Scikit-image 简明教程
Scikit Image Tutorial
Scikit Image Tutorial
Scikit-Image，通常缩写为 skimage，是用于 Python 编程语言的开源图像处理库之一。它为各种图像处理和计算机视觉任务提供了强大的算法和函数工具箱。它建立在流行的科学库之上，如 NumPy 和 SciPy.ndimage。
Scikit-Image, often abbreviated as skimage, one of the open-source image-processing libraries for the Python programming language. It provides a powerful toolbox of algorithms and functions for various image processing and computer vision tasks. And it is built on top of popular scientific libraries like NumPy and SciPy.ndimage.
它提供多个插件和方法来读写多种格式的图像，例如 JPEG、PNG、TIFF 等。这样，您就可以轻松地从不同的来源读取图像，并在完成后将它们保存回输出。
It offers multiple plugins and methods to read and write images of various formats, such as JPEG, PNG, TIFF, and more. So that you can easily read the images from different sources and save them back out when you’re done with the image processing tasks.
在本教程中，我们将采用动手方式学习 Skimage 库的各种功能。从基本图像操作到图像处理任务，如图像增强、对象分割、特征提取等等。
In this tutorial, we’ll take a hands-on approach to learning into various functionalities of Skimage library. From basic image operation to image processing tasks like image enhancement, objects segmention, extracting features and any more.
Why to learn Scikit Image?
学习 skimage 是一项必备技能，适用于那些想使用 Python 以清晰而有意义的方式可视化和分析图像中存在的数据的人。它与其他科学 Python 库（如 NumPy 和 SciPy）相集成，使其成为多种任务的宝贵工具，包括高级计算机视觉项目。通过学习 Scikit-Image，您可以获得从图像中提取有意义信息、识别对象并从可视数据中获得有价值信息的能力。
Learning skimage is an essential skill for the persons who has interested in visualizing and analizing data present in the images in a clear and meaningful way using Python. Its integration with other scientific Python libraries like NumPy and SciPy makes it a valuable tool for a variety of tasks, including advanced computer vision projects. By learning Scikit-Image, you gain the ability to extract meaningful information from images, identify objects, and obtain valuable information from visual data.
Applications of Scikit-Image
Scikit-Image 是一个强大的工具，广泛用于涉及图像处理和计算机视觉的各种应用中。无论是提高图像质量、分割对象还是提取特征，Scikit-Image 都提供了一个丰富的算法工具箱来处理各种与图像相关的任务。
Scikit-Image, is a powerful tool widely used in various applications involving image processing and computer vision. Whether you’re enhancing image quality, segmenting objects, or extracting features, Scikit-Image offers a rich toolbox of algorithms to tackle various image-related tasks.
Who Should Learn skimage?
本教程旨在作为指南，为那些希望利用 Python 数据可视化和数据分析来提高其图像处理技能的人而设计。对于计算机科学、工程或相关领域的希望将图像处理任务集成到其应用中的学生和研究人员，本教程也很有用。
This tutorial is designed to work as a guide for individuals who are looking to enhance their image processing skills using Python data visualization, and data analysis. It is also useful for students and researchers in computer science, engineering, or related fields who want to integrate image processsing tasks into their applications.
Prerequisites to learn skimage
要开始使用 Scikit-Image，熟悉 Python 编程非常重要，了解数组、函数和库等概念将帮助您更有效地学习 Skimage 的功能。对 NumPy 和 SciPy 的基本了解也会有益，但不是强制性的。熟悉使用 pip 安装 Python 依赖项（如“pip install package_name”）也很有帮助。让我们开始吧！
To get started with Scikit-Image, familiarity with Python programming is essential, having knowlegde on concepts like arrays, functions, and libraries will help you to learn Skimage’s functionality more effectively. Basic understanding of NumPy and SciPy can also be beneficial but not mandatory. Along with familiarity with installing Python dependencies using pip (like "pip install package_name") also helpful. Let’s get started!
Scikit Image Jobs and Opportunities
精通 Scikit-Image 为以下行业开辟了一系列职业机会：
Proficiency in Scikit-Image opens up a range of career opportunities in industries such as −
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Healthcare
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Automotive
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Security
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Entertainment
像图像处理工程师、计算机视觉研究员、专注于图像分析的数据科学家、机器学习/人工智能工程师、数据分析师和数据工程师这样的工作角色通常需要了解 Skimage。
The job roles like Image processing engineer, Computer vision researcher, Data scientist specializing in image analysis, Machine Learning/AI Engineer, Data Analyst and Data Engineer, often require knowledge of Skimage.