$20^{\mathrm {th}}$ • Implement advanced techniques in the right way in Python and TensorFlow Finally, we discussed how to perform basic image manipulations with different Python libraries. How to install pip for different OSes or platforms can be found here: https://stackoverflow.com/questions/6587507/how-to-install-pip-with-python-3. The following code block shows how the libraries that we are going to use can be downloaded and installed with pip from a Python prompt (interactive mode): There may be some additional installation instructions, depending on the OS platform you are going to use. The field of optical The scikit-image, mahotas, and opencv libraries will be used for different image processing algorithms. This book will touch the core of image processing, from concepts to code using Python. First try to solve the problems on your own. However, the lack of software for annotating the specialized images has been a hurdle of fully exploiting the images for educating and researching, and enabling intelligent systems for automatic diagnosis or phenotype-genotype association study. The ones we are going to use are: NumPy, SciPy, scikit-image, PIL (Pillow), OpenCV, scikit-learn, SimpleITK, and Matplotlib. We are going to use the pip(orpip3) tool to install the libraries, so—if it isn't already installed—we need to install pip first. The details about how this technique works will be described in the next chapter: Now let us do the reverse: start with a large image of the Victoria Memorial Hall (of a size of 720 x 540) and create a smaller-sized image. The next line of code shows how to use the point() function for a power-law transformation, where γ = 0.6: im_g.point(lambda x: 255*(x/255)**0.6).show(). This book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. For example, for the scikit-image library, detailed installation instructions for different OS platforms can be found here: http://scikit-image.org/docs/stable/install.html. © 2008-2021 ResearchGate GmbH. The word 'Packt' and the Packt logo are registered trademarks belonging to In this book, we are going to use a few Python packages to process an image. The scikit-learn library will be used for building machine-learning models for image processing, and scipy will be used mainly for image enhancements. For example, the next code block shows how to compute the difference image from two successive frames from a video recording (from YouTube) of a match from the 2018 FIFA World Cup: The next figure shows the output of the code, with the consecutive frame images followed by their difference image: The subtract() function can be used to first subtract two images, followed by dividing the result by scale (defaults to 1.0) and adding the offset (defaults to 0.0). Each point is called a pixel or pel (picture element). The next code snippet shows the large image to start with: The output of the previous code, the large image of the Victoria Memorial Hall, is shown as follows: The next line of code shows how the resize() function can be used to shrink the previous image of the Victoria Memorial Hall (by a factor of 5) to resize it to an output image of a size 25 times smaller than the input image by using anti-aliasing (a high-quality down-sampling technique). The following code block shows how to convert from the PIL Image object into numpy ndarray (to be consumed by scikit-image): The next figure shows the output of the previous code, which is an image of flowers: The following code block shows how to convert from numpy ndarray into a PIL Image object. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. In the last few chapters of this book, we will need to use a different setup when we use deep-learning-based methods. Figures from the book. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Sign up to our emails for regular updates, bespoke offers, exclusive
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