Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4) and Python
Apply machine learning and deep learning techniques with TensorFlow and Keras
Discover the modern design patterns you should avoid when developing efficient computer vision applications
Description
OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language.
In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras.
By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.
What you will learn
Handle files and images, and explore various image processing techniques
Explore image transformations, including translation, resizing, and cropping
Gain insights into building histograms
Brush up on contour detection, filtering, and drawing
Work with Augmented Reality to build marker-based and markerless applications
Work with the main machine learning algorithms in OpenCV
Explore the deep learning Python libraries and OpenCV deep learning capabilities
Create computer vision and deep learning web applications
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Alberto Fernndez Villn is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various projects related to monitoring systems for industrial plants, applying both Internet of Things (IoT) and big data technologies. He has a Ph.D. in computer vision (2017), a deep learning certification (2018), and several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. As of 2013, he is a registered and active user (albertofernandez) on the Q&A OpenCV forum.
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