opencv-python API中的findContours、drawContours与hierachy解析

官方文档：

官方文档对hierachy的解释
简单的使用（demo1）：

def draw():image=np.zeros((300,300,1),dtype=np.uint8)cv2.rectangle(image,(10,10),(100,100),(255,255,255),10)cv2.rectangle(image, (100,10), (200,200), (255, 255, 255), 10)cv2.circle(image,(100,100),50,(255,255,255),10)mask=image.copy()mask=cv2.cvtColor(mask,cv2.COLOR_GRAY2BGR)image,contours,hierachy=cv2.findContours(image,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)cv2.drawContours(mask,contours,-1,(0,0,255),2)cv2.imshow('mask',mask)cv2.waitKey(0)
draw()

结果

关于findContours

 image,contours,hierachy=cv2.findContours(image,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)

第一个参数是寻找轮廓的图像；

第二个参数表示轮廓的检索模式，有四种（本文介绍的都是新的cv2接口）：
cv2.RETR_EXTERNAL表示只检测外轮廓
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，上面的一层为外边界，里面的一层为内孔的边界信息。如果内孔内还有一个连通物体，这个物体的边界也在顶层。
cv2.RETR_TREE建立一个等级树结构的轮廓。

第三个参数method为轮廓的近似办法
cv2.CHAIN_APPROX_NONE存储所有的轮廓点，相邻的两个点的像素位置差不超过1，即max（abs（x1-x2），abs（y2-y1））==1
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cv2.CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS使用teh-Chinl chain 近似算法
返回值：
cv2.findContours()函数返回三个值，一个是图像，一个是轮廓本身，还有一个是每条轮廓对应的属性。

关于drawContours()

cv2.drawContours(mask,contours,-1,(0,0,255),2)#参数：图像、轮廓、轮廓序号（负数就画出全部轮廓）、颜色、粗细

还有一些参数，但是目前用这些参数就够了

关于hierachy

来段官方解释
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.

翻译太累了。。。。上官方解释。。。

需要注意的关键点
在hierachy中的数值指的是在hierachy中的顺序而不是contours中的序号，比如在hierachy中某轮廓的子轮廓是2，则它的子轮廓的信息是在hierachy中下标为2的那个
1. RETR_LIST
This is the simplest of the four flags (from explanation point of view). It simply retrieves all the contours, but doesn’t create any parent-child relationship. Parents and kids are equal under this rule, and they are just contours. ie they all belongs to same hierarchy level.

So here, 3rd and 4th term in hierarchy array is always -1. But obviously, Next and Previous terms will have their corresponding values. Just check it yourself and verify it.

Below is the result I got, and each row is hierarchy details of corresponding contour. For eg, first row corresponds to contour 0. Next contour is contour 1. So Next = 1. There is no previous contour, so Previous = 0. And the remaining two, as told before, it is -1.

>>> hierarchy
array([[[ 1, -1, -1, -1],[ 2,  0, -1, -1],[ 3,  1, -1, -1],[ 4,  2, -1, -1],[ 5,  3, -1, -1],[ 6,  4, -1, -1],[ 7,  5, -1, -1],[-1,  6, -1, -1]]])

This is the good choice to use in your code, if you are not using any hierarchy features.
2. RETR_EXTERNAL
If you use this flag, it returns only extreme outer flags. All child contours are left behind. We can say, under this law, Only the eldest in every family is taken care of. It doesn’t care about other members of the family :).

So, in our image, how many extreme outer contours are there? ie at hierarchy-0 level?. Only 3, ie contours 0,1,2, right? Now try to find the contours using this flag. Here also, values given to each element is same as above. Compare it with above result. Below is what I got :

>>> hierarchy
array([[[ 1, -1, -1, -1],[ 2,  0, -1, -1],[-1,  1, -1, -1]]])

You can use this flag if you want to extract only the outer contours. It might be useful in some cases.
3. RETR_CCOMP
This flag retrieves all the contours and arranges them to a 2-level hierarchy. ie external contours of the object (ie its boundary) are placed in hierarchy-1. And the contours of holes inside object (if any) is placed in hierarchy-2. If any object inside it, its contour is placed again in hierarchy-1 only. And its hole in hierarchy-2 and so on.

Just consider the image of a “big white zero” on a black background. Outer circle of zero belongs to first hierarchy, and inner circle of zero belongs to second hierarchy.

We can explain it with a simple image. Here I have labelled the order of contours in red color and the hierarchy they belongs to, in green color (either 1 or 2). The order is same as the order OpenCV detects contours.

So consider first contour, ie contour-0. It is hierarchy-1. It has two holes, contours 1&2, and they belong to hierarchy-2. So for contour-0, Next contour in same hierarchy level is contour-3. And there is no previous one. And its first is child is contour-1 in hierarchy-2. It has no parent, because it is in hierarchy-1. So its hierarchy array is [3,-1,1,-1]

Now take contour-1. It is in hierarchy-2. Next one in same hierarchy (under the parenthood of contour-1) is contour-2. No previous one. No child, but parent is contour-0. So array is [2,-1,-1,0].

Similarly contour-2 : It is in hierarchy-2. There is not next contour in same hierarchy under contour-0. So no Next. Previous is contour-1. No child, parent is contour-0. So array is [-1,1,-1,0].

Contour - 3 : Next in hierarchy-1 is contour-5. Previous is contour-0. Child is contour-4 and no parent. So array is [5,0,4,-1].

Contour - 4 : It is in hierarchy 2 under contour-3 and it has no sibling. So no next, no previous, no child, parent is contour-3. So array is [-1,-1,-1,3].

Remaining you can fill up. This is the final answer I got:

>>> hierarchy
array([[[ 3, -1,  1, -1],[ 2, -1, -1,  0],[-1,  1, -1,  0],[ 5,  0,  4, -1],[-1, -1, -1,  3],[ 7,  3,  6, -1],[-1, -1, -1,  5],[ 8,  5, -1, -1],[-1,  7, -1, -1]]])

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.

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:

>>> hierarchy
array([[[ 7, -1,  1, -1],[-1, -1,  2,  0],[-1, -1,  3,  1],[-1, -1,  4,  2],[-1, -1,  5,  3],[ 6, -1, -1,  4],[-1,  5, -1,  4],[ 8,  0, -1, -1],[-1,  7, -1, -1]]])

参考文章

基于的比较老版本的opencv