Python Code to Apply Gamma Correction to Image

Question

I'm working on a computer vision project where I need to perform various image processing techniques like Gamma Correction, rotation, and thresholding on a set of images. Could you provide me with a well-documented Python script that includes these functionalities?

Answer 1

import cv2

import numpy as np

from skimage import data

import matplotlib.pyplot as plt

def display_image_from_array(image_array, title='Image', cmap=None):

"""

Display an image from a given numpy array without altering the color.

Parameters

----------

image_array : numpy.ndarray

The image data. Shape (height, width, 3) for color images or (height, width) for grayscale.

title : str, optional

Title for the image window.

cmap : str, optional

Colormap for grayscale images.

"""

if cmap:

plt.imshow(image_array, cmap=cmap)

else:

plt.imshow(image_array)

plt.title(title)

plt.axis('off')

plt.show()

def gamma_correction(image, gamma=1.0):

"""

Apply Gamma Correction to the input image.

Parameters

----------

image : numpy.ndarray

The input image data. Shape (height, width, 3).

gamma : float, optional

The gamma value to adjust the image brightness.

> 1: darken, < 1: brighten, = 1: no change.

Returns

-------

numpy.ndarray

The gamma-corrected image.

"""

invGamma = 1.0 / gamma

table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")

corrected = cv2.LUT(image, table)

return corrected

# Reload the astronaut image from scikit-image for demonstration

image = data.astronaut()

# Display the original image correctly

display_image_from_array(image, 'Original Image')

# Apply and display the Gamma Correction technique correctly

gamma_corrected_image = gamma_correction(image, 2)

display_image_from_array(gamma_corrected_image, 'Gamma Corrected Image')