OpenCV-Python

Contours - 3 : Extraction

Published: June 16, 2012 | 00:05

Hi,

This is our third article on contours and direct continuation of Contours 1 : Getting Started and Contours - 2 : Brotherhood. Hope you have read and understood it well before reading this.

In this article, we won't be using any new function from OpenCV, instead we use the methods from previous article to extract useful data of a contour or an object. You will be using some of these routines in your codes often. So we can get into the topic now.

What are these features actually ? Yes, that is a relative question, i think. It can be anything you want to find about an object and it directly depends on your goals. Some times, you may be interested in its size, sometimes its center, or its average color, or minimum and maximum intensity of that object, and even its orientation, ie its slope etc. I would like to list some of the normally used features.

1 - Area and Perimeter :

This, we have already discussed in last articles, which can be found out using cv2.contourArea() and cv2.arcLength() functions, respectively. You can refer that.

2 - Centroid :

Centroids are found using cv2.Moments() function where centroid can be defined as :

centroid_x = M10/M00 and centroid_y = M01/M00

M = cv2.moments(cnt)
	centroid_x = int(M['m10']/M['m00'])
	centroid_y = int(M['m01']/M['m00'])

Remember, actual result obtained will be 'float', so convert it into 'int'.

If you draw a circle at that point, you can see the centroid.

3 - Aspect Ratio :

Aspect Ratio is the ratio of width to height.

It will be useful in the cases where you want to filter out some shapes. The best example which comes to my mind is ANPR (Automatic Number Plate Recognition). ANPR is used in several traffic surveillance systems to track vehicles going that way. So, in such scenarios, first step is to extract rectangles in the image (since number plate is a rectangle). But there may be false ones also. So use aspect ratio to remove unwanted rectangles (You can google several papers using this method)

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

4 - Extent :

Extent is the ratio of contour area to bounding rectangle area.

area = cv2.contourArea(cnt)
	x,y,w,h = cv2.boundingRect(cnt)
	rect_area = w*h
	extent = float(area)/rect_area

5 - Solidity :

Solidity is the ratio of contour area to its convex hull area.

area = cv2.contourArea(cnt)
	hull = cv2.convexHull(cnt)
	hull_area = cv2.contourArea(hull)
	solidity = float(area)/hull_area

6 - Equivalent Diameter :

Equivalent Diameter is the diameter of the circle whose area is same as the contour area.

It is calculated as, Equivalent Diameter = √ 4 * A / Π
where A = Area of contour

area = cv2.contourArea(cnt)
	equi_diameter = np.sqrt(4*area/np.pi)

7 - Orientation :

Orientation is the angle at which object is directed.

(x,y),(MA,ma),angle = cv2.fitEllipse(cnt)

8 - Pixel Points :

In some cases, we may need all the points which comprises that object. It can be done as follows:

mask = np.zeros(imgray.shape,np.uint8)
	cv2.drawContours(mask,[cnt],0,255,-1)
	pixelpoints = np.transpose(np.nonzero(mask))

9 - Maximum Value and Minimum Value :

We can find these parameters using a mask image.

min_val, max_val, min_loc,max_loc = cv2.minMaxLoc(imgray,mask = mask)

where mask is same as above. Remember, this is for grayscale images, not for color images.

10 - Mean Color or Mean Intensity :

Here, we can find the average color of an object. Or it can be average intensity of the object in grayscale mode. We again use the same mask to do it.

mean_val = cv2.mean(im,mask = mask)

Remember, if you are trying for color matching or color based object tracking, first convert image to HSV space, because HSV is more better representation of color that RGB space. We will deal it in more detail in another article.

11 - Extreme Points :

Extreme Points means topmost,bottommost,rightmost,leftmost points of the object.

leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])
	rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])
	topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
	bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])

For eg, if I apply it to an Indian map, I get the following result :

Extreme Points

For those who couldn't understand above piece of code, I will explain one for you, ie leftmost.

