卡尔曼滤波器和连续自适应漂移组合进行目标跟踪:kalman+camshift
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2024-03-25 22:06:52
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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: