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

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