The python code below is developed using source material from https://pyimagesearch.com/2015/09/21/opencv-track-object-movement/ and making it work for dots. The colour of the skin was changed to green (maybe blue at a later point) to distinguish between skin and marking clearly. The first tests were done using coloured paper and a webcam. The next tests were done using camera recordings from the iPhone to post-process the frames. Here, multiple challenges arose, as the green dot on the skin was hard to locate through the plexiglass, while the red dot on the plexiglass was easier to track.
#Packages
import argparse
import imutils
import datetime
import time
import cv2 as cv
import numpy as np
from imutils.video import VideoStream
from collections import deque
#Defining arguments to locate and play files
ap = argparse.ArgumentParser()
ap.add_argument("-v","--video",help="Path to the video file")
ap.add_argument("-b","--buffer",type=int,default=32,help="Max buffer size")
args = vars(ap.parse_args())
#Defining upper and lower bounds to locate the dots
red_lower = np.array([136, 87, 111], np.uint8)
red_upper = np.array([180, 255, 255], np.uint8)
green_lower = np.array([25, 52, 72], np.uint8)
green_upper = np.array([102, 255, 255], np.uint8)
blue_lower = np.array([94, 80, 2], np.uint8)
blue_upper = np.array([120, 255, 255], np.uint8)
#Defining variables for the locations shown on frame
counter = 0
counter1= 0
pts1 = deque(maxlen=args["buffer"])
pts2 = deque(maxlen=args["buffer"])
(dx,dy) = (0,0)
(dx_r,dy_r) = (0,0)
#Reading files or use webcam to capture
if not args.get("video",False):
v = imutils.video.VideoStream(src=0).start()
frame_width = int(v.stream.get(cv.CAP_PROP_FRAME_WIDTH))
frame_height = int(v.stream.get(cv.CAP_PROP_FRAME_HEIGHT))
else:
v = cv.VideoCapture(args["video"])
frame_width = int(v.get(cv.CAP_PROP_FRAME_WIDTH))
frame_height = int(v.get(cv.CAP_PROP_FRAME_HEIGHT))
time.sleep(2.0)
index=0
#While loop to go through frames and track
while True:
frame = v.read()
if args.get("video", False):
frame = frame[1]
else:
frame = frame
if frame is None:
break
#Working the frames to be able to locate and track red dot
frame = imutils.resize(frame,width=600)
blur = cv.GaussianBlur(frame,(11,11),4)
hsv = cv.cvtColor(blur,cv.COLOR_BGR2HSV)
canny = cv.Canny(hsv,125,175)
#cv.imshow("Canny",canny)
#Making a mask
mask1 = cv.inRange(hsv,green_lower,green_upper)
mask1 = cv.erode(mask1,None,iterations=2)
mask1 = cv.dilate(mask1,None,iterations=2)
mask2 = cv.inRange(hsv,red_lower,red_upper)
mask2 = cv.erode(mask2,None,iterations=2)
mask2 = cv.dilate(mask2,None,iterations=2)
cv.imshow("mask",mask1)
cv.imshow("mask1",mask2)
contours1 = cv.findContours(mask1.copy(),cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
contours1 = imutils.grab_contours(contours1)
contours2 = cv.findContours(mask2.copy(),cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)
contours2 = imutils.grab_contours(contours2)
center1 = None
center2 = None
#Making circle and track
if len(contours1)>0:
c1 = max(contours1,key=cv.contourArea)
((x,y),radius) = cv.minEnclosingCircle(c1)
M1 = cv.moments(c1)
center1 = (int(M1["m10"]/M1["m00"]),int(M1["m01"]/M1["m00"]))
if radius > 10:
cv.circle(frame,(int(x),int(y)),int(radius),(0,255,255),2)
cv.circle(frame,center1,5,(0,0,255),-1)
pts1.appendleft(center1)
