OpenCV-Python | category: contour approximation



OpenCV Python Tutorials

Contours - 2 : Brotherhood


This article is the direct continuation of this article : Contours - 1: Getting Started

In this article, we will learn usage of several functions closely related to Contours. Once this functions are learnt, we can find almost all features of Contours.

1 - Image Moments

Image moments help you to calculate some features like center of mass of the object, area of the object etc. Check out the wikipedia page :

The function cv2.moments() gives a dictionary of moment values calculated. See below :

moments = cv2.moments(cnt)

If you print moments, you get a dictionary:

{'mu02': 10888082.359906793, 'mu03': 0.005234025965704581, 'm11': 368666693.125,
'nu02': 0.10815497152071127, 'm12': 69763579350.98334, 'mu21': 101313.30416250229, 'mu20': 6674463.831166983,
'nu20': 0.06629968636479547, 'm30': 84692116672.95001, 'nu21': 1.0046975468372928e-05, 'mu11': -1980114.5675549507,
'mu12': -33122544.260385513, 'nu11': -0.019669141689288665, 'nu12': -0.0032846761082870463, 'm02': 352044973.5833333,
'm03': 68983799276.15001, 'm00': 10033.5, 'm01': 1850134.5, 'mu30': 8633090.369003296, 'nu30': 0.0008561209988226333,
'm10': 2010061.8333333333, 'm20': 409360323.5833333, 'm21': 74691021944.88333}

Now you can have calculations using these dictionary keys. For example to find the area of the object:

area = moments['m00']

More we will learn in next article.

2 - Contour Area:

Area of contour is same as number of pixels inside the contour. It can be found out using cv2.contourArea() function.

area = cv2.contourArea(cnt)

3 - Contour Perimeter:

It is also called arc length. It can be found out using cv2.arcLength() function.

perimeter = cv2.arcLength(cnt,True)

4 - Contour Approximation :

Contour Approximation will remove small curves, there by approximating the contour more to straight line. This is done using cv2.approxPolyDP() function.

Contours - 2 : Brotherhood
To understand this, suppose you are trying to find a square in an image, but due to some problems in the image, you got only what is shown at right side.

So when you try to find the contours, you will get all the curves also. But with contour approximation, you can avoid all those problems and approximates it to a perfect square.

Check below image. Red region is the actual contour area. Where green line shows approximated contour. You can see, approximated contour is a perfect rectangle.

approx = cv2.approxPolyDP(cnt,0.1*cv2.arcLength(cnt,True),True)

Contours - 2 : Brotherhood
epsilon = 10% of arc length
It also reduces number of points to operate. In original contour, there was 210 points, while approximated contour has only four points which corresponds to four corners of rectangle.

In this, second argument is called epsilon, which is maximum distance from contour to approximated contour. It is an accuracy parameter. In above case, i have taken it as 10% of arc length.

Contours - 2 : Brotherhood
epsilon = 1% of arc length

What will happen if you take it as 1% of arc length? Check out this left image. Approximation detects the defects also. And number of points in approximated contour is now 22.

So a wise selection of epsilon is needed and it all depends on your application.

5 - Convex Hull :

Contours - 2 : Brotherhood
convex hull
Once the approximation is over, Convex Hull is next. This will look similar to contour approximation, but not. Here, cv2.convexHull() function checks a curve for convexity defects and corrects it. Generally speaking, convex curves are the curves which are always bulged out, or at-least flat. And if it is bulged inside, it is called convexity defects. For example, in above case, we can see there are some inside curves for that square. They are the convexity defects. If we find convex hull for this, we get image at right.

(Actually this image is same as above, because both results are same. But it doesn't mean approximation is convex hull, although a contour can be approximated to get a convex hull by selecting suitable epsilon)

Contours - 2 : Brotherhood

Still for those who didn't understand convex hull, OpenCV documentation has a nice picture which demonstrats convex hull and convexity defects. As you can see, the black curve ( hand ) is the original contour. Red curve surrounding it is the convex hull, and convexity defects are marked at gaps between fingers, which are the local maximum deviations of hull from contours.

Syntax :

hull = cv2.convexHull(points[, hull[, clockwise[, returnPoints]]]) 

Points are the contours we pass in to.
Hull is the output, normally we avoid it.
Direction : Orientation flag. If it is true, the output convex hull is oriented clockwise. Otherwise, it is oriented counter-clockwise. (Actually i haven't used this flag anywhere)

So to get a convex hull as in above image, following is sufficient.

hull = cv2.convexHull(cnt)

If we print hull, we get a list: [[[234 202]], [[ 51 202]], [[ 51 79]], [[234 79]]], where each value denotes the corners of rectangle, actually coordinates of corners of rectangle.

To draw a convex hull, you need to do as shown above.

But there is a fourth argument, returnPoints, which is by default True. Then it returns the coordinates. But if it is False, it return the indices of those of convex hull points with respect to contours.

For example, execute the following :

hull = cv2.convexHull(cnt,returnPoints = False)

Now if we print hull, we get : [[129],[ 67],[ 0],[142]]. If you check corresponding values in cnt, it will be same as coordinates we have already found. for example, cnt[129] = [[234, 202]] and so others.

