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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

Putting it all together

In order to run our app, we will need to execute the main function routine (chapter8.py) that loads the pre-trained cascade classifier and the pre-trained MLP and applies them to each frame of the webcam live stream.

However, this time, instead of collecting more training samples, we will start the program with a different option, shown here:

 $ python chapter8.py demo --classifier data/clf1

This will start the application with a new FacialExpressionRecognizerLayout layout, which is a subclass of BasicLayout without any extra UI elements. Let's go over the constructor first, as follows:

  1. It reads and initializes all the data that was stored by the training script, like this:
class FacialExpressionRecognizerLayout(BaseLayout):
def __init__(self, *args,
clf_path=None,
**kwargs):
super().__init__(*args, **kwargs...
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