python-3.x – 如何区分对象曲率中的两种不同类型的异常？

人们可以进行结构分析,并且可以观察到正常的洋葱具有几乎平滑的曲率,而异常的洋葱则没有.因此,我非常简单地想要基于对象的边缘构建分类算法.

然而,有时洋葱皮会使曲线不规则.看图像,有一小部分皮肤超出实际曲率.我想区分由于皮肤引起的凸起部分与在两个子部分相遇处产生的畸形,然后重建对象的轮廓以供进一步分析.

是否有一个数学上的东西可以帮助我,因为我有大部分点使洋葱的外缘包括两个不规则？

请参阅以下代码：

import cv2
import numpy as np 
import sys

cv2.ocl.setUSEOpenCL(False)


cv2.namedWindow('test',cv2.WINDOW_NORMAL)
cv2.namedWindow('orig',cv2.WINDOW_NORMAL)
cv2.resizeWindow('test',600,600)
cv2.resizeWindow('orig',600)





image = cv2.imread('./buffer/crp'+str(sys.argv[1])+'.JPG')

tim = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
hsv_image =  cv2.cvtColor(image,cv2.COLOR_BGR2HSV)
frame_threshed = cv2.inRange(hsv_image,np.array([70,0],np.uint8),np.array([140,255,255],np.uint8))

canvas = np.zeros(image.shape,np.uint8)
framhreshed=cv2.threshold(frame_threshed,10,cv2.THRESH_BINARY_INV)
kernel = np.ones((3,3),np.uint8)
frame_threshed = cv2.erode(frame_threshed,kernel,iterations = 1)
kernel = np.ones((5,5),iterations = 1)
kernel = np.ones((7,7),iterations = 1)

_,cnts,hierarchy = cv2.findContours(frame_threshed.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnts= sorted(cnts,key=cv2.contourArea,reverse=True)

big_contours = [c for c in cnts if cv2.contourArea(c) > 100000]

for cnt in big_contours:
perimeter = cv2.arcLength(cnt,True)
epsilon = 0.0015*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
# print(len(approx))
hull = cv2.convexHull(cnt,returnPoints = False)
# try:
defects = cv2.convexityDefects(cnt,hull)
for i in range(defects.shape[0]):
    s,e,f,d = defects[i,0]
    start = tuple(cnt[s][0])
    end = tuple(cnt[e][0])
    far = tuple(cnt[f][0])
    cv2.line(canvas,start,end,[255,2)
    cv2.circle(canvas,far,5,-1)

cv2.drawContours(image,[approx],-1,(0,255),5)
cv2.drawContours(canvas,5)



cv2.imshow('orig',image)
cv2.imshow('test',canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()