Module opencv::dnn::prelude [−][src]
Traits
Derivatives of this class encapsulates functions of certain backends.
Derivatives of this class wraps cv::Mat for different backends and targets.
Partial List of Implemented Layers
This class represents high-level API for classification models.
Constant layer produces the same data blob at an every forward pass.
This class represents high-level API for object detection networks.
Detection output layer.
This class implements name-value dictionary, values are instances of 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.
Element wise operation on inputs
Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2
This class represents high-level API for keypoints models
LSTM recurrent layer
%Layer factory allows to create instances of registered layers.
This class provides all data needed to initialize layer.
This interface class allows to build new Layers - are building blocks of networks.
This class is presented high-level API for neural networks.
This class allows to create and manipulate comprehensive artificial neural networks.
inline formula
- normalization layer.Adds extra values for specific axes.
Classical recurrent layer
Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
This class represents high-level API for segmentation models
Permute channels of 4-dimensional input blob.
Slice layer has several modes:
This class represents high-level API for text detection DL networks compatible with DB model.
This class represents high-level API for text detection DL networks compatible with EAST model.
This class represents high-level API for text recognition networks.