Package org.opencv.dnn
Class Dnn
java.lang.Object
org.opencv.dnn.Dnn
public class Dnn extends Object
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Field Summary
Fields Modifier and Type Field Description static int
DNN_BACKEND_CANN
static int
DNN_BACKEND_CUDA
static int
DNN_BACKEND_DEFAULT
static int
DNN_BACKEND_HALIDE
static int
DNN_BACKEND_INFERENCE_ENGINE
static int
DNN_BACKEND_OPENCV
static int
DNN_BACKEND_TIMVX
static int
DNN_BACKEND_VKCOM
static int
DNN_BACKEND_WEBNN
static int
DNN_LAYOUT_NCDHW
static int
DNN_LAYOUT_NCHW
static int
DNN_LAYOUT_ND
static int
DNN_LAYOUT_NDHWC
static int
DNN_LAYOUT_NHWC
static int
DNN_LAYOUT_PLANAR
static int
DNN_LAYOUT_UNKNOWN
static int
DNN_PMODE_CROP_CENTER
static int
DNN_PMODE_LETTERBOX
static int
DNN_PMODE_NULL
static int
DNN_TARGET_CPU
static int
DNN_TARGET_CPU_FP16
static int
DNN_TARGET_CUDA
static int
DNN_TARGET_CUDA_FP16
static int
DNN_TARGET_FPGA
static int
DNN_TARGET_HDDL
static int
DNN_TARGET_MYRIAD
static int
DNN_TARGET_NPU
static int
DNN_TARGET_OPENCL
static int
DNN_TARGET_OPENCL_FP16
static int
DNN_TARGET_VULKAN
static int
SoftNMSMethod_SOFTNMS_GAUSSIAN
static int
SoftNMSMethod_SOFTNMS_LINEAR
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Constructor Summary
Constructors Constructor Description Dnn()
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Method Summary
Modifier and Type Method Description static Mat
blobFromImage(Mat image)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor, Size size)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor, Size size, Scalar mean)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
Creates 4-dimensional blob from image.static Mat
blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
Creates 4-dimensional blob from image.static Mat
blobFromImages(List<Mat> images)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor, Size size)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
Creates 4-dimensional blob from series of images.static Mat
blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
Creates 4-dimensional blob from series of images.static Mat
blobFromImagesWithParams(List<Mat> images)
Creates 4-dimensional blob from series of images with given params.static void
blobFromImagesWithParams(List<Mat> images, Mat blob)
static void
blobFromImagesWithParams(List<Mat> images, Mat blob, Image2BlobParams param)
static Mat
blobFromImagesWithParams(List<Mat> images, Image2BlobParams param)
Creates 4-dimensional blob from series of images with given params.static Mat
blobFromImageWithParams(Mat image)
Creates 4-dimensional blob from image with given params.static void
blobFromImageWithParams(Mat image, Mat blob)
static void
blobFromImageWithParams(Mat image, Mat blob, Image2BlobParams param)
static Mat
blobFromImageWithParams(Mat image, Image2BlobParams param)
Creates 4-dimensional blob from image with given params.static List<Integer>
getAvailableTargets(int be)
static String
getInferenceEngineBackendType()
Deprecated.static String
getInferenceEngineCPUType()
Returns Inference Engine CPU type.static String
getInferenceEngineVPUType()
Returns Inference Engine VPU type.static void
imagesFromBlob(Mat blob_, List<Mat> images_)
Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).static void
NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
Performs non maximum suppression given boxes and corresponding scores.static void
NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
Performs non maximum suppression given boxes and corresponding scores.static void
NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
Performs non maximum suppression given boxes and corresponding scores.static void
NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices)
Performs batched non maximum suppression on given boxes and corresponding scores across different classes.static void
NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
Performs batched non maximum suppression on given boxes and corresponding scores across different classes.static void
NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
Performs batched non maximum suppression on given boxes and corresponding scores across different classes.static void
NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
static void
NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
static void
NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
static Net
