使用Python 训练OpenCV Cascade分类器 - 5

测试。本例使用了3个opencv的分类器：haarcascade_frontalface_alt和haarcascade_eye是自带的。分别识别人脸和人眼。还有一个是cascade，也就是自己训练出来的分类器。此处测试取决于在这一步传给OPENCV的图片参数，也就是你想要识别的特征图（本例为手表）：

opencv_createsamples.exe -img watch5050.jpg -bg bg.txt -info info/info.lst -pngoutput info -maxxangle 0.5 -maxyangle 0.5 -maxzangle 0.5 -num 1950

下面的代码中，就是使用cv2打开视像头，对每帧图片进行解析，如果识别出模型，则用矩形标记出来。

import numpy as np
import cv2


face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

#this is the cascade we just made. Call what you want
watch_cascade = cv2.CascadeClassifier('./data/cascade.xml')

cap = cv2.VideoCapture(0)

while 1:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    
    # add this
    # image, reject levels level weights.
    watches = watch_cascade.detectMultiScale(gray, 50, 50)
    
    # add this
    for (x,y,w,h) in watches:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)

    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),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)
        for (ex,ey,ew,eh) in eyes:
            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

    cv2.imshow('img',img)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()