一、轮廓发现原理

轮廓发现是在图像边缘提取的基础上寻找对象轮廓的方法，故边缘提取的阈值的选定会影响到最终轮廓发现的结果。
其本质是基于二值图像的，边缘提取常用Canny进行提取边缘
轮廓发现也是基于拓扑结构，扫描连通图，最后建立其树形结构的拓扑图
通常情况可以利用梯度来避免阈值的错误设定

二、代码实现

cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
binary：二值图像
cv2.RETR_TREE：二值图像返回结果构建模型的方法，即构建轮廓的方法，返回树形结构
cv2.CHAIN_APPROX_SIMPLE：一般为简单的返回形式
cloneImage：克隆图片信息，可忽略
contours：存放返回轮廓信息，最有用
heriachy ：存放轮廓层次信息，可忽略

cv2.drawContours(image,contours,i,(0,0,255),2)
image：图像信息
contours：存放轮廓信息，绘制哪个轮廓，会从contours中找
(0,0,255)：绘制轮廓的颜色
2：绘制轮廓的线宽

import cv2
import numpy as np
from matplotlib import pyplot as plt


def contour(image):
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)#先转换为灰度图
    ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)#二值化
    cv2.imshow("binary image",binary)
    
    cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    for i,contour in enumerate(contours):
        cv2.drawContours(image,contours,i,(0,0,255),2)
        print(i)
    cv2.imshow("detect contours",image)
    
    
src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\calc.jpg")
cv2.imshow("image",src)
cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)
contour(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

效果图如下：

三、加上滤波代码实现

cv2.GaussianBlur(image,(3,3),0)高斯滤波，卷积核大小为3*3

import cv2
import numpy as np
from matplotlib import pyplot as plt


def contour(image):
    dst = cv2.GaussianBlur(image,(3,3),0)
    gray = cv2.cvtColor(dst,cv2.COLOR_BGR2GRAY)#先转换为灰度图
    ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)#二值化
    cv2.imshow("binary image",binary)
    
    cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    for i,contour in enumerate(contours):
        cv2.drawContours(image,contours,i,(0,0,255),2)
        print(i)
    cv2.imshow("detect contours",image)
    
    
src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\calc.jpg")
cv2.imshow("image",src)
cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)
contour(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

效果图如下：

四、找大轮廓

cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.RETR_EXTERNAL：返回最大轮廓结果

import cv2
import numpy as np
from matplotlib import pyplot as plt


def contour(image):
    dst = cv2.GaussianBlur(image,(3,3),0)
    gray = cv2.cvtColor(dst,cv2.COLOR_BGR2GRAY)#先转换为灰度图
    ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)#二值化
    cv2.imshow("binary image",binary)
    
    cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    for i,contour in enumerate(contours):
        cv2.drawContours(image,contours,i,(0,0,255),2)
        print(i)
    cv2.imshow("detect contours",image)
    
    
src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\calc.jpg")
cv2.imshow("image",src)
cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)
contour(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

效果图如下：

五、填充轮廓

cv2.drawContours(image,contours,i,(0,0,255),-1)
最后一个参数为绘制轮廓线段的宽度，-1表示填充

import cv2
import numpy as np
from matplotlib import pyplot as plt


def contour(image):
    dst = cv2.GaussianBlur(image,(3,3),0)
    gray = cv2.cvtColor(dst,cv2.COLOR_BGR2GRAY)#先转换为灰度图
    ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)#二值化
    cv2.imshow("binary image",binary)
    
    cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    for i,contour in enumerate(contours):
        cv2.drawContours(image,contours,i,(0,0,255),-1)
        print(i)
    cv2.imshow("detect contours",image)
    
    
src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\calc.jpg")
cv2.imshow("image",src)
cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)
contour(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

效果图如下：

六、Canny提取轮廓

import cv2
import numpy as np
from matplotlib import pyplot as plt


def edge(image):
    blurred = cv2.GaussianBlur(image,(3,3),0)
    gray = cv2.cvtColor(blurred,cv2.COLOR_BGR2GRAY)
    xgrad = cv2.Sobel(gray,cv2.CV_16SC1,1,0)#x Gradient
    ygrad = cv2.Sobel(gray,cv2.CV_16SC1,0,1)#y Gradient
    edge_output = cv2.Canny(gray,50,150)
    cv2.imshow("Canny Edge",edge_output)
    return edge_output


def contour(image):
    binary = edge(image)
    
    cloneImage,contours,heriachy = cv2.findContours(binary,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    for i,contour in enumerate(contours):
        cv2.drawContours(image,contours,i,(0,0,255),-1)
        print(i)
    cv2.imshow("detect contours",image)
    
    
src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\calc.jpg")
cv2.imshow("image",src)
cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)
contour(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

效果图如下：