We have contour points (x,y) stored as a [rows,1,2]. Number of rows equal to number of contour points. So to find the leftmost point, we need to find the point where 'x' is minimum. 'y' doesn't matter. So for that, we extract 'x' coordinates of all the points.

x = cnt[ : , : , 0]

Now we find the location of minimum value in it.( Not minimum value, but position or index of the minimum value)

x_min_loc = x.argmin()

Now we find the point (x,y) in cnt at this location(x_min_loc).

point = cnt[x_min_loc]

Sometimes, there may be more than one leftmost points, like rectangles. So we have to take only one of them. And convert it into tuple.

leftmost = tuple(point[0])

That gives you the answer.

So these are some of the features used frequently.

Now only few more things are there to explain about the contours like convexity defects, point polygon test etc. We will be dealing it in next article.

Send me your feedbacks, comments etc.

Regards,
ARK

Source: opencvpython.blogspot.com

9 Comments labels (12)Flag

All angle area average of contours bottommost centroid contour leftmost mask image mean value orientation rightmost topmost

Contours - 2 : Brotherhood

Published: June 11, 2012 | 17:39

Hi,

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 : http://en.wikipedia.org/wiki/Image_moment

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.

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)

approximation
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.

approximation
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 :

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)

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)
	cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)

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 = cv2.cv.BoxPoints(rect)
	box = np.int0(box)
	cv2.drawContours(im,[box],0,(0,0,255),2)

(x,y) - center point of the box
(w,h) - width and height of the box
theta - angle of rotation

Bounding rectangle

But to draw rectangles, we need coordinate points. For this cv2.cv.BoxPoints() 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

CircumCircle

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)
	cv2.circle(im,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)
	cv2.ellipse(im,ellipse,(0,255,0),2)

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,

Regards,
ARK

Contours - 1 : Getting Started

Published: June 10, 2012 | 10:32

Hi, this article is a tutorial which try to cover all relevant functions in OpenCV dealing with Structural Analysis and Shape Descriptors, which are mainly related to contours.

Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. For example, consider image at left.

Assuming it is a binary image,we can say, its contour is the curve joining the all the boundary white points.

So if we find a contour in a binary image, we are finding the boundaries of objects in an image. That is why, OpenCV doc says, "The contours are a useful tool for shape analysis and object detection and recognition".

Finding Contours:

We start with a simple image as above. First we find the contours.

import numpy as np
	import cv2

	im = cv2.imread('test.jpg')
	imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
	ret,thresh = cv2.threshold(imgray,127,255,0)
	contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

Points to remember :

For better accuracy, use binary images. So before finding contours, apply threshold or canny edge detection.
FindContours function modifies the source image, so 'thresh' before and after finding contours are different image. So if you want 'thresh' as such after finding contours, store it to some other variables.
In OpenCV, its operation is like finding white object from black background. So remember, object to be found should be white and background in black.

What is structure of resulting contours?

The result "contours" is a Python list, where it contains all objects boundary points as separate lists. So to find number of objects, find length of list "contours", where in this case, it is one. Only one object. So we take it as "cnt".

>>> len(contours)
	1
	>>> cnt = contours[0]
	>>> len(cnt)
	244

Here, number of points in cnt is 244. What these points denote? They are the boundary points of the object.

But, does it include all the boundary? Not exactly. The points are selected such that, contours can be drawn as straight line joining these points. So, if object is a horizontal or vertical line, only end points are stored. ie length of cnt = 2. If object is a rectangle, only 4 vertices are stored.

Contour points for a rectangle

Thus in our image, there are no direct horizontal or vertical lines. So most of the points will be stored. To visualise it as above, you can draw circles for each value in cnt.

How to draw the contours?

For this, there is a function, cv2.drawContours(). Let's try it:

cv2.drawContours(im,contours,-1,(0,255,0),3)

This draws a 3-pixel wide green outline of the object. If you want to fill the object with a particular color, pass value of -1 to line thickness.

cv2.drawContours(im,contours,-1,(0,255,0),-1)

Contours drawn filled

Contours drawn 3 px wide

Also, the third argument in cv2.drawContours() is also to be noted. Suppose, you want to draw only fourth contour(not here), third argument should be set to 3. If it is -1, all contours are drawn.

