Neural Network Computer Vision with OpenCV 5: Build computer vision solutions using Python and DNN module

Unlocking computer vision with Python and OpenCV. Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, Deep Learning and CNNs by using Deep Learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems. Whether you are building intelligent systems, automating processes, or working on image-related projects, this book equips you with the skills to revolutionize your approach to visual data. Deep Learning has revolutionized the field of Artificial Intelligence, enabling remarkable progress in areas such as computer vision, natural language processing, and machine translation. This chapter explores the multifaceted landscape of Deep Learning. Moreover, it investigates various architectural approaches, such as convolutional neural networks (CNNs), elucidating their mathematical foundations, strengths, and applications. Furthermore, the chapter introduces training and inference processes in Deep Learning, focusing on techniques for efficient and accurate predictions. It highlights the significance of optimization functions, activation functions, and model compression techniques in enhancing inference speed, reducing computational requirements, and ensuring robustness. What you will learn: - Acquire expertise in image manipulation techniques. - Apply knowledge to practical scenarios in computer vision. - Implement robust systems for face detection and recognition. - Enhance projects with accurate object localization capabilities. - Extract text information from images effectively. Who this book is for: This book is designed for those with basic Python skills, from beginners to intermediate-level readers. Whether you are building intelligent robots that perceive their surroundings or crafting advanced vision systems for object detection and image analysis, this book will equip you with the tools and skills to push the boundaries of AI perception. Cover Page Title Page Copyright Page Dedication About the Author About the Reviewer Acknowledgement Preface Table of Contents 1. Introduction to Computer Vision Introduction Structure Objectives History of computer imaging Retrieving information from images Image processing Representation Manipulation Flexibility Reproducibility Digital image processing Conclusion Exercises 2. Basics of Imaging Introduction Structure Objectives Pixels and image representation Pixels Color spaces Primary colors Additive colors Subtractive colors Grayscale Other color spaces Pixels and color spaces Examples Image filetypes Video files Images and videos Programming for image data A brief history of computer image programming OpenCV: History and overview Image processing code samples Opening, viewing and closing image files CPP code Python code Videos and frames Programming with color spaces Grayscale RGB image Conclusion Exercises 3. Challenges in Computer Vision Introduction Structure Objectives Topics in computer vision Complexity in image processing Image classification Object localization Image segmentation Character recognition Conclusion Exercises Key terms 4. Classical Solutions Introduction Structure Objectives Solutions for challenges in computer vision Classical solutions Modern solutions Algorithm families Morphological operations Erosion and dilation of images Closing and opening images Thresholding Detecting edges and corners Image transformations Region growing Clustering Template matching Watershed algorithm Foreground and background detection Superpixels Image pyramids Convolution Conclusion Exercises Key terms 5. Deep Learning and CNNs Introduction Structure Objectives History of deep learning Perceptron Shallow learning networks Deep learning networks Weights, biases, and activation functions Weight Bias Activation function Optimization function Convolutional neural networks CNNs versus fully connected networks Deep learning process Training Techniques in training Inference process Techniques/tricks in inference Conclusion Key terms Exercises 6. OpenCV DNN Module Introduction Structure Objectives Deep learning frameworks TensorFlow PyTorch Keras Inference for computer vision Local inferencing Local CPUs Local GPUs Cloud Edge computing OpenCV DNN module History Features and limitations Capabilities Limitations Considerations Supported layers Unsupported layers and operations Important classes Conclusion Exercises 7. Modern Solutions for Image Classification Introduction Structure Objectives CNNS for classification Inception-v3 Keras OpenCV DNN module ResNet Keras implementation OpenCV DNN implementation MobileNetV2 Keras implementation OpenCV DNN implementation Comparison of models Parameters for blobFromImage() Conclusion Exercises 8. Modern Solutions for Object Detection Introduction Structure Convolutional neural networks architecture for object detection Faster region convolutional neural network Single shot multibox detector You only look once YOLOv3 Overview of NMSBoxes() API YOLOv5 Differences between YOLOv3 and v5 Obtaining v5 model ONNX file Working with v6, v7 and v8 Conclusion Exercises 9. Faces and Text Introduction Structure Objectives Face detection Haar cascades Deep learning approaches: YuNet Face recognition Face detection versus recognition Face recognition using landmarks Face recognizer module Labeled Faces in the Wild dataset FaceRecognizerSF class Comparing faces Text recognition Text detection Text recognition OpenCV Model Zoo Conclusion Exercises Key terms 10. Running the Code Introduction Structure Objectives Sequence of steps Setting up Anaconda Installing Anaconda on Windows Installing Anaconda on Ubuntu Linux Installing Git Installing Git on Windows Installing Git on Ubuntu Setting up Python environment Fetching the code Downloading the code Fetch the weights Installing the libraries Running the code Conclusion Exercises 11. End-to-end Demo Introduction Structure Objectives Code main_app.py video_app_ui.py image_processor.py numberplate_recognizor.py object_detector.py Running the code Application design Notes about codes Conclusion Exercises Index Unlocking computer vision with Python and OpenCVDESCRIPTIONNeural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition.This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, deep learning and CNNs by using deep learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples.You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems. Whether you are building intelligent systems, automating processes, or working on image-related projects, this book equips you with the skills to revolutionize your approach to visual data.WHAT YOU WILL LEARN● Acquire expertise in image manipulation techniques.● Apply knowledge to practical scenarios in computer vision.● Implement robust systems for face detection and recognition.● Enhance projects with accurate object localization capabilities.● Extract text information from images effectively.WHO THIS BOOK IS FORThis book is designed for those with basic Python skills, from beginners to intermediate-level readers. Whether you are building intelligent robots that perceive their surroundings...