I am trying to generate denseflow for ucf101 dataset but I keep getting the following error:
['ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', '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', 'JumpRope', 'Kayaking', 'Knitting', 'LongJump', 'Lunges', 'MilitaryParade', 'Mixing', 'MoppingFloor', 'Nunchucks', 'ParallelBars', 'PizzaTossing', 'PlayingCello', 'PlayingDaf', 'PlayingDhol', 'PlayingFlute', 'PlayingGuitar', 'PlayingPiano', 'PlayingSitar', 'PlayingTabla', 'PlayingViolin', 'PoleVault', 'PommelHorse', 'PullUps', 'Punch', 'PushUps', 'Rafting', 'RockClimbingIndoor', 'RopeClimbing', 'Rowing', 'SalsaSpin', 'ShavingBeard', 'Shotput', 'SkateBoarding', 'Skiing', 'Skijet', 'SkyDiving', 'SoccerJuggling', 'SoccerPenalty', 'StillRings', 'SumoWrestling', 'Surfing', 'Swing', 'TableTennisShot', 'TaiChi', 'TennisSwing', 'ThrowDiscus', 'TrampolineJumping', 'Typing', 'UnevenBars', 'VolleyballSpiking', 'WalkingWithDog', 'WallPushups', 'WritingOnBoard', 'YoYo'] 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 704, in runfile execfile(filename, namespace)
File "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile exec(compile(f.read(), filename, 'exec'), namespace)
File "D:/Clones/py-denseflow-master/denseflow.py", line 186, in pool.map(dense_flow,zip(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
NameError: name 'videos_root' is not defined
How do I resolve this?
`
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))
The variable videos_root is not accessible by the dense_flow and the get_videos_list function, you need to pass it during function call as an argument
Like this
video_list,len_videos=get_video_list(videos_root)
also make changes to the function definition to reflext this change, as such
def get_video_list(videos_root):