Now you want to draw "cnt" only. It can be done as follows:

cv2.drawContours(im,[cnt],0,(255,0,0),-1)

Note the square bracket around "cnt". Third argument set to 0, means only that particular contour is drawn.

Now we end after one more important concept, called Mask.

Mask : What and Why?

Mask can be considered as a binary image where only our desired area is white and all others are blacked out. They are used to isolate a part of image and do operations on that part only without affecting or operating on other parts of the image. This can also be considered as a ROI (Region of Interest) which can have any shape.

Consider a scenario, where you are asked to find average colors of each shapes in the image at right. So simply threshold the image to binarize it (please don't ask me if white ball can be detected using thresholding, it is just an example). Find contours in the binary image, then for each contour, create a mask image of that shape. ie, if first ball is cosidered, the region of that ball in mask image will be white, while all other shapes and backgrounds are blacked out. Now if you can find the mean color of that shape only. So for every shapes.

(OK, just for this case, I will do it in this image, not on our original image at the beginning)

First we find the contours as we did before. (Adjust the threshold value to detect all). Now we will see how to do it:

First create a mask image where all elements are zero (ie just a black image) with size same as source, but single channel (ie grayscale).

Then for each contour, we draw it on the mask image filled with white color. Then we find mean using mean() function, taking our mask as operating mask.

for h,cnt in enumerate(contours):
	    mask = np.zeros(imgray.shape,np.uint8)
	    cv2.drawContours(mask,[cnt],0,255,-1)
	    mean = cv2.mean(im,mask = mask)

Mask Images

See the result at left side.

(All the resulting images are animated to a single image)

I think it is sufficient for now. Keep these three in mind, ie Find Contours, Draw Contours and Mask Image. Now we can find some contour features in next post.

Regards,
ARK

Contour features

Published: April 19, 2012 | 04:44

For more details on contours, visit :

1) Contours - 1 : Getting Started

2) Contours - 2 : Brotherhood

''' filename : contourfeatures.py

This sample calculates some useful parameters of a contour. This is an OpenCV implementation of regionprops function in matlab with some additional features.

Benefit : Learn to find different parameters of a contour region.
Get familier with different contour functions in OpenCV.

Level : Beginner or Intermediate

Usage : python contourfeatures.py <image_file>

Abid Rahman 3/25/12 '''

import cv2
import numpy as np

class Contour:
''' Provides detailed parameter informations about a contour

Create a Contour instant as follows: c = Contour(src_img, contour)
where src_img should be grayscale image.

Attributes:

c.area -- gives the area of the region
c.parameter -- gives the perimeter of the region
c.moments -- gives all values of moments as a dict
c.centroid -- gives the centroid of the region as a tuple (x,y)
c.bounding_box -- gives the bounding box parameters as a tuple => (x,y,width,height)
c.bx,c.by,c.bw,c.bh -- corresponds to (x,y,width,height) of the bounding box
c.aspect_ratio -- aspect ratio is the ratio of width to height
c.equi_diameter -- equivalent diameter of the circle with same as area as that of region
c.extent -- extent = contour area/bounding box area
c.convex_hull -- gives the convex hull of the region
c.convex_area -- gives the area of the convex hull
c.solidity -- solidity = contour area / convex hull area
c.center -- gives the center of the ellipse
c.majoraxis_length -- gives the length of major axis
c.minoraxis_length -- gives the length of minor axis
c.orientation -- gives the orientation of ellipse
c.eccentricity -- gives the eccentricity of ellipse
c.filledImage -- returns the image where region is white and others are black
c.filledArea -- finds the number of white pixels in filledImage
c.convexImage -- returns the image where convex hull region is white and others are black
c.pixelList -- array of indices of on-pixels in filledImage
c.maxval -- corresponds to max intensity in the contour region
c.maxloc -- location of max.intensity pixel location
c.minval -- corresponds to min intensity in the contour region
c.minloc -- corresponds to min.intensity pixel location
c.meanval -- finds mean intensity in the contour region
c.leftmost -- leftmost point of the contour
c.rightmost -- rightmost point of the contour
c.topmost -- topmost point of the contour
c.bottommost -- bottommost point of the contour
c.distance_image((x,y)) -- return the distance (x,y) from the contour.
c.distance_image() -- return the distance image where distance to all points on image are calculated
'''
def __init__(self,img,cnt):
self.img = img
self.cnt = cnt
self.size = len(cnt)

