基于python实现目标运动与目标跟踪

# USAGE # python object_movement.py --video object_tracking_example.mp4 # python object_movement.py # import the necessary packages 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=32, help="max buffer size") args = vars(ap.parse_args()) # define the lower and upper boundaries of the "green" # ball in the HSV color space greenLower = (29, 86, 6) greenUpper = (64, 255, 255) # initialize the list of tracked points, the frame counter, # and the coordinate deltas pts = deque(maxlen=args["buffer"]) counter = 0 (dX, dY) = (0, 0) direction = "" # 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) # 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) # 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) cnts=cnts[1] 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) pts.appendleft(center) # loop over the set of tracked points for i in np.arange(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 # check to see if enough points have been accumulated in # the buffer if counter >= 10 and i == 1 and pts[-10] is not None: # compute the difference between the x and y # coordinates and re-initialize the direction # text variables dX = pts[-10][0] - pts[i][0] dY = pts[-10][1] - pts[i][1] (dirX, dirY) = ("", "") # ensure there is significant movement in the # x-direction if np.abs(dX) > 20: dirX = "East" if np.sign(dX) == 1 else "West" # ensure there is significant movement in the # y-direction if np.abs(dY) > 20: dirY = "North" if np.sign(dY) == 1 else "South" # handle when both directions are non-empty if dirX != "" and dirY != "": direction = "{}-{}".format(dirY, dirX) # otherwise, only one direction is non-empty else: direction = dirX if dirX != "" else dirY # 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 movement deltas and the direction of movement on # the frame cv2.putText(frame, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 3) cv2.putText(frame, "dx: {}, dy: {}".format(dX, dY), (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1) # show the frame to our screen and increment the frame counter cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF counter += 1 # 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()