我正在尝试在python中将opencv的卡尔曼滤波器应用于跟踪球的位置。我已经可以检测到它，但是仍然需要消除一些噪音。我要测量两个变量-x和y位置-我想获取四个变量-x和y位置以及x和y速度-但我什么也没有。当我在屏幕上显示x0，y0，vy和vx时，我得到“[.0]”。
另一个问题是我无法将控制矩阵应用于kalman.predict()函数，因为出现以下错误:
OpenCV Error: Assertion failed (a_size.width == len) in gemm, file /tmp/opencv3-20170518-8732-1bjq2j7/opencv-3.2.0/modules/core/src/matmul.cpp, line 1537
Traceback (most recent call last):
File "kalman.py", line 128, in <module>
kalmanout = kalman.predict(kalman.controlMatrix)
cv2.error: /tmp/opencv3-20170518-8732-1bjq2j7/opencv-3.2.0/modules/core/src/matmul.cpp:1537: error: (-215) a_size.width == len in function ge
这是我用于卡尔曼滤波器的代码(对于控制矩阵应用程序，我在最后使用行kalmanout = kalman.predict(kalman.controlMatrix):
# import the necessary packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2
import time
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=10,
help="max buffer size")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())
# define the lower and upper boundaries of the "blue"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (48, 62, 88)
greenUpper = (151, 238, 255)
pts = deque(maxlen=args["buffer"])
tintervals = deque(maxlen=args["buffer"])
tPrev = 0;
pRad = 0
mapix = 0
mspeed = 0
# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
camera = cv2.VideoCapture(0)
# otherwise, grab a reference to the video file
else:
camera = cv2.VideoCapture(args["video"])
# keep looping
#initialize background subtraction
fgbg = cv2.createBackgroundSubtractorMOG2()
while True:
# grab the current frame
(grabbed, frame) = camera.read()
displayx = 0
# start counting time
tPrev = time.time()
# if we are viewing a video and we did not grab a frame,
# then we have reached the end of the video
if args.get("video") and not grabbed:
break
# resize the frame and apply background subtraction
frame = imutils.resize(frame, width=500)
mask = fgbg.apply(frame)
res = cv2.bitwise_and(frame, frame, mask = mask)
# blur the frame and convert it to the HSV
blurred = cv2.GaussianBlur(res, (11, 11), 0)
hsv = cv2.cvtColor(res, cv2.COLOR_BGR2HSV)
# construct a mask for the color "blue", then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the ball
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
# only proceed if at least one contour was found
if len(cnts) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
pRad = radius
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
# only proceed if the radius meets a minimum size
if radius > 10:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(frame, center, 5, (0, 0, 255), -1)
# update time intervals queue
tintervals.appendleft(time.time() - tPrev)
# update the points queue
pts.appendleft(center)
# predict position of the ball
if (pRad > 0 and len(pts) > 5):
if pts[0] != None and pts[1] != None:
apix = 98.1/(0.032/pRad)
mapix = apix
y0 = pts[0][1]
x0 = pts[0][0]
kalmanin = np.array((2,1), np.float32) # measurement
kalmanout = np.zeros((4,1), np.float32) # tracked / prediction
kalmanin = np.array([[np.float32(x0)],[np.float32(y0)]])
tkalman = 0.01
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,tkalman,0],[0,1,0,tkalman],[0,0,1,0],[0,0,0,1]],np.float32)
kalman.controlMatrix = np.array([[0],[0.5*(tkalman**2.0)], [0],[tkalman]],np.float32) * mapix
kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03
kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03
kalman.measurementNoiseCov = np.array([[1,0],[0,1]],np.float32) * 0.00009
kalman.correct(kalmanin)
kalmanout = kalman.predict(kalman.controlMatrix)
x0 = kalmanout[0]
y0 = kalmanout[1]
vx = kalmanout[2]
vy = kalmanout[3]
displayx = x0
listX = []
listY = []
for i in range(1, 11):
t = 0.01 * i
y = y0 + vy * t + (apix * (t ** 2)) / 2
x = x0 + vx * t
listX.append(int(x))
listY.append(int(y))
mspeed = vy
for i in range(0, 9):
cv2.line(frame, (listX[i], listY[i]), (listX[i+1], listY[i+1]), (255, 0, 0), 4)
# loop over the set of tracked points
for i in xrange(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
cv2.putText(frame, "y axis speed: {}".format(displayx),
(120, frame.shape[0] - 70), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
cv2.putText(frame, "radius in px: {}".format(pRad),
(120, frame.shape[0] - 30), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
cv2.putText(frame, "apix: {}".format(mapix),
(120, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
if (mapix != 0):
cv2.putText(frame, "radius in meters: {}".format((9.81*pRad)/mapix),
(120, frame.shape[0] - 50), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 1)
# shows x, y position, (newest input from pts)
cv2.putText(frame, "x, y: {}".format(pts[0]),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
最佳答案
首先，我将卡尔曼滤波器的初始化移到循环外。代码的主要问题是已设置控制矩阵。如果我了解您的任务，那么您只是在观察系统，而不是在控制它。只需跳过kalman.controlMatrix初始化或将其设置为零矩阵即可。在循环中，您只需使用
kalmanout = kalman.predict()
kalman.correct(kalmanin)
关于python - 二维对象跟踪中没有卡尔曼滤波器的输出，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/44159887/