[−][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.
Structs
AbsLayer | |
BNLLLayer | |
BackendNode | Derivatives of this class encapsulates functions of certain backends. |
BatchNormLayer | |
BlankLayer | Partial List of Implemented Layers |
ChannelsPReLULayer | |
ConcatLayer | |
ConstLayer | Constant layer produces the same data blob at an every forward pass. |
ConvolutionLayer | |
CropAndResizeLayer | |
CropLayer | |
DeconvolutionLayer | |
DetectionOutputLayer | |
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. |
ELULayer | |
EltwiseLayer | |
FlattenLayer | |
InnerProductLayer | |
InterpLayer | Bilinear resize layer from https://github.com/cdmh/deeplab-public |
LRNLayer | |
LayerFactory | %Layer factory allows to create instances of registered layers. |
LayerParams | This class provides all data needed to initialize layer. |
MVNLayer | |
MaxUnpoolLayer | |
Net | This class allows to create and manipulate comprehensive artificial neural networks. |
NormalizeBBoxLayer | L_p - normalization layer. |
PaddingLayer | Adds extra values for specific axes. |
PermuteLayer | |
PoolingLayer | |
PowerLayer | |
PriorBoxLayer | |
ProposalLayer | |
ReLU6Layer | |
ReLULayer | |
RegionLayer | |
ReorgLayer | |
ReshapeLayer | |
ResizeLayer | Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. |
ScaleLayer | |
ShiftLayer | |
ShuffleChannelLayer | Permute channels of 4-dimensional input blob. |
SigmoidLayer | |
SliceLayer | Slice layer has several modes: |
SoftmaxLayer | |
SplitLayer | |
TanHLayer |
Constants
Traits
ActivationLayer | |
BackendWrapper | Derivatives of this class wraps cv::Mat for different backends and targets. |
BaseConvolutionLayer | |
Dict | This class implements name-value dictionary, values are instances of DictValue. |
LSTMLayer | |
Layer | This interface class allows to build new Layers - are building blocks of networks. |
RNNLayer | Classical recurrent layer |
Functions
blob_from_image | 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_image_1 | 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_images | 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. |
blob_from_images_1 | 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. |
clamp | |
clamp_1 | |
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_rotated | C++ default parameters: |
nms_boxes_rotated_f64 | C++ default parameters: |
read_net | 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_1 | Read deep learning network represented in one of the supported formats. |
read_net_from_caffe | Reads a network model stored in Caffe framework's format. |
read_net_from_caffe_1 | Reads a network model stored in Caffe model in memory. |
read_net_from_caffe_2 | 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_1 | Reads a network model stored in Darknet model files. |
read_net_from_darknet_2 | 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_onnx | Reads a network model ONNX. |
read_net_from_tensorflow | Reads a network model stored in TensorFlow framework's format. |
read_net_from_tensorflow_1 | Reads a network model stored in TensorFlow framework's format. |
read_net_from_tensorflow_2 | 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). |
shape | |
shape_1 | |
shape_2 | C++ default parameters: |
shrink_caffe_model | Convert all weights of Caffe network to half precision floating point. |
slice | |
slice_1 | |
slice_2 | |
slice_3 | |
write_text_graph | Create a text representation for a binary network stored in protocol buffer format. |