174 lines
5.1 KiB
Python
174 lines
5.1 KiB
Python
# This script recognizes the ball in a given video file
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# python ball_tracking.py --video test3.avi
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# import the necessary packages
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from collections import deque
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import numpy as np
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import argparse
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import imutils
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import cv2
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from time import sleep
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-v", "--video",
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help="path to the (optional) video file")
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ap.add_argument("-b", "--buffer", type=int, default=64,
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help="max buffer size")
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args = vars(ap.parse_args())
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# define the lower and upper boundaries of the "green"
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# ball in the HSV color space, then initialize the
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# list of tracked points
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#greenLower = (0, 17, 225)
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#greenUpper = (42, 255, 255)
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greenLower=(0,184,170)
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greenUpper=(2,255,255)
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#greenLower = (29, 86, 6)
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#greenUpper = (64, 255, 255)
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pts = deque(maxlen=args["buffer"])
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# if a video path was not supplied, grab the reference
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# to the webcam
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if not args.get("video", False):
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camera = cv2.VideoCapture(0)
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# otherwise, grab a reference to the video file
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else:
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camera = cv2.VideoCapture(args["video"])
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# keep looping
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while True:
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# grab the current frame
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(grabbed, frame) = camera.read()
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# if we are viewing a video and we did not grab a frame,
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# then we have reached the end of the video
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if args.get("video") and not grabbed:
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break
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# resize the frame, blur it, and convert it to the HSV
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# color space
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# frame = imutils.resize(frame, width=600)
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# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
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# hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# construct a mask for the color "green", then perform
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# a series of dilations and erosions to remove any small
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# blobs left in the mask
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'''
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mask = cv2.inRange(hsv, greenLower, greenUpper)
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mask = cv2.erode(mask, None, iterations=2)
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mask = cv2.dilate(mask, None, iterations=2)
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'''
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# create hsv and do some mask stuff
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frame_HSV = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# frame_test=cv2.cvtColor(frame_HSV,cv2.COLOR_HSV2BGR)
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# frame_gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
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# frame_threshold=cv2.inRange(frame_HSV,(0,9,139),(180,81,255))
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frame_threshold=cv2.inRange(frame_HSV,(0,0,182),(180,60,255))
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frame_threshold = cv2.GaussianBlur(frame_threshold,(9,9),3,3)
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erode_element = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
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#dilate_element = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
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eroded_mask = cv2.erode(frame_threshold,erode_element)
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#dilated_mask = cv2.dilate(eroded_mask,dilate_element)
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#frame_threshold=eroded_mask
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# preparation for edge detection
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res = cv2.bitwise_and(frame,frame, mask=eroded_mask)
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res=cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
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# use canny edge
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frame_edge=cv2.Canny(res,90,494,apertureSize=3)
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#frame_edge=cv2.Canny(res,0,123,apertureSize=3)
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# use hough lines
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lines = cv2.HoughLines(frame_edge,1,1*np.pi/180,80,0,0)
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for rho,theta in lines[0]:
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# print(rho, theta)
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a*rho
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y0 = b*rho
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x1 = int(x0 + 200*(-b))
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y1 = int(y0 + 200*(a))
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x2 = int(x0 - 200*(-b))
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y2 = int(y0 - 200*(a))
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if (theta>np.pi/180*170 or theta<np.pi/180*10):
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#if (theta>np.pi/180*80 and theta<np.pi/180*100):
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cv2.line(frame,(x1,y1),(x2,y2),(0,0,255),2)
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'''
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# find contours in the mask and initialize the current
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# (x, y) center of the ball
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cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)[-2]
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center = None
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# only proceed if at least one contour was found
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if len(cnts) > 0:
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# find the largest contour in the mask, then use
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# it to compute the minimum enclosing circle and
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# centroid
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c = max(cnts, key=cv2.contourArea)
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((x, y), radius) = cv2.minEnclosingCircle(c)
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M = cv2.moments(c)
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center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
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# only proceed if the radius meets a minimum size
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if radius > 10:
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# draw the circle and centroid on the frame,
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# then update the list of tracked points
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cv2.circle(frame, (int(x), int(y)), int(radius),
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#(0, 255, 255), 2)
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(0,255,255),2)
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cv2.circle(frame, center, 5, (0, 0, 255), -1)
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# update the points queue
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pts.appendleft(center)
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# loop over the set of tracked points
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for i in xrange(1, len(pts)):
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# if either of the tracked points are None, ignore
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# them
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if pts[i - 1] is None or pts[i] is None:
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continue
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# otherwise, compute the thickness of the line and
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# draw the connecting lines
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thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 1.25)
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cv2.line(frame, pts[i - 1], pts[i], (0, 255, 0), thickness)
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'''
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# show the frame to our screen
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#cv2.imshow("Frame", frame)
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cv2.imshow("Frame_threshold",frame_threshold)
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cv2.imshow("eroded_mask",eroded_mask)
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cv2.imshow("frame edge",frame_edge)
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cv2.imshow("result eroded_mask applied",res)
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cv2.imshow("frame with lines",frame)
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key = cv2.waitKey(1) & 0xFF
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sleep(0.05)
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# if the 'q' key is pressed, stop the loop
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if key == ord("q"):
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break
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# cleanup the camera and close any open windows
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camera.release()
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cv2.destroyAllWindows()
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