BackgroundSubtractor

Opencv 有三種背景分割器

• K-Nearest：KNN

• Mixture of Gaussian（MOG2）

• Geometric Multigid（GMG）

借助 BackgroundSubtractor 類，可檢測陰影，用閾值排除陰影，從而關注實際特征

createBackgroundSubtractorMOG2

OpenCV圖像處理- 視頻背景消除與前景ROI提取

API：

cv2.createBackgroundSubtractorMOG2(
int history = 500,
double varThreshold = 16,
bool detectShadows = true
)
參數解釋如下：

• history表示過往幀數，500幀，選擇history = 1就變成兩幀差
• varThreshold表示像素與模型之間的馬氏距離，值越大，只有那些最新的像素會被歸到前景，值越小前景對光照越敏感。
• detectShadows 是否保留陰影檢測，請選擇False這樣速度快點。

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 += 1frame_h, frame_w, _ = frame.shapefgmask = 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"):breakcamera.release()
cv2.destroyAllWindows()

做 gif 的時候只設置了播放一次，重復播放需要刷新

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 += 1frame_h, frame_w, _ = frame.shapefgmask = 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"):breakcamera.release()
cv2.destroyAllWindows()