Scikit-image 简明教程

Scikit Image - Image Processing

Image processing ，一般来说，是指分析和处理数字图像以提高其质量、提取有用的信息或对图像数据执行各种操作。它涉及将算法和技术应用于图像，以便更改或提取特征、去除噪声或增强视觉外观。

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.

图像处理涉及的任务包括：

The tasks involved in image processing include −

  1. 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.

  2. Basic Image Manipulations − This includes fundamental operations such as cropping, flipping, rotating, and other similar transformations.

  3. 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.

  4. 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.

  5. 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.

  6. Feature Extraction − Extracting meaningful characteristics or patterns from images, such as edges, textures, or color features.

这些是图像处理中的基本任务，广泛应用于计算机视觉、医学成像、遥感和许多其他领域。

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

使用各种库和工具可在Python中执行图像处理。以下是用于Python图像处理的一些流行程序包：

Image processing in Python can be performed using various libraries and tools. Following are some popular packages for Python image processing −

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

这些库为Python中的图像处理提供了大量的工具和算法。根据你的特定需求，可以选择最合适的库或组合多个库来完成所需的图像处理任务。

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.