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Open Source Computer Vision Library
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89 lines
3.1 KiB
89 lines
3.1 KiB
#!/usr/bin/python |
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""" |
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Tracking of rotating point. |
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Rotation speed is constant. |
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Both state and measurements vectors are 1D (a point angle), |
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Measurement is the real point angle + gaussian noise. |
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The real and the estimated points are connected with yellow line segment, |
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the real and the measured points are connected with red line segment. |
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(if Kalman filter works correctly, |
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the yellow segment should be shorter than the red one). |
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Pressing any key (except ESC) will reset the tracking with a different speed. |
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Pressing ESC will stop the program. |
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""" |
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import cv2 |
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from math import cos, sin |
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import numpy as np |
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if __name__ == "__main__": |
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img_height = 500 |
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img_width = 500 |
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kalman = cv2.KalmanFilter(2, 1, 0) |
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code = -1L |
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cv2.namedWindow("Kalman") |
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while True: |
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state = 0.1 * np.random.randn(2, 1) |
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kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]]) |
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kalman.measurementMatrix = 1. * np.ones((1, 2)) |
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kalman.processNoiseCov = 1e-5 * np.eye(2) |
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kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1)) |
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kalman.errorCovPost = 1. * np.ones((2, 2)) |
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kalman.statePost = 0.1 * np.random.randn(2, 1) |
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while True: |
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def calc_point(angle): |
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return (np.around(img_width/2 + img_width/3*cos(angle), 0).astype(int), |
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np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int)) |
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state_angle = state[0, 0] |
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state_pt = calc_point(state_angle) |
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prediction = kalman.predict() |
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predict_angle = prediction[0, 0] |
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predict_pt = calc_point(predict_angle) |
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measurement = kalman.measurementNoiseCov * np.random.randn(1, 1) |
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# generate measurement |
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measurement = np.dot(kalman.measurementMatrix, state) + measurement |
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measurement_angle = measurement[0, 0] |
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measurement_pt = calc_point(measurement_angle) |
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# plot points |
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def draw_cross(center, color, d): |
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cv2.line(img, |
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(center[0] - d, center[1] - d), (center[0] + d, center[1] + d), |
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color, 1, cv2.LINE_AA, 0) |
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cv2.line(img, |
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(center[0] + d, center[1] - d), (center[0] - d, center[1] + d), |
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color, 1, cv2.LINE_AA, 0) |
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img = np.zeros((img_height, img_width, 3), np.uint8) |
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draw_cross(np.int32(state_pt), (255, 255, 255), 3) |
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draw_cross(np.int32(measurement_pt), (0, 0, 255), 3) |
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draw_cross(np.int32(predict_pt), (0, 255, 0), 3) |
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cv2.line(img, state_pt, measurement_pt, (0, 0, 255), 3, cv2.LINE_AA, 0) |
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cv2.line(img, state_pt, predict_pt, (0, 255, 255), 3, cv2.LINE_AA, 0) |
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kalman.correct(measurement) |
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process_noise = kalman.processNoiseCov * np.random.randn(2, 1) |
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state = np.dot(kalman.transitionMatrix, state) + process_noise |
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cv2.imshow("Kalman", img) |
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code = cv2.waitKey(100) % 0x100 |
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if code != -1: |
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break |
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if code in [27, ord('q'), ord('Q')]: |
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break |
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cv2.destroyWindow("Kalman")
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