OpenCV卡尔曼滤波器python

Question

谁能给我提供示例代码或python 2.7和openCV 2.4.13中的卡尔曼过滤器实现示例

我想在视频中实现它来跟踪人，但是，我没有任何学习参考，也找不到任何python示例。

我知道openCV中的Kalman筛选器以cv2.KalmanFilter的形式存在，但我不知道如何使用它。任何指导将不胜感激

Answer 1

kalman.py下面的代码是github 中OpenCV 3.2源代码中包含的示例。如果需要，可以很容易地将语法更改回2.4。

#!/usr/bin/env 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.
"""
# Python 2/3 compatibility
import sys
PY3 = sys.version_info[0] == 3

if PY3:
    long = int

import cv2
from math import cos, sin, sqrt
import numpy as np

if __name__ == "__main__":

    img_height = 500
    img_width = 500
    kalman = cv2.KalmanFilter(2, 1, 0)

    code = long(-1)

    cv2.namedWindow("Kalman")

    while True:
        state = 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))

            state_angle = state[0, 0]
            state_pt = calc_point(state_angle)

            prediction = kalman.predict()
            predict_angle = prediction[0, 0]
            predict_pt = calc_point(predict_angle)

            measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)

            # generate 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)

            img = np.zeros((img_height, img_width, 3), np.uint8)
            draw_cross(np.int32(state_pt), (255, 255, 255), 3)
            draw_cross(np.int32(measurement_pt), (0, 0, 255), 3)
            draw_cross(np.int32(predict_pt), (0, 255, 0), 3)

            cv2.line(img, state_pt, measurement_pt, (0, 0, 255), 3, cv2.LINE_AA, 0)
            cv2.line(img, state_pt, predict_pt, (0, 255, 255), 3, cv2.LINE_AA, 0)

            kalman.correct(measurement)

            process_noise = sqrt(kalman.processNoiseCov[0,0]) * np.random.randn(2, 1)
            state = np.dot(kalman.transitionMatrix, state) + process_noise

            cv2.imshow("Kalman", img)

            code = cv2.waitKey(100)
            if code != -1:
                break

        if code in [27, ord('q'), ord('Q')]:
            break

    cv2.destroyWindow("Kalman")

这是关于卡尔曼滤波器的OpenCV 2.4文档。希望能有所帮助。