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OpenCV 3 Computer Vision with Python Cookbook

OpenCV 3 Computer Vision with Python Cookbook

By : Aleksei Spizhevoi, Rybnikov
3.3 (3)
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OpenCV 3 Computer Vision with Python Cookbook

OpenCV 3 Computer Vision with Python Cookbook

3.3 (3)
By: Aleksei Spizhevoi, Rybnikov

Overview of this book

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.
Table of Contents (11 chapters)
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Getting input and output tensors' shapes for all layers

Sometimes it's necessary to get information about what's going on with the data shape during a forward pass in deep neural networks. For example, some models allow the usage of various input spatial size and, in that case, you may want to know the output tensors' shapes. OpenCV has an option to get all shapes for all tensors (including intermediate tensors) without inference. This recipe reviews ways of using such functionality along with other useful routines relevant to neural nets.

Getting ready

Before you proceed with this recipe, you need to install the OpenCV 3.3.1 (or higher) Python API package.

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