OpenCV——图像轮廓

我们的目标：

能够掌握轮廓查找与绘制的方法

能够掌握轮廓面积及长度的计算方法

能够编程实现形状匹配

能够掌握轮廓的几何形状拟合方法

任务一 凸包绘制

案例一 获取凸包

import cv2
# 读取图片并转至灰度模式
img = cv2.imread('contours2.png', 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 二值化
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
# 图片轮廓
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
cnt = contours[0]
# 寻找凸包并绘制凸包（轮廓）
hull = cv2.convexHull(cnt)
cv2.polylines(img,[hull],True,(255,0,0),2)
# 显示图片
cv2.imshow('line', img)
cv2.waitKey()
cv2.destroyAllWindows()

练习：实物凸包检测

import cv2
# 读取图片并转至灰度模式
img = cv2.imread('hand.png', 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 二值化
ret, binary = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)
# 图片轮廓
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
n=len(contours)       #轮廓个数
contoursImg=[]
x=0 #初始化要绘制的轮廓索引
for i in range(n):
    area = cv2.contourArea(contours[i])
    if area>10000:    
        print(f"轮廓{i}的面积：\n{area}")
        x=i
cnt = contours[x]
cv2.imshow("binary",binary)
#寻找凸包并绘制凸包（轮廓）
hull = cv2.convexHull(cnt)
cv2.polylines(img,[hull],True,(0,255,0),2)
cv2.drawContours(img,contours,x,(0,0,255), 3)
# 显示图片
cv2.imshow('line', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

任务二 凸缺陷检测

案例二：凸缺陷

import cv2
o = cv2.imread('contours2.png') 
gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY) 
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) 
contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

cnt = contours[0]
hull = cv2.convexHull(cnt,returnPoints = False)            
defects = cv2.convexityDefects(cnt,hull)         

for i in range(defects.shape[0]):
    s,e,f,d = defects[i,0]     
    start = tuple(cnt[s][0])           # 得到的是索引，要再轮廓中选出来
    end = tuple(cnt[e][0])
    far = tuple(cnt[f][0])
    cv2.line(o,start,end,[0,0,255],2)
    cv2.circle(o,far,5,[255,0,0],-1)

cv2.imshow('result',o)
cv2.waitKey(0)
cv2.destroyAllWindows()

任务三 轮廓常见特征值

案例一:宽高比

import cv2
o = cv2.imread('binaryhand.png')  
cv2.imshow("original",o)
gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY)  
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)  
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)  
x,y,w,h = cv2.boundingRect(contours[0])
cv2.rectangle(o,(x,y),(x+w,y+h),(255,255,255),3)
aspectRatio = float(w)/h
print(aspectRatio)
cv2.imshow("result",o)
cv2.waitKey()
cv2.destroyAllWindows()

案例二:Extend

import cv2
o = cv2.imread('binaryhand.png')  
cv2.imshow("original",o)
gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY)  
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)  
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)  
x,y,w,h = cv2.boundingRect(contours[0])
cv2.drawContours(o,contours[0],-1,(0,0,255),3) 
cv2.rectangle(o,(x,y),(x+w,y+h),(255,0,0),3)
rectArea=w*h
cntArea=cv2.contourArea(contours[0])
extend=float(cntArea)/rectArea
print(extend)
cv2.imshow("result",o)
cv2.waitKey()
cv2.destroyAllWindows()

案例三:最大值和最小值及它们的位置

import cv2
import numpy as np
o = cv2.imread('ct2.jpg')  
cv2.imshow("original",o)
gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY)  
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)  
contours, hierarchy = cv2.findContours(binary,
                                             cv2.RETR_LIST,
                                             cv2.CHAIN_APPROX_SIMPLE)  
cnt=contours[3]   #coutours[0]、coutours[1]是左侧字母R
#--------使用掩膜获取感兴趣区域的最值-----------------
#需要注意minMaxLoc处理的对象为灰度图像，本例中处理对象为灰度图像gray
#如果希望获取彩色图像的，需要提取各个通道，将每个通道独立计算最值
mask = np.zeros(gray.shape,np.uint8)
mask=cv2.drawContours(mask,[cnt],-1,255,-1)   
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(gray,mask = mask)
print("minVal=",minVal)
print("maxVal=",maxVal)
print("minLoc=",minLoc)
print("maxLoc=",maxLoc)
#--------使用掩膜获取感兴趣区域并显示-----------------
masko = np.zeros(o.shape,np.uint8)
masko=cv2.drawContours(masko,[cnt],-1,(255,255,255),-1)
loc=cv2.bitwise_and(o,masko) 
cv2.imshow("mask",loc)
#显示灰度结果
#loc=cv2.bitwise_and(gray,mask) 
#cv2.imshow("mask",loc)
#--------释放窗口-----------------
cv2.waitKey()
cv2.destroyAllWindows()

案例四:平均颜色及平均灰度

meanVal = cv2.mean(o,mask = mask)  #mask是区域，所以必须是单通道的
print("meanVal=\n",meanVal)

任务四 实践：轮廓特征值的应用场景

案例:分类1

import cv2
import numpy as np
img = cv2.imread('face.png')  
cv2.imshow("original",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  
ret, binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY)  
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)  
h,w,c=img.shape
mask = np.zeros((h,w,c),np.uint8)
for cnt in contours:
    
    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,255),3)
    ratio = float(w)/h
    if ratio<1:
        cv2.drawContours(mask,[cnt],-1,(0,0,255), -1)
        #n+=1
        print(ratio,"是长的椭圆")
    else:
        cv2.drawContours(mask,[cnt],-1,(0,255,255), -1)
        #n+=1
        print(ratio,"是扁的椭圆")
cv2.imshow("result",mask)
cv2.waitKey()
cv2.destroyAllWindows()

案例:分类2

import cv2
import numpy as np
img = cv2.imread('face.png')  
cv2.imshow("original",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  
ret, binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY)  
contours, hierarchy = cv2.findContours(binary,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)  
h,w,c=img.shape
mask = np.zeros((h,w,c),np.uint8)

for cnt in contours:
    
    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,255),3)
    ratio = float(w)/h
    rectArea=w*h
    cntArea=cv2.contourArea(contours[0])
    extend=float(cntArea)/rectArea
    print(extend)
    
    if ratio<1:
        cv2.drawContours(mask,[cnt],-1,(0,0,255), -1)
        print(ratio,"是长的椭圆")
    else:
        cv2.drawContours(mask,[cnt],-1,(0,255,255), -1)
        print(ratio,"是扁的椭圆")
    if extend==0.9818696450428397:
        cv2.drawContours(mask,[cnt],-1,(255,0,255), -1)
        
cv2.imshow("result",mask)
cv2.waitKey()
cv2.destroyAllWindows()

我们下次见拜拜！