readNet(String model)
Read deep learning network represented in one of the supported formats.static Net
readNet(String model, String config)
Read deep learning network represented in one of the supported formats.static Net
readNet(String model, String config, String framework)
Read deep learning network represented in one of the supported formats.static Net
readNet(String framework, MatOfByte bufferModel)
Read deep learning network represented in one of the supported formats.static Net
readNet(String framework, MatOfByte bufferModel, MatOfByte bufferConfig)
Read deep learning network represented in one of the supported formats.static Net
readNetFromCaffe(String prototxt)
Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.static Net
readNetFromCaffe(String prototxt, String caffeModel)
Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.static Net
readNetFromCaffe(MatOfByte bufferProto)
Reads a network model stored in Caffe model in memory.static Net
readNetFromCaffe(MatOfByte bufferProto, MatOfByte bufferModel)
Reads a network model stored in Caffe model in memory.static Net
readNetFromDarknet(String cfgFile)
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.static Net
readNetFromDarknet(String cfgFile, String darknetModel)
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.static Net
readNetFromDarknet(MatOfByte bufferCfg)
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.static Net
readNetFromDarknet(MatOfByte bufferCfg, MatOfByte bufferModel)
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.static Net
readNetFromModelOptimizer(String xml, String bin)
Load a network from Intel's Model Optimizer intermediate representation.static Net
readNetFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)
Load a network from Intel's Model Optimizer intermediate representation.static Net
readNetFromONNX(String onnxFile)
Reads a network model <a href="https://onnx.ai/">ONNX</a>.static Net
readNetFromONNX(MatOfByte buffer)
Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer.static Net
readNetFromTensorflow(String model)
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.static Net
readNetFromTensorflow(String model, String config)
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.static Net
readNetFromTensorflow(MatOfByte bufferModel)
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.static Net
readNetFromTensorflow(MatOfByte bufferModel, MatOfByte bufferConfig)
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.static Net
readNetFromTFLite(String model)
Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.static Net
readNetFromTFLite(MatOfByte bufferModel)
Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.static Net
readNetFromTorch(String model)
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.static Net
readNetFromTorch(String model, boolean isBinary)
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.static Net
readNetFromTorch(String model, boolean isBinary, boolean evaluate)
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.static Mat
readTensorFromONNX(String path)
Creates blob from .pb file.static Mat
readTorchBlob(String filename)
Loads blob which was serialized as torch.Tensor object of Torch7 framework.static Mat
readTorchBlob(String filename, boolean isBinary)
Loads blob which was serialized as torch.Tensor object of Torch7 framework.static void
releaseHDDLPlugin()
Release a HDDL plugin.static void
resetMyriadDevice()
Release a Myriad device (binded by OpenCV).static String
setInferenceEngineBackendType(String newBackendType)
Deprecated.static void
shrinkCaffeModel(String src, String dst)
Convert all weights of Caffe network to half precision floating point.static void
shrinkCaffeModel(String src, String dst, List<String> layersTypes)
Convert all weights of Caffe network to half precision floating point.static void
softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices)
Performs soft non maximum suppression given boxes and corresponding scores.static void
softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k)
Performs soft non maximum suppression given boxes and corresponding scores.static void
softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma)
Performs soft non maximum suppression given boxes and corresponding scores.static void
writeTextGraph(String model, String output)
Create a text representation for a binary network stored in protocol buffer format.
