Python闭眼时长标准差脚本使用实例代码讲解

#遍历文件夹中的所有txt文件
def scanner_txt(inputSrc,txt_list):
    file_list = os.listdir(inputSrc)
    for file in file_list:
        curr_file=path.join(inputSrc,file)
        #递归实现
        if(path.isdir(curr_file)):
            scanner_txt(curr_file,txt_list)
        else:
            curr_file_name=curr_file.split(".")
            curr_file_type=curr_file_name[len(curr_file_name)-1]
            if curr_file_type=="txt":
                txt_list.append(curr_file)
    return txt_list

def readTxtDataProprocessing(filepath,kss7sd,kss8sd,kss9sd):
    resFrame=[]
    print(filepath)
    with open(filepath,encoding='utf-8') as f:
        for line in f:
            frame_info=line.split(',')
            if(len(frame_info)<3):
                continue
            framenum=frame_info[0]
            frametime=frame_info[2].split('\n')[0]
            resFrame.append([int(framenum),int(frametime)])
    f.close()
    #文件内闭眼时长为空的不处理
    if(len(resFrame)==0):
        return
    #去掉重复的值，归并成统一的值
    totalSD=deduplicationData(resFrame)
    filename=filepath.split('\\')[-1]
    kssLevel=filename.split('-')[2]
    print("文件:{}  总体闭眼时长标准差为:{}".fORMat(filename,totalSD))
    print()
    if(kssLevel=='KSS7'):
        kss7sd.append(totalSD)
    elif(kssLevel=='KSS8'):
        kss8sd.append(totalSD)
    elif(kssLevel=='KSS9'):
        kss9sd.append(totalSD)

def deduplicationData(resFrame):
    uniqueRes = []
    framelength = len(resFrame)
    i = 0
    while (i < framelength):
        if (resFrame[i][0] == 17924):
            t = 1;
        count = 1
        if (i + 1 < framelength):
            if (resFrame[i][1] == resFrame[i + 1][1]):
                count += 1
        if (i + 1 >= framelength):
            break
        if (i + 2 < framelength):
            if (resFrame[i][1] == resFrame[i + 2][1]):
                count += 1

        if (count != 1):
            uniqueRes.append([resFrame[i][0], resFrame[i][1]])
        i += count
        #计算闭眼时长标准差
    return closeEyeTimeSD(uniqueRes)
    # for i in range(len(uniqueRes)):
    #     print("frameNum:", uniqueRes[i][0], "frameTime:", uniqueRes[i][1], "ms")

#data 经过去重和选帧后得到的列表
def closeEyeTimeSD(data):
    #按每一分钟的时间间隔划分区间：1秒钟30帧，1分钟1800帧
    #建立分割数组保存第n分钟内的睁闭眼数据,划分成12段
    dividArr=[]
    for i in range(12):
        dividArr.append([])
    for i in range(len(data)):
        index=int(data[i][0]/1800)
        dividArr[index].append(data[i][1])
    #获取标准差
    sdArr=[]
    for i in range(len(dividArr)):
        avergeTime,sdArray=countTotalSD(dividArr[i])
        if(dividArr[i]!=[]):
            sdArr.append(sdArray)
            print("第{}分钟平均眨眼时长为:{}，闭眼时长标准差为:{}".format(i+1,avergeTime,sdArray))
    totalSD=countTotalSD(sdArr)[1]
    return totalSD

def countTotalSD(sdArray):
    if(len(sdArray)==0):
        return [0,0]
    sumArray = 0
    for i in sdArray:
        sumArray += i
    # 计算全部的标准差
    averageSD = sumArray / len(sdArray)
    sumSD = 0
    for i in sdArray:
        sumSD += pow((i - averageSD), 2)
    totalSD = math.sqrt(sumSD / len(sdArray))
    return [averageSD,totalSD]