如何使用opencv识别和标记形状

如何解决如何使用opencv识别和标记形状

我正在尝试使用opencv在圆锥周围创建一个矩形。我目前所处的位置是概述了导致产生三角形形状的代码。如何使用opencv在三角形周围创建矩形。

到目前为止，我的代码：

import cv2
import numpy as np

img = cv2.imread('image.jpg')

ret,mask = cv2.threshold(img[:,:,2],235,255,cv2.THRESH_BINARY)

mask3 = np.zeros_like(img)
mask3[:,0] = mask
mask3[:,1] = mask
mask3[:,2] = mask

orange = cv2.bitwise_and(img,mask3)


cv2.imwrite("output.jpg",orange)

im = cv2.imread('output.jpg')

imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray,127,0)
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

cv2.drawContours(im,contours,-1,(0,0),3)
cv2.imshow('img',im)

cv2.waitKey(0)
cv2.destroyAllWindows

Jpeg文件：

解决方法

一种方法是使用多尺度模板匹配

• 1. 您裁剪要查找的对象：

     

• 1. 应用Canny边缘检测来找到边缘

  edged = cv2.Canny(resized,50,200)
  

• 1. 使用matchTemplate
  查找匹配的模板

  result = cv2.matchTemplate(edged,template,cv2.TM_CCOEFF)
  

结果：

代码：

import numpy as np
import imutils
import glob
import cv2

template = cv2.imread("template.jpg")
template = cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template,200)
(h,w) = template.shape[:2]

for imagePath in glob.glob("img2" + "/pXobJ.jpg"):
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    found = None

    for scale in np.linspace(0.2,1.0,20)[::-1]:
        resized = imutils.resize(gray,width=int(gray.shape[1] * scale))
        r = gray.shape[1] / float(resized.shape[1])

        if resized.shape[0] < h or resized.shape[1] < w:
            break

        edged = cv2.Canny(resized,200)
        result = cv2.matchTemplate(edged,cv2.TM_CCOEFF)
        (_,maxVal,_,maxLoc) = cv2.minMaxLoc(result)

        if found is None or maxVal > found[0]:
            found = (maxVal,maxLoc,r)

    (_,r) = found
    (startX,startY) = (int(maxLoc[0] * r),int(maxLoc[1] * r))
    (endX,endY) = (int((maxLoc[0] + w) * r),int((maxLoc[1] + h) * r))

    cv2.rectangle(image,(startX,startY),(endX,endY),(0,255),2)
    cv2.imwrite("img2/out.jpg",image)
    print("Table coordinates: ({},{},{})".format(startX,startY,endX,endY))

• 您还可以将深度学习对象检测与经过训练的网络一起使用。