OpenCV轮廓形状匹配

案例来源于© Fu Xianjun. All Rights Reserved

目标：

1. 能够掌握轮廓查找与绘制的方法
2. 能够掌握轮廓面积及长度的计算方法
3. 能够编程实现形状匹配
4. 能够掌握轮廓的几何形状拟合方法

***— 查找轮廓

cv2.RETR_EXTERNAL 只检测外轮廓
cv2.RETR_LIST检测的轮廓不建立等级关系
cv2.RETR_CCOMP建立两个等级的轮廓
cv2.RETR_TREE建立一个等级树结构的轮廓
method:
cv2.CHAIN_APPROX_NONE存储所有的轮廓点
cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息。

img = cv2.imread("shape.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#-----------#阈值处理
ret,binary = cv2.threshold(gray,200,255,0)

#-----------#查找轮廓
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
img = cv2.drawContours(img,contours,-1,(255,255,0),5)
#计算图像的矩特征
retval = cv2.moments(contours[1])
print(retval["m00"])
area = cv2.contourArea(contours[1])
print(area)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

1.显示面积小于20000的轮廓

img = cv2.imread("shape.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#-----------#阈值处理
ret,binary = cv2.threshold(gray,200,255,0)

#-----------#查找轮廓
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)

for i in range(len(contours)):
    area = cv2.contourArea(contours[i])
    if area<20000:
        img = cv2.drawContours(img,contours,i,(255,255,0),5)
        
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

2.显示长度小于600的轮廓

img = cv2.imread("shape.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#-----------#阈值处理
ret,binary = cv2.threshold(gray,200,255,0)

#-----------#查找轮廓
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
for i in range(len(contours)):
    length = cv2.arcLength(contours[i],True)
    print(length)
    if length<600:
        img = cv2.drawContours(img,contours,i,(255,255,0),5)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

二.使用Hu特征进行形状匹配

img1 = cv2.imread("1.png")
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
ret,binary1 = cv2.threshold(gray1,200,255,0)
contours1,hierarchy = cv2.findContours(binary1,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)

img2 = cv2.imread("2.png")
gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
ret,binary2 = cv2.threshold(gray2,200,255,0)
contours2,hierarchy = cv2.findContours(binary2,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)

img3 = cv2.imread("3.png")
gray3 = cv2.cvtColor(img3,cv2.COLOR_BGR2GRAY)
ret,binary3 = cv2.threshold(gray3,200,255,0)
contours3,hierarchy = cv2.findContours(binary3,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)

retval1 = cv2.matchShapes(contours1[0],contours2[0],1,0.0)
retval2 = cv2.matchShapes(contours1[0],contours3[0],1,0.0)
print(retval1,retval2)

三.轮廓拟合

1.矩形包围框

img = cv2.imread("shape.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#-----------#阈值处理
ret,binary = cv2.threshold(gray,200,255,0)

#-----------#查找轮廓
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0])
cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),1)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

2.最小包围矩形框

img = cv2.imread("shape.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#-----------#阈值处理
ret,binary = cv2.threshold(gray,200,255,0)

#-----------#查找轮廓
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
rect = cv2.minAreaRect(contours[1])
print(rect)
points = cv2.boxPoints(rect)
print(rect)
points = np.int64(points)
img = cv2.drawContours(img,[points],0,(0,0,0),2)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

3.最小包围圆形

img1 = cv2.imread("shape.jpg")
cv2.imshow("o",img1)
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
ret,binary1 = cv2.threshold(gray1,200,255,0)
contours1,hierarchy = cv2.findContours(binary1,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
(x,y),r = cv2.minEnclosingCircle(contours[1])
center = (int(x),int(y))
r = int(r)
cv2.circle(img1,center,r,(0,0,0),2)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

4.最优拟合椭圆

import cv2
img = cv2.imread("shape.jpg")
cv2.imshow("o",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,binary = cv2.threshold(gray,127,255,0)
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
ellipse = cv2.fitEllipse(contours[1])
print("ellipse=",ellipse)
cv2.ellipse(img,ellipse,(0,255,0),3)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()

5.最优拟合直线

import cv2
img = cv2.imread("shape.jpg")
cv2.imshow("o",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,binary = cv2.threshold(gray,127,255,0)
contours,hierarchy = cv2.findContours(binary,cv2.RETR_LIST,\
                                     cv2.CHAIN_APPROX_SIMPLE)
r,c = img.shape[:2]
[vx,vy,x,y] = cv2.fitLine(contours[1],cv2.DIST_L2,0,0.01,0.01)
lefty = int((-x*vy/vx) + y)
righty = int(((c-x)*vy/vx) + y)
cv2.line(img,(c-1,righty),(0,lefty),(0,255,0),2)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()