if len(contours2)>0:
c2 = max(contours2,key=cv.contourArea)
((x1,y1),radius1) = cv.minEnclosingCircle(c2)
M = cv.moments(c2)
center2 = (int(M["m10"]/M["m00"]),int(M["m01"]/M["m00"]))
if radius1 > 10:
cv.circle(frame,(int(x1),int(y1)),int(radius1),(0,255,255),2)
cv.circle(frame,center2,5,(0,0,255),-1)
pts2.appendleft(center2)
#Setting counter and difference in locations between frames
for i in np.arange(1,len(pts1)):
if pts1[i-1] is None or pts1[i] is None:
continue
if counter >= 10 and i==1 and pts1[-10] is not None:
dx = pts1[-10][0] - pts1[i][0]
dy = pts1[-10][1] - pts1[i][1]
thickness = int(np.sqrt(args["buffer"]/float(i+1))*2.5)
cv.line(frame,pts1[i-1],pts1[i],(0,255,0),thickness)
if len(pts2) >= 10:
if None not in pts2:
dx_r = pts2[-1][0] - pts2[0][0]
dy_r = pts2[-1][1] - pts2[0][1]
for l in range(1, len(pts2)):
thickness2 = int(np.sqrt(args["buffer"] / float(len(pts2)))*2.5)
if pts2[l-1] is not None and pts2[l] is not None:
cv.line(frame, pts2[l-1], pts2[l], (0, 0, 255), thickness2)
cv.putText(frame,"dx_red:{},dy_red:{}".format(dx_r,dy_r),(10,frame.shape[0]-20),cv.FONT_HERSHEY_TRIPLEX,0.35,(0,255,0),1)
cv.putText(frame,"dx_green:{},dy_green:{}".format(dx,dy),(10,frame.shape[0]-10),cv.FONT_HERSHEY_TRIPLEX,0.35,(0,255,0),1)
cv.putText(frame,datetime.datetime.now().strftime("%A %d %B %Y %H:%M:%S%p"),(10,30),cv.FONT_HERSHEY_TRIPLEX,0.35,(0,255,0),1)
name = './Videosaved/frame' + str(index) + '.jpg'
cv.imwrite(name, frame)
index+=1
# next frame
index += 1
cv.imshow("Frame",frame)
key = cv.waitKey(1) & 0xFF
counter += 1
if key == ord("d"):
break
if not args.get("video",False):
v.stop()
else:
v.release()
cv.destroyAllWindows()
|
Some frames from first test with webcam:
Some frames from test with recording:
As you can see, the tracking is quite accurate for the first test, but struggles in the second. The system/code must be more robust going forward.
The way I addressed this, to make the system optimized for the colours currently on the 2D setup, was to add a slider were I could manually tune the values while seeing the frame and the masks to correctly identify the dots. The code is taken from here: https://www.geeksforgeeks.org/detecting-objects-of-similar-color-in-python-using-opencv/?ref=lbp.
cv.namedWindow('Tracking')
def nothing(x):
pass
#Set trackbar to change value of red on frame
cv.createTrackbar("LH", "Tracking",
0, 255, nothing)
cv.createTrackbar("LS", "Tracking",
0, 255, nothing)
cv.createTrackbar("LV", "Tracking",
0, 255, nothing)
cv.createTrackbar("HH", "Tracking",
0, 255, nothing)
cv.createTrackbar("HS", "Tracking",
0, 255, nothing)
cv.createTrackbar("HV", "Tracking",
0, 255, nothing)
#Making a mask
l_h = cv.getTrackbarPos("LH",
"Tracking")
# find LS trackbar position
l_s = cv.getTrackbarPos("LS",
"Tracking")
# find LV trackbar position
l_v = cv.getTrackbarPos("LV",
"Tracking")
# find HH trackbar position
h_h = cv.getTrackbarPos("HH",
"Tracking")
# find HS trackbar position
h_s = cv.getTrackbarPos("HS",
"Tracking")
# find HV trackbar position
h_v = cv.getTrackbarPos("HV",
"Tracking")
# create a given numpy array
l_b = np.array([l_h, l_s,
l_v])
# create a given numpy array
u_b = np.array([h_h, h_s,
h_v])
mask1 = cv.inRange(hsv,l_b,u_b) #Changed for red as well to tune the colour.
mask1 = cv.erode(mask1,None,iterations=2)
mask1 = cv.dilate(mask1,None,iterations=2) |
