python 视频处理，提取视频相关帧，读取Excel

一共这几个模块：

class videoReader 读取视频

class videoFramesExtractor(videoReader):继承了读取视频，主要是用来限制读取视频中的哪些帧，并保存。

read_excel_single(excel_path,event_instance): 处理Excel中的数组，找到事件的帧数范围：

Excel内容是这样的，需要的只是第二列的帧范围，另外第二列中的这些值还有重复的所以需要去除重复：

每一个范围，例如:23450:23461之内的帧要存放在一个文件夹里。



[python] view plain copy

</pre><pre name="code" class="python"># -*- coding: cp936 -*-

import cv2.cv as cv

import os

import cv2

import numpy

import Image

import xlrd

class videoReader:

frame_count=0

def __init__(self,videoPath):

self.videoPath=videoPath

def video_init(self):

self.capture = cv.CaptureFromFile(self.videoPath)

self.win_name = "test"

cv.NamedWindow(self.win_name, cv.CV_WINDOW_AUTOSIZE)

def read(self):

self.video_init()

while 1:

self.frame_count+=1

image = cv.QueryFrame(self.capture )

cv.ShowImage(self.win_name, image)

print self.frame_count

c = cv.WaitKey(10)

if c == 27:

break

cv.DestroyWindow(self.win_name)

class videoFramesExtractor(videoReader):

save_path='d:/'

def __init__(self,videoPath,frameSpan,image_prefix):#image_prefix=event_name

videoReader.__init__(self,videoPath)

self.frameSpan=frameSpan

self.image_prefix=image_prefix

def read(self):

capture = cv2.VideoCapture(self.videoPath)

win_name = "test"

cv.NamedWindow(win_name, cv.CV_WINDOW_AUTOSIZE)

success,frame = capture.read()

init_row=0

while success:

self.frame_count+=1

success,frame = capture.read()

#cv2.imshow(win_name,frame) #显示照片浪费时间

time_duration=self.frameSpan[init_row][1]-self.frameSpan[init_row][0]+1

if self.frame_count>=self.frameSpan[init_row][0] and \

self.frame_count<=self.frameSpan[init_row][1] :

self.image_save(frame,init_row,time_duration)

if self.frame_count==self.frameSpan[init_row][1]:

init_row+=1

if init_row==len(self.frameSpan):

cv.DestroyWindow(win_name)

return 0

c = cv.WaitKey(10)

if c == 27:

break

cv.DestroyWindow(win_name)

def image_save(self,frame,init_row,time_duration):

save_path=self.make_folder(init_row,time_duration)

#frame=Image.fromarray(frame)

#frame.resize((300,300))

#frame.save('%s//%s.jpg' % (save_path,self.frame_count))

frame=cv2.resize(frame,(300,300))#

cv2.imwrite( '%s//%s.jpg' % (save_path,self.frame_count), frame)

def make_folder(self,init_row,time_duration):

temp= self.image_prefix+'_'+str(init_row)+'_'+str(time_duration)#事件名称_事件顺序_

new_path = os.path.join(self.save_path,temp)

if not os.path.isdir(new_path):

os.makedirs(new_path)

return new_path

def read_excel_single(excel_path,event_instance):

data=[]

data=xlrd.open_workbook(excel_path)

#read the first sheets

table = data.sheets()[0]

#read the num fo cols

nrows = table.nrows

rowmsg=[]#用来存储帧的范围，及事件的类别，描述。

for i in xrange(nrows):

start_frame,end_frame=table.row_values(i)[1].split(':')

rowmsg.append([int(start_frame),int(end_frame),table.row_values(i)[3]])

rowmsg.sort(lambda x,y:cmp(x[0],y[0]))#按照数字大小排序~

event_id=[]

event_id=event_instance

#print event_id

event=[]

for item in xrange(len(rowmsg)):

if rowmsg[item][2]==event_id:#

event.append(rowmsg[item])

if event==[]:

print ("there is no %s occurs" % event_instance)

return 0

#去除重复

cur_pos=1

new_rowmsg=[]

pre_s=event[0][0]

pre_e=event[0][1]

cur_s=event[cur_pos][0]

cur_e=event[cur_pos][1]

while cur_pos<len(event)-1:

while not (cur_s>pre_e):

if cur_s>pre_s:

cur_s=pre_s

if cur_e<pre_e:

cur_e=pre_e

still_pos=[cur_s,cur_e,event_id]

cur_pos+=1

if cur_pos==len(event)-1:

break

pre_s=cur_s

pre_e=cur_e

cur_s=event[cur_pos][0]

cur_e=event[cur_pos][1]

new_rowmsg.append(still_pos)

if cur_pos==len(event)-1:

break

cur_pos+=1

pre_s=cur_s

pre_e=cur_e

cur_s=event[cur_pos][0]

cur_e=event[cur_pos][1]

return new_rowmsg

excel_source='E:/08ann/dev_20071101/LGW_20071101_E1_CAM1.mpeg.xlsx'

event_instance='CellToEar'

row_msg=read_excel_single(excel_source,event_instance)

new_row=[ item[0:2] for item in row_msg ]

filename = "G:/TrecvidData/08/DEV/LGW_20071101_E1_CAM1.mpg"

videoread=videoFramesExtractor(filename,new_row,'CellToEar')

videoread.read()

有几点发现：

cv2.的函数输出的数据类型基本都是numpy，而cv.不行。所以尽量在Python中使用cv2.的函数。

通过numpy转换后的图片会在颜色上有些失真，不过影响不大，如果是灰度图的话则完全没有影响。