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Field Details
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DNN_BACKEND_DEFAULT
- See Also:
- Constant Field Values
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DNN_BACKEND_HALIDE
- See Also:
- Constant Field Values
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DNN_BACKEND_INFERENCE_ENGINE
- See Also:
- Constant Field Values
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DNN_BACKEND_OPENCV
- See Also:
- Constant Field Values
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DNN_BACKEND_VKCOM
- See Also:
- Constant Field Values
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DNN_BACKEND_CUDA
- See Also:
- Constant Field Values
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DNN_BACKEND_WEBNN
- See Also:
- Constant Field Values
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DNN_BACKEND_TIMVX
- See Also:
- Constant Field Values
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DNN_BACKEND_CANN
- See Also:
- Constant Field Values
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DNN_LAYOUT_UNKNOWN
- See Also:
- Constant Field Values
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DNN_LAYOUT_ND
- See Also:
- Constant Field Values
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DNN_LAYOUT_NCHW
- See Also:
- Constant Field Values
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DNN_LAYOUT_NCDHW
- See Also:
- Constant Field Values
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DNN_LAYOUT_NHWC
- See Also:
- Constant Field Values
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DNN_LAYOUT_NDHWC
- See Also:
- Constant Field Values
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DNN_LAYOUT_PLANAR
- See Also:
- Constant Field Values
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DNN_PMODE_NULL
- See Also:
- Constant Field Values
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DNN_PMODE_CROP_CENTER
- See Also:
- Constant Field Values
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DNN_PMODE_LETTERBOX
- See Also:
- Constant Field Values
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SoftNMSMethod_SOFTNMS_LINEAR
- See Also:
- Constant Field Values
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SoftNMSMethod_SOFTNMS_GAUSSIAN
- See Also:
- Constant Field Values
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DNN_TARGET_CPU
- See Also:
- Constant Field Values
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DNN_TARGET_OPENCL
- See Also:
- Constant Field Values
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DNN_TARGET_OPENCL_FP16
- See Also:
- Constant Field Values
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DNN_TARGET_MYRIAD
- See Also:
- Constant Field Values
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DNN_TARGET_VULKAN
- See Also:
- Constant Field Values
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DNN_TARGET_FPGA
- See Also:
- Constant Field Values
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DNN_TARGET_CUDA
- See Also:
- Constant Field Values
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DNN_TARGET_CUDA_FP16
- See Also:
- Constant Field Values
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DNN_TARGET_HDDL
- See Also:
- Constant Field Values
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DNN_TARGET_NPU
- See Also:
- Constant Field Values
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DNN_TARGET_CPU_FP16
- See Also:
- Constant Field Values
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Constructor Details
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Method Details
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getAvailableTargets
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readNetFromDarknet
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.- Parameters:
cfgFile
- path to the .cfg file with text description of the network architecture.darknetModel
- path to the .weights file with learned network.- Returns:
- Network object that ready to do forward, throw an exception in failure cases.
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readNetFromDarknet
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.- Parameters:
cfgFile
- path to the .cfg file with text description of the network architecture.- Returns:
- Network object that ready to do forward, throw an exception in failure cases.
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readNetFromDarknet
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.- Parameters:
bufferCfg
- A buffer contains a content of .cfg file with text description of the network architecture.bufferModel
- A buffer contains a content of .weights file with learned network.- Returns:
- Net object.
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readNetFromDarknet
Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.- Parameters:
bufferCfg
- A buffer contains a content of .cfg file with text description of the network architecture.- Returns:
- Net object.
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readNetFromCaffe
Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.- Parameters:
prototxt
- path to the .prototxt file with text description of the network architecture.caffeModel
- path to the .caffemodel file with learned network.- Returns:
- Net object.
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readNetFromCaffe
Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.- Parameters:
prototxt
- path to the .prototxt file with text description of the network architecture.- Returns:
- Net object.
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readNetFromCaffe
Reads a network model stored in Caffe model in memory.- Parameters:
bufferProto
- buffer containing the content of the .prototxt filebufferModel
- buffer containing the content of the .caffemodel file- Returns:
- Net object.
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readNetFromCaffe
Reads a network model stored in Caffe model in memory.- Parameters:
bufferProto
- buffer containing the content of the .prototxt file- Returns:
- Net object.
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readNetFromTensorflow
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.- Parameters:
model
- path to the .pb file with binary protobuf description of the network architectureconfig
- path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.- Returns:
- Net object.
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readNetFromTensorflow
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.- Parameters:
model
- path to the .pb file with binary protobuf description of the network architecture Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.- Returns:
- Net object.
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readNetFromTensorflow
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.- Parameters:
bufferModel
- buffer containing the content of the pb filebufferConfig
- buffer containing the content of the pbtxt file- Returns:
- Net object.
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readNetFromTensorflow
Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.- Parameters:
bufferModel
- buffer containing the content of the pb file- Returns:
- Net object.
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readNetFromTFLite
Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.- Parameters:
model
- path to the .tflite file with binary flatbuffers description of the network architecture- Returns:
- Net object.
