opencv学习记录之图像轮廓之四

凸包

凸包是指完全包含原有轮廓，并且仅有轮廓上的点所构成的多边形

凸包的每一处都是凸的，在凸包内任意三个连续点的内角小于180度

函数形式：

hull = cv2.convexHull( points [, clockwise[ , returnPoints]])

hull为凸包角点

clockwise布尔型值，凸包角点按顺时针方向排列；该值为False时逆时针排列

returnPoints 布尔型值，默认为True，该函数返回凸包角点的x，y轴坐标，当为False时，函数返回轮廓中凸包角点的索引

 1 import cv2                                                                       
 2 o = cv2.imread("hand.bmp")
 3 cv2.imshow("original" , o) 
 4 gray = cv2.cvtColor(o , cv2.COLOR_BGR2GRAY)
 5 ret , binary = cv2.threshold(gray , 127 , 255 , cv2.THRESH_BINARY)
 6 contours , hierarchy = cv2.findContours(binary , cv2.RETR_LIST ,
 7         cv2.CHAIN_APPROX_SIMPLE)
 8 hull = cv2.convexHull(contours[0])
 9 cv2.polylines(o , [hull] , True , (0,255,0) , 2) 
10 cv2.imshow("result" , o) 
11 cv2.waitKey()
12 cv2.destroyAllWindows()

绿色的多边形即为凸包，在多边形和手之间的部分为凸缺陷，其可以用来处理手势识别等问题

凸缺陷获取函数

convexityDefects = cv2.convexityDefects( contour , convexHull )

convexityDefects为凸缺点集，包含[ 起点，终点，轮廓上距离凸包最远的点，最远点到凸包的近似距离]

contour为轮廓

convexHull为凸包

 1 import cv2                                                                       
 2 img = cv2.imread("hand.bmp")
 3 cv2.imshow("original" , img)
 4 gray = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY)
 5 ret , binary = cv2.threshold(gray , 127 , 255 ,0)
 6 contours , hierarchy = cv2.findContours(binary , cv2.RETR_LIST ,  
 7         cv2.CHAIN_APPROX_SIMPLE)
 8 #
 9 cnt = contours[0]
10 hull = cv2.convexHull(cnt , returnPoints = False)
11 defects = cv2.convexityDefects(cnt , hull)
12 print("defects = 
" ,defects)
13 #
14 for i in range(defects.shape[0]):
15     s,e,f,d = defects[i , 0] 
16     start = tuple(cnt[s][0])
17     end = tuple(cnt[e][0])
18     far = tuple(cnt[f][0])
19     cv2.line(img , start , end , [0,0,255] , 2) 
20     cv2.circle(img , far , 5 , [ 255,0,0] ,-1)
21 #
22 cv2.imshow("result" , img)
23 cv2.waitKey()
24 cv2.destroyAllWindows()

将起点和终点相连，在最远点画一个圆圈

defects = 
 [[[  305   311   306   114]]

 [[  311   385   342 13666]]

 [[  385   389   386   395]]

 [[  389   489   435 20327]]

 [[    0   102    51 21878]]

 [[  103   184   150 13876]]

 [[  185   233   220  4168]]

 [[  233   238   235   256]]

 [[  238   240   239   247]]

 [[  240   294   255  2715]]

 [[  294   302   295   281]]

 [[  302   304   303   217]]]