Python opencv contour discovery function based on image edge extraction

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Python OpenCV 365 Day study plan , Go into the field of image with the eraser . This blog is the third in this series 36 piece .

Basic knowledge

In the image , The outline can be simply understood as connecting all continuous points with the same color （ The border ） The curve of , Contours can be used for shape analysis and object detection 、 Identification and other fields .

The principle of contour discovery ： Firstly, the target object is extracted by threshold segmentation , Then the contour of the target object is extracted by edge detection . An outline is a series of points （ Pixels ）, These points form an ordered set of points .

Use cv2.findContours Function can be used to detect the edge of an image .

Function prototype description

contours, hierarchy = cv2.findContours(image, mode, method[, contours[, hierarchy[, offset]]])
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I use Python OpenCV yes 4.0 Above version , If you're using 3.0 above , There may be a return value difference problem . The parameters are described as follows ：

• image： The input image ;
• mode： Contour retrieval mode , Please refer to the following text for details ;
• method： Contour approximation method , For details, please refer to the following text ;
• contours： The outline of the return ;
• hierachy： Attributes corresponding to each contour ;
• offset： Optional offset for each contour point movement .

remarks ：image The parameter needs to be a binary graph , Not grayscale , The result is contour and hierarchy .

Contour retrieval mode , There are four kinds.

• cv2.RETR_EXTERNAL： Indicates that only the outer contour is detected ;
• cv2.RETR_LIST： Detected contour , Do not establish hierarchical relationships ;
• cv2.RETR_CCOMP： Build two levels of outline , The upper layer is the outer boundary , The inner layer is the boundary information of the inner hole . If there is a connected object in the inner hole , The boundary of this object is also at the top ;
• cv2.RETR_TREE： Create a hierarchical tree structure outline .
• Above , Can be found on this website ： Official website address

Contour approximation method

• cv2.CHAIN_APPROX_NONE： Store all contour points , The pixel position difference between two adjacent points shall not exceed 1, namely max(abs(x1-x2),abs(y2-y1))==1, It's not used in general ;
• cv2.CHAIN_APPROX_SIMPLE： Compress the horizontal direction , vertical direction , Diagonal elements , Only the coordinates of the end point in this direction are reserved , For example, a rectangular outline only needs 4 Points to save profile information .
• cv2.CHAIN_APPROX_TC89_L1,cv2.CV_CHAIN_APPROX_TC89_KCOS： Use teh-Chinl chain The approximate algorithm （ Didn't look for information to learn ）.

After understanding the above , You can apply the contour discovery function , The code is as follows ：

import cv2 as cv

src = cv.imread("./both.jpeg")

gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 150, 255, 0)
cv.imshow("thresh",thresh)
#  Look for the outline 
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
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After the contour is found , And through cv2.drawContours Function to draw the outline , The prototype of this function is as follows ：

image = cv2.drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]])
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The parameters are described as follows ：

• image： The input image ;
• contours： outline , stay Python There is a list, Namely cv2.findContours The point set found by the function , A list of ;
• contourIdx： Index of contour , Specifies the sketch profile list Which outline in the , To draw all the outlines , Please deliver -1;
• color： Color ;
• thickness： The thickness of the , If it is -1, Indicates filling ;
• lineType： Linetype ;
• hierarchy： Optional information for the hierarchy ;
• maxLevel： The maximum level at which the outline is drawn ,0： Draw only the specified profile ,1： Draw the profile and all nested profiles ,2： Draw the outline , All nested profiles , All nested to nested contours ;
• offset： Contour offset parameters .

The test code and running results are as follows ：

import cv2 as cv
# help(cv.drawContours)
src = cv.imread("./both.jpeg")

gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 150, 255, 0)
cv.imshow("thresh",thresh)
#  Look for the outline 
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

# print(contours)
# print(hierarchy)
#  Draw the outline 
cv.drawContours(src,contours,-1,(200,0,150),2)

cv.imshow('src',src)
cv.waitKey(0)
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