import random


def calculate_confusion_matrix(predict, result):
    '''計算並畫出混淆矩陣'''
    tp, fp, tn, fn = 0
    for i in range(len(predict)):
        if predict[i] == [1., 0., 0.] and result[i] == [1., 0., 0.]:
           pass 


def shuffle_data(image, label, mode = 1):
    '''
    ## 被用來做資料打亂的用途
    ### 有兩種不同的需求
    1. 打亂影像資料(讀完檔後的影像) => 回傳Label與Image Root兩個List
    2. 打亂路徑資料(影像的路徑資料，還沒讀檔前) => 回傳打亂後的Dict
    '''
    if mode == 1:
        shuffle_image, shuffle_label = [], []

        total = list(zip(image, label))
        random.shuffle(total)

        for total_data in total:
            shuffle_image.append(total_data[0])
            shuffle_label.append(total_data[1])

        return shuffle_image, shuffle_label
    else:
        shuffle_image = {
        label[0] : []
        }

        for i in range(1, len(label)):
            shuffle_image.update({label[i] : []})
            
        for Label in label:
            shuffle_image[Label] = image[Label]
            random.shuffle(shuffle_image[Label])

        return shuffle_image
    
def Balance_Process(Data_Dict_Data, Labels):
    # Data_Dict_Data = shuffle_data(Data_Content, Labels, 2)
    Train_Size = 0

    Train_Size = min(len(Data_Dict_Data[Labels[0]]), len(Data_Dict_Data[Labels[1]]))
    for i in range(1, len(Labels) - 1):
        Train_Size = min(Train_Size, len(Data_Dict_Data[Labels[i + 1]]))

    for i in range(len(Labels)):
        Data_Dict_Data[Labels[i]] = Data_Dict_Data[Labels[i]][0 : Train_Size]

    return Data_Dict_Data, Train_Size