ball_track.py

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())

greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
pts = deque(maxlen=args["buffer"])

# allow the camera or video file to warm up
time.sleep(2.0)

class VideoCamera(object):
    def __init__(self):
        # Using OpenCV to capture from device 0. If you have trouble capturing
        # from a webcam, comment the line below out and use a video file
        # instead.
        self.video = cv2.VideoCapture(0)
        # If you decide to use video.mp4, you must have this file in the folder
        # as the main.py.
        # self.video = cv2.VideoCapture('video.mp4')
    
    def __del__(self):
        self.video.release()
    
    def get_frame(self):
        success, frame = self.video.read()
        # handle the frame from VideoCapture or VideoStream
        #frame = frame[1] if args.get("video", False) else frame
        # We are using Motion JPEG, but OpenCV defaults to capture raw images,
        # so we must encode it into JPEG in order to correctly display the
        # video stream.
                # 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)

        # 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)

        # 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

        ret, jpeg = cv2.imencode('.jpg', frame)
        return jpeg.tobytes()