Contours in Images

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Akula Hemanth Kumar

Published in

The Startup

4 min readFeb 23, 2020

Contours

Why Contours?

Issues with edges

Edges have no identity.
Edges have no continuity.
Edges cannot be easily counted and worked upon.
Edges cannot be differentiated from one another.
Edges in themselves have no defined shape.

Contours

Contours are an outline representing or bounding the shape or form of something.

Contours in image processing

Contours are designed using edges.
They are edges with an identity, geometrical parameters and are continuous.
They are useful for shape analysis and object recognition.

Contour detection using OpenCV

Retrieval methods

CV_RETR_EXTERNAL gives “outer” contours, so if you have (say) one contour enclosing another (like concentric circles), only the outermost is given.
CV_RETR_LIST gives all the contours and doesn’t even bother calculating the hierarchy — good if you only want the contours and don’t care whether one is nested inside another.
CV_RETR_CCOMP gives contours and organizes them into outer and inner contours. Every contour is either the outline of an object or the outline of an object inside another object (i.e. hole). The hierarchy is adjusted accordingly. This can be useful if (say) you want to find all the holes.
CV_RETR_TREE calculates the full hierarchy of the contours. So you can say that object1 is nested 4 levels deep within object2 and object3 is also nested 4 levels deep.

Contour properties

Contour Moments

2. Contour Area -> Area Enclosed inside a closed contour in pixels

3. Contour Perimeter — Number of pixels a contour covers in pixels

4. Contour approximation — Approximating the shape of a contour from an irregular shape.

5. Convex Hull

6. Bounding rectangle, circle, ellipse fitting across a contour.

Contour Moments

Moments are a set of statistical parameters to measure a distribution.

Three moments are commonly used:

1. Mean: the average.

2. Variance: an indication of how closely the values are spread about the mean.

3. Standard deviation: It is the square root of the variance.

Output

Number of contours = 262

Image contours

Image moments help you to calculate some features like the center of mass of the object, area of the object.

Contour Area

# Contour Area
cnt = contours[109]
area = cv2.contourArea(cnt)
print(area)

Output

6045.0

List of areas of all contours

lst_arr = [];
for cnt in contours:
    lst_arr.append(cv2.contourArea(cnt))

print("Minimum area = {}".format(min(lst_arr)))
print("Maximum area = {}".format(max(lst_arr)))

Output

Minimum area = 0.0
Maximum area = 152881.0

Contour Perimeter

cnt = contours[109]
perimeter = cv2.arcLength(cnt, True) # True -> closed length
print(perimeter)

Output

326.48528122901917

List of perimeters of all contours

lst_per = [];
for cnt in contours:
    lst_per.append(cv2.arcLength(cnt, True))

print("Minimum perimeter = {}".format(min(lst_per)))
print("Maximum perimeter = {}".format(max(lst_per)))

Output

Minimum perimeter = 0.0
Maximum perimeter = 1564.0

Contour Approximation based on length

*Contours approximation over the original image*

Convex Hull

The convex hull or convex envelope or convex closure of a set X points in the Euclidean plane or in a Euclidean space is the smallest convex set that contains X.