Question

我试图修改此代码以允许跟踪相同颜色的多个对象并绘制对象行进的路径。 Currenlty代码只是根据颜色跟踪最大的对象，当对象围绕视频移动时，行进路径会消失。最后，我可以使用一些指导来了解如何创建捕获路径的新视频文件。这是我的第一篇文章，所以我不确定代码是否正确发布大声笑。在我身上轻松一下;）

from collections import deque
import numpy as np
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,
help="max buffer size")
args = vars(ap.parse_args())

# define the lower and upper boundaries of the "green"
# object in the HSV color space, then initialize the
# list of tracked points
greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
pts = deque(maxlen=args["buffer"])

# 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
while True:
# grab the current frame
(grabbed, frame) = camera.read()

# 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, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=600)
# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

# construct a mask for the color "green", 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)
    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 the points queue
pts.appendleft(center)

# 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)

# 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()

使用openCV在python中进行对象跟踪

0 个答案: