Finding minimum enclosing rectangle in OpenCV Python

In this article, we are going to see how to draw the minimum enclosing rectangle covering the object using OpenCV Python. For that, we will be using the concepts of Contours. 

Import-Module and read images

In this step, we will import the OpenCV and NumPy library and then read the image with its help.

Python3

import cv2
import numpy as np

# To read image
img = cv2.imread("cloud.png", cv2.IMREAD_COLOR)

Output:

Convert image to grayscale

To get a clear image we need to convert it into grayscale.

Python3

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, 0)

Find the contours in the image 

Contours can be extracted by using cv2.findContours() function. We can select a contour cnt or loop over the all contour

Python3

# finding the contours
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE,
                               cv2.CHAIN_APPROX_SIMPLE)

# take the first contour
cnt = contours[0]

Drawing and Displaying Rectangle Boundaries 

In this step, we will see how the rectangle will cover the cloud in the image.

Python3

# computing the bounding rectangle of the contour
x, y, w, h = cv2.boundingRect(cnt)

# draw contour
img = cv2.drawContours(img, [cnt], 0, (0, 255, 255), 2)

# draw the bounding rectangle
img = cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

# display the image with bounding rectangle drawn on it
cv2.imshow("Bounding Rectangle", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

Minimum Enclosing Rectangle

Now, let’s talk about the minimum enclosing rectangle. Is it the minimum size? To check this, let’s use the minAreaRect() function and OpenCV.

Python3

rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
img = cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

# display the image with bounding rectangle drawn on it
cv2.imshow("Bounding Rectangle", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output: