data"):
        super().__init__("smartdoc")
        self.data = []
        self.labels = []
        for d in directory:
            self.directory = d
            self.train_transform = transforms.Compose(
                [
                    getTransformsByImgaug(),
                    transforms.ToTensor(),
                ]
            )

            self.test_transform = transforms.Compose(
                [
                    iaa.Sequential(
                        [
                            iaa.Resize(32),
                        ]
                    ).augment_image,
                    transforms.ToTensor(),
                ]
            )

            logger.info("Pass train/test data paths here")

            self.classes_list = {}

            file_names = []
            with open(os.path.join(self.directory, "gt.csv"), "r") as csvfile:
                spamreader = csv.reader(
                    csvfile, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL
                )
                import ast

                for row in spamreader:
                    file_names.append(row[0])
                    self.data.append(os.path.join(self.directory, row[0]))
                    test = row[1].replace("array", "")
                    self.labels.append((ast.literal_eval(test)))
        self.labels = np.array(self.labels)

        self.labels = np.reshape(self.labels, (-1, 2))
        logger.debug("Ground Truth Shape: %s", str(self.labels.shape))
        logger.debug("Data shape %s", str(len(self.data)))

        self.myData = [self.data, self.labels]