NameError:未定义名称“videos_root”

Question

我正在尝试为ucf101数据集生成denseflow，但我一直收到以下错误：

‘'ApplyEyeMakeup'，'ApplyLipstick'，'Archery'，'BabyCrawling'，'BalanceBeam'，'BandMarching'，'BaseballPitch'，’篮球‘，’Basketball Diving‘，'BenchPress'，'Biking'，'Billiards'，'BlowDryHair'，'BlowingCandles'，'BodyWeightSquats'，'Bowling'，'BoxingPunchingBag'，'BoxingSpeedBag'，'BreastStroke'，'BrushingTeeth'，'CleanAndJerk'，'CliffDiving'，'CricketBowling'，'CricketShot'，'CuttingInKitchen'，'Diving’“‘Drumming”，“Fencing”，“FieldHockeyPenalty”，“FloorGymnastics”，“FrisbeeCatch”，“FrontCrawl”，“GolfSwing”，“Haircut”，“Hammering”，“HammerThrow”，“HandstandPushups”，“HandstandWalking”，“HeadMassage”，“HighJump”，“HorseRace”，“HorseRiding”，“HulaHoop”，“IceDancing”，“JavelinThrow”，“JugglingBalls”，“JumpingJack”，“JumpingJack”“‘MilitaryParade”、“Mixing”、“MoppingFloor”、“Nunchucks”、“ParallelBars”、“PizzaTossing”、“PlayingCello”、“PlayingDaf”、“PlayingDhol”、“PlayingFlute”、“PlayingGuitar”、“PlayingPiano”、“PlayingSitar”、“PlayingTabla”、“PlayingViolin”、“PoleVault”、“PommelHorse”、“PullUps”、“Punch”、“PushUps”、“漂流”、“RockClimbingIndoor”、“Rlaying气候”、“划船”、“SalsaSpin”、“ShavingBeard”、“ShavingBeard”。‘射击’，‘滑板’，‘滑雪’，‘斯基耶’，‘跳伞’，‘足球比赛’，'StillRings'，‘相扑’，‘冲浪’，‘摇摆’，'TableTennisShot'，'TaiChi'，'TennisSwing'，'ThrowDiscus'，'Typing'，‘打字’，'UnevenBars'，‘排球扣球’，'WalkingWithDog'，'WallPushups'，'WritingOnBoard'，'YoYo‘查找13320个视频。完成视频列表！回溯(最近一次调用)：

文件""，第1行，克隆wdir=‘D:/ runfile('D:/Clones/py-denseflow-master/denseflow.py'，/py-denseflow-runfile(’D:/Clones/py-denseflow-master/denseflow.py‘，’)

运行文件execfile(文件名，命名空间)中的文件"C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py"，行704

文件名第108行，在execfile exec( "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py"，(f.read()，filename，'exec')，命名空间)中)

Pool.map中的文件"D:/Clones/py-denseflow-master/denseflow.py"，第186行(dense_flow，zip(video_list，flows_dirs，step*len(video_list)，bound*len(Video_list)

文件返回行290，在map return self._map_async( "C:\Users\sancy\Anaconda3\lib\multiprocessing\pool.py"，，iterable，mapstar，chunksize).get()

get raise self._value中的文件"C:\Users\sancy\Anaconda3\lib\multiprocessing\pool.py"，行683

NameError:未定义名称“videos_root”

我该如何解决这个问题？

`

import os,sys
    import numpy as np
    import cv2
    from PIL import Image
    from multiprocessing import Pool
    import argparse
    from IPython import embed #to debug
    import skvideo.io
    import scipy.misc


    def ToImg(raw_flow,bound):
        '''
        this function scale the input pixels to 0-255 with bi-bound

        :param raw_flow: input raw pixel value (not in 0-255)
        :param bound: upper and lower bound (-bound, bound)
        :return: pixel value scale from 0 to 255
        '''
        flow=raw_flow
        flow[flow>bound]=bound
        flow[flow<-bound]=-bound
        flow-=-bound
        flow*=(255/float(2*bound))
        return flow

    def save_flows(flows,image,save_dir,num,bound):
        '''
        To save the optical flow images and raw images
        :param flows: contains flow_x and flow_y
        :param image: raw image
        :param save_dir: save_dir name (always equal to the video id)
        :param num: the save id, which belongs one of the extracted frames
        :param bound: set the bi-bound to flow images
        :return: return 0
        '''
        #rescale to 0~255 with the bound setting
        flow_x=ToImg(flows[...,0],bound)
        flow_y=ToImg(flows[...,1],bound)
        if not os.path.exists(os.path.join(data_root,new_dir,save_dir)):
            os.makedirs(os.path.join(data_root,new_dir,save_dir))

        #save the image
        save_img=os.path.join(data_root,new_dir,save_dir,'img_{:05d}.jpg'.format(num))
        scipy.misc.imsave(save_img,image)

