背景分割器BackgroundSubtractor是专门用来视频分析的，会对视频中的每一帧进行“学习”，比较，计算阴影，排除检测图像的阴影区域，按照时间推移的方法提高运动分析的结果。而且BackgroundSubtractor不仅可以用于背景分割，而且还可以提高背景检测的效果。在opencv中有三种分割器：KNN，MOG2，GMG。
通过mog2实现
import numpy as np
import cv2
cap=cv2.VideoCapture(1)
mog = cv2.createBackgroundSubtractorMOG2()
while(1):
ret,frame= cap.read()
fgmask = mog.apply(frame)
cv2.imshow('frame',fgmask)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
通过KNN实现
实现思想：
1.定义1个KNN背景分割器对象
2.定义视频对象
while True：
3.一帧帧读取视频
4.计算前景掩码
5.二值化操作
6.膨胀操作
7.查找轮廓
8.轮廓筛选
9.画出轮廓（在原图像）
10.显示图像帧，
代码实现：
# coding:utf8
import cv2
def detect_video(video):
camera = cv2.VideoCapture(video)
history = 500 # 训练帧数
bs = cv2.createBackgroundSubtractorKNN(detectShadows=True) # 背景减除器，设置阴影检测
bs.setHistory(history)
frames = 0
while True:
res, frame = camera.read()
if not res:
break
fg_mask = bs.apply(frame) # 获取 foreground mask
if frames < history:
frames += 1
continue
# 对原始帧进行膨胀去噪
th = cv2.threshold(fg_mask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2)
# 获取所有检测框
image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#print(len(contours))
tempjs = 0
for c in contours:
# 获取矩形框边界坐标
x, y, w, h = cv2.boundingRect(c)
# 计算矩形框的面积
area = cv2.contourArea(c)
if 500 < area:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
tempjs = tempjs +1
print(tempjs)
cv2.imshow("detection", frame)
cv2.imshow("back", dilated)
if cv2.waitKey(110) & 0xff == 27:
break
camera.release()
if __name__ == '__main__':
#video = 'person.avi'
detect_video(1)
#-*- coding:utf-8 -*-
import cv2
import numpy as np
# 1.常见一个BackgroundSubtractorKNN接口
bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
#2.读取视频
camera = cv2.VideoCapture('traffic.flv')
#定义卷积核圆形
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
while True:
ret,frame = camera.read()
#3. apply()函数计算了前景掩码
fgmask = bs.apply(frame)
#4. 获得前景掩码（含有白色值以及阴影的灰色值），通过设定阈值将非白色（244~255）的所有像素都设为0，而不是1；
th = cv2.threshold(fgmask.copy(),244,255,cv2.THRESH_BINARY)[1] #二值化操作
dilated = cv2.dilate(th,kernel,iterations =2) #5.膨胀操作
#cv2.getStructuringElement 构建一个椭圆形的核
#3x3卷积核中有2个1那就设置为1
#6. findContours函数参数说明cv2.RETR_EXTERNAL只检测外轮廓，
# cv2.CHAIN_APPROX_SIMPLE只存储水平，垂直，对角直线的起始点。
image,contours,hier = cv2.findContours(dilated,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) #查找轮廓
for c in contours: #从list列表取出每个轮廓
if cv2.contourArea(c) < 1500: #进行轮廓筛选 轮廓面积小于1500
continue
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow("mog",fgmask)
cv2.imshow("thresh",th)
cv2.imshow("detection",frame)
if cv2.waitKey(100) & 0xff == ord("q"):
break
camera.release()
cv2.destroyAllWindows()
