99 lines
3.7 KiB
Python
Executable File
99 lines
3.7 KiB
Python
Executable File
#!/usr/bin/python
|
|
"""
|
|
Tracking of rotating point.
|
|
Rotation speed is constant.
|
|
Both state and measurements vectors are 1D (a point angle),
|
|
Measurement is the real point angle + gaussian noise.
|
|
The real and the estimated points are connected with yellow line segment,
|
|
the real and the measured points are connected with red line segment.
|
|
(if Kalman filter works correctly,
|
|
the yellow segment should be shorter than the red one).
|
|
Pressing any key (except ESC) will reset the tracking with a different speed.
|
|
Pressing ESC will stop the program.
|
|
"""
|
|
import urllib2
|
|
import cv2.cv as cv
|
|
from math import cos, sin, sqrt
|
|
import sys
|
|
|
|
if __name__ == "__main__":
|
|
A = [ [1, 1], [0, 1] ]
|
|
|
|
img = cv.CreateImage((500, 500), 8, 3)
|
|
kalman = cv.CreateKalman(2, 1, 0)
|
|
state = cv.CreateMat(2, 1, cv.CV_32FC1) # (phi, delta_phi)
|
|
process_noise = cv.CreateMat(2, 1, cv.CV_32FC1)
|
|
measurement = cv.CreateMat(1, 1, cv.CV_32FC1)
|
|
rng = cv.RNG(-1)
|
|
code = -1L
|
|
|
|
cv.Zero(measurement)
|
|
cv.NamedWindow("Kalman", 1)
|
|
|
|
while True:
|
|
cv.RandArr(rng, state, cv.CV_RAND_NORMAL, cv.RealScalar(0), cv.RealScalar(0.1))
|
|
|
|
kalman.transition_matrix[0,0] = 1
|
|
kalman.transition_matrix[0,1] = 1
|
|
kalman.transition_matrix[1,0] = 0
|
|
kalman.transition_matrix[1,1] = 1
|
|
|
|
cv.SetIdentity(kalman.measurement_matrix, cv.RealScalar(1))
|
|
cv.SetIdentity(kalman.process_noise_cov, cv.RealScalar(1e-5))
|
|
cv.SetIdentity(kalman.measurement_noise_cov, cv.RealScalar(1e-1))
|
|
cv.SetIdentity(kalman.error_cov_post, cv.RealScalar(1))
|
|
cv.RandArr(rng, kalman.state_post, cv.CV_RAND_NORMAL, cv.RealScalar(0), cv.RealScalar(0.1))
|
|
|
|
|
|
while True:
|
|
def calc_point(angle):
|
|
return (cv.Round(img.width/2 + img.width/3*cos(angle)),
|
|
cv.Round(img.height/2 - img.width/3*sin(angle)))
|
|
|
|
state_angle = state[0,0]
|
|
state_pt = calc_point(state_angle)
|
|
|
|
prediction = cv.KalmanPredict(kalman)
|
|
predict_angle = prediction[0, 0]
|
|
predict_pt = calc_point(predict_angle)
|
|
|
|
cv.RandArr(rng, measurement, cv.CV_RAND_NORMAL, cv.RealScalar(0),
|
|
cv.RealScalar(sqrt(kalman.measurement_noise_cov[0, 0])))
|
|
|
|
# generate measurement
|
|
cv.MatMulAdd(kalman.measurement_matrix, state, measurement, measurement)
|
|
|
|
measurement_angle = measurement[0, 0]
|
|
measurement_pt = calc_point(measurement_angle)
|
|
|
|
# plot points
|
|
def draw_cross(center, color, d):
|
|
cv.Line(img, (center[0] - d, center[1] - d),
|
|
(center[0] + d, center[1] + d), color, 1, cv.CV_AA, 0)
|
|
cv.Line(img, (center[0] + d, center[1] - d),
|
|
(center[0] - d, center[1] + d), color, 1, cv.CV_AA, 0)
|
|
|
|
cv.Zero(img)
|
|
draw_cross(state_pt, cv.CV_RGB(255, 255, 255), 3)
|
|
draw_cross(measurement_pt, cv.CV_RGB(255, 0,0), 3)
|
|
draw_cross(predict_pt, cv.CV_RGB(0, 255, 0), 3)
|
|
cv.Line(img, state_pt, measurement_pt, cv.CV_RGB(255, 0,0), 3, cv. CV_AA, 0)
|
|
cv.Line(img, state_pt, predict_pt, cv.CV_RGB(255, 255, 0), 3, cv. CV_AA, 0)
|
|
|
|
cv.KalmanCorrect(kalman, measurement)
|
|
|
|
cv.RandArr(rng, process_noise, cv.CV_RAND_NORMAL, cv.RealScalar(0),
|
|
cv.RealScalar(sqrt(kalman.process_noise_cov[0, 0])))
|
|
cv.MatMulAdd(kalman.transition_matrix, state, process_noise, state)
|
|
|
|
cv.ShowImage("Kalman", img)
|
|
|
|
code = cv.WaitKey(100) % 0x100
|
|
if code != -1:
|
|
break
|
|
|
|
if code in [27, ord('q'), ord('Q')]:
|
|
break
|
|
|
|
cv.DestroyWindow("Kalman")
|