Back ArrowExplore all kits

How to use skimage.morphology

share link

by Dejaswarooba dot icon Updated: Oct 19, 2023

technology logo
technology logo

Solution Kit Solution Kit  

Scikit-image, often abbreviated as skimage, is a powerful and widely used open-source Python library. It is designed for image processing and computer vision tasks.


It provides a comprehensive set of tools and functions. Especially, for manipulating, analyzing, and enhancing images. This makes it an invaluable resource for researchers, developers, and data scientists. Especially, for those working in fields such as computer vision and image analysis.  

 

Scikit-image is built on top of the popular scientific computing library NumPy. It is part of the larger SciPy ecosystem. Its friendly UI makes it accessible to both beginners and experienced users. With scikit-image, we perform tasks like image filtering, segmentation, feature extraction, and more. Thus, making it an essential toolkit for anyone working with digital images.  


skimage.morphology is a vital module within the scikit-image library. It offers a range of mathematical morphology operations for image processing and analysis. These operations play a fundamental role in extracting essential features from images. It also enhances image quality and performs tasks. It includes image segmentation, skeletonization, and noise reduction.  

 

The module provides efficient implementations of morphology operations. It includes fast binary morphological opening and dilation. These operations involve manipulating binary images to remove small noise regions. It also bridges gaps in objects or expands object boundaries, preserving their shapes.  


One significant application of skimage.morphology is morphological thinning. It extracts the skeleton of objects within an image. This can be essential for various purposes. It includes shape analysis, object tracking, and pattern recognition. Skimage offers several thinning algorithms and methodologies for generating skeleton models.  

 

Morphological operations in skimage.morphology extends to grayscale images as well. It has functions for grayscale erosion, dilation, opening, and closing. These operations are valuable for a lot of operations. It includes image enhancement, contrast adjustment, and feature extraction in grayscale images.  

 

The module also facilitates morphological grayscale reconstruction. Here, it reconstructs an image's shape and intensity. This is useful for tasks like image segmentation and object tracking.  

 

Skimage.morphology includes a wide variety of structuring elements. It includes diamond, star-shaped, disk-shaped, and octagon-shaped elements. It helps to tailor morphology operations to specific needs.  

 

The watershed algorithm, commonly used in image segmentation, is also available. Thus enabling the partitioning of an image into regions. This partitioning is based on markers and gradients.  

 

The module supports working with binary and grayscale images. Its efficient algorithms are optimized for performance and ease of use. It finds applications in various fields. It ranges from computer vision and image analysis to geospatial data processing.  


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

Code

This code generates binary blobs using the `skimage` library and applies a morphological operation to remove small objects.

How to apply the OpenCV threshold method to the morphology image ouput from the Skimage method?

PythondotLines of Code : 9dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import cv2
import skimage.morphology

blobs_labels = skimage.data.binary_blobs()
constant = 16
b = skimage.morphology.remove_small_objects(blobs_labels, constant)
b = b.astype('uint8')
img = cv2.threshold(b, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

Follow the steps carefully to get the output easily.

  • Download and install VS Code on your desktop.
  • Open VS Code and create a new file in the editor.
  • Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
  • Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
  • pip install scikit-image - Use this line in the command prompt to install skimage.
  • pip install matplotlib - Use this line in the command prompt to install matplotlib.
  • Remove the first line and add import matplotlib.pyplot as plt in the beginning.
  • Remove the last line and add the following -
plt.imshow(b, cmap='gray')
plt.show()
  • To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac).


I hope you found this useful. I have added the dependencies and their version information below.


I found this code snippet by searching for "skimage.morphology" in kandi. You can try any such use case!

Dependencies

matplotlibby matplotlib

Python doticonstar image 17559 doticonVersion:v3.7.1doticon
no licences License: No License (null)

matplotlib: plotting with Python

Support
    Quality
      Security
        License
          Reuse

            matplotlibby matplotlib

            Python doticon star image 17559 doticonVersion:v3.7.1doticonno licences License: No License

            matplotlib: plotting with Python
            Support
              Quality
                Security
                  License
                    Reuse

                      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

                                          If you do not have Scikit-image that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the page in kandi.


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

                                          Environment tested

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


                                          1. The solution is created and tested using Vscode 1.77.2 version
                                          2. This code was tested using Python version 3.8.0
                                          3. This code was tested using matplotlib version 3.7.1
                                          4. This code was tested using scikit-image version 0.21.0


                                          By using this technique, you can utilise various functionalities of skimage.morphology module. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code.

                                          FAQ

                                          1. What is fast binary morphological dilation, and how is it used in skimage.morphology?  

                                          Fast binary morphological dilation in skimage.morphology expands object boundaries. It is often used for noise reduction and feature enhancement.  

                                           

                                          2. How does Morphological Image Analysis help with skimage.morphology?  

                                          Morphological Image Analysis aids shape recognition and texture characterization with skimage.morphology.  

                                           

                                          3. What advantages does fast binary morphological erosion offer when using skimage.morphology?   

                                          Fast binary morphological erosion reduces object sizes and removes noise in binary images.  

                                           

                                          4. How can greyscale morphological dilation be applied to image processing with skimage.morphology?  

                                          Greyscale morphological dilation enhances image features and contrasts in skimage.morphology.  


                                          5. Where do Image and Sequence Processing techniques fit into the usage of skimage.morphology?  

                                          Image and Sequence Processing techniques integrate seamlessly with skimage.morphology. This helps with image segmentation and feature extraction. 

                                          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.