OpenCV卡尔曼滤波器python [英] OpenCV Kalman Filter python

问题描述

任何人都可以给我提供示例代码或python 2.7和openCV 2.4.13中的Kalman过滤器实现示例

Can anyone provide me a sample code or some sort of example of Kalman filter implementation in python 2.7 and openCV 2.4.13

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

I want to implement it in a video to track a person but, I don't have any reference to learn and I couldn't find any python examples.

我知道openCV中的Kalman过滤器以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

I know Kalman Filter exists in openCV as cv2.KalmanFilter but I have no idea how to use it. Any guidance would be appreciated

推荐答案

下面的kalman.py代码是

The kalman.py code below is the example included in OpenCV 3.2 source in github. It should be easy to change the syntax back to 2.4 if needed.

#!/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")

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