OpenCV Computer Vision with Python

Joseph Howse

買這商品的人也買了...

相關主題

商品描述

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.

商品描述(中文翻譯)

學習使用Python和OpenCV庫捕捉視頻、操縱圖像和追蹤物體。

概述:
- 在Windows、Mac或Ubuntu上設置OpenCV、其Python綁定和可選的Kinect驅動程序。
- 創建一個追蹤和操縱臉部的應用程序。
- 使用普通彩色圖像和深度圖像識別面部區域。

詳細內容:
計算機視覺可以通過網絡攝像頭、相機手機和Kinect等遊戲傳感器在各種情境下接觸消費者。OpenCV的Python綁定可以幫助開發人員滿足這些消費者對於捕捉圖像、改變外觀和從中提取信息的應用程序的需求,使用高級語言和與NumPy和SciPy等科學庫相互操作的標準化數據格式。

《OpenCV Computer Vision with Python》是一本實用的、實踐導向的指南,涵蓋了計算機視覺的基本任務——捕捉、過濾和分析圖像,並提供了編寫應用程序和可重用庫類的逐步指導。

《OpenCV Computer Vision with Python》向您展示如何使用OpenCV的Python綁定。通過清晰而簡潔的示例,您將開發一個計算機視覺應用程序,可以在實時視頻中追蹤面部並對其應用特殊效果。如果您一直想學習使用哪個版本的這些綁定,如何與跨平台的Kinect驅動程序集成以及如何使用NumPy和SciPy高效處理圖像數據,那麼這本書就是為您而寫的。

本書將教您以下內容:
- 在Windows、Mac或Ubuntu上安裝OpenCV和相關軟件,如Python、NumPy、SciPy、OpenNI和SensorKinect。
- 捕捉、顯示和保存照片和實時視頻。
- 使用OpenCV的HighGui模塊或Pygame處理窗口事件和輸入事件。
- 了解OpenCV的圖像格式以及如何使用NumPy和SciPy對OpenCV圖像執行高效操作。
- 應用“曲線”和其他顏色轉換來模擬舊照片、電影或視頻遊戲的外觀。
- 只對圖像中的邊緣應用效果。
- 複製和調整圖像的部分。
- 使用來自深度傳感器(如Kinect)的數據,只對圖像中的特定深度應用效果。
- 使用預建數據集追蹤面部、眼睛、鼻子和嘴巴。
- 通過創建原始數據集追蹤任意對象。

這是一本針對Python開發人員和愛好者的實用、基於項目的教程,旨在讓他們從頭開始使用OpenCV和Python進行計算機視覺。

本書的讀者對於圖像數據(例如像素和色彩通道)有一定的理解會更有幫助。至少需要一個網絡攝像頭才能完成某些練習。某些練習需要額外的硬件,如第二個網絡攝像頭、Microsoft Kinect或符合OpenNI標準的深度傳感器,如Asus Xtion PRO。