计算图像中对象的中心 [英] Calculating center of an object in an image
问题描述
我正在阅读此帖子使用OpenCV
计算图像的中心,而OpenCV
使用Moments
.但是我正在尝试计算使用HoughLinesP检测到的对象的中心. OpenCV有办法做到这一点吗?
I was reading this post to calculate the center of an image using OpenCV
which uses Moments
. But I am trying to calculate the center of an object I detected using HoughLinesP. Is there a way with OpenCV I could do this?
这是我要为其计算中心的图像.
Here is the image for which I am trying to calculate the centers.
找到线段,输出图像如下:
The line segments were found and the output image looks like:
import cv2
import numpy as np
import math
img = cv2.imread("./images/octa.jpg")
b,g,r = cv2.split(img)
smoothed = cv2.GaussianBlur(g, (3,3), 0)
edges = cv2.Canny(smoothed, 15, 60, apertureSize = 3)
lines = cv2.HoughLinesP(edges,1,np.pi/180,35, 30, 20)
print("length of lines detected ", lines.shape)
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(img,(x1,y1),(x2,y2),(255,0,0),2)
print("x1,y1", x1,",",y1, " --- ", "x2,y2", x2,",",y2)
cv2.imshow('detected',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
使用坐标如何计算该图像的中心?如何在这里使用Moments
?
Using the coordinates how could I calculate the center of this image? How could I use Moments
here?
我的一个限制是我不能使用OpenCV
附带的Contour
方法.
One constraint I have is that I cannot use Contour
methods included with OpenCV
.
推荐答案
以下代码与3.3.1
的cv2
版本一起使用.
The following code was used with cv2
version of 3.3.1
.
我密切关注了 opencv文档,它确实有效美好的.
I closely followed the opencv docs and it worked fine.
import cv2
img = cv2.imread("octa.jpg", 0)
ret,thresh = cv2.threshold(img,100,255,0)
im2, contours, hierachy = cv2.findContours(thresh, 1, 2)
cnt = contours[0]
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
im2 = cv2.cvtColor(im2, cv2.COLOR_GRAY2RGB)
cv2.polylines(im2, cnt, True, (0, 0, 255), 2)
cv2.circle(im2, (cx, cy), 5, (0, 0, 255), 1)
cv2.imshow("res", im2)
两个注意事项:
- 您需要将
0
自变量添加到imread
中,否则轮廓查找将不起作用 - 我将阈值设置得低一点,所以只找到了八边形的轮廓
- you need to add the argument
0
toimread
otherwise the contour finding would not work - I set the threshold just a little bit lower, so only the contours of the octagon were found
结果:
如果使用其他版本的cv2
,则只需将文档更改为您的版本即可;文档真的很好.
If you use a different version of cv2
, you can just change the docs to your version; the documentation is really good.
您可能还想稍微模糊图像或进行其他一些预处理,但是在这种情况下,不需要它.
You also may want to blur your image a bit or do some other preprocessing, but in this case, there was no need for it.
编辑,不带轮廓:
我从这篇文章中获得了有用的评论,并做了一些修改.这不使用轮廓.它找到线并用它们找到中心
I took the helpful comments from this post and tinkered around a bit. This does not use contours. It finds lines and uses them to find the center
import cv2
import numpy as np
mg = cv2.imread('octa.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)
ret,thresh = cv2.threshold(blur_gray,100,255,0)
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(thresh, low_threshold, high_threshold)
rho = 1 # distance resolution in pixels of the Hough grid
theta = np.pi / 180 # angular resolution in radians of the Hough grid
threshold = 15 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 50 # minimum number of pixels making up a line
max_line_gap = 50 # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0 # creating a blank to draw lines on
# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),2)
lines_edges = cv2.addWeighted(img, 0.5, line_image, 1, 0)
line_image_gray = cv2.cvtColor(line_image, cv2.COLOR_RGB2GRAY)
M = cv2.moments(line_image_gray)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
cv2.circle(lines_edges, (cx, cy), 5, (0, 0, 255), 1)
cv2.imshow("res", lines_edges)
结果: 找到的线用蓝色绘制;中心为红色
Result: Found lines are drawn in blue; the center in red
这篇关于计算图像中对象的中心的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!