Hands-On Computer Vision with Julia: Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking. Key Features Build a full-fledged image processing application using JuliaImages Perform basic to advanced image and video stream processing with Julia's APIs Understand and optimize various features of OpenCV with easy examples Book Description Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image com

OpenCV三大开发宝典之一。Mastering Open CV 不多解释。

Photoshop 的色相/饱和度调整，可以对全图、红、黄、绿、青、蓝、洋红六个通道进行设置。每个通道可设置：　色相（hue）, 饱和度(satuation), 明度(Lightness)三个调整值。 另包含 多个颜色空间转换函数

GAC模型，CV模型，RSF模型，都可以使用，对每种模型有几种不同的实现版本

全景平面映射鱼眼矫正 实现全景图片、球面图片的平面映射，使之看上去没有变形。内附测试照片。半径是源图片宽度为周长的圆的半径。opencv2.4，vs2010下运行毫无问题。（测试图更新）

斯坦福计算机视觉课程CS231N中文讲义，对于想学习计算机视觉的同学来说很有用。学完这个课程能够对kNN，SVM，CNN，目标检测，GAN，风格迁移等基础与前沿理论都有足够的了解。

#include "opencv2/core/core_c.h" #include "opencv2/core/core.hpp" 所以这下要做的事儿比较明白了，就是把%opencv2.2%/vs2010/include也在属性页里添加到VC++目录->include那栏里，这样就能够找到这俩头文件了。 继续生成，然后继续报错！告诉我cv210d.lib找不到，再看看，去整个文件夹里搜，发觉是没有这东西啊。那2.2版本的运行库都是啥啊。这就没啥诀窍了，去官网上看看吧，这里列出来了我用的这几个 opencv_imgproc220d.lib opencv_legacy220d.lib opencv_ml220d.lib opencv_video220d.lib opencv_highgui220d.lib opencv_core220d.lib 基本上就是把原来的加上一个opencv_么...... 添加了这些库之后，编译通过。

基于ffmpeg实现了解析rtsp视频为cv::Mat格式，此时可以对cv::Mat进行处理，然后再将cv::Mat数据推送到rtmp流媒体服务。处理、转码一条龙。另外，工程中关于OpenCV、ffmpeg的库和头文件的路径需要自己根据实际路径配置，如果没有这两种依赖库，官网上可以很轻松的下载到。

Emgu.CV 打开视频与人脸检测，可以运行，Emgu.CV应用3.4.0，BIN中有相关DLL，打开视频方法：USB，RTSP，HTTP取图片，本地录像。打开视频方法切换时先停止上一次打开。打开视频后分析检测视频中人脸

C#下的openCV(Emgu_CV)教程,使用Emgu_CV进行图像处理的同学可以用来参考一下，压缩包内容： Emgu_CV_Tutorial_Skander(EmguCV教程) Emgu_CV_Essentials 还有一个入门的教程