Contours

Contours: Getting Started

Goals

We will understand what contours are.
We will learn to find and draw contours.
We will learn these functions: cv2.findContours(), cv2.drawContours()

What are Contours?

Contours can be simply explained as a curve joining all the continuous points (along the boundary) having the same color or intensity. Contours are a useful tool for shape analysis and object detection and recognition.

For better accuracy, binary images are used ( binary images. ). So before finding contours, apply threshold or canny edge detection.
findContours function modifies the source image. So if you want to keep the source image even after finding contours, store it in some other variable.
Finding contours in OpenCV is like finding white object from black background. Remember, object to be found should be white and background should be black.

Let’s see how to find contours of a binary image:

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

You can see that the cv2.findContours() function has 3 arguments:

Source image
Contour retrieval mode ( contour retrieval mode )
Contour approximation method ( contour approximation method )

And it outputs the image, contours and hierarchy.

contours is a Python list of all the contours in the image. Each individual contour is a Numpy array of (x,y) coordinates of boundary points of the object.

How to Draw Contours?

To draw the contours, cv2.drawContours function is used. It can also be used to draw any shape provided you have its boundary points.

First argument is source image ( source image )
Second argument is the contours which should be passed as a Python list.
Third argument is index of contours (useful when drawing individual contour. To draw all contours, pass -1) and remaining arguments are color ( color ), thickness ( thickness ) etc.

To draw all the contours in an image:

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

To draw an individual contour, say 4th contour:

img = cv2.drawContours(img, contours, 3, (0,255,0), 3)

But most of the time, below method will be useful:

cnt = contours[4]
img = cv2.drawContours(img, [cnt], 0, (0,255,0), 3)

Note: The last two methods are same, but when you go forward, you will see that the last one is more useful.

Contour Approximation Method

Contour Approximation Method

This is the third argument of the cv2.findContours function. What does it actually mean?

Above, we told that contours are the boundaries of a shape with same intensity. It stores the (x, y) coordinates of the boundary of a shape. But does it store all the coordinates? That is specified by this contour approximation method.

If you pass cv2.CHAIN_APPROX_NONE, all the boundary points are stored. But actually, do we need all the points? For example, you found the contour of a straight line. Do you need all the points on the line to represent that line? No, we need just two end points of that line. This is what cv2.CHAIN_APPROX_SIMPLE does. It removes all redundant points and compresses the contour, thereby saving memory.

The below image of a rectangle demonstrate this technique. Just draw a circle on all the coordinates in the contour array (drawn in blue color). First image shows points I got with cv2.CHAIN_APPROX_NONE (734 points) and second image shows the one with cv2.CHAIN_APPROX_SIMPLE (only 4 points). See, how much memory it saves!!!