[−][src]Module opencv::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.
Modules
| prelude |
Structs
| BackendNode | Derivatives of this class encapsulates functions of certain backends. |
| BaseConvolutionLayer | |
| BlankLayer | Partial List of Implemented Layers |
| ClassificationModel | This class represents high-level API for classification models. |
| ConcatLayer | |
| ConstLayer | Constant layer produces the same data blob at an every forward pass. |
| ConvolutionLayer | |
| CropAndResizeLayer | |
| CropLayer | |
| DeconvolutionLayer | |
| DetectionModel | This class represents high-level API for object detection networks. |
| DetectionOutputLayer | |
| Dict | This class implements name-value dictionary, values are instances of DictValue. |
| DictValue | This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. |
| EltwiseLayer | Element wise operation on inputs |
| FlattenLayer | |
| InnerProductLayer | |
| InterpLayer | Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2 |
| KeypointsModel | This class represents high-level API for keypoints models |
| LRNLayer | |
| Layer | This interface class allows to build new Layers - are building blocks of networks. |
| LayerFactory | %Layer factory allows to create instances of registered layers. |
| LayerParams | This class provides all data needed to initialize layer. |
| MVNLayer | |
| MaxUnpoolLayer | |
| Model | This class is presented high-level API for neural networks. |
| Net | This class allows to create and manipulate comprehensive artificial neural networks. |
| NormalizeBBoxLayer |
inline formula - normalization layer. |
| PaddingLayer | Adds extra values for specific axes. |
| PermuteLayer | |
| PoolingLayer | |
| PriorBoxLayer | |
| ProposalLayer | |
| RegionLayer | |
| ReorgLayer | |
| ReshapeLayer | |
| ResizeLayer | Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. |
| ScaleLayer | |
| SegmentationModel | This class represents high-level API for segmentation models |
| ShiftLayer | |
| ShuffleChannelLayer | Permute channels of 4-dimensional input blob. |
| SliceLayer | Slice layer has several modes: |
| SoftmaxLayer | |
| SplitLayer | |
| _Range |
Enums
| Backend | Enum of computation backends supported by layers. |
| Target | Enum of target devices for computations. |
Constants
| CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH | |
| CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API | |
| CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 | |
| CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X | |
| CV_DNN_INFERENCE_ENGINE_VPU_TYPE_UNSPECIFIED | |
| DNN_BACKEND_CUDA | |
| DNN_BACKEND_DEFAULT | 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_HALIDE | 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_INFERENCE_ENGINE | Intel's Inference Engine computational backend |
| DNN_BACKEND_OPENCV | |
| DNN_BACKEND_VKCOM | |
| DNN_TARGET_CPU | |
| DNN_TARGET_CUDA | |
| DNN_TARGET_CUDA_FP16 | |
| DNN_TARGET_FPGA | FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. |
| DNN_TARGET_MYRIAD | |
| DNN_TARGET_OPENCL | |
| DNN_TARGET_OPENCL_FP16 | |
| DNN_TARGET_VULKAN | |
| OPENCV_DNN_API_VERSION |
Traits
| AbsLayer | |
| ActivationLayer | |
| BNLLLayer | |
| BackendNodeTrait | Derivatives of this class encapsulates functions of certain backends. |
| BackendWrapper | Derivatives of this class wraps cv::Mat for different backends and targets. |
| BaseConvolutionLayerTrait | |
| BatchNormLayer | |
| BlankLayerTrait | Partial List of Implemented Layers |
| ChannelsPReLULayer | |
| ClassificationModelTrait | This class represents high-level API for classification models. |
| ConcatLayerTrait | |
| ConstLayerTrait | Constant layer produces the same data blob at an every forward pass. |
| ConvolutionLayerTrait | |
| CropAndResizeLayerTrait | |
| CropLayerTrait | |
| DeconvolutionLayerTrait | |
| DetectionModelTrait | This class represents high-level API for object detection networks. |
| DetectionOutputLayerTrait | |
| DictTrait | This class implements name-value dictionary, values are instances of DictValue. |
| DictValueTrait | This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. |
| ELULayer | |
| EltwiseLayerTrait | Element wise operation on inputs |
| FlattenLayerTrait | |
| InnerProductLayerTrait | |
| InterpLayerTrait | Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2 |
| KeypointsModelTrait | This class represents high-level API for keypoints models |
| LRNLayerTrait | |
| LSTMLayer | LSTM recurrent layer |
| LayerFactoryTrait | %Layer factory allows to create instances of registered layers. |
| LayerParamsTrait | This class provides all data needed to initialize layer. |
| LayerTrait | This interface class allows to build new Layers - are building blocks of networks. |
| MVNLayerTrait | |
| MaxUnpoolLayerTrait | |
| MishLayer | |
