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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

Preparing an inference script

Our inference script is quite simple. It will first prepare a drawing function, then load the model and connect it to the camera. Then, it will loop over the frames from the video stream. In the loop, for each frame of the stream, it will use the imported model to make an inference and the drawing function to display the results. Let's create a complete script using the following steps:

  1. First, we import the required modules:
import numpy as np
import cv2
import tensorflow.keras as K

In this code, besides importing NumPy and OpenCV, we have also imported Keras. We are going to use Keras to make predictions in this script; additionally, we will use it to create and train our models throughout the chapter.

  1. Then, we define a function to draw localization bounding boxes on a frame:
def draw_box(frame: np.ndarray, box: np.ndarray) -> np.ndarray...
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