Book Image

Mastering Computer Vision with TensorFlow 2.x

By : Krishnendu Kar
Book Image

Mastering Computer Vision with TensorFlow 2.x

By: Krishnendu Kar

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Introduction to Computer Vision and Neural Networks
6
Section 2: Advanced Concepts of Computer Vision with TensorFlow
11
Section 3: Advanced Implementation of Computer Vision with TensorFlow
14
Section 4: TensorFlow Implementation at the Edge and on the Cloud

Object detection on Raspberry Pi using TensorFlow Lite

The Python quickstart package listed under TensorFlow Lite (https://www.tensorflow.org/lite/guide/python) describes how to install the TensorFlow Lite package for Raspberry Pi. However, there are several notable exceptions. Due to this, we have listed the entire process here:

  1. First and foremost, install the TensorFlow Lite interpreter. Raspberry Pi has ARM7 and Python3.7 installed, so run the following two commands in the Terminal:
$sudo su
$pip3 install tflite_runtime-1.14.0-cp37-cp37m-linux_armv7l.whl
  1. As per the official TensorFlow Lite documentation, some changes need to be made in the label_image.py file (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/python):
$import tflite_runtime.interpreter as tflite,
$interpreter = tf.lite.Interpreter(model_path=args.model_file)
Note that when those...