# MAIN PARAMETERS

#Contour.area - Area bounded by the contour region'''
self.area = cv2.contourArea(self.cnt)

# contour perimeter
self.perimeter = cv2.arcLength(cnt,True)

# centroid
self.moments = cv2.moments(cnt)
if self.moments['m00'] != 0.0:
self.cx = self.moments['m10']/self.moments['m00']
self.cy = self.moments['m01']/self.moments['m00']
self.centroid = (self.cx,self.cy)
else:
self.centroid = "Region has zero area"

# bounding box
self.bounding_box=cv2.boundingRect(cnt)
(self.bx,self.by,self.bw,self.bh) = self.bounding_box

# aspect ratio
self.aspect_ratio = self.bw/float(self.bh)

# equivalent diameter
self.equi_diameter = np.sqrt(4*self.area/np.pi)

# extent = contour area/boundingrect area
self.extent = self.area/(self.bw*self.bh)

### CONVEX HULL ###

# convex hull
self.convex_hull = cv2.convexHull(cnt)

# convex hull area
self.convex_area = cv2.contourArea(self.convex_hull)

# solidity = contour area / convex hull area
self.solidity = self.area/float(self.convex_area)

### ELLIPSE ###

self.ellipse = cv2.fitEllipse(cnt)

# center, axis_length and orientation of ellipse
(self.center,self.axes,self.orientation) = self.ellipse

# length of MAJOR and minor axis
self.majoraxis_length = max(self.axes)
self.minoraxis_length = min(self.axes)

# eccentricity = sqrt( 1 - (ma/MA)^2) --- ma= minor axis --- MA= major axis
self.eccentricity = np.sqrt(1-(self.minoraxis_length/self.majoraxis_length)**2)

### CONTOUR APPROXIMATION ###

self.approx = cv2.approxPolyDP(cnt,0.02*self.perimeter,True)

### EXTRA IMAGES ###

# filled image :- binary image with contour region white and others black
self.filledImage = np.zeros(self.img.shape[0:2],np.uint8)
cv2.drawContours(self.filledImage,[self.cnt],0,255,-1)

# area of filled image
filledArea = cv2.countNonZero(self.filledImage)

# pixelList - array of indices of contour region
self.pixelList = np.transpose(np.nonzero(self.filledImage))

# convex image :- binary image with convex hull region white and others black
self.convexImage = np.zeros(self.img.shape[0:2],np.uint8)
cv2.drawContours(self.convexImage,[self.convex_hull],0,255,-1)

### PIXEL PARAMETERS

# mean value, minvalue, maxvalue
self.minval,self.maxval,self.minloc,self.maxloc = cv2.minMaxLoc(self.img,mask = self.filledImage)
self.meanval = cv2.mean(self.img,mask = self.filledImage)

### EXTREME POINTS ###

# Finds the leftmost, rightmost, topmost and bottommost points
self.leftmost = tuple(self.cnt[self.cnt[:,:,0].argmin()][0])
self.rightmost = tuple(self.cnt[self.cnt[:,:,0].argmax()][0])
self.topmost = tuple(self.cnt[self.cnt[:,:,1].argmin()][0])
self.bottommost = tuple(self.cnt[self.cnt[:,:,1].argmax()][0])
self.extreme = (self.leftmost,self.rightmost,self.topmost,self.bottommost)

### DISTANCE CALCULATION

def distance_image(self,point=None):

