Представьте, что у меня есть набор цветных шаров (например, красный, синий и зеленый). Я хотел бы узнать, какого цвета имеет шар, который находится в кадре в данный момент. В данный момент в кадре будет только один мяч. Тогда я бы хотел отследить этот мяч. У меня есть отслеживающая и направленная части, если я заранее знаю цвет мяча.
Что может быть хорошим способом динамического определения цвета мяча? Я видел несколько примеров ( ball_tracking.py или этот ), где цвета фиксированы заранее, а один постоянно перебирает возможные цвета. Это обычный подход? Я чувствую, что должен быть способ, не зная заранее цвета, например, сначала определить форму, а затем найти цвет формы.
Я немного поиграл с cv2.HoughCircles(), но получаю много ложных срабатываний даже для неподвижного изображения. Я также попробовал BlobDecetor поставить этот подход, кажется, еще хуже.
Я бы оценил любые отзывы или указатели. Ниже приведен пример изображения:
Вот код, который у меня есть для отслеживания, если цвет был заранее установлен.
# this is heavily based on
# https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/
# https://www.pyimagesearch.com/2015/09/21/opencv-track-object-movement/
# import the necessary packages
from collections import deque
from imutils.video import VideoStream
import numpy as np
import argparse
import cv2
import imutils #pip install --upgrade imutils
import time
#global variables
north,east,south,west = "Up","Right","Down","Left"
defaultGreenLower = (50,97,0)
defaultGreenUpper = (84,255,255)
defaultRedLower = (0, 179, 106)
defaultRedUpper = (38, 255, 255)
defaultBlueLower = (78, 75, 0)
defaultBlueUpper = (140, 255, 152)
windowTitle = "Tracking & Treshold"
hsvStr = "HSV"
def get_arguments():
# 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("-bf", "--buffer", type=int, default=64,
help="max buffer size")
ap.add_argument("-t", "--threshold", help="Direction_detection. Lower = more sensitive. Default: 20",default=20,type=int)
# color flags
group = ap.add_mutually_exclusive_group()
group.add_argument("-r", "--red",
help="track red objects",action='store_true')
group.add_argument("-b", "--blue",
help="track blue objects",action='store_true')
return vars(ap.parse_args())
def callback(value):
pass
def setup_trackbars(lowerBound, upperBound):
cv2.namedWindow(windowTitle,0)
for i in ["MIN", "MAX"]:
v = lowerBound if i == "MIN" else upperBound
for j,k in zip(hsvStr,v):
cv2.createTrackbar("%s_%s" % (j, i), windowTitle, k, 255, callback)
def get_trackbar_values():
values = []
for i in ["MIN", "MAX"]:
for j in hsvStr:
v = cv2.getTrackbarPos("%s_%s" % (j, i), windowTitle)
values.append(v)
return values
def main():
args = get_arguments()
# initialize the list of tracked points, the frame counter,
# and the coordinate deltas
pts = deque(maxlen=args["buffer"])
counter = 0
(dX, dY) = (0, 0)
direction = ""
# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
vs = VideoStream(src=0).start()
# otherwise, grab a reference to the video file
else:
vs = cv2.VideoCapture(args["video"])
# allow the camera or video file to warm up
time.sleep(2.0)
#use green as the default color
if not args['red'] and not args['blue']:
setup_trackbars(defaultGreenLower,defaultGreenUpper)
if args['red']:
setup_trackbars(defaultRedLower,defaultRedUpper)
if args['blue']:
setup_trackbars(defaultBlueLower,defaultBlueUpper)
if args['threshold']:
direction_detection = args['threshold']
# keep looping
while True:
# grab the current frame
frame = vs.read()
# handle the frame from VideoCapture or VideoStream
frame = frame[1] if args.get("video", False) else frame
# if we are viewing a video and we did not grab a frame,
# then we have reached the end of the video
if frame is None:
break
# resize the frame, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=600)
#flip image
frame = cv2.flip(frame, 1 )
#create copy for overlays
overlay = frame.copy()
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsvFrame = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
#connect slider to image
h_min, s_min, v_min, h_max, s_max, v_max = get_trackbar_values()
# construct a mask for the color set at the slider, then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
org_mask = cv2.inRange(hsvFrame, (h_min, s_min, v_min), (h_max, s_max, v_max))
mask = cv2.erode(org_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)
cnts = imutils.grab_contours(cnts)
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(overlay, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(overlay, center, 5, (0, 0, 255), -1)
# update the points queue
pts.appendleft(center)
# loop over the set of tracked points
for i in range(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
# check to see if enough points have been accumulated in
# the buffer
if counter >= 10 and i == 1 and pts[-10] is not None:
# compute the difference between the x and y
# coordinates and re-initialize the direction
# text variables
dX = pts[-10][0] - pts[i][0]
dY = pts[-10][1] - pts[i][1]
(dirX, dirY) = ("", "")
# ensure there is significant movement in the
# x-direction
if np.abs(dX) > direction_detection:
dirX = west if np.sign(dX) == 1 else east
# ensure there is significant movement in the
# y-direction
if np.abs(dY) > direction_detection:
dirY = north if np.sign(dY) == 1 else south
# handle when both directions are non-empty
if dirX != "" and dirY != "":
direction = "{}-{}".format(dirY, dirX)
# otherwise, only one direction is non-empty
else:
direction = dirX if dirX != "" else dirY
# 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(overlay, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the movement deltas and the direction of movement on the frame
cv2.putText(overlay, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,0.65, (0, 0, 255), 2)
cv2.putText(overlay, "dx: {}, dy: {}".format(dX, dY), (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
if direction:
print(direction)
#add third channel to threshold view (required for side-by-side view)
display_mask1 = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# combine frames into a single window
gui = np.hstack((overlay, display_mask1))
# display frame
cv2.imshow(windowTitle,gui)
key = cv2.waitKey(1) & 0xFF
counter += 1
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
print(get_trackbar_values())
break
# if we are not using a video file, stop the camera video stream
if not args.get("video", False):
vs.stop()
# otherwise, release the camera
else:
vs.release()
# close all windows
cv2.destroyAllWindows()
if __name__ == '__main__':
main()