Opencv中KNN背景分割器

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()