寻找矩形区域的中心线和中心点

Question

我运行了以下代码来创建一个矩形轮廓：

#import the necessary packages
import argparse
import imutils
import cv2
import numpy as np

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to the input image")
args = vars(ap.parse_args())

# load the image, convert it to grayscale, blur it slightly, and threshold it
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)

# threshold the image, then perform a series of erosions + dilations to remove any small regions of noise
thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)

contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

# Find the index of the largest contour
areas = [cv2.contourArea(c) for c in contours]
max_index = np.argmax(areas)
cnt=contours[max_index]

x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)

# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)

我想找到矩形轮廓的中心线和中心点，请指教。最初的回答：无需解释。

- dfahsjdahfsudaf

Answer 1

由于您已经在上述代码中使用 x,y,w,h = cv2.boundingRect(cnt) 获得了所需轮廓的 (x, y, w, h)，因此垂直中线的中心可以通过 (x+w//2, y+h//2) 给出，并且可以使用以下代码绘制垂直线：

x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
# center line
cv2.line(image, (x+w//2, y), (x+w//2, y+h), (0, 0, 255), 2)
# below circle to denote mid point of center line
center = (x+w//2, y+h//2)
radius = 2
cv2.circle(image, center, radius, (255, 255, 0), 2)

输出：

- Anubhav Singh

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工作得非常完美。感谢您的帮助。 - undefined

欢迎。我很高兴能够帮助你。谢谢。 :) - undefined

Answer 2

自已有边界框，可以使用 cv2.moments() 函数，找到中心坐标。这将给我们质心（即目标的中心 (x, y) 坐标）。

M = cv2.moments(cnt)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])

中心点就是简单的 (cX, cY)，您可以使用 cv2.circle() 绘制它。

cv2.circle(image, (cX, cY), 5, (36, 255, 12), -1)

同样地，我们可以使用cv2.line()或Numpy切片来绘制中心线。

cv2.line(image, (x + int(w/2), y), (x + int(w/2), y+h), (0, 0, 255), 3)
image[int(cY - h/2):int(cY+h/2), cX] = (0, 0, 255)

import imutils
import cv2
import numpy as np

# load the image, convert it to grayscale, blur it slightly, and threshold it
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)

# threshold the image, then perform a series of erosions + dilations to remove any small regions of noise
thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)

contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

# Find the index of the largest contour
areas = [cv2.contourArea(c) for c in contours]
max_index = np.argmax(areas)
cnt=contours[max_index]

x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)

M = cv2.moments(cnt)

cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])

cv2.circle(image, (cX, cY), 5, (36, 255, 12), -1)

# To draw line you can use cv2.line or numpy slicing
cv2.line(image, (x + int(w/2), y), (x + int(w/2), y+h), (0, 0, 255), 3)
# image[int(cY - h/2):int(cY+h/2), cX] = (36, 255, 12)

# show the output image
cv2.imshow("Image", image)
cv2.imwrite("Image.png", image)
cv2.waitKey(0)

- nathancy

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非常感谢你。我很感激。 - undefined

@dfahsjdahfsudaf 没问题，很高兴能帮到你！ - undefined