This is the code repository for Mastering Computer Vision with TensorFlow 2.x, published by Packt.
Build advanced computer vision applications using machine learning and deep learning techniques
Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
This book covers the following exciting features:
- Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
- Use TensorFlow for various visual search methods for real-world scenarios
- Build neural networks or adjust parameters to optimize the performance of models
- Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
- Evaluate your model and optimize and integrate it into your application to operate at scale
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
faceresize = cv2.resize(detected_face, (img_size,img_size))
img_name = "dataset/opencv_frame_{}.jpg".format(img_counter)
cv2.imwrite(img_name, faceresize)
Following is what you need for this book: This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Jupyter Notebook | Windows, Mac OS X, and Linux (Any) |
| 1-10 | TensorFlow 2.0, Google Colab | Windows, Mac OS X, and Linux (Any) |
| 11-12 | Amazon Web Services, Azure and Google Cloud Platform | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
https://drive.google.com/open?id=1rvhB5sE5u6lzXAb4o8hsZcbhXZ2oiQHY
Although the book has gone through many reviews, we noticed few errors and additional clarifications required after the book was published - we believe listing these will help the readers - the link for these can be found below. This is a dynamic section and is a means to work with our readers to make this book even better.
https://drive.google.com/drive/folders/1lTgWdYdhJPju9oOJ0Hns9QRjvtdhG6Qq?usp=sharing
Krishnendu Kar is passionate about research on computer vision and solving AI problems to make our life simpler. His core expertise is deep learning - computer vision, IoT, and agile software development. Krish is also a passionate app developer and has a dash cam-based object and lane detection and turn by turn navigation and fitness app in the iOS app store - Nity Map AI Camera & Run timer.
Here is the author linkedin page if you have questions on the book. He will try to respond all answers within 48 hours - his average response time is 12 hours. https://www.linkedin.com/in/krish-kar-554739b2/
Click here if you have any feedback or suggestions.