如果您使用的是opencv2.4，那么这是个坏消息：KalmanFilter不可用，因为您无法设置过渡（或任何其他）矩阵。
对于opencv3.0，它可以正常工作，如下所示：
import cv2, numpy as npmeas=[]pred=[]frame = np.zeros((400,400,3), np.uint8) # drawing canvasmp = np.array((2,1), np.float32) # measurementtp = np.zeros((2,1), np.float32) # tracked / predictiondef onmouse(k,x,y,s,p): global mp,meas mp = np.array([[np.float32(x)],[np.float32(y)]]) meas.append((x,y))def paint(): global frame,meas,pred for i in range(len(meas)-1): cv2.line(frame,meas[i],meas[i+1],(0,100,0)) for i in range(len(pred)-1): cv2.line(frame,pred[i],pred[i+1],(0,0,200))def reset(): global meas,pred,frame meas=[] pred=[] frame = np.zeros((400,400,3), np.uint8)cv2.namedWindow("kalman")cv2.setMouseCallback("kalman",onmouse);kalman = cv2.KalmanFilter(4,2)kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32)kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32)kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03#kalman.measurementNoiseCov = np.array([[1,0],[0,1]],np.float32) * 0.00003while True: kalman.correct(mp) tp = kalman.predict() pred.append((int(tp[0]),int(tp[1]))) paint() cv2.imshow("kalman",frame) k = cv2.waitKey(30) &0xFF if k == 27: break if k == 32: reset()