'''find the distance between a point and adjacent point on contour specified. Point should be a tuple or list (x,y)
If no point is given, distance to all point is calculated and distance image is returned'''
if type(point) == tuple:
if len(point)==2:
self.dist = cv2.pointPolygonTest(self.cnt,point,True)
return self.dist
else:
dst = np.empty(self.img.shape)
for i in xrange(self.img.shape[0]):
for j in xrange(self.img.shape[1]):
dst.itemset(i,j,cv2.pointPolygonTest(self.cnt,(j,i),True))

dst = dst+127
dst = np.uint8(np.clip(dst,0,255))

# plotting using palette method in numpy
palette = []
for i in xrange(256):
if i<127:
palette.append([2*i,0,0])
elif i==127:
palette.append([255,255,255])
elif i>127:
l = i-128
palette.append([0,0,255-2*l])
palette = np.array(palette,np.uint8)
self.h2 = palette[dst]
return self.h2

#### DEMO ######
if __name__=='__main__':

import sys
if len(sys.argv)>1:
image = sys.argv[1]
else:
image = 'new.bmp'
print "Usage : python contourfeatures.py <image_file>"

im = cv2.imread(image)
imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(imgray,255,0,1,11,2)
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
k = 1000
for cnt in contours:

# first shows the original image
im2 = im.copy()
c = Contour(imgray,cnt)
print c.leftmost,c.rightmost
cv2.putText(im2,'original image',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
cv2.imshow('image',im2)
if cv2.waitKey(k)==27:
break

im2 = im.copy()

# Now shows original contours, approximated contours, convex hull
cv2.drawContours(im2,[cnt],0,(0,255,0),4)
string1 = 'green : original contour'
cv2.putText(im2,string1,(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
cv2.imshow('image',im2)
if cv2.waitKey(k)==27:
break

approx = c.approx
cv2.drawContours(im2,[approx],0,(255,0,0),2)
string2 = 'blue : approximated contours'
cv2.putText(im2,string2,(20,40), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
cv2.imshow('image',im2)
if cv2.waitKey(k)==27:
break

hull = c.convex_hull
cv2.drawContours(im2,[hull],0,(0,0,255),2)
string3 = 'red : convex hull'
cv2.putText(im2,string3,(20,60), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
cv2.imshow('image',im2)
if cv2.waitKey(k)==27:
break

im2 = im.copy()

# Now mark centroid and bounding box on image
(cx,cy) = c.centroid
cv2.circle(im2,(int(cx),int(cy)),5,(0,255,0),-1)
cv2.putText(im2,'green : centroid',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))

(x,y,w,h) = c.bounding_box
cv2.rectangle(im2,(x,y),(x+w,y+h),(0,0,255))
cv2.putText(im2,'red : bounding rectangle',(20,40), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))

(center , axis, angle) = c.ellipse
cx,cy = int(center[0]),int(center[1])
ax1,ax2 = int(axis[0]),int(axis[1])
orientation = int(angle)
cv2.ellipse(im2,(cx,cy),(ax1,ax2),orientation,0,360,(255,255,255),3)
cv2.putText(im2,'white : fitting ellipse',(20,60), cv2.FONT_HERSHEY_PLAIN, 1.0,(255,255,255))

cv2.circle(im2,c.leftmost,5,(0,255,0),-1)
cv2.circle(im2,c.rightmost,5,(0,255,0))
cv2.circle(im2,c.topmost,5,(0,0,255),-1)
cv2.circle(im2,c.bottommost,5,(0,0,255))
cv2.imshow('image',im2)
if cv2.waitKey(k)==27:
break

# Now shows the filled image, convex image, and distance image
filledimage = c.filledImage
cv2.putText(filledimage,'filledImage',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,255)
cv2.imshow('image',filledimage)
if cv2.waitKey(k)==27:
break

conveximage = c.convexImage
cv2.putText(conveximage,'convexImage',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,255)
cv2.imshow('image',conveximage)
if cv2.waitKey(k)==27:
break

distance_image = c.distance_image()
cv2.imshow('image',distance_image)
cv2.putText(distance_image,'distance_image',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(255,255,255))
if cv2.waitKey(k)==27:
break

cv2.destroyAllWindows()

Source: opencvpython.blogspot.com

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