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دانلود کتاب OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision applications with OpenCV and C++, 4th Edition
دانلود کتاب OpenCV 4 Computer Vision Application Programming Cookbook: ساخت برنامه های پیچیده بینایی کامپیوتری با OpenCV و C نسخه 4
مشخصات کتاب
OpenCV 4 Computer Vision Application Programming Cookbook: Build complex computer vision applications with OpenCV and C++, 4th Edition
ویرایش: نویسندگان: David Millán Escrivá, Robert Laganiere سری: ISBN (شابک) : 9781789345285, 1789345286 ناشر: Packt Publishing Ltd سال نشر: 2019 تعداد صفحات: 494 [479] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 33 Mb
قیمت کتاب (تومان) : 55,000
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توجه داشته باشید کتاب OpenCV 4 Computer Vision Application Programming Cookbook: ساخت برنامه های پیچیده بینایی کامپیوتری با OpenCV و C نسخه 4 نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب OpenCV 4 Computer Vision Application Programming Cookbook: ساخت برنامه های پیچیده بینایی کامپیوتری با OpenCV و C نسخه 4
دستور العمل های جالبی را برای کمک به درک مفاهیم تشخیص اشیا، پردازش تصویر و تشخیص چهره کشف کنید. قابلیت های یادگیری ماشین توضیحات کتاب OpenCV یک کتابخانه پردازش تصویر و ویدئو است که برای انواع تجزیه و تحلیل تصویر و ویدئو استفاده می شود. در سراسر کتاب، شما با دستور العمل هایی کار خواهید کرد که وظایف مختلفی مانند تشخیص و تشخیص چهره را اجرا می کنند. این کتاب با 70 آموزش مستقل، نقاط درد رایج و بهترین شیوه ها را برای توسعه دهندگان بینایی کامپیوتر (CV) بررسی می کند. هر دستور غذا به یک مشکل خاص می پردازد و یک راه حل اثبات شده و بهترین عمل را با بینش هایی در مورد نحوه کار ارائه می دهد، به طوری که می توانید کد و فایل های پیکربندی را کپی کنید و آنها را مطابق با نیاز خود تغییر دهید. این کتاب با تنظیم OpenCV شروع می شود و نحوه دستکاری پیکسل ها را توضیح می دهد. شما خواهید فهمید که چگونه می توانید تصاویر را با کلاس ها پردازش کنید و پیکسل ها را با هیستوگرام بشمارید. همچنین تشخیص، توصیف و تطبیق نقاط علاقه را خواهید آموخت. با پیشروی در فصل‌ها، با تخمین روابط تصویری در تصاویر، بازسازی صحنه‌های سه‌بعدی، پردازش توالی‌های ویدیویی و ردیابی حرکت بصری آشنا می‌شوید. در فصل های پایانی، مفاهیم یادگیری عمیق مانند تشخیص چهره و اشیا را پوشش خواهید داد. تا پایان کتاب، می‌توانید با اطمینان الگوریتم‌های بینایی بین رایانه‌ای را برای برآوردن نیازهای فنی پروژه‌های CV پیچیده خود پیاده‌سازی کنید. اشیاء معنی دار استفاده از فیلترهای تصویر برای افزایش محتوای تصویر بهره برداری از هندسه تصویر برای انتقال نماهای مختلف از یک صحنه تصویر شده کالیبره کردن دوربین از مشاهدات تصویری مختلف تشخیص افراد و اشیاء در تصاویر با استفاده از تکنیک های یادگیری ماشینی بازسازی صحنه 3 بعدی از تصاویر کاوش تشخیص چهره با استفاده از یادگیری عمیق این کتاب برای چه کسی است اگر شما شما یک توسعه‌دهنده CV یا حرفه‌ای هستید که قبلاً از OpenCV برای ساختن نرم‌افزار بینایی کامپیوتری استفاده می‌کنید یا می‌خواهید از آن استفاده کنید، این کتاب برای شماست. اگر یک برنامه نویس C هستید و به دنبال گسترش مهارت های بینایی کامپیوتر خود با یادگیری OpenCV هستید، این کتاب برای شما مفید خواهد بود.
