718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
77 lines
3.7 KiB
Markdown
77 lines
3.7 KiB
Markdown
# Why Graph API? {#gapi_purposes}
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# Motivation behind G-API {#gapi_intro_why}
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G-API module brings graph-based model of execution to OpenCV. This
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chapter briefly describes how this new model can help software
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developers in two aspects: optimizing and porting image processing
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algorithms.
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## Optimizing with Graph API {#gapi_intro_opt}
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Traditionally OpenCV provided a lot of stand-alone image processing
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functions (see modules `core` and `imgproc`). Many of that functions
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are well-optimized (e.g. vectorized for specific CPUs, parallel, etc)
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but still the out-of-box optimization scope has been limited to a
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single function only -- optimizing the whole algorithm built atop of that
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functions was a responsibility of a programmer.
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OpenCV 3.0 introduced _Transparent API_ (or _T-API_) which allowed to
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offload OpenCV function calls transparently to OpenCL devices and save
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on Host/Device data transfers with cv::UMat -- and it was a great step
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forward. However, T-API is a dynamic API -- user code still remains
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unconstrained and OpenCL kernels are enqueued in arbitrary order, thus
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eliminating further pipeline-level optimization potential.
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G-API brings implicit graph model to OpenCV 4.0. Graph model captures
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all operations and its data dependencies in a pipeline and so provides
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G-API framework with extra information to do pipeline-level
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optimizations.
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The cornerstone of graph-based optimizations is _Tiling_. Tiling
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allows to break the processing into smaller parts and reorganize
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operations to enable data parallelism, improve data locality, and save
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memory footprint. Data locality is an especially important aspect of
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software optimization due to diffent costs of memory access on modern
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computer architectures -- the more data is reused in the first level
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cache, the more efficient pipeline is.
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Definitely the aforementioned techniques can be applied manually --
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but it requires extra skills and knowledge of the target platform and
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the algorithm implementation changes irrevocably -- becoming more
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specific, less flexible, and harder to extend and maintain.
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G-API takes this responsibility and complexity from user and does the
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majority of the work by itself, keeping the algorithm code clean from
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device or optimization details. This approach has its own limitations,
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though, as graph model is a _constrained_ model and not every
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algorithm can be represented as a graph, so the G-API scope is limited
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only to regular image processing -- various filters, arithmetic,
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binary operations, and well-defined geometrical transformations.
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## Porting with Graph API {#gapi_intro_port}
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The essence of G-API is declaring a sequence of operations to run, and
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then executing that sequence. G-API is a constrained API, so it puts a
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number of limitations on which operations can form a pipeline and
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which data these operations may exchange each other.
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This formalization in fact helps to make an algorithm portable. G-API
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clearly separates operation _interfaces_ from its _implementations_.
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One operation (_kernel_) may have multiple implementations even for a
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single device (e.g., OpenCV-based "reference" implementation and a
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tiled optimized implementation, both running on CPU). Graphs (or
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_Computations_ in G-API terms) are built only using operation
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interfaces, not implementations -- thus the same graph can be executed
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on different devices (and, of course, using different optimization
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techniques) with little-to-no changes in the graph itself.
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G-API supports plugins (_Backends_) which aggregate logic and
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intelligence on what is the best way to execute on a particular
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platform. Once a pipeline is built with G-API, it can be parametrized
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to use either of the backends (or a combination of it) and so a graph
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can be ported easily to a new platform.
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@sa @ref gapi_hld
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