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，

#-*- 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()

# coding:utf-8

import cv2

import numpy as np

#from MyCvUtils import MyCvUtils

#datapath = "D:/imgData/video/"

bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)

camera = cv2.VideoCapture("traffic.flv")

ret, frame = camera.read()

while True:

    ret, frame = camera.read()

    # 计算前景掩码，包含 前景的白色值 以及 阴影的灰色值

    fgmask = bs.apply(frame)

    # 前景区域二值化，将非白色（0-244）的非前景区域（包含背景以及阴影）均设为0，前景的白色（244-255）设置为255

    th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]

    # 前景区域形态学处理

    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)

    # 绘制前景图像的轮廓矩形

    image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in contours:

        #       对轮廓设置最小区域，对检测结果降噪

        if cv2.contourArea(c) > 1000:

            (x, y, w, h) = cv2.boundingRect(c)

            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)

    cv2.imshow("mog", fgmask)

    cv2.imshow("thresh", th)

    cv2.imshow("diff", frame & cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR))

    cv2.imshow("detection", frame)

    if (cv2.waitKey(30) & 0xFF) == 27:

        break

    if (cv2.waitKey(30) & 0xFF) == ord('q'):

        break

camera.release()

cv2.destroyAllWindows()

def createBackgroundSubtractorKNN(history=None, dist2Threshold=None, detectShadows=None): # real signature unknown; restored from __doc__

    """

    createBackgroundSubtractorKNN([, history[, dist2Threshold[, detectShadows]]]) -> retval

    .   @brief Creates KNN Background Subtractor

    .   @param history    
Length of the history.

    .   @param dist2Threshold      
Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.

    .   @param detectShadows    
If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

    """

    pass

@简单创建KNN背景减法器

@param历史

历史长度。

@param dist2threshold

阈值像素和样本之间的平方距离，以决定像素是否接近该样本。此参数不影响后台更新。

@param detectshadows

如果为真，该算法将检测阴影并标记它们。它会稍微降低速度，所以如果您不需要这个特性，请将参数设置为false。