Code Analysis

https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/

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

define the green's range (29, 86, 6) ~ (64, 255, 255). pts is buffer maximum size, i use 64 dequeue's size

blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)

For processing the frame faster, blur the frame to reduce high frequency noise and allow to focus on the structural objects inside the frame.

	mask = cv2.inRange(hsv, greenLower, greenUpper)
	mask = cv2.erode(mask, None, iterations=2)
	mask = cv2.dilate(mask, None, iterations=2)

Ref

https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html

<cv2.erode()>

thickness or size of the foreground object decreases or simply white region decreases in the image. It is useful for removing small white noises.

<cv2.dilate()>

It is just opposite of erosion. erosion removes white noises, but it also shrinks our object. So we dilate it.

cv2. erosion()

cv2.dilation()

erosion, dilation is use to remove any small noise

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"]))

I find the largest contour inthe cnts list. -> compute minimum enclosing circle of the blob. -> compute the center (x, y)-coordinates

If complete computing (x,y)-coordinate, append value in dequeu "pts"

thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)

compute the thickness of the contrail and then draw it on the frame using cv2.line()