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Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library Overview Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu Create an application that tracks and manipulates faces Identify face regions using normal color images and depth images In Detail Computer Vision can reach consumers in various Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library Overview Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu Create an application that tracks and manipulates faces Identify face regions using normal color images and depth images In Detail Computer Vision can reach consumers in various contexts via webcams, camera phones and gaming sensors like Kinect. OpenCV's Python bindings can help developers meet these consumer demands for applications that capture images, change their appearance and extract information from them, in a high-level language and in a standardized data format that is interoperable with scientific libraries such as NumPy and SciPy. "OpenCV Computer Vision with Python" is a practical, hands-on guide that covers the fundamental tasks of computer vision-capturing, filtering and analyzing images-with step-by-step instructions for writing both an application and reusable library classes. "OpenCV Computer Vision with Python" shows you how to use the Python bindings for OpenCV. By following clear and concise examples you will develop a computer vision application that tracks faces in live video and applies special effects to them. If you have always wanted to learn which version of these bindings to use, how to integrate with cross-platform Kinect drivers and and how to efficiently process image data with NumPy and SciPy then this book is for you. What you will learn from this book Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect-all on Windows, Mac or Ubuntu Capture, display, and save photos and real-time videos Handle window events and input events using OpenCV's HighGui module or Pygame Understand OpenCV's image format and how to perform efficient operations on OpenCV images with NumPy and SciPy Apply "curves" and other color transformations to simulate the look of old photos, movies or video games Apply an effect only to edges in an image Copy and resize segments of an image Apply an effect only to certain depths in an image by using data from a depth sensor such as Kinect Track faces, eyes, noses and mouths by using prebuilt datasets Track arbitrary objects by creating original datasets Approach A practical, project-based tutorial for Python developers and hobbyists who want to get started with computer vision with OpenCV and Python. Who this book is written for OpenCV Computer Vision with Python is written for Python developers who are new to computer vision and want a practical guide to teach them the essentials. Some understanding of image data (for example, pixels and color channels) would be beneficial. At a minimum you will need access to at least one webcam. Certain exercises require additional hardware like a second webcam, a Microsoft Kinect or an OpenNI-compliant depth sensor such as the Asus Xtion PRO.


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Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library Overview Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu Create an application that tracks and manipulates faces Identify face regions using normal color images and depth images In Detail Computer Vision can reach consumers in various Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library Overview Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu Create an application that tracks and manipulates faces Identify face regions using normal color images and depth images In Detail Computer Vision can reach consumers in various contexts via webcams, camera phones and gaming sensors like Kinect. OpenCV's Python bindings can help developers meet these consumer demands for applications that capture images, change their appearance and extract information from them, in a high-level language and in a standardized data format that is interoperable with scientific libraries such as NumPy and SciPy. "OpenCV Computer Vision with Python" is a practical, hands-on guide that covers the fundamental tasks of computer vision-capturing, filtering and analyzing images-with step-by-step instructions for writing both an application and reusable library classes. "OpenCV Computer Vision with Python" shows you how to use the Python bindings for OpenCV. By following clear and concise examples you will develop a computer vision application that tracks faces in live video and applies special effects to them. If you have always wanted to learn which version of these bindings to use, how to integrate with cross-platform Kinect drivers and and how to efficiently process image data with NumPy and SciPy then this book is for you. What you will learn from this book Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect-all on Windows, Mac or Ubuntu Capture, display, and save photos and real-time videos Handle window events and input events using OpenCV's HighGui module or Pygame Understand OpenCV's image format and how to perform efficient operations on OpenCV images with NumPy and SciPy Apply "curves" and other color transformations to simulate the look of old photos, movies or video games Apply an effect only to edges in an image Copy and resize segments of an image Apply an effect only to certain depths in an image by using data from a depth sensor such as Kinect Track faces, eyes, noses and mouths by using prebuilt datasets Track arbitrary objects by creating original datasets Approach A practical, project-based tutorial for Python developers and hobbyists who want to get started with computer vision with OpenCV and Python. Who this book is written for OpenCV Computer Vision with Python is written for Python developers who are new to computer vision and want a practical guide to teach them the essentials. Some understanding of image data (for example, pixels and color channels) would be beneficial. At a minimum you will need access to at least one webcam. Certain exercises require additional hardware like a second webcam, a Microsoft Kinect or an OpenNI-compliant depth sensor such as the Asus Xtion PRO.

44 review for Opencv Computer Vision with Python

  1. 5 out of 5

    Fahad Naeem

    A good book for those who want to learn image processing/manipulation using OpenCV module in Python. The only bad thing about this book is, providing codes in object-oriented programming, which does not lead to better understanding in terms of individuality.

  2. 4 out of 5

    Pranay Kothapalli

    Most of the flags, methods and classes seem to have been renamed/removed from the updated version of OpenCV. Had to refer to the official documentation every time the interpreter threw an error. I suggest you to get an updated version of the book. Overall, a good book to get you started, but not entirely beginner friendly. Doesn't dwell deep into the basics of computer vision stuff, just a scratch on the surface. Wasn't entirely satisfied, but a good read overall.

  3. 4 out of 5

    James Schultz

    It's a good book for beginners but sometimes I wish the author went a little further and deeper. The book doesn't cover anything that you couldn't find online, and although the book is relatively short, a lot of time is spent on installation instructions.

  4. 4 out of 5

    Hector Cuesta

  5. 5 out of 5

    Jamey Fraser

  6. 4 out of 5

    John

  7. 4 out of 5

    Subhajit Das

  8. 4 out of 5

    Ricardo Galli

  9. 4 out of 5

    Priyanka

  10. 4 out of 5

    Mr R E Imber

  11. 5 out of 5

    Huyen

  12. 4 out of 5

    Antonio

  13. 4 out of 5

    Simon

  14. 4 out of 5

    Turan Erdem

  15. 4 out of 5

    Nick Merrill

  16. 4 out of 5

    Priyanka

  17. 5 out of 5

    Jacob Silva

  18. 5 out of 5

    abidrahmank

  19. 4 out of 5

    Brendan

  20. 5 out of 5

    Muthanna Alwahash

  21. 4 out of 5

    Elie De Brauwer

  22. 4 out of 5

    Daniele

  23. 5 out of 5

    Williamguillermo

  24. 4 out of 5

    Tyler

  25. 4 out of 5

    Gutemann

  26. 5 out of 5

    Ben

  27. 5 out of 5

    Dfc

  28. 5 out of 5

    Biblioteca Sardegna Ricerche

  29. 4 out of 5

    Alexander Holik

  30. 4 out of 5

    Alberto Zapatero

  31. 4 out of 5

    Kami Bee

  32. 4 out of 5

    Farideh Hosseini

  33. 4 out of 5

    Fahimeh Saleh

  34. 4 out of 5

    Rainer Jenning

  35. 5 out of 5

    Vikarti

  36. 4 out of 5

    Tarun Vangani

  37. 4 out of 5

    Mark Bastourous

  38. 4 out of 5

    Christian Breuer

  39. 4 out of 5

    Tincy Thomas

  40. 4 out of 5

    Rafal Kijewski

  41. 5 out of 5

    Alif

  42. 4 out of 5

    Rui Figueiredo

  43. 4 out of 5

    Ueliton Freitas

  44. 5 out of 5

    Hariharan Ramshankar

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