python如何解决IndexError：列表索引超出范围？

我正在尝试为ucf101数据集生成密集流，但我不断收到以下错误：

我尝试在第68行中将video_name.split（''）[1]更改为video_name.split（''）[0]，已编译代码，但出现读取错误（请参见第70行）

这是我尝试运行的python代码，但我一直在获取IndexError：列表索引超出范围：

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 ''' videos_root,video_name,save_dir,step,bound=augs #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(videos_root): 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(videos_root)
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(videos_root , video_list,flows_dirs,[step]*len(video_list),[bound]*len(video_list)))
    #pool.map(dense_flow(zip(video_list,flows_dirs,[step]*len(video_list),[bound]*len(video_list)),videos_root))
else: #mode=='debug
    dense_flow((videos_root, video_list[0],flows_dirs[0],step,bound))

我得到以下结果

runfile('D:/Clones/py-denseflow-master/denseflow.py', wdir='D:/Clones/py-denseflow-master') ['ApplyEyeMakeup', 'ApplyLipstick1', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk'] find 13320 videos. get videos list done! Traceback (most recent call last):

File " ", line 1, in runfile('D:/Clones/py-denseflow-master/denseflow.py', wdir='D:/Clones/py-denseflow-master')

File "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile execfile(filename, namespace)

File "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile exec(compile(f.read(), filename, 'exec'), namespace)

File "D:/Clones/py-denseflow-master/denseflow.py", line 187, in pool.map(dense_flow,zip(videos_root , video_list,flows_dirs,[step]*len(video_list),[bound]*len(video_list)))

File "C:\Users\sancy\Anaconda3\lib\multiprocessing\pool.py", line 290, in map return self._map_async(func, iterable, mapstar, chunksize).get()

File "C:\Users\sancy\Anaconda3\lib\multiprocessing\pool.py", line 683, in get raise self._value

IndexError: list index out of range