But why would we need such a feature ? It is necessary when we find the convexity defects. We need to pass these indices to cv2.convexityDefects() function to find convexity defects. We will deal with it in another article, but keep this in mind.

6 - Is contour Convex:

There is a function to check if a curve is convex or not, cv2.isContourConvex(). It just return whether True or False. Not a big deal.

k = cv2.isContourConvex(cnt)

7 - Bounding Rectangle :

There are two types of bounding rectangles.

1) Just an upright bounding rectangle which covers the full object. It doesn't consider the rotation of the object.

Let (x,y) be the starting coordinate of rectangle, (w,h) be its width and height.

Then we can find and draw the bounding rect as follows (Green color). See result below:

x,y,w,h = cv2.boundingRect(cnt)

2) Rotated rectangle where a bounding rectangle is drawn with minimum area, so it considers the rotation also. The function used is cv2.minAreaRect(). It returns a Box2D structure - (x,y),(w,h),theta.

rect = cv2.minAreaRect(cnt)
box =
box = np.int0(box)

(x,y) - center point of the box
(w,h) - width and height of the box
theta - angle of rotation
Contours - 2 : Brotherhood
Bounding rectangle

But to draw rectangles, we need coordinate points. For this function is used.

Both the rectangles are shown in a single image. Green rectangle shows the normal bounding rect. Red rectangle is the rotated rect.

Area of normal bounding rect = 15972

Area of rotated rect = 8853

Contours - 2 : Brotherhood
8 - Minimum Enclosing Circle :

Next we find the circumcircle of an object using the function cv2.minEnclosingCircle(). It is a circle which completely covers the object with minimum area.

You can see the result in this image.

(x,y),radius = cv2.minEnclosingCircle(cnt)
center = (int(x),int(y))
radius = int(radius),center,radius,(0,255,0),2)

9 - Fit Ellipse :

Next one is to fit an ellipse to an object. It returns the rotated rectangle in which the ellipse is inscribed.

ellipse = cv2.fitEllipse(cnt)

Contours - 2 : Brotherhood
Fit ellipse


So, these are some major functions related to Contours.

There are some other functions like, cv2.pointPolygonTest(), cv2.convexityDefects() etc which we will deal in another article.

Hope you like this,


Sudoku Solver - Part 2


This is the continuation of the article : Sudoku Solver - Part 1

So we start implementing here.

Load the image :

Below is the image I used to work with.

Sudoku Solver - Part 2
Original  Image
So, first we import necessary libraries.

import cv2
import numpy as np

Then we load the image, and convert to grayscale.

img =  cv2.imread('sudoku.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

Image Pre-processing :

I have done just noise removal and thresholding. And it is working. So I haven't done anything extra.

gray = cv2.GaussianBlur(gray,(5,5),0)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)

Below is the result :

Sudoku Solver - Part 2
Result of adaptive thresholding
Now two questions may arise :

1) What is the need of smoothing here?
2) Why Adaptive Thresholding ? Why not normal Thresholding using cv2.threshold()  ? 

Find the answers here : Some Common Questions

Find Sudoku Square and Corners :

Now we find the sudoku border. For that, we are taking a practical assumption : The biggest square in the image should be Sudoku Square. In short, image should be taken close to Sudoku, as you can see in the input image of demo.

So a lot of things are clear from this : Image should have only one square, Sudoku Square, or not, Sudoku Square must be the biggest. If this condition is not true, method fails.

It is because, we find the sudoku square by finding the biggest blob ( an independant particle) in the image. So if biggest blob is something other than Sudoku, that blob is processed. So, I think you will keep an eye on it.

We start by finding contours in the thresholded image:

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Now we find the biggest blob, ie blob with max. area.

For this, first we find area of each blob. Then we filter them by area. We consider a blob for next processing only if its area is greater than a particular value (here, it is 100). If so, we approximate the contours. It removes unwanted coordinate values in the contour and keep only the corners. So if number of corners equal to four, that is a square (actually, a rectangle). If it has the maximum area among all detected squares, it is out Sudoku square.

biggest = None
max_area = 0
for i in contours:
area = cv2.contourArea(i)
if area > 100:
peri = cv2.arcLength(i,True)
approx = cv2.approxPolyDP(i,0.02*peri,True)
if area > max_area and len(approx)==4:
biggest = approx
max_area = area

For you to understand between original contour and approximated contour, I have drawn it on the image (using cv2.drawContours() function). Red line is the original contour, Green line is the approximated contour and corners marked in blue color circles.

Sudoku Solver - Part 2
Border and corners detected
Look at the top edge of sudoku. Original contour ( Red line) grazes on the edge of square and it is curved. Approximated contour ( Green line) just made it into a straight line.

Now, a simple question may arise. What is the benefit of filtering contours with respect to area? What is the need of removing them ? In simple words, it is done for speed up of the program. Although it may give you a little performance ( in the range of few milliseconds), even that will be good for those who want to implement it in real time. For more explanation, visit : Some Common Questions

Summary :

So, in this section, we have found the boundary of sudoku. Next part is the image transformation. I will explain it in next post.

Until then, I would like to know your feedback, doubts etc.

With Regards
Contours - 2 : BrotherhoodSudoku Solver - Part 2

Report "OpenCV-Python"

Are you sure you want to report this post for ?