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Python Image Processing Cookbook

Python Image Processing Cookbook

By : Sandipan Dey
2 (2)
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Python Image Processing Cookbook

Python Image Processing Cookbook

2 (2)
By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)
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Image Manipulation and Transformation

Image transformation is the art of transforming an image. With image transformation and manipulation, we can enhance the appearance of an image. The transformation and manipulation operation can also be used as preprocessing steps for more complex image processing tasks, such as classification or segmentation, which you will get more acquainted with in later chapters. In this chapter, you are going to learn how to use different Python libraries (NumPy, SciPy, scikit-image, OpenCV-Python, Mahotas, and Matplotlib) for image manipulation and transformation. Different recipes will help you to learn how to write Python code to implement color space transformation, geometric transformations, perspective transforms/homography, and so on.

In this chapter, we will cover the following recipes:

  • Transforming color space (RGB → Lab)
  • Applying affine transformation
  • Applying perspective transformation and homography
  • Creating pencil sketches from images
  • Creating cartoonish images
  • Simulating light art/long exposure
  • Object detection using color in HSV
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