Net class

Net allows you to create and manipulate comprehensive artificial neural networks.

For further details, please see: https://docs.opencv.org/master/db/d30/classcv_1_1dnn_1_1Net.html

Inheritance
Available extensions

Constructors

Net.empty()
factory
Net.fromBytes(String framework, Uint8List bufferModel, {Uint8List? bufferConfig})
Read deep learning network represented in one of the supported formats.
factory
Net.fromCaffe(String prototxt, String caffeModel)
Reads a network model stored in Caffe framework's format. https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga7117752a0216d9f84a9677eefdabf514
factory
Net.fromCaffeBytes(Uint8List bufferProto, Uint8List bufferModel)
Reads a network model stored in Caffe model in memory. https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga5b1fd56ca658f10c3bd544ea46f57164
factory
Net.fromFile(String path, {String config = "", String framework = ""})
Read deep learning network represented in one of the supported formats.
factory
Net.fromOnnx(String path)
Reads a network model ONNX.
factory
Net.fromOnnxBytes(Uint8List bufferModel)
Reads a network model from ONNX in-memory buffer.
factory
Net.fromPointer(NetPtr ptr, [bool attach = true])
factory
Net.fromTensorflow(String path, {String config = ""})
Reads a network model stored in TensorFlow framework's format.
factory
Net.fromTensorflowBytes(Uint8List bufferModel, {Uint8List? bufferConfig})
Reads a network model stored in TensorFlow framework's format.
factory
Net.fromTFLite(String path)
Reads a network model stored in TFLite framework's format.
factory
Net.fromTFLiteBytes(Uint8List bufferModel)
Reads a network model stored in TensorFlow framework's format.
factory
Net.fromTorch(String path, {bool isBinary = true, bool evaluate = true})
Reads a network model stored in Torch7 framework's format. https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga73785dd1e95cd3070ef36f3109b053fe
factory

Properties

hashCode → int
The hash code for this object.
no setterinherited
isEmpty → bool
Empty returns true if there are no layers in the network.
no setter
props → List<Object?>
no setterinherited
ptr ↔ Pointer<Net>
getter/setter pairinherited
ref → Net
no setteroverride
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

dispose() → void
dump() → String
forward({String outputName = ""}) → Mat
Forward runs forward pass to compute output of layer with name outputName.
forwardLayers(List<String> names) → VecMat
OpenVINO not supported yet, this is not available ForwardAsync runs forward pass to compute output of layer with name outputName.
getInputDetails() → (VecF32, VecI32)
Returns input scale and zeropoint for a quantized Net. https://docs.opencv.org/4.x/db/d30/classcv_1_1dnn_1_1Net.html#af82a1c7e7de19712370a34667056102d
getLayer(int index) → Layer
GetLayer returns pointer to layer with specified id from the network.
getLayerNames() → List<String>
GetLayerNames returns all layer names.
getPerfProfile() → (int, VecF64)
GetPerfProfile returns overall time for inference and timings (in ticks) for layers
getUnconnectedOutLayers() → List<int>
GetUnconnectedOutLayers returns indexes of layers with unconnected outputs.
getUnconnectedOutLayersNames() → List<String>
getUnconnectedOutLayersNames
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
setInput(InputArray blob, {String name = "", double scalefactor = 1.0, Scalar? mean}) → void
SetInput sets the new value for the layer output blob.
setPreferableBackend(int backendId) → void
SetPreferableBackend ask network to use specific computation backend.
setPreferableTarget(int targetId) → void
SetPreferableTarget ask network to make computations on specific target device.
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited

Static Properties

finalizer → NativeFinalizer
final