############################################################################################

#

# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project

# Repository:    ALL Detection System 2019

# Project:       Facial Authentication Server

#

# Author:        Adam Milton-Barker (AdamMiltonBarker.com)

# Contributors:

# Title:         Training Data Class

# Description:   Training Data class for the ALL Detection System 2019 NCS1 Classifier.

# License:       MIT License

# Last Modified: 2020-07-16

#

############################################################################################

import os, random, sys

from Classes.Helpers import Helpers

from Classes.Data import Data as DataProcess

class Data():

    """ Trainer Data Class

    Sorts the ALL Detection System 2019 NCS1 Classifier training data.

    """

    def __init__(self):

        """ Initializes the Data Class """

        self.Helpers = Helpers("Data")

        self.confs = self.Helpers.confs

        self.DataProcess = DataProcess()

        self.labelsToName = {}

        self.Helpers.logger.info("Data class initialization complete.")

    def sortData(self):

        """ Sorts the training data """

        humanStart, clockStart = self.Helpers.timerStart()

        self.Helpers.logger.info("Loading & preparing training data.")

        dataPaths, classes = self.DataProcess.processFilesAndClasses()

        classId = [int(i) for i in classes]

        classNamesToIds = dict(zip(classes, classId))

        # Divide the training datasets into train and test

        numValidation = int(

            self.confs["Classifier"]["ValidationSize"] * len(dataPaths))

        self.Helpers.logger.info("Number of classes: " + str(classes))

        self.Helpers.logger.info("Validation data size: " + str(numValidation))

        random.seed(self.confs["Classifier"]["RandomSeed"])

        random.shuffle(dataPaths)

        trainingFiles = dataPaths[numValidation:]

        validationFiles = dataPaths[:numValidation]

        # Convert the training and validation sets

        self.DataProcess.convertToTFRecord(

            'train', trainingFiles, classNamesToIds)

        self.DataProcess.convertToTFRecord(

            'validation', validationFiles, classNamesToIds)

        # Write the labels to file

        labelsToClassNames = dict(zip(classId, classes))

        self.DataProcess.writeLabels(labelsToClassNames)

        self.Helpers.logger.info(

            "Loading & preparing training data completed.")

    def cropTestData(self):

        """ Crops the testing data """

        self.DataProcess.cropTestDataset()

        self.Helpers.logger.info(

            "Testing data resized.")

if __name__ == "__main__":

    ProcessData = Data()

    ProcessData.sortData()