Pickleball app

pickleball/wall-ball-app/README.md

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+# Wall Ball Tracking App
+
+Based on this: https://pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/
+
+
+
+```
+python ball_tracking.py --video ball_tracking_example.mp4
+```
+
+if you want to execute the script using your webcam rather than the supplied video file, simply omit the --video
+switch:
+```
+python ball_tracking.py
+```

pickleball/wall-ball-app/ball_tracking.py

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+# import the necessary packages
+
+
+# Lines 2-8 handle importing our necessary packages. We’ll be using deque
+# , a list-like data structure with super fast appends and pops to maintain a list of the past N (x, y)-locations of the ball in our video stream. Maintaining such a queue allows us to draw the “contrail” of the ball as its being tracked.
+
+# We’ll also be using imutils
+# , my collection of OpenCV convenience functions to make a few basic tasks (like resizing) much easier. If you don’t already have imutils
+# installed on your system, you can grab the source from GitHub or just use pip
+# to install it:
+
+# pip install --upgrade imutils
+
+# From there, Lines 11-16 handle parsing our command line arguments. The first switch, --video
+# is the (optional) path to our example video file. If this switch is supplied, then OpenCV will grab a pointer to the video file and read frames from it. Otherwise, if this switch is not supplied, then OpenCV will try to access our webcam.
+
+# If this your first time running this script, I suggest using the --video
+# switch to start: this will demonstrate the functionality of the Python script to you, then you can modify the script, video file, and webcam access to your liking.
+
+# A second optional argument, --buffer
+# is the maximum size of our deque
+# , which maintains a list of the previous (x, y)-coordinates of the ball we are tracking. This deque
+# allows us to draw the “contrail” of the ball, detailing its past locations. A smaller queue will lead to a shorter tail whereas a larger queue will create a longer tail (since more points are being tracked):
+
+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)
+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(2.0)
+
+
+# Lines 21 and 22 define the lower and upper boundaries of the color green in the HSV color space (which I determined using the range-detector script in the imutils
+# library). These color boundaries will allow us to detect the green ball in our video file. Line 23 then initializes our deque
+# of pts
+# using the supplied maximum buffer size (which defaults to 64
+# ).
+
+# From there, we need to grab access to our vs
+# pointer. If a --video
+# switch was not supplied, then we grab reference to our webcam (Lines 27 and 28) — we use the imutils.video
+# VideoStream
+# threaded class for efficiency. Otherwise, if a video file path was supplied, then we open it for reading and grab a reference pointer on Lines 31 and 32 (using the built in cv2.VideoCapture
+# ).
+
+
+
+
+
+
+# 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
+	mask = cv2.inRange(hsv, greenLower, greenUpper)
+	mask = cv2.erode(mask, None, iterations=2)
+	mask = cv2.dilate(mask, None, iterations=2)
+
+
+# Line 38 starts a loop that will continue until (1) we press the q
+# key, indicating that we want to terminate the script or (2) our video file reaches its end and runs out of frames.
+
+# Line 40 makes a call to the read
+# method of our camera
+# pointer which returns a 2-tuple. The first entry in the tuple, grabbed
+# is a boolean indicating whether the frame
+# was successfully read or not. The frame
+# is the video frame itself. Line 43 handles VideoStream
+# vs VideoCapture
+# implementations.
+
+# In the case we are reading from a video file and the frame is not successfully read, then we know we are at the end of the video and can break from the while
+# loop (Lines 47 and 48).
+
+# Lines 52-54 preprocess our frame
+# a bit. First, we resize the frame to have a width of 600px. Downsizing the frame
+# allows us to process the frame faster, leading to an increase in FPS (since we have less image data to process). We’ll then blur the frame
+# to reduce high frequency noise and allow us to focus on the structural objects inside the frame
+# , such as the ball. Finally, we’ll convert the frame
+# to the HSV color space.
+
+# Lines 59 handles the actual localization of the green ball in the frame by making a call to cv2.inRange
+# . We first supply the lower HSV color boundaries for the color green, followed by the upper HSV boundaries. The output of cv2.inRange
+# is a binary mask
+# , like this one:
+# Figure 2: Generating a mask for the green ball using the cv2.inRange function.
+# Figure 2: Generating a mask for the green ball using the cv2.inRange function.
+
+# As we can see, we have successfully detected the green ball in the image. A series of erosions and dilations (Lines 60 and 61) remove any small blobs that may be left on the mask.
+
+# Alright, time to perform compute the contour (i.e. outline) of the green ball and draw it on our frame
+# :
+
+
+
+
+
+
+	# 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(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)
+
+
+# We start by computing the contours of the object(s) in the image on Line 65 and 66. On the subsequent line, make the function compatible with all versions of OpenCV. You can read more about why this change to cv2.findContours
+# is necessary in this blog post. We’ll also initialize the center
+# (x, y)-coordinates of the ball to None
+# on Line 68.
+
+# Line 71 makes a check to ensure at least one contour was found in the mask
+# . Provided that at least one contour was found, we find the largest contour in the cnts
+# list on Line 75, compute the minimum enclosing circle of the blob, and then compute the center (x, y)-coordinates (i.e. the “centroids) on Lines 77 and 78.
+
+# Line 81 makes a quick check to ensure that the radius
+# of the minimum enclosing circle is sufficiently large. Provided that the radius
+# passes the test, we then draw two circles: one surrounding the ball itself and another to indicate the centroid of the ball.
+
+# Finally, Line 89 appends the centroid to the pts
+# list.
+
+# The last step is to draw the contrail of the ball, or simply the past N (x, y)-coordinates the ball has been detected at. This is also a straightforward process:
+
+
+	# 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
+# 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()
+
+
+# We start looping over each of the pts
+# on Line 92. If either the current point or the previous point is None
+# (indicating that the ball was not successfully detected in that given frame), then we ignore the current index continue looping over the pts
+# (Lines 95 and 96).
+
+# Provided that both points are valid, we compute the thickness
+# of the contrail and then draw it on the frame
+# (Lines 100 and 101).
+
+# The remainder of our ball_tracking.py
+# script simply performs some basic housekeeping by displaying the frame
+# to our screen, detecting any key presses, and then releasing the vs
+# pointer.