openCV卡尔曼滤波器的使用很少有文献记载,只有少数(如果有的话)和C ++中的例子。我已经移植了一个假设工作的,简单的C ++示例(Opencv kalman filter prediction without new observtion)。我的端口运行,但无法正常工作。
我做错了什么?
谷歌搜索提供了一些有用的C ++示例和一些不起作用的旧Python示例。 openCV文档引用了C ++“对OpenCV的卡尔曼滤波器的c调用的示例”,即不太有用。
measurement = np.zeros((2,1),dtype=np.float32)
state = np.zeros((4,1),dtype=np.float32) # (x, y, Vx, Vy)
kalman = cv2.KalmanFilter(4,2,0)
def initKalman(x,y): # init to 0,0
measurement[0][0] = x
measurement[1][0] = y
kalman.statePre = np.zeros((4,1),dtype=np.float32)
kalman.statePre[0,0] = x
kalman.statePre[1,0] = y
kalman.statePost = np.zeros((4,1),dtype=np.float32)
kalman.statePost[0,0] = x
kalman.statePost[1,0] = y
cv2.setIdentity(kalman.measurementMatrix)
cv2.setIdentity(kalman.processNoiseCov, .01)
cv2.setIdentity(kalman.measurementNoiseCov, .1)
cv2.setIdentity(kalman.errorCovPost, .1)
kalman.transitionMatrix = np.array([[1,0,1,0],
[0,1,0,1],
[0,0,1,0],
[0,0,0,1]],np.float32)
def kalmanPredict():
prediction = kalman.predict()
predictPr = [prediction[0,0],prediction[1,0]]
return predictPr
def kalmanCorrect(x,y):
measurement[0,0] = x
measurement[1,0] = y
estimated = kalman.correct(measurement)
return [estimated[0,0],estimated[1,0]]
def runK():
initKalman(0,0)
p = kalmanPredict(); # first time - should be the initial x,y, i.e., 0,0
print("first",p)
s = kalmanCorrect(10, 10);
print("C",s) # should be (per example) 5,5 -- but I get 0,0
p = kalmanPredict()
print("P",p) # should be (per example) 5,5 -- but I get 0,0
s = kalmanCorrect(20, 20);
print("C",s) # should be (per example) 10,10 -- but I get 0,0
p = kalmanPredict()
print("P",p) # should be (per example) 10,10 -- but I get 0,0
s = kalmanCorrect(30, 30); # -- but I get 0,0
print("C",s)
p = kalmanPredict() # -- but I get 0,0
print("P",p)
runK()
---- with the output ----
first [0.0, 0.0]
C [0.0, 0.0]
P [0.0, 0.0]
C [0.0, 0.0]
P [0.0, 0.0]
C [0.0, 0.0]
P [0.0, 0.0]
我期待C ++示例的结果。相反,我收到全零,即不好的结果。
谢谢!!!!
即使您的代码看起来不错,但似乎setidentity不像名称所暗示的那样工作。就像现在一样,它只会使矩阵为0:
print (kalman.measurementMatrix )
cv2.setIdentity(kalman.measurementMatrix)
print (kalman.measurementMatrix )
得到:
[[0. 0. 0. 0.]
[0. 0. 0. 0.]]
[[0. 0. 0. 0.]
[0. 0. 0. 0.]]
您需要将函数的结果分配给变量,如文档mtx=cv.setIdentity(mtx[, s])
中所述。在你的代码中它将是这样的:
kalman.measurementMatrix = cv2.setIdentity(kalman.measurementMatrix)
或使用numpy eye功能
kalman.measurementMatrix = np.eye(2,M=4, dtype=np.float32)
对initKalman
函数中的所有有问题的行进行修复将导致如下所示:
def initKalman(x,y): # init to 0,0
measurement[0][0] = x
measurement[1][0] = y
kalman.statePre = np.zeros((4,1),dtype=np.float32)
kalman.statePre[0,0] = x
kalman.statePre[1,0] = y
kalman.statePost = np.zeros((4,1),dtype=np.float32)
kalman.statePost[0,0] = x
kalman.statePost[1,0] = y
kalman.measurementMatrix=cv2.setIdentity(kalman.measurementMatrix)
kalman.processNoiseCov=cv2.setIdentity(kalman.processNoiseCov, .01)
kalman.measurementNoiseCov=cv2.setIdentity(kalman.measurementNoiseCov, .1)
kalman.errorCovPost=cv2.setIdentity(kalman.errorCovPost, .1)
kalman.transitionMatrix = np.array([[1,0,1,0],
[0,1,0,1],
[0,0,1,0],
[0,0,0,1]],np.float32)
这会产生以下结果:
first [0.0, 0.0]
C [6.774194, 6.774194]
P [10.0, 10.0]
C [16.875, 16.875]
P [23.538307, 23.538307]
C [27.827488, 27.827488]
P [36.32232, 36.32232]