Question

我是新手，使用pyimagesearch代码通过python 2.7和opencv进行球跟踪。

https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/

我正在尝试将跟踪对象的x，y坐标写入.csv文件。我将pts转换为字符串，然后写入.csv文件。我得到一组这样的数字：（255 386）（266 399）这些是x，y坐标吗？如果是的话，它们相对于图像意味着什么？

#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
import csv

#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 = (0, 0, 0)
greenUpper = (180, 255, 40)
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)

#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 = cnts[0] if imutils.is_cv2() else 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 adn 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, 225), -1)

#update the points queue
    pts.appendleft(center)

#loop over the set of tracket 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, 225), thickness)
    #write info to file
    f = open("foo11.csv", "w+")
    s = str(pts)
    f.write(s)
    f.close()

#show the frame to our screen
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

#if the 'q' key is press, 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()

Answer 1

将以下网格视为图像：

据说此图像的形状为（7x7）。高度为7个像素（沿y），宽度为7个像素（沿x））。因此，该图像被称为具有49像素，这是该图像的大小。
原点（0，0）在左上角。这是图像的最左上角的像素。
现在随着轮廓（球）的质心移动，它出现在这49个像素之一中。
结果，.txt文件将这些像素坐标存储在(x, y)的元组中。

opencv，不确定pts输出意味着什么

1 个答案: