** Watch the Full Video Here: **

So far in our Contour Detection 101 series, we have made significant progress unpacking many of the techniques and tools that you will need to build useful vision applications. In part 1, we learned the basics, how to detect and draw the contours, in part 2 we learned to do some contour manipulations.

Now in the third part of this series, we will be learning about analyzing contours. This is really important because by doing contour analysis you can actually recognize the object being detected and differentiate one contour from another. We will also explore how you can identify different properties of contours to retrieve useful information. Once you start analyzing the contours, you can do all sorts of cool things with them. The application below that I made, uses contour analysis to detect the shapes being drawn!

You can build this too! In fact, I have an entire course that will help you ** master contours for building computer vision applications**, where you learn by building all sorts of cool vision applications!

This post will be the third part of the Contour Detection 101 series. All 4 posts in the series are titled as:

*Contour Detection 101: The Basics**Contour Detection 101: Contour Manipulation**Contour Detection 101: Contour Analysis (This Post)**Vehicle Detection with OpenCV using Contours + Background Subtraction*

So if you haven’t seen any of the previous posts make sure you do check them out since in this part we are going to build upon what we have learned before so it will be helpful to have the basics straightened out if you are new to contour detection.

Alright, now we can get started with the Code.

## Download Code

**Import the Libraries**

**Import the Libraries**

Let’s start by importing the required libraries.

1 2 3 4 5 6 | import cv2 import math import numpy as np import pandas as pd import transformations import matplotlib.pyplot as plt |

**Read an Image**

Next, let’s read an image containing a bunch of shapes.

1 2 3 4 5 6 | # Read the image image1 = cv2.imread('media/image.png') # Display the image plt.figure(figsize=[10,10]) plt.imshow(image1[:,:,::-1]);plt.title("Original Image");plt.axis("off"); |

**Detect and draw Contours**

Next, we will detect and draw external contours on the image using** **** cv2.findContours()** and

`cv`

**functions that we have also discussed thoroughly in the previous posts.**

`2.drawContours()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | image1_copy = image1.copy() # Convert to grayscale gray_scale = cv2.cvtColor(image1_copy,cv2.COLOR_BGR2GRAY) # Find all contours in the image contours, hierarchy = cv2.findContours(gray_scale, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Draw all the contours. contour_image = cv2.drawContours(image1_copy, contours, -1, (0,255,0), 3); # Display the results. plt.figure(figsize=[10,10]) plt.imshow(contour_image[:,:,::-1]);plt.title("Image Contours");plt.axis("off"); |

The result is a list of detected contours that can now be further analyzed for their properties. These properties are going to prove to be really useful when we build a vision application using contours. We will use them to provide us with valuable information about an object in the image and distinguish it from the other objects.

Below we will look at how you can retrieve some of these properties.

**Image Moments**

Image moments are like the weighted average of the pixel intensities in the image. They help calculate some features like the center of mass of the object, area of the object, etc. Finding image moments is a simple process in OpenCV which we can get by using the function ** cv2.moments()** that returns a dictionary of various properties to use.

**Function Syntax:**

**Parameters:**

– Single-channel, 8-bit or floating-point 2D array`array`

**Returns:**

– A python dictionary containing different moments properties`retval`

1 2 3 4 5 6 7 8 | # Select a contour contour = contours[1] # get its moments M = cv2.moments(contour) # print all the moments print(M) |

{‘m00’: 28977.5, ‘m10’: 4850112.666666666, ‘m01’: 15004570.666666666, ‘m20’: 878549048.4166666, ‘m11’: 2511467783.458333, ‘m02’: 7836261882.75, ‘m30’: 169397190630.30002, ‘m21’: 454938259986.68335, ‘m12’: 1311672140996.85, ‘m03’: 4126888029899.3003, ‘mu20’: 66760837.58548939, ‘mu11’: 75901.88486719131, ‘mu02’: 66884231.43453884, ‘mu30’: 1727390.3746643066, ‘mu21’: -487196.02967071533, ‘mu12’: -1770390.7230567932, ‘mu03’: 495214.8310546875, ‘nu20’: 0.07950600793808808, ‘nu11’: 9.03921532296414e-05, ‘nu02’: 0.07965295864597088, ‘nu30’: 1.2084764986041665e-05, ‘nu21’: -3.408407043976586e-06, ‘nu12’: -1.238559397771768e-05, ‘nu03’: 3.4645063088656135e-06}

The values returned represent different kinds of image movements including raw moments, central moments, scale/rotation invariant moments, and so on.

For more information on image moments and how they are calculated, you can read this Wikipedia article. Below we will discuss how some of the image moments can be used to analyze the contours detected.

**Find the center of a contour**

Let’s start by finding the centroid of the object in the image using the contour’s image moments. The X and Y coordinates of the Centroid are given by two relations of the central image moments, **Cx=M10/M00 and Cy=M01/M00**.

1 2 3 4 5 6 7 8 | # Calculate the X-coordinate of the centroid cx = int(M['m10'] / M['m00']) # Calculate the Y-coordinate of the centroid cy = int(M['m01'] / M['m00']) # Print the centroid point print('Centroid: ({},{})'.format(cx,cy)) |

Centroid: (167,517)

Let’s repeat the process for the rest of the contours detected and draw a circle using ** cv2.circle()** to indicate the centroids on the image.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | image1_copy = image1.copy() # Loop over the contours for contour in contours: # Get the image moments for the contour M = cv2.moments(contour) # Calculate the centroid cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) # Draw a circle to indicate the contour cv2.circle(image1_copy,(cx,cy), 10, (0,0,255), -1) # Display the results plt.figure(figsize=[10,10]) plt.imshow(image1_copy[:,:,::-1]);plt.axis("off"); |

**Finding Contour Area**

We are already familiar with one way of finding the area of contour in the last post, using function ** cv2.contourArea()**.

1 2 3 4 5 6 7 | # Select a contour contour = contours[1] # Get the area of the selected contour area_method1 = cv2.contourArea(contour) print('Area:',area_method1) |

Area: 28977.5

Additionally, you can also find the area using the ** m00** moment of the contour which contains the contour’s area.

1 2 3 4 5 6 7 | # get selected contour moments M = cv2.moments(contour) # Get the moment containing the Area area_method2 = M['m00'] print('Area:',area_method2) |

Area: 28977.5

As you can see, both of the methods give the same result.

**Contour Properties**

When building an application using contours, information about the properties of a contour is vital. These properties are often invariant to one or more transformations such as translation, scaling, and rotation. Below, we will have a look at some of these properties.

Let’s start by detecting the external contours of an image.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Read the image image4 = cv2.imread('media/sword.jpg') # Create a copy image4_copy = image4.copy() # Convert to gray-scale imageGray = cv2.cvtColor(image4_copy,cv2.COLOR_BGR2GRAY) # create a binary thresholded image _, binary = cv2.threshold(imageGray, 220, 255, cv2.THRESH_BINARY_INV) # Detect and draw external contour contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Select a contour contour = contours[0] # Draw the selected contour cv2.drawContours(image4_copy, contour, -1, (0,255,0), 3) # Display the result plt.figure(figsize=[10,10]) plt.imshow(image4_copy[:,:,::-1]);plt.title("Sword Contour");plt.axis("off"); |

Now using a custom ** transform()** function from the

**module which you will find included with the code for this post, we can conveniently apply and display different transformations to an image.**

`transformation.py`

**Function Syntax:**

`transformations.transform(translate=True, scale=False, rotate=False, path='media/sword.jpg', display=True)`

By default, only translation is applied but you may scale and rotate the image as well.

1 | modified_contour = transformations.transform(rotate = True,scale=True) |

Applied Translation of x: 44, y: 30

Applied rotation of angle: 80

Image resized to: 95.0

**Aspect ratio**

Aspect ratio is the ratio of width to height of the bounding rectangle of an object. It can be calculated as ** AR=width/height**. This value is always invariant to translation.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Get the up-right bounding rectangle for the image x,y,w,h = cv2.boundingRect(contour) # calculate the aspect ratio aspect_ratio = float(w)/h print("Aspect ratio intitially {}".format(aspect_ratio)) # Apply translation to the image and get its detected contour modified_contour = transformations.transform(translate=True) # Get the bounding rectangle for the detected contour x,y,w,h = cv2.boundingRect(modified_contour) # Calculate the aspect ratio for the modified contour aspect_ratio = float(w)/h print("Aspect ratio After Modification {}".format(aspect_ratio)) |

Aspect ratio initially 0.9442231075697212

Applied Translation of x: -45 , y: -49

Aspect ratio After Modification 0.9442231075697212

**Extent **

Another useful property is the extent of a contour which is the ratio of contour area to its bounding rectangle area. Extent is invariant to Translation & Scaling.

To find the extend we start by calculating the contour area for the selected contour using the function cv2.contourArea(). Next, the bounding rectangle is found using cv2.boundingRect(). The area of the bounding rectangle is calculated using **rectarea=width×height**. Finally, the extent is then calculated as **extent=contourarea/rectarea**.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # Calculate the area for the contour original_area = cv2.contourArea(contour) # find the bounding rectangle for the contour x,y,w,h = cv2.boundingRect(contour) # calculate the area for the bounding rectangle rect_area = w*h # calcuate the extent extent = float(original_area)/rect_area print("Extent intitially {}".format(extent)) # apply scaling and translation to the image and get the contour modified_contour = transformations.transform(translate=True,scale = True) # Get the area of modified contour modified_area = cv2.contourArea(modified_contour) # Get the bounding rectangle x,y,w,h = cv2.boundingRect(modified_contour) # Calculate the area for the bounding rectangle modified_rect_area = w*h # calcuate the extent extent = float(modified_area)/modified_rect_area print("Extent After Modification {}".format(extent)) |

Extent intitially 0.2404054667406324

Applied Translation of x: 38 , y: 44

Image resized to: 117.0%

Extent After Modification 0.24218788234718347

**Equivalent Diameter **

Equivalent Diameter is the diameter of the circle whose area is the same as the contour area. It is Invariant to Translation & Rotation. The equivalent diameter can be calculated by first getting the area of contour with cv2.boundingRect(), the area of the circle is given by **area=2×π×d ^{2}/4** where d is the diameter of the circle.

So to find the diameter we just have to make **d** the subject in the above equation, giving us: **d= ** **√(4×rectArea/π).**

1 2 3 4 5 6 7 8 9 10 11 12 13 | # Calculate the diameter equi_diameter = np.sqrt(4*original_area/np.pi) print("Equi diameter intitially {}".format(equi_diameter)) # Apply rotation and transformation modified_contour = transformations.transform(rotate= True) # Get the area of modified contour modified_area = cv2.contourArea(modified_contour) # Calculate the diameter equi_diameter = np.sqrt(4*modified_area/np.pi) print("Equi diameter After Modification {}".format(equi_diameter)) |

Equi diameter intitially 134.93924087995146

Applied Translation of x: -39 , y: 38

Applied rotation of angle: 38

Equi diameter After Modification 135.06184863765444

**Orientation **

Orientation is simply the angle at which an object is rotated.

1 2 | # Rotate and translate the contour modified_contour = transformations.transform(translate=True,rotate= True,display = True) |

Applied Translation of x: 48 , y: -37

Applied rotation of angle: 176

**Now Let’s take a look at an elliptical angle on the sword contour above**

1 2 3 4 5 | # Fit and ellipse onto the contour similarly to minimum area rectangle (x,y),(MA,ma),angle = cv2.fitEllipse(modified_contour) # Print the angle of rotation of ellipse print("Elliptical Angle is {}".format(angle)) |

Elliptical Angle is 46.882904052734375

**Below method also gives the angle of the contour by fitting a rotated box instead of an ellipse**

1 2 | (x,y),(w,mh),angle = cv2.minAreaRect(modified_contour) print("RotatedRect Angle is {}".format(angle)) |

RotatedRect Angle is 45.0

**Note:** *Don’t be confused by why all three angles are showing different results, they all calculate angles differently, for e.g ellipse fits an ellipse and then calculates the angle that an ellipse makes, similarly the rotated rectangle calculates the angle the rectangle makes. For triggering decisions based on the calculated angle you would first need to find what angle the respective method is making at the given orientations of the object.*

**Hu moments**

Hu moments are a set of 7 numbers calculated using the central moments. What makes these 7 moments special is the fact that out of these 7 moments, the first * 6* of the Hu moments are invariant to translation, scaling, rotation and reflection. The

*Hu moment is also invariant to these transformations, except that it changes its sign in case of reflection. Below we will calculate the Hu moments for the sword contour, using the moments of the contour.*

**7th**You can read this paper if you want to know more about hu-moments and how they are calculated.

1 2 3 4 5 6 7 | # Calculate moments M = cv2.moments(contour) # Calculate Hu Moments hu_M = cv2.HuMoments(M) print(hu_M) |

[[5.69251998e-01]

[2.88541572e-01]

[1.37780830e-04]

[1.28680955e-06]

[2.45025329e-12]

[3.54895392e-07]

[1.69581763e-11]]

As you can see the different hu-moments have varying ranges (e.g. compare hu-moment 1 and 7) so to make the Hu-moments more comparable with each other, we will transform them to log-scale and bring them all to the same range.

1 2 3 4 5 6 | # Log scale hu moments for i in range(0,7): hu_M[i] = -1* math.copysign(1.0, hu_M[i]) * math.log10(abs(hu_M[i])) df = pd.DataFrame(hu_M,columns=['Hu-moments of original Image']) df |

Next up let’s apply transformations to the image and find the Hu-moments again.

1 2 | # Apply translation to the image and get its detected contour modified_contour = transformations.transform(translate = True, scale = True, rotate = True) |

Applied Translation of x: -31 , y: 48

Applied rotation of angle: 122

Image resized to: 87.0%

1 2 3 4 5 6 7 8 9 10 11 12 | # Calculate moments M_modified = cv2.moments(modified_contour) # Calculate Hu Moments hu_Modified = cv2.HuMoments(M_modified) # Log scale hu moments for i in range(0,7): hu_Modified[i] = -1* math.copysign(1.0, hu_Modified[i]) * math.log10(abs(hu_Modified[i])) df['Hu-moments of Modified Image'] = hu_Modified df |

The difference is minimal because of the invariance of Hu-moments to the applied transformations.

**Summary**

In this post, we saw how useful contour detection can be when you analyze the detected contour for its properties, enabling you to build applications capable of detecting and identifying objects in an image.

We learned how image moments can provide us useful information about a contour such as the center of a contour or the area of contour.

We also learned how to calculate different contour properties invariant to different transformations such as rotation, translation, and scaling.

Lastly, we also explored seven unique image moments called **Hu-moments** which are really helpful for object detection using contours since they are invariant to translation, scaling, rotation, and reflection at once.

This concludes the third part of the series. In the next and final part of the series, we will be building a Vehicle Detection Application using many of the techniques we have learned in this series.

You can reach out to me **personally for a 1 on 1 consultation** session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directly **here.**

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