توضیحاتی درمورد کتاب به خارجی
Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key FeaturesExplore the latest features and APIs in OpenCV 4 and build computer vision algorithmsDevelop effective, robust, and fail-safe vision for your applicationsBuild computer vision algorithms with machine learning capabilitiesBook Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you\'ll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by setting up OpenCV, and explains how to manipulate pixels. You\'ll understand how you can process images with classes and count pixels with histograms. You\'ll also learn detecting, describing, and matching interest points. As you advance through the chapters, you\'ll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you\'ll cover deep learning concepts such as face and object detection. By the end of the book, you\'ll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects What you will learnInstall and create a program using the OpenCV librarySegment images into homogenous regions and extract meaningful objectsApply image filters to enhance image contentExploit image geometry to relay different views of a pictured sceneCalibrate the camera from different image observationsDetect people and objects in images using machine learning techniquesReconstruct a 3D scene from imagesExplore face detection using deep learningWho this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV.
فهرست مطالب
Cover Title Page Copyright and Credits About Packt Contributors Table of Contents Preface Chapter 1: Playing with Images Installing the OpenCV library Getting ready How to do it... How it works... There's more... Using Qt for OpenCV developments The OpenCV developer site See also Loading, displaying, and saving images Getting ready How to do it... How it works... There's more... Clicking on images Drawing on images Running the example with Qt See also Exploring the cv::Mat data structure How to do it... How it works... There's more... The input and output arrays See also Defining regions of interest Getting ready How to do it... How it works... There's more... Using image masks See also Chapter 2: Manipulating the Pixels Accessing pixel values Getting ready How to do it... How it works... There's more... The cv::Mat_ template class See also Scanning an image with pointers Getting ready How to do it... How it works... There's more... Other color reduction formulas Having input and output arguments Efficient scanning of continuous images Low-level pointer arithmetics See also Scanning an image with iterators Getting ready How to do it... How it works... There's more... See also Writing efficient image-scanning loops How to do it... How it works... There's more... See also Scanning an image with neighbor access Getting ready How to do it... How it works... There's more... See also Performing simple image arithmetic Getting ready How to do it... How it works... There's more... Overloaded image operators Splitting the image channels Remapping an image How to do it... How it works... See also Chapter 3: Processing Color Images with Classes Comparing colors using the strategy design pattern How to do it... How it works... There's more... Computing the distance between two color vectors Using OpenCV functions The functor or function object The OpenCV base class for algorithms See also Segmenting an image with the GrabCut algorithm How to do it... How it works... See also Converting color representations Getting ready How to do it... How it works... See also Representing colors with hue, saturation, and brightness How to do it... How it works... There's more... Using colors for detection – skin tone detection Chapter 4: Counting the Pixels with Histograms Computing the image histogram Getting started How to do it... How it works... There's more... Computing histograms of color images See also Applying lookup tables to modify the image's appearance How to do it... How it works... There's more... Stretching a histogram to improve the image contrast Applying a lookup table on color images Equalizing the image histogram How to do it... How it works... Backprojecting a histogram to detect specific image content How to do it... How it works... There's more... Backprojecting color histograms Using the mean shift algorithm to find an object How to do it... How it works... See also Retrieving similar images using histogram comparison How to do it... How it works... See also Counting pixels with integral images How to do it... How it works... There's more... Adaptive thresholding Visual tracking using histograms See also Chapter 5: Transforming Images with Morphological Operations Eroding and dilating images using morphological filters Getting ready How to do it... How it works... There's more... See also Opening and closing images using morphological filters How to do it... How it works... See also Detecting edges and corners using morphological filters Getting ready How to do it... How it works... See also Segmenting images using watersheds How to do it... How it works... There's more... See also Extracting