Back ArrowExplore all kits

How to use skimage.exposure function

share link

by vinitha@openweaver.com dot icon Updated: Oct 27, 2023

technology logo
technology logo

Solution Kit Solution Kit  

Skimage.exposure refers to a module within the scikit-image library. It provides functions for working with the brightness and contrast of images. 

 

Scikit-image is a popular Python library for various image-processing tasks. The skimage.exposure module includes functions for tasks such as:  

  • Histogram Equalization  
  • Histogram Stretching  
  • Adjusting Gamma correction  
  • Inverse Logarithmic Correction  
  • Contrast Adjustment and contrast image enhancement  
  • Adaptive Histogram Equalization  


We can design these functions to provide various tools. It helps enhance the exposure and contrast of images. It helps in computer vision, image processing, and medical imaging applications. It helps improve image quality and aids in feature extraction and analysis.  


In scikit-image, you use other Python libraries to load and manipulate image data. You can use the functions provided by skimage for image analysis.  


Here's a general overview of how to import and work with image data in skimage:  

  • Import the necessary libraries, which usually include skimage numpy for visualization  
  • Load image data  
  • Image manipulation and analysis  
  • visualization  
  • Further analysis  


In conclusion, scikit-image is a powerful Python library for image processing and analysis. It offers a comprehensive set of functions for working with image data. It makes a valuable resource for a wide range of applications.  

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

How can I use thresholding to improve image quality after rotating an image with skimage.transform?

PythondotLines of Code : 25dotLicense : Strong Copyleft (CC BY-SA 4.0)

import cv2
import numpy as np
import skimage.exposure

# load image
img = cv2.imread('122.png')

# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# blur threshold image
blur = cv2.GaussianBlur(gray, (0,0), sigmaX=2, sigmaY=2, borderType = cv2.BORDER_DEFAULT)

# stretch so that 255 -> 255 and 127.5 -> 0
result = skimage.exposure.rescale_intensity(blur, in_range=(127.5,255), out_range=(0,255)).astype(np.uint8)

# save output
cv2.imwrite('122_antialiased.png', result)

# Display various images to see the steps
cv2.imshow('result', result)

cv2.waitKey(0)
cv2.destroyAllWindows()

Instructions

Follow the steps carefully to get the output easily.


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


I hope you found this useful.


I found this code snippet by searching for ' How can I use thresholding to improve image quality ' 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. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. Scikit image library 0.21.0 version



Using this solution, we can able to use skimage.exposure function 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 use skimage.exposure function.

Dependency library

scikit-imageby scikit-image

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

Image processing in Python

Support
    Quality
      Security
        License
          Reuse

            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 is Lightness, and how does it affect the max intensity of an image?  

                      We can compute the Lightness by taking a weighted average of the RGB color channels. The weights used in this calculation can vary depending on our color space. The Lightness effect on the maximum intensity depends on the color space. It also depends on how you define Lightness within that space:  

                      • RGB Color Space  
                      • HSL and Lab Color Spaces  

                       

                      2. What techniques can enhance the contrast of an image?  

                      Enhancing the contrast of an image is a common image-processing task. It helps make the details in an image distinguishable and improve its visual quality. We can employ several techniques to achieve this goal:  

                      • Histogram Equalization  
                      • Contrast Stretching  
                      • Gamma Correction  
                      • Logarithmic Correction  
                      • Histogram Specification  
                      • Adaptive Histogram Equalization  
                      • Sigmoid Correction and Histogram Thresholding  

                       

                      3. How do I import data into skimage for analysis?  

                      You use Python libraries to import and load image data into scikit-image for analysis. Here are the steps to import and load image data:  

                      • Start by importing the necessary libraries, including skimage and numpy  
                      • Load Image Data  
                      • Perform Analysis  
                      • Visualize Results  
                      • Further Analysis  

                       

                      4. What are the benefits of using skimage's exposure methods? How does it help manipulate image intensity values compared to other software packages?  

                      Scikit-image's skimage.exposure methods offer several benefits. When compared to other software packages, they offer:  

                      • Open Source and Free  
                      • Integration with Scikit-Image  
                      • Ease of Use  
                      • Python Ecosystem  

                       

                      5. Is negative sigmoid correction useful to improve contrast in images processed?  

                      Negative sigmoid correction is not recommended for contrast enhancement. It happens in standard image processing tasks when using scikit-image's exposure functions. Other methods are more appropriate for these purposes. However, negative sigmoid correction helps in creative or artistic effects in image manipulation.  

                      Support

                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page

                      See similar Kits and Libraries

                      Open Weaver – Develop Applications Faster with Open Source

                      kandi

                      Community and Support

                      Company

                      Follow

                      • © 2023 Open Weaver Inc.