到这一步就是进行人脸识别了。
流程图：
代码：
import cv2 def recognize(cam): recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read(\'face_trainer/trainer.yml\') cascadePath = "haarcascade_frontalface_default.xml" faceCascade = cv2.CascadeClassifier(cascadePath) # 字体 font = cv2.FONT_HERSHEY_DUPLEX names = [\'zhiqiang\', \'jiayu\', \'jinze\'] cam = cv2.VideoCapture(0) minW = 0.1*cam.get(3) minH = 0.1*cam.get(4) while True: ret, img = cam.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=5, minSize=(int(minW), int(minH)) ) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2) idnum, confidence = recognizer.predict(gray[y:y+h, x:x+w]) print(idnum) if confidence < 100: idnum = names[idnum] confidence = "{0}%".format(round(100 - confidence)) else: idnum = "unknown" confidence = "{0}%".format(round(100 - confidence)) cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1) cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (0, 0, 0), 1) cv2.imshow(\'camera\', img) k = cv2.waitKey(10) if k == 27: break cam.release() cv2.destroyAllWindows() if __name__ == \'__main__\': cam = cv2.VideoCapture(0) recognize(cam)