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How to use skimage.filters

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by l.rohitharohitha2001@gmail.com dot icon Updated: Oct 11, 2023

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Scikit-image is often referred to as skimage. It is a Python library specifically designed for image processing. It is part of the larger sci-kit-learn ecosystem. 


It includes various libraries for tasks like ML, data analysis, and scientific computing. Scikit-image focuses exclusively on image processing. It provides comprehensive functions and algorithms for working with digital images.  

Tips for Finding the Right Image:  

Basic Adjustments:  

  1. Identify the Problem: Determine what needs adjustment in your image. Is it the brightness, contrast, color balance, or something else? Pinpoint the specific issue before selecting a filter.  
  2. Use Histogram: Check the image histogram to understand the tonal distribution. This can help you decide which adjustment filter to use.  
  3. Start with Essentials: Basic filters like Levels, Curves, Brightness/Contrast, and Hue/Saturation. They provide fundamental control over exposure, tonal range, and color.  
  4. Real-time Preview: Many filters in Photoshop offer real-time previews. Use this feature to see the filter's effect before applying it permanently. It helps fine-tune adjustments.  

Color Correction:  

  1. Identify Color Issues: Determine whether you need to correct color casts. You can do so to adjust the overall color balance or modify specific color channels.  
  2. Hue/Saturation for Targeted Changes: Use this filter for targeted color adjustments. Select the color range you want to modify using the dropdown menu.  
  3. Color Balance for Global Adjustments: Use the Color Balance filter to balance color. 

Sharpening and Blur:  

  1. Evaluate Image Sharpness: Zoom in to assess the image's sharpness. If it's slightly soft, consider sharpening. If it's too sharp or noisy, use blur filters for a smoother look.  
  2. Use Smart Sharpen: For sharpening, try the Smart Sharpen filter. It provides advanced control over sharpening, including reducing noise in shadows and highlights.  
  3. Gaussian Blur for Softening: Gaussian Blur is a versatile filter for softening images. Adjust the radius to control the degree of blurring.  

  

In conclusion, scikit-image is a valuable Python library for image processing. It provides various tools and functions for working with digital images. It stands as a crucial component in the scikit-learn ecosystem. It includes various data science, machine learning, and scientific computing libraries.  

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution we are using Scikit image library of Python.

Split Image using Skimage

PythondotLines of Code : 27dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from skimage import data
import matplotlib.pyplot as plt

image = data.camera()

nrows, ncols = image.shape
rsplit, csplit = nrows // 2, ncols // 2

quadrants = [
    image[:rsplit, :csplit],
    image[:rsplit, csplit:],
    image[rsplit:, :csplit],
    image[rsplit:, csplit:],
]

fig, axes = plt.subplots(2, 2)

for quadrant, ax in zip(quadrants, axes.flat):
    ax.imshow(quadrant, cmap='gray')
    ax.set_axis_off()

from skimage.util import view_as_blocks

quadrants = view_as_blocks(image, (rsplit, csplit)).reshape(
    (4, rsplit, csplit)
)

Instructions


Follow the steps carefully to get the output easily.


  1. Download and Install the Jupyter Notebook on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Create a new Python file on your Notebook.
  4. Copy the snippet using the 'copy' button and paste it into your Python.
  5. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for 'split image using Skimage' in Kandi. You can try any such use case!

Environment Tested


I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. Jupyter Notebook (anaconda 3) 6.0.1 Version
  2. The solution is created in Python 3.8 Version
  3. Scikit image 0.21.0 Version.


Using this solution, we can be able to use skimage.filters using Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to skimage.filters using Python.

Dependent Library


scikit-imageby scikit-image

Python doticonstar image 5440 doticonVersion:v0.21.0doticon
License: Others (Non-SPDX)

Image processing in Python

Support
    Quality
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            scikit-imageby scikit-image

            Python doticon star image 5440 doticonVersion:v0.21.0doticon License: Others (Non-SPDX)

            Image processing in Python
            Support
              Quality
                Security
                  License
                    Reuse

                      You can search for any dependent library on kandi like 'scikit-image'.

                      FAQ:  

                      1. What are Image Derivatives, and how do they work in skimage.filters?   

                      Image derivatives show how pixel values in an image change based on location. They describe how pixel values in an image change as you move across the image in the x and y directions. Image derivatives are essential in image processing for various tasks. It includes edge detection, feature extraction, and gradient-based optimization.  

                        

                      2. What is the New Criterion for Automatic Multilevel Thresholding, and how does it work?  

                      The New Criterion for Automatic Multilevel Thresholding (NCAMT) is an image processing technique. It helps with automatic image thresholding. It is a crucial step in tasks like image segmentation. Image thresholding separates an image into distinct regions by determining a threshold value. It separates pixels into two groups: 

                      • foreground  
                      • background  

                        

                      3. What is Minimum Cross Entropy Thresholding? How can I implement it using the skimage library?   

                      Minimum Cross Entropy Thresholding is a thresholding method used for image segmentation. It depends on the concept of entropy, a measure of the information content in an image. The goal of this technique is to find the threshold value. It minimizes the cross-entropy between two classes of pixels, effectively separating them. It does so in a way that minimizes the information loss.  

                        

                      4. How does Digital Image Processing relate to the skimage library's filter functions?   

                      Digital Image Processing (DIP) is a broad field. It encompasses various techniques and methods for manipulating and analyzing digital images. It involves enhancing image quality, extracting features, and detecting objects. One crucial aspect is that various image filters to process and improve images. It is where the scikit-image library's filter functions come into play.  

                        

                      5. Are there convolution filters available within the scope of the scikit image library?   

                      Yes, the scikit-image is a skimage library. It provides a variety of convolution filters for image processing. These filters help perform edge detection, blurring, sharpening, and more. The skimage.filters module includes several convolution filter functions. It will help you use it for these purposes.  

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