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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

By : Rever
1 (1)
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

1 (1)
By: Rever

Overview of this book

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.
Table of Contents (9 chapters)
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Evaluating the results

After we finished training, as we can see from the following screenshot, we get a result of over 99%, so that is significantly better than what we got with softmax or our SVM:

Deep learning is probably the most powerful machine learning technique, due to its ability to learn very complex pattern recognition. It's just dominating everything else for advanced computer vision, speech processing, and more—stuff that conventional machine learning techniques haven't been all that successful at. However, that doesn't necessarily mean that you want to use deep learning for everything. Deep learning generally acquires a large number of examples—many thousands, if not millions sometimes—and it can also be very computationally expensive. So, it's not always the best solution, although it is very powerful, as we have seen right...

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