Atitit opencv3.0  3.1 3.2 新特性attilax总结

 

 

Atitit opencv3.0  3.1 3.2 新特性attilax总结

 

 

1. 3.0OpenCV 3 的改动在哪?1

1.1. 模块构成该看哪些模块?2

2. 3.1新特性 2015-12-213

2.1.1. Changes3

3. 3.2 新特性 20161224 - OpenCV 3.2.0 发布5

3.1. version:3.25

 

1. 3.0OpenCV 3 的改动在哪?

· C 风格的API很快将会消失,完全被C++的API替代,代码更加简洁、不易出错。读者如果想借助opencv最新的功能,记得清理代码中的C风格API。

· C++ API 将更加简洁。

 

 

apps/,包含进行 haar 分类器训练的工具,OpenCV 进行人脸检测便是基于 haar 分类器。如果你想检测人脸以外的图片,千万不要错过这几个工具。

 

 

1.1. 模块构成该看哪些模块?

androidcamera/,仅用于Android平台,使得可以通过与其他平台相同的接口来控制android设备的相机。

core/,核心功能模块,定义了基本的数据结构,包括最重要的 Mat 类、XML 读写、opengl三维渲染等。

imgproc/,全称为 image processing,即图像处理。包括图像滤波、集合图像变换、直方图计算、形状描述子等。图像处理是计算机视觉的重要工具。

 

imgcodec/,负责各种格式的图片的读写,这个模块是从以前的 highgui 中剥离的。

highgui/,高级图形界面及与 QT 框架的整合。

video/,视频分析模块。包括背景提取、光流跟踪、卡尔曼滤波等,做视频监控的读者会经常使用这个模块。

videoio/,负责视频文件的读写,也包括摄像头、Kinect 等的输入。

calib3d/,相机标定以及三维重建。相机标定用于去除相机自身缺陷导致的画面形变,还原真实的场景,确保计算的准确性。三维重建通常用在双目视觉(立体视觉),即两个标定后的摄像头观察同一个场景,通过计算两幅画面中的相关性来估算像素的深度。

features2d/,包含 2D 特征值检测的框架。包含各种特征值检测器及描述子,例如 FAST、MSER、OBRB、BRISK等。各类特征值拥有统一的算法接口,因此在不影响程序逻辑的情况下可以进行替换。

objdetect/,物体检测模块。包括haar分类器、SVM检测器及文字检测。

ml/,全称为 Machine Learning,即机器学习。包括统计模型、K最近邻、支持向量机、决策树、神经网络等经典的机器学习算法。

flann/,用于在多维空间内聚类及搜索的近似算法,做图像检索的读者对它不会陌生。

photo/,计算摄影学。包括图像修补、去噪、HDR成像、非真实感渲染等。如果读者想实现Photoshop的高级功能,那么这个模块必不可少。

stitching/,图像拼接,可用于制作全景图。

shape/,形状匹配算法模块。用于描述形状、比较形状。

 

2. 3.1新特性 2015-12-21

 

OpenCV 3.1 release is finally ready, right before the XMas holidays. This is the first stabilization update in 3.x series. It shall be reminded that since OpenCV 3.0 we’ve changed the version enumeration scheme, so that 3.1 is the same sort of update to 3.0 as 2.4.1 was to 2.4.0.

2.0.1. Changes

The detailed list of changes since 3.0 can be found at https://github.com/Itseez/opencv/wiki/ChangeLog. Here is the short summary:

· A lot of new functionality has been introduced during Google Summer of Code 2015:

· “Omnidirectional Cameras Calibration and Stereo 3D Reconstruction” – opencv_contrib/ccalib module (Baisheng Lai, Bo Li)

· “Structure From Motion” – opencv_contrib/sfm module (Edgar Riba, Vincent Rabaud)

· “Improved Deformable Part-based Models” – opencv_contrib/dpm module (Jiaolong Xu, Bence Magyar)

· “Real-time Multi-object Tracking using Kernelized Correlation Filter” – opencv_contrib/tracking module (Laksono Kurnianggoro, Fernando J. Iglesias Garcia)

· “Improved and expanded Scene Text Detection” – opencv_contrib/text module (Lluis Gomez, Vadim Pisarevsky)

· “Stereo correspondence improvements” – opencv_contrib/stereo module (Mircea Paul Muresan, Sergei Nosov)

· “Structured-Light System Calibration” – opencv_contrib/structured_light (Roberta Ravanelli, Delia Passalacqua, Stefano Fabri, Claudia Rapuano)

· “Chessboard+ArUco for camera calibration” – opencv_contrib/aruco (Sergio Garrido, Prasanna, Gary Bradski)

· “Implementation of universal interface for deep neural network frameworks” – opencv_contrib/dnn module (Vitaliy Lyudvichenko, Anatoly Baksheev)

· “Recent advances in edge-aware filtering, improved SGBM stereo algorithm” – opencv/calib3d and opencv_contrib/ximgproc (Alexander Bokov, Maksim Shabunin)

· “Improved ICF detector, waldboost implementation” – opencv_contrib/xobjdetect (Vlad Shakhuro, Alexander Bovyrin)

· “Multi-target TLD tracking” – opencv_contrib/tracking module (Vladimir Tyan, Antonella Cascitelli)

· “3D pose estimation using CNNs” – opencv_contrib/cnn_3dobj (Yida Wang, Manuele Tamburrano, Stefano Fabri)

· Many great contributions made by the community, such as:

· Support for HDF5 format

· New/Improved optical flow algorithms

· Multiple new image processing algorithms for filtering, segmentation and feature detection

· Superpixel segmentation

· and much more

· IPPICV is now based on IPP 9.0.1, which should make OpenCV even faster on modern Intel chips

· opencv_contrib modules can now be included into the opencv2.framework for iOS

· Newest operating systems are supported: Windows 10 and OSX 10.11 (Visual Studio 2015 and XCode 7.1.1)

· Interoperability between T-API and OpenCLOpenGLDirectX and Video Acceleration API on Linux, as well as Android 5 camera.

· HAL (Hardware Acceleration Layer) module functionality has been moved into corresponding basic modules; the HAL replacement mechanism has been implemented along with the examples

 

3. 3.2 新特性 20161224 - OpenCV 3.2.0 发布

OpenCV Change Logs

3.1. version:3.2

December, 2016

Long-awaited update to OpenCV 3.x release series, with tons of improvements and bug fixes. Big thanks to everyone who particpated! If you contributed something but your name is missing, please, let us know.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

· Results from 11 GSoC 2016 projects have been submitted to the library, 9 of them have been integrated already, 2 still pending (the numbers below are the id’s of the Pull Requests in opencv or opencv_contrib repository):

· Ambroise Moreau (Delia Passalacqua) – sinusoidal patterns for structured light and phase unwrapping module (711)

· Alexander Bokov (Maksim Shabunin) – DIS optical flow (excellent dense optical flow algorithm that is both significantly better and significantly faster than Farneback’s algorithm – our baseline), and learning-based color constancy algorithms implementation (689, 708, 722, 736, 745, 747)

· Tyan Vladimir (Antonella Cascitelli) – CNN based tracking algorithm (GOTURN) (718, 899)

· Vladislav Samsonov (Ethan Rublee) – PCAFlow and Global Patch Collider algorithms implementation (710, 752)

· João Cartucho (Vincent Rabaud) – Multi-language OpenCV Tutorials in Python, C++ and Java (7041)

· Jiri Horner (Bo Li) – New camera model and parallel processing for stitching pipeline (6933)

· Vitaliy Lyudvichenko (Anatoly Baksheev) – Optimizations and improvements of dnn module (707, 750)

· Iric Wu (Vadim Pisarevsky) – Base64 and JSON support for file storage (6697, 6949, 7088). Use names like `“myfilestorage.xml?base64”` when writing file storage to store big chunks of numerical data in base64-encoded form.

· Edgar Riba (Manuele Tamburrano, Stefano Fabri) – tiny_dnn improvements and integration (720: pending)

· Yida Wang (Manuele Tamburrano, Stefano Fabri) – Quantization and semantic saliency detection with tiny_dnn

· Anguelos Nicolaou (Lluis Gomez) – Word-spotting CNN based algorithm (761: pending)

big thanks to all the pa

 

OpenCV 3 将会为我们带来什么? - 开源中国社区.html

OpenCV3.0 3.1版本的改进 - shiter编写程序的艺术 - 博客频道 - CSDN.NET.html

OpenCV 3 新特性 - z605928171的博客 - 博客频道 - CSDN.NET.html

 

 

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