public class opencv_dnn extends opencv_dnn
| Modifier and Type | Class and Description |
|---|---|
static class |
opencv_dnn._LayerStaticRegisterer
\}
\}
|
static class |
opencv_dnn.Blob
\brief This class provides methods for continuous n-dimensional CPU and GPU array processing.
|
static class |
opencv_dnn.BlobPointerVector |
static class |
opencv_dnn.BlobShape
\addtogroup dnn
\{
|
static class |
opencv_dnn.BlobVector |
static class |
opencv_dnn.Dict
\brief This class implements name-value dictionary, values are instances of DictValue.
|
static class |
opencv_dnn.DictValue
\addtogroup dnn
\{
|
static class |
opencv_dnn.Importer
\brief Small interface class for loading trained serialized models of different dnn-frameworks.
|
static class |
opencv_dnn.Layer
\brief This interface class allows to build new Layers - are building blocks of networks.
|
static class |
opencv_dnn.LayerFactory
\addtogroup dnn
\{
\defgroup dnnLayerFactory Utilities for New Layers Registration
\{
|
static class |
opencv_dnn.LayerParams
\brief This class provides all data needed to initialize layer.
|
static class |
opencv_dnn.Net
\brief This class allows to create and manipulate comprehensive artificial neural networks.
|
| Constructor and Description |
|---|
opencv_dnn() |
map@Namespace(value="cv::dnn") public static void initModule()
/** \brief Initialize dnn module and built-in layers. This function automatically called on most of OpenCV builds, but you need to call it manually on some specific configurations (iOS for example).
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createCaffeImporter(@opencv_core.Str BytePointer prototxt, @opencv_core.Str BytePointer caffeModel)
prototxt - path to the .prototxt file with text description of the network architecture.caffeModel - path to the .caffemodel file with learned network.@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createCaffeImporter(@opencv_core.Str BytePointer prototxt)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createCaffeImporter(@opencv_core.Str String prototxt, @opencv_core.Str String caffeModel)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createCaffeImporter(@opencv_core.Str String prototxt)
@Namespace(value="cv::dnn") @ByVal public static opencv_dnn.Net readNetFromCaffe(@opencv_core.Str BytePointer prototxt, @opencv_core.Str BytePointer caffeModel)
@Namespace(value="cv::dnn") @ByVal public static opencv_dnn.Net readNetFromCaffe(@opencv_core.Str BytePointer prototxt)
@Namespace(value="cv::dnn") @ByVal public static opencv_dnn.Net readNetFromCaffe(@opencv_core.Str String prototxt, @opencv_core.Str String caffeModel)
@Namespace(value="cv::dnn") @ByVal public static opencv_dnn.Net readNetFromCaffe(@opencv_core.Str String prototxt)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTensorflowImporter(@opencv_core.Str BytePointer model)
model - path to the .pb file with binary protobuf description of the network architecture.@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTensorflowImporter(@opencv_core.Str String model)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTorchImporter(@opencv_core.Str BytePointer filename, @Cast(value="bool") boolean isBinary)
filename - path to the file, dumped from Torch by using torch.save() function.isBinary - specifies whether the network was serialized in ascii mode or binary.opencv_dnn_BUILD_TORCH_IMPORTER flag to compile its.
\note Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language,
which has various bit-length on different systems.
The loading file must contain serialized nn.Module object
with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
List of supported layers (i.e. object instances derived from Torch nn.Module class):
- nn.Sequential
- nn.Parallel
- nn.Concat
- nn.Linear
- nn.SpatialConvolution
- nn.SpatialMaxPooling, nn.SpatialAveragePooling
- nn.ReLU, nn.TanH, nn.Sigmoid
- nn.Reshape
Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTorchImporter(@opencv_core.Str BytePointer filename)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTorchImporter(@opencv_core.Str String filename, @Cast(value="bool") boolean isBinary)
@Namespace(value="cv::dnn") @opencv_core.Ptr public static opencv_dnn.Importer createTorchImporter(@opencv_core.Str String filename)
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