上接《OpenCV3-Python人脸识别方法—基于图像》，本篇介绍实时摄像头下人脸检测。
1. Viola-Jones分类器
Viola-Jones分类器在级联的每个节点中使用AdaBoost来学习一个高检测率低拒绝率的多层树分类器，其有以下几点创新：
（1）使用类Haar输入特征，对矩形图像区域的“和”或者“差”进行阈值化。
（2）积分图像技术加速矩形图像区的45°旋转值的计算，被用来加速类Haar输入特征的计算。
（3）使用统计boosting创建两类问题（人脸、非人脸）的分类器节点（高通过率、低拒绝率）。
（4）把弱分类节点组成筛选式级联。换句话说：第一组分类器是最优，能通过包含物体的图像区域，同时允许一些不包含物体的图像通过；第二组分类器次优分类器，也是有较低的拒绝率；以此类推，在测试模式下，只要图像区域通过了整个级联，则认为里面有物体。
2. 摄像头实时人脸识别
加载人脸检测器文件haarcascade_frontalface_default.xml和人眼检测器文件 haarcascade_eye.xml；调用 cv2.VideoCapture(0)打开USB摄像头，获取实时图像，程序会根据获取的图像，检测并绘制出人脸和眼睛。
若佩戴着眼镜，请加载检测器文件 haarcascade_eye_tree_eyeglasses.xml 。
import cv2 def detect(): face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('./cascades/haarcascade_eye.xml') #eye_cascade = cv2.CascadeClassifier('./cascades/haarcascade_eye_tree_eyeglasses.xml') camera = cv2.VideoCapture(0) while (True): ret, frame = camera.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) #img = cv2.imread(frame) for (x,y,w,h) in faces: img = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) roi_gray = gray[y:y+h, x:x+w] ''' cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects Parameters: image – Matrix of the type CV_8U containing an image where objects are detected. scaleFactor – Parameter specifying how much the image size is reduced at each image scale. minNeighbors – Parameter specifying how many neighbors each candidate rectangle should have to retain it. flags – Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade. minSize – Minimum possible object size. Objects smaller than that are ignored. maxSize – Maximum possible object size. Objects larger than that are ignored. objects – Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image. ''' eyes = eye_cascade.detectMultiScale(roi_gray, 1.03, 5, 0, (80,80)) for (ex,ey,ew,eh) in eyes: cv2.rectangle(img,(x+ex,y+ey),(x+ex+ew,y+ey+eh),(0,255,0),2) cv2.imshow("camera", frame) if cv2.waitKey(100) & 0xff == ord("q"): break camera.release() cv2.destroyAllWindows() if __name__ == "__main__": detect()
3. 识别结果****
**4. 注意事项 **
实际中进行眼睛检测时，其结果可能会和鼻子混淆，此时可通过添加minSize来限制最小区域：
minSize – Minimum possible object size. Objects smaller than that are ignored.
评论 (0)
暂无评论，快来抢沙发吧！