How To Check Image Blur Or Not Using Python

WebSolutionStuff | May-13-2022 | Categories : Python

In this article, we will see how to check image blur or not using python. For blur image check we are using the OpenCV package. OpenCV is a huge open-source library for computer vision, machine learning, and image processing. It can process images and videos to identify objects, faces, or even the handwriting of a human.

So, let's see blur detection in image python, Image blur detection in Python using OpenCV and imutils package, how to check blur image in python, python blur image OpenCV, blur image OpenCV python, image blur detection OpenCV python, openCV blur image detection python.

Step 1: Install PIP

In this step, we will install PIP. PIP is a package management system used to install and manage software packages written in Python. It stands for “preferred installer program” or “Pip Installs Packages.”

Download the file and store it in the same directory as python is installed.


So, check PIP installed using the below command.

pip -V

After installing pip we will install imutils and other needed packages.

pip install imutils

NumPy is the fundamental package for array computing with Python.

pip3 install numpy

Wrapper package for OpenCV python bindings.

pip install opencv-python

basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much easier with OpenCV and both Python 2.7 and Python 3.



Step 2: Write Python Script

Now, we will write the python script for image checking.

from imutils import paths

import argparse
import cv2
import sys

def variance_of_laplacian(image):
    return cv2.Laplacian(image, cv2.CV_64F).var()

#path of image URL
imagePath = sys.argv[1]	

image = cv2.imread(imagePath)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

fm = variance_of_laplacian(gray)

text = "Image is not blurred..!"

if fm < 100:
    text = "Image is blurred..!"	
print text

Now, run the script.

python -i images -t 100


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