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videio_spilit_global-通用-多进程.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 2 19:29:11 2021
@author: yejohnny
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import os
import time
from multiprocessing import Pool
from multiprocessing import Process
import pandas as pd
from tqdm import tqdm
##############################################################################
def video_split(file, start_frame, end_frame, outpath):
cap = cv2.VideoCapture(file)
fps = cap.get(cv2.CAP_PROP_FRAME_COUNT)
start_frame = start_frame # 开始帧,从1开始计数
end_frame = end_frame # 结束帧
# 获取视频分辨率
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# 输出文件编码，Linux下可选X264
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
# 视频帧率
fps = cap.get(cv2.CAP_PROP_FPS)
success, image = cap.read()
count = 0
success = True
# 从视频开头获取每一帧，直到到达开始帧
while success:
success, image = cap.read()
count += 1
if (count == start_frame):
success = False
# 开始帧的时间(单位ms)，相当于ffmpeg的ss参数
ss = int(cap.get(cv2.CAP_PROP_POS_MSEC))
# 输出
out = cv2.VideoWriter(outpath, fourcc, fps, size)
# 读取开始帧到结束帧的每一帧并写入新视频
while (count < end_frame):
success, image = cap.read()
out.write(image)
count += 1
# 结束帧的时间，相对于ffmpeg的to参数
to = int(cap.get(cv2.CAP_PROP_POS_MSEC))
cap.release()
out.release()
info_df = pd.read_csv(
'H:/Z01_3D_behavior/SP_square_box/result/df_nearest1.csv')
raw_video_dir = 'H:/Z01_3D_behavior/SP_square_box/split_video' # 输入视频存放的文件夹
output_dir = 'H:/Z01_3D_behavior/SP_square_box/video_resplit'
### 获取输入文件夹下待切割视频路径
raw_video_path = {}
file_name_list = info_df['file'].unique()
for file_name in file_name_list:
raw_video_path.setdefault(file_name, [])
for v_file_name in os.listdir(raw_video_dir):
if v_file_name.endswith('.avi'):
if v_file_name.startswith(file_name):
raw_video_path[file_name].append(raw_video_dir +'/'+ v_file_name)
if __name__ == '__main__':
t1 = time.time()
pool = Pool(5)
for i in info_df.index:
label = info_df.loc[i, 'movement_label']
boundary = info_df.loc[i, 'boundary']
file_name = info_df.loc[i, 'file']
start_frame = int(boundary.split(',')[0].strip('('))
end_frame = int(boundary.split(',')[1].strip(')')) + 1
video_list = raw_video_path[file_name]
video_list.sort()
input_file = video_list[2]
sub_output_dir = output_dir + '/{0}/{1}'.format(label, input_file[-12:-4])
folder = os.path.exists(sub_output_dir)
if not folder:
os.makedirs(sub_output_dir)
#print('--- establish {} ---\n'.format(sub_output_dir))
output_file = sub_output_dir + '/{0}_{1}_{2}.avi'.format(file_name, start_frame, end_frame)
pool.apply_async(video_split, args=(input_file, start_frame, end_frame, output_file))
pool.close()
pool.join()
t2 = time.time()
print('\n finish! Total time:{:.2f} min \n'.format((t2 - t1) / 60))
'''
### 开始循环处理视频
print('processing data ......\n')
p = Pool(6)
for i in info_df.index:
label = info_df.loc[i, 'movement_label']
boundary = info_df.loc[i, 'boundary']
file_name = info_df.loc[i, 'file']
start_frame = int(boundary.split(',')[0].strip('('))
end_frame = int(boundary.split(',')[1].strip(')')) + 1
video_list = raw_video_path[file_name]
for video_path in video_list[2:4]:
input_file = video_path
sub_output_dir = output_dir + '/{0}/{1}'.format(label, video_path[-12:-4])
folder = os.path.exists(sub_output_dir)
if not folder:
os.makedirs(sub_output_dir)
#print('--- establish {} ---\n'.format(sub_output_dir))
output_file = sub_output_dir + '/{0}_{1}_{2}.avi'.format(file_name, start_frame, end_frame)
#video_split(input_file, start_frame, end_frame, output_file)
p.apply_async(video_split, args=(input_file, start_frame, end_frame, output_file))
print(video_path,output_file)
p.close()
p.join()
t2 = time.time()
print('\n finish! Total time:{:.2f} min \n'.format((t2 - t1) / 60))
'''