Day5--扩展：移动对象跟踪-Toy模板网

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OpenCV Track Object Movement

作为day5扩展–https://pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/?utm_source=Drip&utm_medium=Email&utm_campaign=CVandDLCrashCourse&utm_content=email5

参考博客：https://pyimagesearch.com/2015/09/21/opencv-track-object-movement/

跟踪图像中的对象移动，确定对象的移动方向

# 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)
	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)

# -----------与之前的轨迹绘制代码  开始  不同-----------

# 实际跟踪物体的移动，然后使用这个物体的移动来计算物体的移动方向，只使用物体的（x，y）坐标
# 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) = ("", "")
            # dx和dy，分别是当前帧和靠近缓冲区末端的帧的x和y坐标之间的差值


#----------此处阈值限制运动范围-----------

			# ensure there is significant movement in the
			# x-direction
			if np.abs(dX) > 20:         # 检查是否有显著差异
				# x坐标之间存在超过20个像素的差异，我们需要确定对象正在朝哪个方向移动
				dirX = "East" if np.sign(dX) == 1 else "West"
				# 正的--向右移动；否则--向左移动

			# 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)
			# 沿对角线移动的情况，并相应地更新方向变量

			# 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()


# $ python object_movement.py --video object_tracking_example.mp4
# https://pyimagesearch.com/2015/09/21/opencv-track-object-movement/

重要的是要注意，执行此计算有一点困难。
显而易见的第一个解决方案是计算对象在当前帧和上一帧之间的方向。
但是，使用当前帧和前一帧是一种不稳定的解决方案。
除非物体移动得非常快，否则(x，y)坐标之间的差值将非常小。
如果我们使用这些增量来报告方向，那么我们的结果将是极其嘈杂的，这意味着即使是轨迹上的微小变化也会被认为是方向变化。
事实上，这些变化可能非常小，以至于肉眼几乎看不见(或者至少是微不足道的)–我们很可能对报告和跟踪如此微小的运动不是很感兴趣。

相反，我们更有可能对更大的对象移动感兴趣，并报告对象移动的方向-因此，我们计算当前帧和队列中更靠后的帧的坐标之间的差值。
执行此操作有助于减少噪音和方向更改的错误报告文章来源地址https://www.toymoban.com/news/detail-507588.html

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