- Scikit Image Tutorial
- Scikit Image - Introduction
- Scikit Image - Image Processing
- Scikit Image - Numpy Images
- Scikit Image - Image datatypes
- Scikit Image - Using Plugins
- Scikit Image - Image Handling
- Scikit Image - Reading Images
- Scikit Image - Writing Images
- Scikit Image - Displaying Images
- Scikit Image - Image Collections
- Scikit Image - Image Stack
- Scikit Image - Multi Images
- Scikit Image - Data Visualization
- Scikit Image - Using Matplotlib
- Scikit Image - Using Plotly
- Scikit Image - Using Mayavi
- Scikit Image - Using Napari
Scikit Image - Image Processing
Image processing, in general, refers to the analysis and manipulation of digital images to enhance their quality, extract useful information, or perform various operations on the image data. It involves applying algorithms and techniques to images in order to alter or extract features, remove noise, or enhance visual appearance.
The tasks involved in image processing include −
- Input/Output and Image Display − This involves handling the input and output of images, as well as displaying them on a screen or other output devices.
- Basic Image Manipulations − This includes fundamental operations such as cropping, flipping, rotating, and other similar transformations.
- Image Filtering − This task involves applying filters to images to achieve specific effects. Common filtering operations include denoising (removing noise), sharpening (enhancing the edges and details), and other similar operations.
- Image Segmentation − It focuses on dividing an image into different regions or objects. It involves labeling each pixel or group of pixels to identify which object they belong to. This is useful for tasks like object detection or boundary extraction.
- Classification − Image classification involves categorizing or labeling images based on their content. This task uses machine learning or pattern recognition algorithms to automatically assign images to predefined classes or categories.
- Feature Extraction − Extracting meaningful characteristics or patterns from images, such as edges, textures, or color features.
These are the fundamental tasks in image processing and are used extensively in various applications such as computer vision, medical imaging, remote sensing, and many other fields.
Image processing in Python
Image processing in Python can be performed using various libraries and tools. Following are some popular packages for Python image processing −
- scikit-image − It is an open-source library that provides a wide range of algorithms for image-processing tasks such as filtering, segmentation, feature extraction, and more. It is designed to be user-friendly and integrates well with other scientific Python libraries.
- scipy.ndimage − This package, part of the SciPy library, offers various functions for n-dimensional image processing. It includes operations like filtering, interpolation, morphology, and measurements. It is particularly useful for scientific and medical image analysis.
- OpenCV − Open Source Computer Vision Library, is a highly popular and extensive computer vision library that provides a great collection of image processing and computer vision algorithms. And it is widely used for tasks like object detection, image recognition, and video processing.
- Pillow − Pillow is a powerful Python imaging library that provides a wide range of image processing functionalities. It supports image reading and writing in various formats, basic manipulations, filters, color space conversions, and more.
- scipy.ndimage − This package, part of the SciPy library, offers various functions for n-dimensional image processing. It includes operations like filtering, interpolation, morphology, and measurements. It is particularly useful for scientific and medical image analysis.
These libraries provide a wide range of tools and algorithms for image processing in Python. Depending on your specific requirements, you can choose the most suitable library or combine multiple libraries to achieve your desired image processing tasks.