卡尔曼滤波器和连续自适应漂移组合进行目标跟踪：kalman+camshift

卡尔曼滤波器和连续自适应漂移组合进行目标跟踪，具体概念解释参考本人OpenCV系列文章，代码实现如下：

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

# take first frame of the video
ret,frame = cap.read()

# setup initial location of window
r,h,c,w = 300,200,400,300  # simply hardcoded the values
track_window = (c,r,w,h)


roi = frame[r:r+h, c:c+w]
hsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((160., 30.,32.)), np.array((180.,120.,255.)))
roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )

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) * 0.03

measurement = np.array((2,1), np.float32) 
prediction = np.zeros((2,1), np.float32)

def center(points):
    x = (points[0][0] + points[1][0] + points[2][0] + points[3][0]) / 4.0
    y = (points[0][1] + points[1][1] + points[2][1] + points[3][1]) / 4.0
    return np.array([np.float32(x), np.float32(y)], np.float32)

while(1):
    ret ,frame = cap.read()

    if ret == True:
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        
        ret, track_window = cv2.CamShift(dst, track_window, term_crit)
        
        pts = cv2.boxPoints(ret)
        pts = np.int0(pts)
        (cx, cy), radius = cv2.minEnclosingCircle(pts)
        kalman.correct(center(pts))
        img2 = cv2.polylines(frame,[pts],True, 255,2)
        prediction = kalman.predict()
        cv2.circle(frame, (prediction[0], prediction[1]), int(radius), (0, 255, 0))
        cv2.imshow('img2',img2)
        k = cv2.waitKey(60) & 0xff
        if k == 27:
            break

    else:
        break

cv2.destroyAllWindows()
cap.release()

效果图：