卡尔曼滤波追踪——鼠标轨迹预测

import cv2 import numpy as np import matplotlib.pyplot as plt frame = cv2.imread
('inference_results/001.png') height, weigth = frame.shape[0], frame.shape[1]
print(height,weigth) last_mes = current_mes = np.array((0,height//2),np.float32)
# 保存当前中心点，可替换为船舶检测出来的中心点坐标格式为[[x][y]] last_pre = current_pre = np.array((0,
height//2),np.float32) # 保存预测[[x][y][x误差][y误差]] def mousemove(event, x,y,s,p):
# x和y需要自己抛出来,中心点左边的x，y global frame, current_mes, last_mes, current_pre,
last_pre last_pre= current_pre last_mes = current_mes current_mes = np.array([[
np.float32(x)],[np.float32(y)]]) kalman.correct(current_mes) current_pre =
kalman.predict() lmx, lmy = last_mes[0],last_mes[1] lpx, lpy = last_pre[0],
last_pre[1] cmx, cmy = current_mes[0],current_mes[1] cpx, cpy = current_pre[0],
current_pre[1] cv2.line(frame, (lmx,lmy),(cmx,cmy),(0,200,0)) # 实际轨迹 cv2.line(
frame, (lpx,lpy),(cpx,cpy),(0,0,200)) # 预测轨迹 cv2.namedWindow("Kalman") cv2.
setMouseCallback("Kalman", mousemove) kalman = cv2.KalmanFilter(4,2) kalman.
measurementMatrix= np.array([[1,0,0,0],[0,1,0,0]],np.float32) kalman.
transitionMatrix= np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]], np.float32
) kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],
np.float32) * 0.003 kalman.measurementNoiseCov = np.array([[1,0],[0,1]], np.
float32) * 1 while(True): cv2.imshow('Kalman',frame) if cv2.waitKey(1) & 0xFF ==
27: break cv2.destroyAllWindows()

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