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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

By : Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler
5 (4)
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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

5 (4)
By: Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler

Overview of this book

OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.
Table of Contents (14 chapters)
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11
Profiling and Accelerating Your Apps
12
Setting Up a Docker Container
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Defining a Dockerfile

Instructions in the Dockerfile start from a base image, and then desired installations and modifications are done on top of that image.

At the time of writing, TensorFlow does not support Python 3.8. If you plan to run Chapter 7, Learning to Recognize Traffic Signs, or Chapter 9, Learning to Classify and Localize Objects, where TensorFlow is used, you can start with Python 3.7 and then install TensorFlow with pip, or you can pick tensorflow/tensorflow:latest-py3 as the base image.

Let's go over the steps to create our environment:

  1. We start from a base image, which is the basic Python image that is based on Debian:
FROM python:3.8
  1. We install useful packages that will particularly be used during the installation process of OpenCV and other dependencies:
RUN apt-get update && apt-get install -y \
build-essential \
cmake \
...
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