When working with images, it is often necessary to determine their sharpness or blurriness. Fortunately, there are several Python libraries available that can help us with this task. In this article, we will explore three different ways to estimate image sharpness or blurriness using existing code libraries in Python.

Option 1: OpenCV

OpenCV is a popular computer vision library that provides various image processing functions. To estimate image sharpness or blurriness using OpenCV, we can utilize the Laplacian operator. The Laplacian operator calculates the second derivative of an image, which can be used as a measure of image sharpness.

import cv2

def estimate_sharpness_opencv(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    laplacian = cv2.Laplacian(gray, cv2.CV_64F).var()
    return laplacian

# Example usage
image = cv2.imread('image.jpg')
sharpness = estimate_sharpness_opencv(image)
print("Sharpness:", sharpness)

This code snippet converts the input image to grayscale and then applies the Laplacian operator to calculate the sharpness. The resulting sharpness value can be used to determine the blurriness or sharpness of the image.

Option 2: scikit-image

scikit-image is another powerful library for image processing in Python. It provides various functions for image analysis, including image sharpness estimation. To estimate image sharpness using scikit-image, we can utilize the Sobel operator.

from skimage import filters

def estimate_sharpness_skimage(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    sobel = filters.sobel(gray)
    sharpness = sobel.var()
    return sharpness

# Example usage
image = cv2.imread('image.jpg')
sharpness = estimate_sharpness_skimage(image)
print("Sharpness:", sharpness)

This code snippet converts the input image to grayscale and then applies the Sobel operator to calculate the sharpness. The resulting sharpness value can be used to determine the blurriness or sharpness of the image.

Option 3: PIL (Python Imaging Library)

PIL (Python Imaging Library) is a widely used library for image processing in Python. Although it does not provide specific functions for sharpness estimation, we can utilize the image filter functionality to achieve similar results.

from PIL import ImageFilter

def estimate_sharpness_pil(image):
    blurred = image.filter(ImageFilter.BLUR)
    sharpness = image.filter(ImageFilter.SHARPEN)
    return sharpness

# Example usage
image = Image.open('image.jpg')
sharpness = estimate_sharpness_pil(image)
print("Sharpness:", sharpness)

This code snippet applies a blur filter and a sharpen filter to the input image. By comparing the original image with the sharpened image, we can estimate the sharpness or blurriness of the image.

After exploring these three options, it is difficult to determine which one is better as it depends on the specific requirements and constraints of the project. OpenCV and scikit-image provide dedicated functions for sharpness estimation, making them more straightforward to use. On the other hand, PIL offers more flexibility with its image filter functionality. Therefore, the best option would be to choose the library that best suits the project’s needs and constraints.