Let’s Start with Image Preprocessing using SKimage

An image is a two-dimensional representation of a visual subject, like a photograph, painting, or drawing. In digital imaging, images are stored as arrays of pixel values, where each pixel represents a sample of the image’s brightness and color. The color of each pixel can be represented by one or multiple channels, like the red, green, and blue (RGB) channels in traditional color images. In this article, you will learn various image preprocessing techniques.

Images can be processed using computer algorithms to change their appearance or extract information. Image processing techniques include operations like resizing, cropping, rotating, filtering, and thresholding. These operations are performed on the pixel values to modify the image or extract information about its content. Image processing is used in so many applications, including computer vision, medical imaging, and digital art.

By learning Image Preprocessing using SKimage, you will be able to:

1. Understand the importance of image preprocessing in image analysis and machine learning.
2. Learn how to use various SKimage functions for image filtering, enhancement, restoration, and transformation.
3. Apply image preprocessing techniques such as noise reduction, edge detection, and image thresholding to improve image quality.
4. Perform common image preprocessing tasks such as image resizing, cropping, and rotation.
5. Use feature extraction techniques to extract meaningful information from images.
6. Implement object detection algorithms for image analysis applications.

1. Using an image processing library: Many image processing libraries, like OpenCV, Pillow, and scikit-image, provide functions to display images. For example, you can use the imshow function to display an image in OpenCV.
2. Using a plotting library: You can use a plotting library, like Matplotlib in Python, to display an image. For example, you can use the imshow function from the Matplotlib Pyplot module to display an image in Python.

Once the image is displayed, you can interact with it by zooming in and out, panning, and readjusting the display settings.

In Python, the “scikit-image” (skimage) library provides several functions for reshaping images. Here is an example of using skimage to reshape an image:

# import colour sub-module
from skimage import color

# reading the image
image = imread('index.png')

# converting image to grayscale
grayscale_image = color.rgb2gray(image)
grayscale_image.shape

import numpy as np
new_shape = (grayscale_image.shape[0]*grayscale_image.shape[1])

# reshape 
image2 = np.reshape(grayscale_image, new_shape)
image2.shape

If converting a 4 by 4 2-D image to 1-D, we will have 4×4=16 values.

Image Preprocessing – Image Rotation

import numpy as np
from skimage import io
from skimage.transform import rotate

# Load an image
image = io.imread("image.jpg")

# Rotate the image by 180 degrees
rotated_image = rotate(image, angle=180, resize=True)

# Save the rotated image
io.imsave("rotated_image.jpg", rotated_image)

import numpy as np
from skimage import io

# Load an image
image = io.imread("image.jpg")
rows, cols = image.shape[:2]
cropped_image = image[rows//4:-rows//4, cols//4:-cols//4]

# Save the cropped image
io.imsave("cropped_image.jpg", cropped_image)

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# reading the image
image = imread('index.png')
image = np.array(image)

imshow(image)
plt.title('Original Image')

# flip image left-to-right
flipLR = np.fliplr(image)

plt.imshow(flipLR)
plt.title('Left to Right Flipped')

from skimage.exposure import adjust_gamma

# read the image
image = imread('index.png')

plt.title('Original Image')
imshow(image)

# brighten the image
bright = adjust_gamma(image,gamma=0.5,gain=1)

imshow(bright)
plt.title('Brightened IMage')