如何检测对象球（使用opencv进行跟踪）是否通过给定区域

Question

我正在尝试开发桌上足球游戏分析应用程序。我可以使用基于opencv颜色的跟踪来跟踪球。真正的问题是目标检测。可能的解决方案是- 如果球经过球门上的标记区域，并且球从桌子上消失了很长时间，则可以将其视为球门，并应增加球门计数器。

我在github和stackoverflow上经历了很多代码，但是不能正确实现需求。

from collections import deque
from imutils.video import VideoStream
import numpy as np
import argparse
import cv2
import imutils
import time

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())
match_score = (0, 0)

greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)

pts = deque(maxlen=args["buffer"])
counter = 0
(dX, dY) = (0, 0)
direction = ""

if not args.get("video", False):
    vs = VideoStream(src=1).start()
else:
    vs = cv2.VideoCapture(args["video"])

time.sleep(2.0)

while True:

frame = vs.read()


frame = frame[1] if args.get("video", False) else frame
if frame is None:
    break

frame = imutils.resize(frame, width=640, height=480)
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)

mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
center = None
cv2.rectangle(frame,(0,0),(640,360),(0,255,255),7)
cv2.line(frame,(10,135),(10,225),(255,0,0),7)
cv2.line(frame,(625,140),(625,230),(255,0,0),7)
cv2.putText(frame, str(match_score[0]) + " - " + str(match_score[1]), (260, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (120, 255, 50), 2)

if len(cnts) > 0:
    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"]))

    if radius > 5:
        # 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)


pts.appendleft(center)
for i in range(1, len(pts)):
    if pts[i - 1] is None or pts[i] is None:
        continue
    if counter >= 10 and i == 1 and pts[-10] is not None:
        dX = pts[-10][0] - pts[i][0]
        dY = pts[-10][1] - pts[i][1]
        (dirX, dirY) = ("", "")

    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, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
    0.65, (0, 0, 255), 3)
cv2.putText(frame, "dx: {}, dy: {}".format(dX, dY),
    (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
    0.5, (0, 0, 255), 1)

cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
counter += 1
if key == ord("q"):
    break

if not args.get("video", False):
    vs.stop()
else:
    vs.release()

cv2.destroyAllWindows()

这些是我当前正在使用的剪辑。 https://drive.google.com/file/d/1Y7Dzzfx_V0fsDZRZAC4d0MuEpSnKnodI/view?usp=sharing https://drive.google.com/file/d/1jazU_-tdp9e-nALTqT04_U_8gj8rovSL/view?usp=sharing

python trackball.py --video test3.mp4

我完全愿意接受新建议！

Answer 1

使用投票算法确定是否已进球。在图像中找到目标的位置。如果跟踪的球位置在球门内，请增加投票数。如果一个目标有几票，则计算该目标。

此投票方法将有助于消除错误的跟踪值。

# Find the ball position (an x,y coordinate)
x, y = ball_position
# Find the goal position (a rectangle)
goal_x, goal_y, width, height = 0, 400, 50, 170
# Is the ball inside the goal?
if x>goal_x and p<goal_x+width and y>goal_y and y<goal_y+height: votes += 1
else: votes -= .1
if votes < 0: votes = 0
if votes > 7: print('goal!')