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Commit ee6fd905 authored by widmerl's avatar widmerl
Browse files

added python cv2 code

parent b73412c5
# import the necessary packages
from collections import deque
from imutils.video import VideoStream
import numpy as np
import argparse
import cv2
import imutils
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=64,
help="max buffer size")
args = vars(ap.parse_args())
# define the lower and upper boundaries of the "green"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
redLower1 = (0, 86, 6)
redUpper1 = (8, 255, 255)
redLower2 = (90, 86, 6)
redUpper2 = (100, 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):
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(0.2)
# 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)
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(blurred, 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
maskGreen = cv2.inRange(hsv, greenLower, greenUpper)
maskGreen = cv2.erode(maskGreen, None, iterations=2)
maskGreen = cv2.dilate(maskGreen, None, iterations=2)
maskRed1 = cv2.inRange(hsv, redLower1, redUpper1)
maskRed2 = cv2.inRange(hsv, redLower1, redUpper1)
maskRed = cv2.bitwise_or(maskRed1, maskRed2)
maskRed = cv2.erode(maskRed, None, iterations=2)
maskRed = cv2.dilate(maskRed, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the ball
cntsGreen = cv2.findContours(maskGreen.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cntsGreen = imutils.grab_contours(cntsGreen)
cntsRed = cv2.findContours(maskRed.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cntsRed = imutils.grab_contours(cntsRed)
centerRed = None
centerGreen = None
# only proceed if at least one Red contour was found
if len(cntsRed) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
cR = max(cntsRed, key=cv2.contourArea)
((xR, yR), radiusR) = cv2.minEnclosingCircle(cR)
print('Red position: ', int(xR), ',',int(yR),'radius: ', int(radiusR))
MR = cv2.moments(cR)
centerR = (int(MR["m10"] / MR["m00"]), int(MR["m01"] / MR["m00"]))
# only proceed if the radius meets a minimum size
if radiusR > 10:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(xR), int(yR)), int(radiusR), (0, 255, 255), 2)
cv2.circle(frame, centerR, 5, (0, 0, 255), -1)
# only proceed if at least one Green contour was found
if len(cntsGreen) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
cG = max(cntsGreen, key=cv2.contourArea)
((xG, yG), radiusG) = cv2.minEnclosingCircle(cG)
print('Green position: ', int(xG), ',',int(yG),'radius: ', int(radiusG))
MG = cv2.moments(cG)
centerG = (int(MG["m10"] / MG["m00"]), int(MG["m01"] / MG["m00"]))
# only proceed if the radius meets a minimum size
if radiusG > 2:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(xG), int(yG)), int(radiusG), (0, 255, 255), 2)
cv2.circle(frame, centerG, 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
##
## # 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)
#cv2.imshow("Blurred", blurred)
#cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
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()
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