How to blackout a specific area in an image using OpenCV

OpenCV is a free, open-source, cross-platform, C++ library for image processing and computer vision with Python and Java bindings. It incorporates many inbuilt functions to execute tasks like face detection, object tracking, landmark detection, and much more.

Application

Let’s walk through some of the most useful applications of this feature:

Hiding the number plate of a car in a photo.
Masking unbidden people from selfies.
Redacting private information like SSN or credit card number in a scanned document.

Note: It is always cautious to protect certain types of information like Protected Health Information (PHI) or any type of Personal Identifying Information (PII).

Example

Take an example where we need to blackout the middle area of an image.

We can achieve this by creating a white-filled image, the same as the input, with a black-filled rectangle on it as a mask to blackout the specific area of an image. Executing bitwise AND operation on the mask and the input image will give us the image with the middle part as blackout.

Let’s implement the code to blackout the middle area of the input image.

import cv2
import numpy as np
import os
#Load the selected image
img = cv2.imread('__ed_input.png')
#Get the image shape and show its dimensions
h,w = img.shape[:2]
print('Image Dimensions: height={}, width={}'.format(h, w))
# Define a black  mask
mask = np.zeros_like(img)
#Flip the mask color to white
mask[:, :] = [255, 255, 255] 
# Draw a black filled rectangle on top of the mask to 
# hide a specific area
start_point = ((w//2),(h//2))
end_point = ((w//2 + 100),(h//2 + 100))
color = (0,0,0)
mask = cv2.rectangle(mask, start_point, end_point, color, -1)
#mask = 1 - mask
# Apply the mask to image
result = cv2.bitwise_and(img,mask)
# save the processed image and mask
output_path = '/usercode/output'
cv2.imwrite(os.path.join(output_path,'img_masked.png'), result)

How to blackout a specific area in an image using OpenCV

Blackout specific part of an image

Application

Example

Explanation