Keras (https://keras.io/) is an open source, high-level neural network API written in Python (compatible with Python 2.7-3.6). It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML, and was developed with a focus on enabling fast experimentation. In this section, we are going to see two examples. In the first example, we are going to see how to solve a linear regression problem using the same input data as the TensorFlow example in the previous section. In the second example, we will classify some handwritten digits using the MNIST dataset in the same way we also performed in the previous section with TensorFlow. This way, you can clearly see the differences between the two libraries when solving the same kind of problems.
Mastering OpenCV 4 with Python
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Mastering OpenCV 4 with Python
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Overview of this book
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.
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Current Title:
Mastering OpenCV 4 with Python
Table of Contents (20 chapters)
Preface
Free Chapter
Section 1: Introduction to OpenCV 4 and Python
Setting Up OpenCV
Image Basics in OpenCV
Handling Files and Images
Constructing Basic Shapes in OpenCV
Section 2: Image Processing in OpenCV
Image Processing Techniques
Constructing and Building Histograms
Thresholding Techniques
Contour Detection, Filtering, and Drawing
Augmented Reality
Section 3: Machine Learning and Deep Learning in OpenCV
Machine Learning with OpenCV
Face Detection, Tracking, and Recognition
Introduction to Deep Learning
Section 4: Mobile and Web Computer Vision
Mobile and Web Computer Vision with Python and OpenCV
Assessments
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