Opencv 之 DNN 与 CUDA综述

news2025/10/31 16:25:32

Opencv 之 DNN 与 CUDA 目录

Opencv官方手稿（包含各模块API介绍及使用例程）
Opencv在github的仓库地址：https://github.com/opencv
Opencv额外的测试数据下载：https://github.com/opencv/opencv_extra

#可通过git下载拉取
git clone https://github.com/opencv/opencv_extra.git

开源Opencv+contrib - 455：https://gitcode.net/openmodel/opencv

1.DNN模块

opencv中的DNN模块对接深度学习框架的一些API、C++复现过来的深度学习模型（目标检测、分割）
官方介绍：https://docs.opencv.org/4.x/d2/d58/tutorial_table_of_content_dnn.html

#ifndef OPENCV_DNN_HPP
#define OPENCV_DNN_HPP

// This is an umbrella header to include into you project.
// We are free to change headers layout in dnn subfolder, so please include
// this header for future compatibility


/** @defgroup dnn Deep Neural Network module
  @{
    This module contains:
        - API for new layers creation, layers are building bricks of neural networks;
        - set of built-in most-useful Layers;
        - API to construct and modify comprehensive neural networks from layers;
        - functionality for loading serialized networks models from different frameworks.

    Functionality of this module is designed only for forward pass computations (i.e. network testing).
    A network training is in principle not supported.
  @}
*/
/** @example samples/dnn/classification.cpp
Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
*/
/** @example samples/dnn/colorization.cpp
*/
/** @example samples/dnn/object_detection.cpp
Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details
*/
/** @example samples/dnn/openpose.cpp
*/
/** @example samples/dnn/segmentation.cpp
*/
/** @example samples/dnn/text_detection.cpp
*/
#include <opencv2/dnn/dnn.hpp>

#endif /* OPENCV_DNN_HPP */

2. CUDA模块

CUDA模块即Opencv中的GPU模块，用于视觉算法实现过程中的加速
官方介绍：https://docs.opencv.org/4.x/da/d2c/tutorial_table_of_content_gpu.html

cuda的使用在目录 .\opencv-contrib\include\opencv2\dnn\dnn.cpp中可以找到

//其中包括
    /**
     * @brief Enum of computation backends supported by layers.
     * @see Net::setPreferableBackend
     */
    enum Backend
    {
        //! DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if
        //! OpenCV is built with Intel's Inference Engine library or
        //! DNN_BACKEND_OPENCV otherwise.
        DNN_BACKEND_DEFAULT = 0,
        DNN_BACKEND_HALIDE,
        DNN_BACKEND_INFERENCE_ENGINE,            //!< Intel's Inference Engine computational backend
                                                 //!< @sa setInferenceEngineBackendType
        DNN_BACKEND_OPENCV,
        DNN_BACKEND_VKCOM,
        DNN_BACKEND_CUDA,
        DNN_BACKEND_WEBNN,
#ifdef __OPENCV_BUILD
        DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000,     // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
        DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019,      // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
#endif
    };

    /**
     * @brief Enum of target devices for computations.
     * @see Net::setPreferableTarget
     */
    enum Target
    {
        DNN_TARGET_CPU = 0,
        DNN_TARGET_OPENCL,
        DNN_TARGET_OPENCL_FP16,
        DNN_TARGET_MYRIAD,
        DNN_TARGET_VULKAN,
        DNN_TARGET_FPGA,  //!< FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
        DNN_TARGET_CUDA,
        DNN_TARGET_CUDA_FP16,
        DNN_TARGET_HDDL
    };