Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • OpenCV 4 with Python Blueprints
  • Toc
  • feedback
OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

By : Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler
5 (4)
close
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)
close
11
Profiling and Accelerating Your Apps
chevron up
12
Setting Up a Docker Container

Profiling and Accelerating Your Apps

When you have a problem with a slow app, first of all, you need to find which exact parts of your code are taking quite a lot of processing time. A good way of finding such parts of the code, which are also called bottlenecks, is to profile the app. One of the good profilers available that allow an app to be profiled without modifications being introduced to the app is called pyinstrument (https://github.com/joerick/pyinstrument). Here, we profile the app of Chapter 10, Learning to Detect and Track Objects, using pyinstrument, as follows:

$ pyinstrument -o profile.html -r html  main.py

We have passed an output .html file where we want the profiling report information to be saved with a -o option.

We have also specified how the report should be rendered with a -r option, to state that we want an HTML output. Once the app is terminated, the...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete