mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 09:49:12 +08:00
d6306f8ccb
Update kalman sample * updated view and comments, fixed dims * updated view and comments, added statePost
105 lines
4.3 KiB
Python
Executable File
105 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python
|
|
"""
|
|
Tracking of rotating point.
|
|
Point moves in a circle and is characterized by a 1D state.
|
|
state_k+1 = state_k + speed + process_noise N(0, 1e-5)
|
|
The speed is constant.
|
|
Both state and measurements vectors are 1D (a point angle),
|
|
Measurement is the real state + gaussian noise N(0, 1e-1).
|
|
The real and the measured points are connected with red line segment,
|
|
the real and the estimated points are connected with yellow line segment,
|
|
the real and the corrected estimated points are connected with green line segment.
|
|
(if Kalman filter works correctly,
|
|
the yellow segment should be shorter than the red one and
|
|
the green segment should be shorter than the yellow one).
|
|
Pressing any key (except ESC) will reset the tracking.
|
|
Pressing ESC will stop the program.
|
|
"""
|
|
# Python 2/3 compatibility
|
|
import sys
|
|
PY3 = sys.version_info[0] == 3
|
|
|
|
if PY3:
|
|
long = int
|
|
|
|
import numpy as np
|
|
import cv2 as cv
|
|
|
|
from math import cos, sin, sqrt, pi
|
|
|
|
def main():
|
|
img_height = 500
|
|
img_width = 500
|
|
kalman = cv.KalmanFilter(2, 1, 0)
|
|
|
|
code = long(-1)
|
|
num_circle_steps = 12
|
|
while True:
|
|
img = np.zeros((img_height, img_width, 3), np.uint8)
|
|
state = np.array([[0.0],[(2 * pi) / num_circle_steps]]) # start state
|
|
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]]) # F. input
|
|
kalman.measurementMatrix = 1. * np.eye(1, 2) # H. input
|
|
kalman.processNoiseCov = 1e-5 * np.eye(2) # Q. input
|
|
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1)) # R. input
|
|
kalman.errorCovPost = 1. * np.eye(2, 2) # P._k|k KF state var
|
|
kalman.statePost = 0.1 * np.random.randn(2, 1) # x^_k|k KF state var
|
|
|
|
while True:
|
|
def calc_point(angle):
|
|
return (np.around(img_width / 2. + img_width / 3.0 * cos(angle), 0).astype(int),
|
|
np.around(img_height / 2. - img_width / 3.0 * sin(angle), 1).astype(int))
|
|
img = img * 1e-3
|
|
state_angle = state[0, 0]
|
|
state_pt = calc_point(state_angle)
|
|
# advance Kalman filter to next timestep
|
|
# updates statePre, statePost, errorCovPre, errorCovPost
|
|
# k-> k+1, x'(k) = A*x(k)
|
|
# P'(k) = temp1*At + Q
|
|
prediction = kalman.predict()
|
|
|
|
predict_pt = calc_point(prediction[0, 0]) # equivalent to calc_point(kalman.statePre[0,0])
|
|
# generate measurement
|
|
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
|
|
measurement = np.dot(kalman.measurementMatrix, state) + measurement
|
|
|
|
measurement_angle = measurement[0, 0]
|
|
measurement_pt = calc_point(measurement_angle)
|
|
|
|
# correct the state estimates based on measurements
|
|
# updates statePost & errorCovPost
|
|
kalman.correct(measurement)
|
|
improved_pt = calc_point(kalman.statePost[0, 0])
|
|
|
|
# plot points
|
|
cv.drawMarker(img, measurement_pt, (0, 0, 255), cv.MARKER_SQUARE, 5, 2)
|
|
cv.drawMarker(img, predict_pt, (0, 255, 255), cv.MARKER_SQUARE, 5, 2)
|
|
cv.drawMarker(img, improved_pt, (0, 255, 0), cv.MARKER_SQUARE, 5, 2)
|
|
cv.drawMarker(img, state_pt, (255, 255, 255), cv.MARKER_STAR, 10, 1)
|
|
# forecast one step
|
|
cv.drawMarker(img, calc_point(np.dot(kalman.transitionMatrix, kalman.statePost)[0, 0]),
|
|
(255, 255, 0), cv.MARKER_SQUARE, 12, 1)
|
|
|
|
cv.line(img, state_pt, measurement_pt, (0, 0, 255), 1, cv.LINE_AA, 0) # red measurement error
|
|
cv.line(img, state_pt, predict_pt, (0, 255, 255), 1, cv.LINE_AA, 0) # yellow pre-meas error
|
|
cv.line(img, state_pt, improved_pt, (0, 255, 0), 1, cv.LINE_AA, 0) # green post-meas error
|
|
|
|
# update the real process
|
|
process_noise = sqrt(kalman.processNoiseCov[0, 0]) * np.random.randn(2, 1)
|
|
state = np.dot(kalman.transitionMatrix, state) + process_noise # x_k+1 = F x_k + w_k
|
|
|
|
cv.imshow("Kalman", img)
|
|
code = cv.waitKey(1000)
|
|
if code != -1:
|
|
break
|
|
|
|
if code in [27, ord('q'), ord('Q')]:
|
|
break
|
|
|
|
print('Done')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print(__doc__)
|
|
main()
|
|
cv.destroyAllWindows()
|