| ModelTrait | This class is presented high-level API for neural networks. |
| NetTrait | This class allows to create and manipulate comprehensive artificial neural networks. |
| NormalizeBBoxLayerTrait |
inline formula - normalization layer. |
| PaddingLayerTrait | Adds extra values for specific axes. |
| PermuteLayerTrait | |
| PoolingLayerTrait | |
| PowerLayer | |
| PriorBoxLayerTrait | |
| ProposalLayerTrait | |
| RNNLayer | Classical recurrent layer |
| ReLU6Layer | |
| ReLULayer | |
| RegionLayerTrait | |
| ReorgLayerTrait | |
| ReshapeLayerTrait | |
| ResizeLayerTrait | Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. |
| ScaleLayerTrait | |
| SegmentationModelTrait | This class represents high-level API for segmentation models |
| ShiftLayerTrait | |
| ShuffleChannelLayerTrait | Permute channels of 4-dimensional input blob. |
| SigmoidLayer | |
| SliceLayerTrait | Slice layer has several modes: |
| SoftmaxLayerTrait | |
| SplitLayerTrait | |
| SwishLayer | |
| TanHLayer | |
| _RangeTrait |
Functions
| blob_from_image | Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. |
| blob_from_image_to | Creates 4-dimensional blob from image. @details This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. |
| blob_from_images | Creates 4-dimensional blob from series of images. Optionally resizes and crops @p images from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. |
| blob_from_images_to | Creates 4-dimensional blob from series of images. @details This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. |
| clamp | |
| clamp_1 | |
| clamp_2 | |
| concat | |
| get_available_targets | |
| get_inference_engine_backend_type | Returns Inference Engine internal backend API. |
| get_inference_engine_vpu_type | Returns Inference Engine VPU type. |
| get_plane | |
| images_from_blob | Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vectorcv::Mat). |
| nms_boxes | Performs non maximum suppression given boxes and corresponding scores. |
| nms_boxes_f64 | C++ default parameters |
| nms_boxes_rotated | C++ default parameters |
C++ default parameters | |
| read_net | Read deep learning network represented in one of the supported formats. |
| read_net_1 | Read deep learning network represented in one of the supported formats. @details This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. |
| read_net_from_caffe | Reads a network model stored in Caffe framework's format. |
| read_net_from_caffe_buffer | Reads a network model stored in Caffe model in memory. |
| read_net_from_caffe_str | Reads a network model stored in Caffe model in memory. @details This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. |
| read_net_from_darknet | Reads a network model stored in Darknet model files. |
| read_net_from_darknet_buffer | Reads a network model stored in Darknet model files. |
| read_net_from_darknet_str | Reads a network model stored in Darknet model files. |
| read_net_from_model_optimizer | Load a network from Intel's Model Optimizer intermediate representation. |
| read_net_from_model_optimizer_1 | Load a network from Intel's Model Optimizer intermediate representation. |
| read_net_from_model_optimizer_2 | Load a network from Intel's Model Optimizer intermediate representation. |
| read_net_from_onnx | Reads a network model ONNX. |
| read_net_from_onnx_buffer | Reads a network model from ONNX in-memory buffer. |
| read_net_from_onnx_str | Reads a network model from ONNX in-memory buffer. |
| read_net_from_tensorflow | Reads a network model stored in TensorFlow framework's format. |
| read_net_from_tensorflow_buffer | Reads a network model stored in TensorFlow framework's format. |
| read_net_from_tensorflow_str | Reads a network model stored in TensorFlow framework's format. @details This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. |
| read_net_from_torch | Reads a network model stored in Torch7 framework's format. |
| read_tensor_from_onnx | Creates blob from .pb file. |
| read_torch_blob | Loads blob which was serialized as torch.Tensor object of Torch7 framework. @warning This function has the same limitations as readNetFromTorch(). |
| reset_myriad_device | Release a Myriad device (binded by OpenCV). |
| set_inference_engine_backend_type | Specify Inference Engine internal backend API. |
| shape | |
| shape_1 | |
| shape_2 | |
| shape_3 | |
| shape_4 | C++ default parameters |
| shrink_caffe_model | Convert all weights of Caffe network to half precision floating point. |
| slice | |
| slice_1 | |
| slice_2 | |
| slice_3 | |
| to_string | C++ default parameters |
| total | C++ default parameters |
| write_text_graph | Create a text representation for a binary network stored in protocol buffer format. |
Type Definitions
| LayerFactory_Constructor | Each Layer class must provide this function to the factory |
| MatShape | |
| Net_LayerId | Container for strings and integers. |