OpenCV使用FindContours进行二维码定位

我使用过FindContours，而且知道有能够直接寻找联通区域的函数。但是我使用的大多只是“最大轮廓”或者"轮廓数目“这些数据。其实轮廓还有另一个很重要的性质，那就是轮廓的相互包含特性。

比如典型的运用在二维码上面

    对于它的3个定位点，认为构造了相互包含的轮廓区域，这种特性，在图上只有三处，而且在自然图片中是不容易重复的（当然二维码内部还有校验机制）。那么基于此就能够很容易地获取准确定位。将二维码的这种机制进行扩展，能够得到对实际物体的定位方法。比如如果我将笔记本的内页设计成这样（当然也可以是答题卡之类），那么就能够为准确的定位、透视变换打下好的基础。

     而首先，就是要能够把轮库这块吃透。最原始和基本的资料就是《学习OpenCV》

 

对于这幅图的解释:

 

直接使用代码进行学习

);
    threshold(src,src,,,THRESH_OTSU);
    vector, ) );
    waitKey();
}

核心函数是findContours，它的几个参数是什么意思了？

第一个参数是输入图像，必须是8通道的，并且应该被转化为二值（所有我threshold了预先）， 已经说明了，用于Contours寻找的图片会被直接修改，如果需要它用，需要先复制一份后再保存。

第二个参数是内存存储器，FindContours找到的轮廓放到内存里面。这里只是一个指针；

第三个参数是mode,可以为

,
    CV_RETR_LIST,
    CV_RETR_CCOMP,
    CV_RETR_TREE,
    CV_RETR_FLOODFILL
};

 

 

向FindContours说明需要的轮廓类型和希望的返回形式。也就是内部轮廓的组织形式。其中显然CV_RETR_TREE模式是最全的模式

 

最后一个参数是轮廓如何被近似

 

,
       CHAIN_APPROX_SIMPLE    ,
       CHAIN_APPROX_TC89_L1   ,
       CHAIN_APPROX_TC89_KCOS 
}

 

不过，如果我们是想获得准确的值的话，这可能更多采用的是NONE，也就是显示所有值。

书本内容学完了，但是并没有解决问题。因为我们需要的是想一个办法，使得轮廓之间的关系能够显示出来。所以需要继续挖掘文档。

 

http://docs.opencv.org/3.1.0/d9/d8b/tutorial_py_contours_hierarchy.html

Also, in the output, we got three arrays, first is the image, second is our contours, and one more output which we named as hierarchy (Please checkout the codes in previous articles). But we never used this hierarchy anywhere. Then what is this hierarchy and what is it for ? What is its relationship with the previous mentioned function argument ?

Consider an example image below :

在这幅图中（白色区域为有数据的区域，黑色为无数据),0,1,2是第一层，然后里面是3，3的里面是4和5。(2a表示是2的内部)

 

In this image, there are a few shapes which I have numbered from 0-5. 2 and 2a denotes the external and internal contours of the outermost box.

Here, contours 0,1,2 are external or outermost. We can say, they are in hierarchy-0 or simply they are in same hierarchy level.

Next comes contour-2a. It can be considered as a child of contour-2 (or in opposite way, contour-2 is parent of contour-2a). So let it be in hierarchy-1. Similarly contour-3 is child of contour-2 and it comes in next hierarchy. Finally contours 4,5 are the children of contour-3a, and they come in the last hierarchy level. From the way I numbered the boxes, I would say contour-4 is the first child of contour-3a (It can be contour-5 also).

I mentioned these things to understand terms like same hierarchy level, external contour, child contour, parent contour, first child etc. Now let's get into OpenCV.

Hierarchy Representation in OpenCV （层级关系)

So each contour has its own information regarding what hierarchy it is, who is its child, who is its parent etc. OpenCV represents it as an array of four values : **[Next, Previous, First_Child, Parent]**

*"Next denotes next contour at the same hierarchical level."*

For eg, take contour-0 in our picture. Who is next contour in its same level ? It is contour-1. So simply put Next = 1. Similarly for Contour-1, next is contour-2. So Next = 2.

What about contour-2? There is no next contour in the same level. So simply, put Next = -1. What about contour-4? It is in same level with contour-5. So its next contour is contour-5, so Next = 5.

*"Previous denotes previous contour at the same hierarchical level."*

It is same as above. Previous contour of contour-1 is contour-0 in the same level. Similarly for contour-2, it is contour-1. And for contour-0, there is no previous, so put it as -1.

*"First_Child denotes its first child contour."*

There is no need of any explanation. For contour-2, child is contour-2a. So it gets the corresponding index value of contour-2a. What about contour-3a? It has two children. But we take only first child. And it is contour-4. So First_Child = 4 for contour-3a.

*"Parent denotes index of its parent contour."*

It is just opposite of First_Child. Both for contour-4 and contour-5, parent contour is contour-3a. For contour-3a, it is contour-3 and so on.

Note

If there is no child or parent, that field is taken as -1

对于Tree这种模式，这样解释很清楚

 

4. RETR_TREE

And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family hierarchy list. It even tells, who is the grandpa, father, son, grandson and even beyond... :).

For examle, I took above image, rewrite the code for cv2.RETR_TREE, reorder the contours as per the result given by OpenCV and analyze it. Again, red letters give the contour number and green letters give the hierarchy order.

image

Take contour-0 : It is in hierarchy-0. Next contour in same hierarchy is contour-7. No previous contours. Child is contour-1. And no parent. So array is [7,-1,1,-1].

Take contour-2 : It is in hierarchy-1. No contour in same level. No previous one. Child is contour-2. Parent is contour-0. So array is [-1,-1,2,0].

And remaining, try yourself. Below is the full answer:

1 >>> hierarchy

2 array([[[ 7, -1, 1, -1],

3  [-1, -1, 2, 0],

4  [-1, -1, 3, 1],

5  [-1, -1, 4, 2],

6  [-1, -1, 5, 3],

7  [ 6, -1, -1, 4],

8  [-1, 5, -1, 4],

9  [ 8, 0, -1, -1],

10  [-1, 7, -1, -1]]])

 

 

 

来自为知笔记(Wiz)