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): break if (i + 2 

#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]