distinctive regions using MSER How to do it... How it works... See also Extracting foreground objects with the GrabCut algorithm How to do it... How it works... See also Chapter 6: Filtering the Images Filtering images using low-pass filters How to do it... How it works... See also Downsampling an image How to do it... How it works... There's more... Interpolating pixel values See also Filtering images using a median filter How to do it... How it works... Applying directional filters to detect edges How to do it... How it works... There's more... Gradient operators Gaussian derivatives See also Computing the Laplacian of an image How to do it... How it works... There's more... Enhancing the contrast of an image using the Laplacian Difference of Gaussians See also Chapter 7: Extracting Lines, Contours, and Components Detecting image contours with the Canny operator How to do it... How it works... See also Detecting lines in images with the Hough transform Getting ready How to do it... How it works... There's more... Detecting circles See also Fitting a line to a set of points How to do it... How it works... There's more... Extracting the components' contours How to do it... How it works... There's more... Computing components' shape descriptors How to do it... How it works... There's more... Quadrilateral detection Chapter 8: Detecting Interest Points Detecting corners in an image How to do it... How it works... There's more... Good features to track The feature detector's common interface See also Detecting features quickly How to do it... How it works... There's more... Adapted feature detection See also Detecting scale-invariant features How to do it... How it works... There's more... The SIFT feature-detection algorithm See also Detecting FAST features at multiple scales How to do it... How it works... There's more... The ORB feature-detection algorithm See also Chapter 9: Describing and Matching Interest Points Matching local templates How to do it... How it works... There's more... Template matching See also Describing local intensity patterns How to do it... How it works... There's more... Cross-checking matches The ratio test Distance thresholding See also Describing keypoints with binary features How to do it... How it works... There's more... FREAK See also Chapter 10: Estimating Projective Relations in Images Computing the fundamental matrix of an image pair Getting ready How to do it... How it works... See also Matching images using a random sample consensus How to do it... How it works... There's more... Refining the fundamental matrix Refining the matches Computing a homography between two images Getting ready How to do it... How it works... There's more... Detecting planar targets in an image How to do it... See also Chapter 11: Reconstructing 3D Scenes Digital image formation Calibrating a camera Getting ready How to do it... How it works... There's more... Calibration with known intrinsic parameters Using a grid of circles for calibration See also Recovering the camera pose How to do it... How it works... There's more... cv::Viz – a 3D visualizer module See also Reconstructing a 3D scene from calibrated cameras How to do it... How it works... There's more... Decomposing a homography Bundle adjustment See also Computing depth from a stereo image Getting ready How to do it... How it works... See also Chapter 12: Processing Video Sequences Reading video sequences How to do it... How it works... There's more... See also Processing video frames How to do it... How it works... There's more... Processing a sequence of images Using a frame processor class See also Writing video sequences How to do it... How it works... There's more... The codec four-character code See also Extracting the foreground objects in a video How to do it... How it works... There's more... The mixture of Gaussian method See also Chapter 13: Tracking Visual Motion Tracing feature points in a video How to do it... How it works... See also Estimating the optical flow Getting ready How to do it... How it works... See also Tracking an object in a video How to do it... How it works... See also Chapter 14: Learning from Examples Recognizing faces using the nearest neighbors of local binary patterns How to do it... How it works... See also Finding objects and faces with a cascade of Haar features Getting ready How to do it... How it works... There's more... Face detection with a Haar cascade See also Detecting objects and people using SVMs and histograms of oriented gradients Getting ready How to do it... How it works... There's more... HOG visualization People detection Deep learning and convolutional neural networks (CNNs) See also Chapter 15: OpenCV Advanced Features Face detection using deep learning How to do it... How it works... See also Object detection with YOLOv3 How to do it... How it works... See also Enabling Halide to improve efficiency How to do it... How it works... See also OpenCV.js introduction How to do it... How it works... Other Books You May Enjoy Index
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