python opencv —— 背景提取（MOG、KNN）、識別與檢測（Haar Cascade）

注意 opencv 的座標軸，x 軸向右，和 width 對應，y 軸向下，和 height 對應； 1. MOG2 與 KNN

MOG：Mixture of Gaussian

import cv2cap = cv2.VideoCapture('./data/video/768x576.avi')knn_sub = cv2.createBackgroundSubtractorKNN()mog2_sub = cv2.createBackgroundSubtractorMOG2()while True:    ret, frame = cap.read()    if not ret:        break    mog_sub_mask = mog2_sub.apply(frame)    knn_sub_mask = knn_sub.apply(frame)    cv2.imshow('original', frame)    cv2.imshow('MOG2', mog_sub_mask)    cv2.imshow('KNN', knn_sub_mask)    key = cv2.waitKey(30) & 0xff    if key == 27 or key == ord('q'):        breakcap.release()cv2.destroyAllWindows()

2. 識別與檢測

Haar Cascade

import cv2import numpy as np# haar 級聯分類器，opencv 源檔案中data檔案夾下會有# https://github.com/opencv/opencv/tree/master/data/haarcascadesface_cascade = cv2.CascadeClassifier(    './data/classifiers/haarcascade_frontalface_default.xml')eye_cascade = cv2.CascadeClassifier('./data/classifiers/haarcascade_eye.xml')cap = cv2.VideoCapture(0)while True:    ret, img = cap.read()    if not ret:        break    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    faces = face_cascade.detectMultiScale(gray, 1.3, 5)    for (x, y, w, h) in faces:        cv2.rectangle(img, (x, y), (x+w, y+h), color=(255, 0, 0), thickness=2)        roi_gray = gray[y:y+h, x:x+w]        roi_color = img[y:y+h, x:x+w]        eyes = eye_cascade.detectMultiScale(roi_gray)        try:            ex, ey, ew, eh = eyes[0]            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh),                 color=(0, 255, 0), thickness=2)            ex1, ey1, ew1, eh1 = eyes[1]            cv2.rectangle(roi_color, (ex1, ey1), (ex1+ew1, ey1+eh1),                 color=(0, 255, 0), thickness=2)        except IndexError:            print('')    cv2.imshow('me', img)    key = cv2.waitKey(30)    if key == 27 or key == ord('q'):        breakcap.release()cv2.destroyAllWindows()