如何識別這個圓形邊緣？

Question

我試圖找出使用Python中的OpenCV這幅畫的圓圈：

我使用網絡攝像頭，這一形象。 這個灰色圓圈裏面還有一個標誌。這個標誌有相同顏色的圓圈外圈（黑色）

我想把這個灰色圓圈和黑色背景分開，並在圓圈周圍劃一條銳利的線條。

我該怎麼辦？

Answer 1

另一種方法可能是用中值過濾器過濾圖像。它減少了噪音並保留了邊緣。之後，您可以使用固定的threshold（您有一個很好的雙倍直方圖）進行二值化，然後執行findContours。然後你可以計算minEnclosingCircle或使用fitEllipse或任何你想實現的。

# Blur the image to supress artefacts 
# in the binary image while preserving edges 
median = cv2.medianBlur(imgray,15) 
# Do a global thresholding 
ret, thresh = cv2.threshold(median,0,255,cv2.THRESH_BINARY + cv2.THRESH_OTSU) 

#Copy image to show binary image (findContours maipulates source) 
copy =thresh.copy() 

#Detect just external contours 
im2, contours, hierarchy = cv2.findContours(copy, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 
height, width, channels = src.shape 

#Image for displaying contour 
drawing = np.zeros((height, width, 3), dtype='uint8') 

#No loop needed, just one external contour 
#Draw contour, minimal enclosing circle and ellispe 
cv2.drawContours(drawing,contours,0,(255,0,0)) 
(x,y), radius = cv2.minEnclosingCircle(contours[0]) 
center = (int(x),int(y)) 
cv2.circle(drawing,center,int(radius),(0,0,255)) 
ellipse = cv2.fitEllipse(contours[0]) 
cv2.ellipse(drawing,ellipse,(0,255,0),1) 

cv2.imshow("Binary", thresh) 
cv2.imshow("Median", median) 
cv2.imshow("Contour", drawing) 

以下是藍色與輪廓的效果，minEnclosingCircle在紅色和橢圓的綠色。

Answer 2

建議： 將圖像轉換爲灰度圖像並使用Hough Circle Transform來檢測圓。 一個示例顯示在opencv3.0.0 documentation中。

Answer 3

這裏是你如何能夠實現這個目標： 首先你是閾值圖像，然後你使用查找座標函數，返回列表的最大值將是你的圓圈。找到峯值並通過它們繪製圓圈。

import cv2 
import numpy as np 
import random 
from random import randint 
import math 

image=cv2.imread('C:/Users/srlatch/Desktop/XtfWR.jpg') 
img = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) 


def f(list): 
    max=[] 
    for i in list: 
     if len(i)>len(max): 
      max=i 
    return max 

def draw_circle(img,ctr): 
    for i in ctr: 
     img[i[0][1]][i[0][0]]=255 


ret,tresh = cv2.threshold(img,40,255,cv2.THRESH_BINARY) 
kernel = np.ones((5,5),np.uint8) 
cv2.erode(img,kernel,iterations=2) 
tresh,c,hr=cv2.findContours(tresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) 
circle=f(c) 


max_y=0 

min_y=circle[0][0][1] 

max_x=0 

min_x=circle[0][0][0] 


pointY=tuple() 
pointy=tuple() 
pointX=tuple() 
pointx=tuple() 


for i in circle: 
    if i[0][1]>max_y: 
     max_y=i[0][1] 
     pointY=(i[0][1], i[0][0]) 

for i in circle: 
    if i[0][1]<=min_y: 
     min_y=i[0][1] 
     pointy=(i[0][1], i[0][0]) 

for i in circle: 
    if i[0][0]>max_x: 
     max_x=i[0][0] 
     pointX=(i[0][1], i[0][0]) 

for i in circle: 
    if i[0][0]<=min_x: 
     min_x=i[0][0] 
     pointx=(i[0][1], i[0][0]) 

cv2.ellipse(image, (pointY[1],pointY[0]),(2,2),0,0,360,(243,0,214),-1) #min y 
cv2.ellipse(image, (pointy[1],pointy[0]),(2,2),0,0,360,(243,0,214),-1) #max y 
cv2.ellipse(image, (pointX[1],pointX[0]),(2,2),0,0,360,(243,0,214),-1) #min y 
cv2.ellipse(image, (pointx[1],pointx[0]),(2,2),0,0,360,(243,0,214),-1) #min y 

center_x=(pointX[0]+pointx[0])/2 
center_y=((pointY[1]+pointy[1])/2)-pointy[0] 

cv2.circle(image, (int(center_x),int(center_y)), int((center_y+pointy[0])-60),(243,0,214), thickness=1, lineType=8, shift=0) 

cv2.imshow('wnd', image) 

cv2.waitKey(0) 

輪廓發現的結果：

最大和最小點：

盤旋點：

祝你好運！