I have been reading a tutorial on creating a Rpi colour based object tracking system but have been unable to test it due to me being a away from my Rpi. However I have read through the code and it seems to me that only one object of x colour will be tracked at a time. This is a problem as I want to track up to 4 at a time. Can anyone tell me how I can go about doing this?
# import the necessary packages from collections import deque import numpy as np import argparse import imutils import cv2 # 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 "yellow object" # (or "ball") in the HSV color space, then initialize the # list of tracked points colorLower = (24, 100, 100) colorUpper = (44, 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, inverted ("vertical flip" w/ 180degrees), # blur it, and convert it to the HSV color space frame = imutils.resize(frame, width=600) frame = imutils.rotate(frame, angle=180) # 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, colorLower, colorUpper) 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 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) 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()
The tutorial: here Up to (and including) Step 5
Note: This is NOT my code!!
Also, is it possible to remove the fade on the red line drawn?