pip install opencv-python==4.1.0.25 -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install opencv-contrib-python==4.1.0.25 -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install multiprocess

import os,sys

import numpyas np

import cv2

from PILimport Image

from multiprocessingimport Pool

import argparse

# from IPython import embed #to debug

def ToImg(raw_flow,bound):

'''

this function scale the input pixels to 0-255 with bi-bound

    :paramraw_flow: input raw pixel value (not in 0-255)

    :parambound: 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

    :paramflows: contains flow_x and flow_y

    :paramimage: raw image

    :paramsave_dir: save_dir name (always equal to the video id)

    :paramnum: the save id, which belongs one of the extracted frames

    :parambound: 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(new_dir,save_dir)):

os.makedirs(os.path.join(new_dir,save_dir))

#save the image

    save_img=os.path.join(new_dir,save_dir,'img_{:05d}.jpg'.format(num))

cv2.imwrite(save_img,image)

#save the flows

    save_x=os.path.join(new_dir,save_dir,'flow_x_{:05d}.jpg'.format(num))

save_y=os.path.join(new_dir,save_dir,'flow_y_{:05d}.jpg'.format(num))

flow_x_img=np.array(flow_x)

flow_y_img=np.array(flow_y)

cv2.imwrite(save_x,flow_x_img)

cv2.imwrite(save_y,flow_y_img)

return 0

def dense_flow(augs):

'''

To extract dense_flow images

    :paramaugs: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_path,save_dir,step,bound=augs

# 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

    videocapture=cv2.VideoCapture(video_path)

# if extract nothing, exit!

    frame_num=0

    image,prev_image,gray,prev_gray=None,None,None,None

    num0=0

    # videocapture.set(cv2.CAP_PROP_POS_FRAMES,0)  #设置要获取的帧号

    # a,frame=videocapture.read()  #read方法返回一个布尔值和一个视频帧。若帧读取成功，则返回True

    while True:

suc,frame=videocapture.read()

num0+=1

        if frame_num==0:

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.optflow.DualTVL1OpticalFlow_create()

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_namesin 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)

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)

    videos_root=os.path.join(r'C:\Users\USER\Desktop\test_cpu\xdw_baseline\data\mod-ucf101\videos')

#specify the augments

    num_workers=4

    step=1

    bound=15

    s_=0

    e_=13320

    new_dir=r'C:\Users\USER\Desktop\test_cpu\xdw_baseline\data\mod-ucf101'+'/'+'flow'

    videos_list = []

flows_dirs = []

for iin os.listdir(r'C:\Users\USER\Desktop\test_cpu\xdw_baseline\data\mod-ucf101\videos'):

videos_list.append(r'C:\Users\USER\Desktop\test_cpu\xdw_baseline\data\mod-ucf101\videos'+'/'+i)

flows_dirs.append(r'C:\Users\USER\Desktop\test_cpu\xdw_baseline\data\mod-ucf101\flow'+'/'+i.split('.')[0])

len_videos=min(13320-s_,13320-s_)# if we choose the ucf101

    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((videos_list[0],flows_dirs[0],step,bound))