One interesting functionality offered by OpenCV in connection with histograms is the cv2.compareHist() function, which can be used to get a numerical parameter expressing how well two histograms match each other. In this sense, as histograms reflect the intensity distributions of the pixel values in the image, this function can be used to compare images. As previously commented, the histograms show only statistical information and not the location of pixels. Therefore, a common approach for image comparison is to divide the image into a certain number of regions (commonly with the same size), calculate the histogram for each region, and, finally, concatenate all the histograms to create the feature representation of the image. In this example, for simplicity, we are not going to divide the image into a certain number of regions, so only one region (the full...
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.
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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