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removed setter methods, replaced by CV_PROP_RW macro

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Juan Carlos Niebles
2014-09-18 09:39:35 -05:00
parent 1162f0ed63
commit dc49e11527
2 changed files with 28 additions and 58 deletions
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49
samples/python2/kalman.py
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@@ -11,21 +11,15 @@
Pressing any key (except ESC) will reset the tracking with a different speed.
Pressing ESC will stop the program.
"""
import urllib2
import cv2
from math import cos, sin, sqrt
import sys
from math import cos, sin
import numpy as np
if __name__ == "__main__":
img_height = 500
img_width = 500
img = np.array((img_height, img_width, 3), np.uint8)
kalman = cv2.KalmanFilter(2, 1, 0)
state = np.zeros((2, 1)) # (phi, delta_phi)
process_noise = np.zeros((2, 1))
measurement = np.zeros((1, 1))
code = -1L
@@ -34,25 +28,17 @@ if __name__ == "__main__":
while True:
state = 0.1 * np.random.randn(2, 1)
transition_matrix = np.array([[1., 1.], [0., 1.]])
kalman.setTransitionMatrix(transition_matrix)
measurement_matrix = 1. * np.ones((1, 2))
kalman.setMeasurementMatrix(measurement_matrix)
process_noise_cov = 1e-5
kalman.setProcessNoiseCov(process_noise_cov * np.eye(2))
measurement_noise_cov = 1e-1
kalman.setMeasurementNoiseCov(measurement_noise_cov * np.ones((1, 1)))
kalman.setErrorCovPost(1. * np.ones((2, 2)))
kalman.setStatePost(0.1 * np.random.randn(2, 1))
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]])
kalman.measurementMatrix = 1. * np.ones((1, 2))
kalman.processNoiseCov = 1e-5 * np.eye(2)
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1))
kalman.errorCovPost = 1. * np.ones((2, 2))
kalman.statePost = 0.1 * np.random.randn(2, 1)
while True:
def calc_point(angle):
return (np.around(img_width/2 + img_width/3*cos(angle), 0).astype(int),
np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int))
np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int))
state_angle = state[0, 0]
state_pt = calc_point(state_angle)
@@ -61,21 +47,22 @@ if __name__ == "__main__":
predict_angle = prediction[0, 0]
predict_pt = calc_point(predict_angle)
measurement = measurement_noise_cov * np.random.randn(1, 1)
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
# generate measurement
measurement = np.dot(measurement_matrix, state) + measurement
measurement = np.dot(kalman.measurementMatrix, state) + measurement
measurement_angle = measurement[0, 0]
measurement_pt = calc_point(measurement_angle)
# plot points
def draw_cross(center, color, d):
cv2.line(img, (center[0] - d, center[1] - d),
(center[0] + d, center[1] + d), color, 1, cv2.LINE_AA, 0)
cv2.line(img, (center[0] + d, center[1] - d),
(center[0] - d, center[1] + d), color, 1, cv2.LINE_AA, 0)
cv2.line(img,
(center[0] - d, center[1] - d), (center[0] + d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
cv2.line(img,
(center[0] + d, center[1] - d), (center[0] - d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
img = np.zeros((img_height, img_width, 3), np.uint8)
draw_cross(np.int32(state_pt), (255, 255, 255), 3)
@@ -87,8 +74,8 @@ if __name__ == "__main__":
kalman.correct(measurement)
process_noise = process_noise_cov * np.random.randn(2, 1)
state = np.dot(transition_matrix, state) + process_noise
process_noise = kalman.processNoiseCov * np.random.randn(2, 1)
state = np.dot(kalman.transitionMatrix, state) + process_noise
cv2.imshow("Kalman", img)