Practical Python and OpenCV 2nd Edition的实例

新手学习-------------------------------------------------------------------------------------------------------------------------------------------------------------------------COPYRIGHTThe contents of this book unless otherwise indicated areCopyrightc2016Adrianrosebrock,Pylmagesearch.comAll rights reservedThis version of the book was published on 6 June 2016Books like this are made possible by the time invested bythe authors. If you received this book and did not purchaseit, please consider making future books possible by buyingacopyathttps://www.pyimagesearch.com/practicalpython-opencv/today.11CONTENTSI INTRODUCTION2 FACE DETECTION3 WEBCAM FACE DETECTION4 OBJECT TRACKING IN VIDEO265 EYE TRACKING366 HANDWRITING RECOGNITION WITH HOG47Z PLANT CLASSIFICATION8 BUILDING AN AMAZON. COM COVER SEARCH 838. I Keypoints, features, and OpenCV3868.2 Identifying the covers of books889 CONCLUSION110ContentsAuthor's Note: All persons government organizationsuniversities, corporations, institutions and museums usedin this book are either fictitious or used fictitiouslyPYTHON AND OPENCV VERSIONSOver a year ago, when i wrote the first edition of practical Python and Opencv Case Studies the current versionof OpenCv was 2. 4.9, which only supported Python 2.7While many scientific developers(myself included)are verymuch accustomed to using Python 2.7, newcomers to computer vision and machine learning were often confused andfrustrated by the lack of Python 3 support-Python 3 is thefuture of the Python programming language, after all!However, this all changed on June 4th, 2015, which markeda momentous date in the history of opencvpencv 3.0 was finally released!The benefits of Opencv 3 o are numerous including improved stability, performance, increases, and even transparent OpenCL supportBut by far the most exciting update to us in the Pythonworld isPython 3 supportAfter years of being stuck and sequestered to Python 2.7,we can now finally use OpenCv with Python 3+ContentsWhile the differences in Opencv(at least with the Pythonbindings) are relatively small between OpenCV 2. 4. X andOpenCv3.0+, there are enough to warrant a new edition ofPractical Python and OpenCv+ Case StudiesIn this edition, I have updated all chapters, code samples,and datasets to be compatible with OpenCV 3.0. Further-more, all code examples will run in both the Python 2.7 andthe python 3+ environmentsIf you are looking for the OpenCV 2. 4. X and Python 2.7version of this book, please look in the download directoryassociated with your purchase inside you will find theOpenCV 2. 4. X+ Python 2. 7 editionThank you so much for making this book possible, andplease enjoy the new edition of Practical Python and Opencv+Case studies!VIPREFACEMy first book Practical Python and Opencv, taught youthe basics of computer vision and image processingNow, this Case Studies book will help you apply yourknowledge to solve actual real-world problemsI've written these Case Studies from the perspective ofprogrammers who are tackling computer vision problemsin their everyday lives. These characters are entirely fictional, but they are meant to convey a single common themethroughout this book -there people are working on andsolving these types of problems using computer vision every single dayIf you're looking to solve real-world problems in computer vision, such as face detection in both images andvideo, object trackingn video handwriting recognitionclassification, and keypoint matching then this is the bookor youRemember, if you have any questions or comments, orif you simply want to say hello, just send me an email atadrian@pyimagesearch.com,orvisitmywebsiteatwww.pImage Search. com and leave a commentI look forward to hearing from you soon!v11Contentsall the bestAdrian rosebrockVIllPREREQUISITESIn order to make the most of these case studies, youwill need to have a little bit of programming experienceAll examples in this book are in the Python programminglanguage. Familiarity with Python or other scripting languages is suggested but not requireda little background in machine learning and use withthe scikit-learn library is also recommended; however, theexamples in this book contain lots of code so even if youare a complete beginner, do not worry! Youll find that theexamples are extremely detailed and heavily documentedto help you follow along