- What is sys.argv[1]?
- Write a piece of code to add a first_number argument of the int type and include the help first number to be added using parser.add_argument().
- Write a piece of code to save the imagine img to disk with the name image.png.
- Create the capture object using cv2.VideoCapture() to read from the first camera that's connected to your computer.
- Create the object capture using cv2.VideoCapture() to read from the first camera connected to your computer and print the CAP_PROP_FRAME_WIDTH property.
- Read an image and save it to disk with the same name but ending in _copy.png (for example, logo_copy.png).
- Create a script (read_video_file_backwards_save_video.py) that loads a video file and creates another played backwards (containing the last frame of the video first and so on).
-
Book Overview & Buying
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Table Of Contents
Mastering OpenCV 4 with Python
By :
Mastering OpenCV 4 with Python
By:
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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