高斯混合模型与差帧法提取前景_前景提取的高斯混合模型-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_45087786/article/details/121865855

该项目使用OpenCV实现前景提取，通过差帧法与高斯混合模型进行视频处理。首先应用背景减除算法创建前景掩模，然后进行形态学操作如膨胀和腐蚀以去除噪声。接着，通过寻找轮廓并设定阈值过滤小物体，识别出满足条件的人形区域，并在原始帧上绘制矩形框进行标注。此外，还提供了带界面的项目下载链接。

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基于opencv的前景提取项目。

视频演示：

opecv差帧法提取前景

b站地址：b站观看

高斯混合模型代码：

import copy
import numpy as np
import cv2 as cv
fgbg=cv.createBackgroundSubtractorMOG2()
kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
def aa(frame):
    frame_copy=copy.deepcopy(frame)
    # kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
    global kernel
    fgmask = fgbg.apply(frame)
    # 形态学处理
    fgmask = cv.morphologyEx(fgmask, cv.MORPH_OPEN, kernel)
    # 膨胀
    kernel = np.ones((3, 3), np.uint8)
    # erroding = cv.erode(fgmask, kernel)
    dilation = cv.dilate(fgmask, kernel)
    # cv.imshow('a12',dilation)
    # cv.waitKey(100)
    _, dilation = cv.threshold(dilation, 200, 255, cv.THRESH_BINARY)
    contours, hierarchy = cv.findContours(dilation, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

    for c in contours:
        perimeter = cv.arcLength(c, True)
        #print(perimeter)
        if perimeter > 55:
            x, y, w, h = cv.boundingRect(c)
            if w>2*h:
                pass
            else:
                cv.rectangle(frame_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
                cv.rectangle(frame_copy, (x, y - 16), (x + 70, y), (0, 0, 255), -1)
                cv.putText(frame_copy, 'persion', (x, y - 5), cv.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 255), 1)
    return frame_copy,dilation

if __name__ == '__main__':
    pass

差帧法代码：

import cv2 as cv
import numpy as np
from gaoshi import aa

cap = cv.VideoCapture("./video/test2.avi")
a=0
zhen2=0
zhen3=0
zhen4=0
zhen5=0
zftu_hou=0
while True:

    a+=1
    ret, frame = cap.read()
    frame=cv.resize(frame,(531,398))
    # 高斯滤波
    frame = cv.GaussianBlur(frame, (3,3), 0)
    gray=cv.cvtColor(frame,cv.COLOR_BGR2GRAY)

    zhen1 = zhen2
    zhen2=zhen3
    zhen3 = zhen4
    zhen4=zhen5
    zhen5=gray

    #print('zhen1=',np.sum(zhen1),'zhen2=',np.sum(zhen2),'zhen3=',np.sum(zhen3))
    if a>=5:
        kernel = np.ones((3, 3), np.uint8)
        diff=cv.absdiff(zhen1,zhen5)
        #膨胀
        diff = cv.dilate(diff, kernel)
        #print(diff.shape)
        _,diff=cv.threshold(diff,10,255,cv.THRESH_BINARY)


        fras,dilation=aa(frame)

        #形态学操作
        # 膨胀

        dilation = cv.dilate(dilation, kernel)
        dilation = cv.erode(dilation, kernel)


        contours, hierarchy = cv.findContours(dilation, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

        for c in contours:
            perimeter = cv.arcLength(c, True)
            #print(perimeter)
            if perimeter > 55:
                x, y, w, h = cv.boundingRect(c)
                if w>2*h:
                    pass
                else:
                    cv.rectangle(fras, (x, y), (x + w, y + h), (0, 255, 0), 2)
                    cv.rectangle(fras, (x, y - 16), (x + 70, y), (0, 0, 255), -1)
                    cv.putText(fras, 'persion', (x, y - 5), cv.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 255), 1)

        jiao=np.where((dilation==255)|(diff==255),255,0)
        jiao=jiao.astype('uint8')

        cv.imshow("jiao", jiao)
        cv.imshow("diff", diff)
        cv.imshow("fras", fras)
        cv.imshow("dilation", dilation)
        # cv.imshow("gray", frame)
        cv.waitKey(100)