OpenCV：OpenCV4.0更新

OpenCV4.0已經釋出一段時間，更新在官方提示中，重要的是添加了ONNX接口和KinectFusion算法；

Release highlights:

• OpenCV is now C++11 library and requires C++11-compliant compiler. Minimum required CMake version has been raised to 3.5.1.
• A lot of C API from OpenCV 1.x has been removed.
• Persistence (storing and loading structured data to/from XML, YAML or JSON) in the core module has been completely reimplemented in C++ and lost the C API as well.
• New module G-API has been added, it acts as an engine for very efficient graph-based image procesing pipelines.
• dnn module was updated with Deep Learning Deployment Toolkit from the OpenVINO™ toolkit R4. See the guide how to build and use OpenCV with DLDT support.
• dnn module now includes experimental Vulkan backend and supports networks in ONNX format.
• The popular Kinect Fusion algorithm has been implemented and optimized for CPU and GPU (OpenCL)
• QR code detector and decoder have been added to the objdetect module
• Very efficient and yet high-quality DIS dense optical flow algorithm has been moved from opencv_contrib to the video module.
• More details can be found in previous announces: 4.0-alpha, 4.0-beta, 4.0-rc and in the changelog

Branch 3.4 will be switched to maintanence mode: only bugfixes and light features will be accepted. BTW, release 3.4.4 is ready too!

重要更新：

        OpenCV 4.0 現在是一個 C++11 庫，要求 C++11 相容的編譯器。所需的 CMake 至少是 3.5.1 版本。

        移除 OpenCV 1.x 中的大量 C API。core 子產品中的 Persistence（用于存儲和加載 XML、YAML 或 JSON 格式的結構化資料）可以完全使用 C++ 來重新實作，是以這裡的 C API 也被移除。

        添加了新子產品 G-API，它可作為基于圖的高效圖像處理流程。

        dnn 子產品包括實驗用 Vulkan 後端，且支援 ONNX 格式的網絡。

        實作了流行的 Kinect Fusion 算法，且為 CPU 和 GPU (OpenCL) 進行優化。

        objdetect 子產品中添加了二維碼檢測器和解碼器。将高效、高品質的 DIS dense optical flow 算法從 opencv_contrib 移到 video 子產品。

        此外，OpenCV 4.0 支援 Mask-RCNN 模型，性能也有所提升，圖像處理操作可實作 15%-30% 的速度提升。