BackgroundSubtractor

Opencv 有三种背景分割器

• K-Nearest：KNN

• Mixture of Gaussian（MOG2）

• Geometric Multigid（GMG）

借助 BackgroundSubtractor 类，可检测阴影，用阈值排除阴影，从而关注实际特征

createBackgroundSubtractorMOG2

OpenCV图像处理- 视频背景消除与前景ROI提取

API：

cv2.createBackgroundSubtractorMOG2(
int history = 500,
double varThreshold = 16,
bool detectShadows = true
)
参数解释如下：

• history表示过往帧数，500帧，选择history = 1就变成两帧差
• varThreshold表示像素与模型之间的马氏距离，值越大，只有那些最新的像素会被归到前景，值越小前景对光照越敏感。
• detectShadows 是否保留阴影检测，请选择False这样速度快点。

import cv2
import os
# bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
bs = cv2.createBackgroundSubtractorMOG2(detectShadows=True)
os.makedirs("frame1", exist_ok=True)
os.makedirs("frame2", exist_ok=True)
os.makedirs("frame3", exist_ok=True)

camera = cv2.VideoCapture('car.mkv')
index = 0
while True:
    ret, frame = camera.read()
    index += 1
    frame_h, frame_w, _ = frame.shape
    fgmask = bs.apply(frame)
    th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
    dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)),
                         iterations=2)
    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in contours:
        # if cv2.contourArea(c) > frame_w*0.075 * frame_h*0.075:
        if cv2.contourArea(c) > 1000:
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255), 5)
    cv2.imshow("mog", fgmask)
    cv2.imwrite("./frame1/{}.jpg".format(index), fgmask)
    cv2.imshow("thresh", th)
    cv2.imwrite("./frame2/{}.jpg".format(index), th)
    cv2.imshow("detection", frame)
    cv2.imwrite("./frame3/{}.jpg".format(index), frame)

    if cv2.waitKey(30) & 0xff == ord("q"):
        break

camera.release()
cv2.destroyAllWindows()

做 gif 的时候只设置了播放一次，重复播放需要刷新

createBackgroundSubtractorKNN

import cv2
import numpy as np
bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
camera = cv2.VideoCapture('car.mkv')
index = 0
while True:
    ret, frame = camera.read()
    index += 1
    frame_h, frame_w, _ = frame.shape
    fgmask = bs.apply(frame)
    th = cv2.threshold(fgmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
    dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)),
                         iterations=2)
    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in contours:
        # if cv2.contourArea(c) > frame_w*0.075 * frame_h*0.075:
        if cv2.contourArea(c) > 1000:
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255), 5)
    cv2.imshow("mog", fgmask)
    cv2.imwrite("./frame1/{}.jpg".format(index), fgmask)
    cv2.imshow("thresh", th)
    cv2.imwrite("./frame2/{}.jpg".format(index), th)
    cv2.imshow("detection", frame)
    cv2.imwrite("./frame3/{}.jpg".format(index), frame)

    if cv2.waitKey(30) & 0xff == ord("q"):
        break

camera.release()
cv2.destroyAllWindows()