opencv-080-视频分析(背景消除与前景ROI提取)

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知识点

通过视频中的背景进行建模，实现背景消除，生成mask图像，通过对mask二值图像分析实现对前景活动对象ROI区域的提取，是很多视频监控分析软件常用的手段之一，该方法很实时！整个步骤如下：

  1. 初始化背景建模对象GMM
  2. 读取视频一帧
  3. 使用背景建模消除生成mask
  4. 对mask进行轮廓分析提取ROI
  5. 绘制ROI对象

代码（c++,python）

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

void process_frame(Mat &image);
Ptr<BackgroundSubtractor> pMOG2 = createBackgroundSubtractorMOG2(500, 100, false);

/*
 * 视频分析(背景消除与前景ROI提取)
 */
int main() {
    VideoCapture capture("../images/vtest.avi");
    if (!capture.isOpened()) {
        printf("could not open camera...\n");
        return -1;
    }

    Mat frame;
    while (true) {
        bool ret = capture.read(frame);
        if (!ret) break;
        imshow("input", frame);
        process_frame(frame);
        imshow("result", frame);
        char c = waitKey(50);
        if (c == 27) {
            break;
        }
    }
    waitKey(0);
    return 0;

    waitKey(0);
    return 0;
}

void process_frame(Mat &image) {
    Mat mask;
    pMOG2->apply(image, mask);

    // 开操作
    Mat se = getStructuringElement(MORPH_RECT, Size(1, 5));
    morphologyEx(mask, mask, MORPH_OPEN, se);

    // 寻找最大轮廓
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(mask, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
    for (size_t t = 0; t < contours.size(); t++) {
        double area = contourArea(contours[t]);
        if (area < 100) {
            continue;
        }
        RotatedRect rect = minAreaRect(contours[t]);
        ellipse(image, rect, Scalar(0, 255, 0), 2);
        circle(image, rect.center, 2, Scalar(255, 0, 0), 2);
    }
}
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import numpy as np
import cv2 as cv

cap = cv.VideoCapture('D:/images/video/vtest.avi')
fgbg = cv.createBackgroundSubtractorMOG2(
    history=500, varThreshold=100, detectShadows=False)


def process(image, opt=1):
    mask = fgbg.apply(frame)
    line = cv.getStructuringElement(cv.MORPH_RECT, (1, 5), (-1, -1))
    mask = cv.morphologyEx(mask, cv.MORPH_OPEN, line)
    cv.imshow("mask", mask)
    # 轮廓提取, 发现最大轮廓
    out, contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for c in range(len(contours)):
        area = cv.contourArea(contours[c])
        if area < 100:
            continue
        rect = cv.minAreaRect(contours[c])
        cv.ellipse(image, rect, (0, 255, 0), 2, 8)
        cv.circle(image, (np.int32(rect[0][0]), np.int32(rect[0][1])), 2, (255, 0, 0), 2, 8, 0)
    return image, mask


while True:
    ret, frame = cap.read()
    cv.imwrite("D:/input.png", frame)
    cv.imshow('input', frame)
    result, m_ = process(frame)
    cv.imshow('result', result)
    k = cv.waitKey(50)&0xff
    if k == 27:
        cv.imwrite("D:/result.png", result)
        cv.imwrite("D:/mask.png", m_)

        break
cap.release()
cv.destroyAllWindows()

结果

代码地址

github