卡尔曼滤波追踪——鼠标轨迹预测_卡尔曼模型鼠标-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_44898889/article/details/120002361

该博客介绍了如何使用OpenCV实现鼠标交互，结合卡尔曼滤波算法对图像中船舶位置进行连续预测和跟踪。通过鼠标点击获取实时位置，然后应用滤波器处理误差，展示预测轨迹与实际轨迹。

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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()