from experiments.experiment import experiments
from Image_Process.load_and_ImageGenerator import Load_ImageGenerator
from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
from Training_Tools.Tools import Tool
from model_data_processing.processing import shuffle_data
from Load_process.LoadData import Load_Data_Prepare
from Calculate_Process.Calculate import Calculate
from merge_class.merge import merge
import tensorflow as tf
import time
import os

if __name__ == "__main__":
    # 測試GPU是否可用
    print('TensorFlow version:', tf.__version__)
    physical_devices = tf.config.experimental.list_physical_devices('GPU')
    print(physical_devices)
    assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
    tf.config.experimental.set_memory_growth(physical_devices[0], True)
    os.environ["CUDA_VISIBLE_DEVICES"]='0'

    Status = 2 # 決定要使用什麼資料集
    # 要換不同資料集就要改
    tool = Tool()
    tool.Set_Labels()
    tool.Set_Save_Roots()

    Labels = tool.Get_Data_Label()
    Trainig_Root, Testing_Root, Validation_Root = tool.Get_Save_Roots(Status) # 一般的
    Generator_Root = tool.Get_Generator_Save_Roots(Status)

    # 取得One-hot encording 的資料
    tool.Set_OneHotEncording(Labels)
    Encording_Label = tool.Get_OneHot_Encording_Label()
    Label_Length = len(Labels)

    Gneerator_size = 0
    Prepare = Load_Data_Prepare()
    loading_data = Load_ImageGenerator(Trainig_Root, Testing_Root, Validation_Root, Generator_Root, Labels)
    experiment = experiments(tool, Status)
    image_processing = Read_image_and_Process_image()
    Merge = merge()
    Calculate_Tool = Calculate()
 
    counter = 5
    
    for i in range(0, counter, 1): # 做規定次數的訓練
        # 讀取資料
        Data_Dict_Data = loading_data.process_main(Label_Length)
        
        Data_Dict_Data = shuffle_data(Data_Dict_Data, Labels, 2)
        tmp = []
        Train_Size = 0
        if len(Data_Dict_Data[Labels[0]]) >= len(Data_Dict_Data[Labels[1]]):
            Train_Size = len(Data_Dict_Data[Labels[1]])
            for j in range(Train_Size):
                tmp.append(Data_Dict_Data[Labels[0]][j])
            Data_Dict_Data[Labels[0]] = tmp
        else:
            Train_Size = len(Data_Dict_Data[Labels[0]])
            for j in range(Train_Size):
                tmp.append(Data_Dict_Data[Labels[1]][j])
            Data_Dict_Data[Labels[1]] = tmp

        # 輸出內容
        print("Negative Data有 " + str(len(Data_Dict_Data[Labels[1]])) + " 筆資料")
        print("Positive Data有 " + str(len(Data_Dict_Data[Labels[0]])) + " 筆資料")
        print("總共有 " + str(len(Data_Dict_Data[Labels[0]]) + len(Data_Dict_Data[Labels[1]])) + " 筆資料")

        # 做出跟資料相同數量的Label
        Negative_Num = image_processing.make_label_list(Train_Size, Encording_Label[1])
        Positive_Num = image_processing.make_label_list(Train_Size, Encording_Label[0])

        # 將資料做成Dict的資料型態
        Prepare.Set_Final_Dict_Data(Labels, Data_Dict_Data, [Positive_Num, Negative_Num], 2)
        Final_Dict_Data = Prepare.Get_Final_Data_Dict()
        keys = list(Final_Dict_Data.keys())

        training_data = Merge.merge_all_image_data(Final_Dict_Data[keys[0]], Final_Dict_Data[keys[1]]) # 將訓練資料合併成一個list
        training_label = Merge.merge_all_image_data(Final_Dict_Data[keys[2]], Final_Dict_Data[keys[3]]) #將訓練資料的label合併成一個label的list

        start = time.time()
        trains_Data_Image = image_processing.Data_Augmentation_Image(training_data) # 多執行續讀檔
        total_trains, train_label = shuffle_data(trains_Data_Image, training_label) # 將資料打亂
        training_data = list(total_trains) # 轉換資料型態

        print(len(training_data))
        training_data, train_label = image_processing.image_data_processing(training_data, train_label) # 將讀出來的檔做正規化。降label轉成numpy array 格式

        end = time.time()
        print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start))

        # 針對其他資料執行值方圖等化加去背
        # with ProcessPoolExecutor() as executor: ## 默认为1，多執行續
            # trains = list(executor.map(adaptive_histogram_equalization, trains_another))

            # # 針對正常資料做值方圖等化加去背
            # train_normal = list(executor.map(image_processing.get_data, normal)) # 多執行續讀檔
            # normal = list(executor.map(Remove_Background, train_normal)) # 多執行續去背
            # normal_data = list(executor.map(adaptive_histogram_equalization, normal))

            # # 針對猴痘水痘進行讀檔
            # Chickenpox = list(executor.map(image_processing.get_data, chickenpox)) # 多執行續讀檔
            # train_chickenpox = list(executor.map(shapen, Chickenpox))

            # Monkeypox = list(executor.map(image_processing.get_data, monkeypox)) # 多執行續讀檔
            # train_monkeypox = list(executor.map(shapen, Monkeypox)) # 銳化
            

            # for All_Normal_Data in normal_data:
            #     trains.append(All_Normal_Data)
            #     training_label.append([0, 0, 0, 0, 0, 1, 0])

            # for All_Chhickenpox_Data in train_chickenpox:
            #     trains.append(All_Chhickenpox_Data)
            #     training_label.append([0, 0, 0, 1, 0, 0, 0])

            # for All_Monkeypox_Data in train_monkeypox:
            #     trains.append(All_Monkeypox_Data)
            #     training_label.append([0, 0, 0, 0, 1, 0, 0])            
        
        loss, accuracy, precision, recall, AUC, f = experiment.processing_main(training_data, train_label, i) # 執行訓練方法
        Calculate_Tool.Append_numbers(loss, accuracy, precision, recall, AUC, f)

    print("實驗結果")
    print("--------------------------------------------")
    print("平均值: ")
    print(Calculate_Tool.Calculate_Mean())
    print("標準差: ")
    print(Calculate_Tool.Calculate_Std())
    print("結果: ")
    print(Calculate_Tool.Output_Style())