        #save the flows
        save_x=os.path.join(data_root,new_dir,save_dir,'flow_x_{:05d}.jpg'.format(num))
        save_y=os.path.join(data_root,new_dir,save_dir,'flow_y_{:05d}.jpg'.format(num))
        flow_x_img=Image.fromarray(flow_x)
        flow_y_img=Image.fromarray(flow_y)
        scipy.misc.imsave(save_x,flow_x_img)
        scipy.misc.imsave(save_y,flow_y_img)
        return 0

    def dense_flow(augs):
        '''
        To extract dense_flow images
        :param augs:the detailed augments:
            video_name: the video name which is like: 'v_xxxxxxx',if different ,please have a modify.
            save_dir: the destination path's final direction name.
            step: num of frames between each two extracted frames
            bound: bi-bound parameter
        :return: no returns
        '''
        video_name,save_dir,step,bound=augs
        video_path=os.path.join(videos_root,video_name.split('_')[1],video_name)

        # provide two video-read methods: cv2.VideoCapture() and skvideo.io.vread(), both of which need ffmpeg support

        # videocapture=cv2.VideoCapture(video_path)
        # if not videocapture.isOpened():
        #     print 'Could not initialize capturing! ', video_name
        #     exit()
        try:
            videocapture=skvideo.io.vread(video_path)
        except:
            print('read error!'.format(video_name))
            return 0
        print (video_name)
        # if extract nothing, exit!
        if videocapture.sum()==0:
            print ('Could not initialize capturing',video_name)
            exit()
        len_frame=len(videocapture)
        frame_num=0
        image,prev_image,gray,prev_gray=None,None,None,None
        num0=0
        while True:
            #frame=videocapture.read()
            if num0>=len_frame:
                break
            frame=videocapture[num0]
            num0+=1
            if frame_num==0:
                image=np.zeros_like(frame)
                gray=np.zeros_like(frame)
                prev_gray=np.zeros_like(frame)
                prev_image=frame
                prev_gray=cv2.cvtColor(prev_image,cv2.COLOR_RGB2GRAY)
                frame_num+=1
                # to pass the out of stepped frames
                step_t=step
                while step_t>1:
                    #frame=videocapture.read()
                    num0+=1
                    step_t-=1
                continue

            image=frame
            gray=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
            frame_0=prev_gray
            frame_1=gray
            ##default choose the tvl1 algorithm
            dtvl1=cv2.createOptFlow_DualTVL1()
            flowDTVL1=dtvl1.calc(frame_0,frame_1,None)
            save_flows(flowDTVL1,image,save_dir,frame_num,bound) #this is to save flows and img.
            prev_gray=gray
            prev_image=image
            frame_num+=1
            # to pass the out of stepped frames
            step_t=step
            while step_t>1:
                #frame=videocapture.read()
                num0+=1
                step_t-=1


    def get_video_list():
        video_list=[]
        for cls_names in os.listdir(videos_root):
            cls_path=os.path.join(videos_root,cls_names)
            for video_ in os.listdir(cls_path):
                video_list.append(video_)
        video_list.sort()
        return video_list,len(video_list)



    def parse_args():
        parser = argparse.ArgumentParser(description="densely extract the video frames and optical flows")
        parser.add_argument('--dataset',default='ucf101',type=str,help='set the dataset name, to find the data path')
        parser.add_argument('--data_root',default='D:/Clones/py-denseflow-master/video_classification/data',type=str)
        parser.add_argument('--new_dir',default='flows',type=str)
        parser.add_argument('--num_workers',default=4,type=int,help='num of workers to act multi-process')
        parser.add_argument('--step',default=1,type=int,help='gap frames')
        parser.add_argument('--bound',default=15,type=int,help='set the maximum of optical flow')
        parser.add_argument('--s_',default=0,type=int,help='start id')
        parser.add_argument('--e_',default=13320,type=int,help='end id')
        parser.add_argument('--mode',default='run',type=str,help='set \'run\' if debug done, otherwise, set debug')
        args = parser.parse_args()
        return args

    if __name__ =='__main__':

        # example: if the data path not setted from args,just manually set them as belows.
        #dataset='ucf101'
        #data_root='/S2/MI/zqj/video_classification/data'
        #data_root=os.path.join(data_root,dataset)

        args=parse_args()
        data_root=os.path.join(args.data_root,args.dataset)
        videos_root=os.path.join(data_root,'videos')
        #print(videos_root)
        print (os.listdir(videos_root))

        #specify the augments
        num_workers=args.num_workers
        step=args.step
        bound=args.bound
        s_=args.s_
        e_=args.e_
        new_dir=args.new_dir
        mode=args.mode
        #get video list
        video_list,len_videos=get_video_list()
        video_list=video_list[s_:e_]

        len_videos=min(e_-s_,13320-s_) # if we choose the ucf101
        print ('find {} videos.'.format(len_videos))
        flows_dirs=[video.split('.')[0] for video in video_list]
        print ('get videos list done! ')

        pool=Pool(num_workers)
        if mode=='run':
            pool.map(dense_flow,zip(video_list,flows_dirs,[step]*len(video_list),[bound]*len(video_list)))
        else: #mode=='debug
            dense_flow((video_list[0],flows_dirs[0],step,bound))

Answer 1

dense_flow和get_videos_list函数无法访问变量videos_root，您需要在函数调用期间将其作为参数传递

像这样

video_list,len_videos=get_video_list(videos_root)

还要对函数定义进行更改，以反映此更改，例如

def get_video_list(videos_root):