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readNetFromTFLite
Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.- Parameters:
bufferModel
- buffer containing the content of the tflite file- Returns:
- Net object.
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readNetFromTorch
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.- Parameters:
model
- path to the file, dumped from Torch by using torch.save() function.isBinary
- specifies whether the network was serialized in ascii mode or binary.evaluate
- specifies testing phase of network. If true, it's similar to evaluate() method in Torch.- Returns:
- Net object.
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 <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> 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 - nn.SoftMax, nn.LogSoftMax Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
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readNetFromTorch
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.- Parameters:
model
- path to the file, dumped from Torch by using torch.save() function.isBinary
- specifies whether the network was serialized in ascii mode or binary.- Returns:
- Net object.
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 <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> 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 - nn.SoftMax, nn.LogSoftMax Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
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readNetFromTorch
Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.- Parameters:
model
- path to the file, dumped from Torch by using torch.save() function.- Returns:
- Net object.
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 <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> 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 - nn.SoftMax, nn.LogSoftMax Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
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readNet
Read deep learning network represented in one of the supported formats.- Parameters:
model
- Binary file contains trained weights. The following file extensions are expected for models from different frameworks: **.caffemodel
(Caffe, http://caffe.berkeleyvision.org/) **.pb
(TensorFlow, https://www.tensorflow.org/) **.t7
|*.net
(Torch, http://torch.ch/) **.weights
(Darknet, https://pjreddie.com/darknet/) **.bin
(DLDT, https://software.intel.com/openvino-toolkit) **.onnx
(ONNX, https://onnx.ai/)config
- Text file contains network configuration. It could be a file with the following extensions: **.prototxt
(Caffe, http://caffe.berkeleyvision.org/) **.pbtxt
(TensorFlow, https://www.tensorflow.org/) **.cfg
(Darknet, https://pjreddie.com/darknet/) **.xml
(DLDT, https://software.intel.com/openvino-toolkit)framework
- Explicit framework name tag to determine a format.- Returns:
- Net object.
This function automatically detects an origin framework of trained model
and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
REF: readNetFromTorch or REF: readNetFromDarknet. An order of
model
andconfig
arguments does not matter.
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readNet
Read deep learning network represented in one of the supported formats.- Parameters:
model
- Binary file contains trained weights. The following file extensions are expected for models from different frameworks: **.caffemodel
(Caffe, http://caffe.berkeleyvision.org/) **.pb
(TensorFlow, https://www.tensorflow.org/) **.t7
|*.net
(Torch, http://torch.ch/) **.weights
(Darknet, https://pjreddie.com/darknet/) **.bin
(DLDT, https://software.intel.com/openvino-toolkit) **.onnx
(ONNX, https://onnx.ai/)config
- Text file contains network configuration. It could be a file with the following extensions: **.prototxt
(Caffe, http://caffe.berkeleyvision.org/) **.pbtxt
(TensorFlow, https://www.tensorflow.org/) **.cfg
(Darknet, https://pjreddie.com/darknet/) **.xml
(DLDT, https://software.intel.com/openvino-toolkit)- Returns:
- Net object.
This function automatically detects an origin framework of trained model
and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
REF: readNetFromTorch or REF: readNetFromDarknet. An order of
model
andconfig
arguments does not matter.
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readNet
Read deep learning network represented in one of the supported formats.- Parameters:
model
- Binary file contains trained weights. The following file extensions are expected for models from different frameworks: **.caffemodel
(Caffe, http://caffe.berkeleyvision.org/) **.pb
(TensorFlow, https://www.tensorflow.org/) **.t7
|*.net
(Torch, http://torch.ch/) **.weights
(Darknet, https://pjreddie.com/darknet/) **.bin
(DLDT, https://software.intel.com/openvino-toolkit) **.onnx
(ONNX, https://onnx.ai/) file with the following extensions: **.prototxt
(Caffe, http://caffe.berkeleyvision.org/) **.pbtxt
(TensorFlow, https://www.tensorflow.org/) **.cfg
(Darknet, https://pjreddie.com/darknet/) **.xml
(DLDT, https://software.intel.com/openvino-toolkit)- Returns:
- Net object.
This function automatically detects an origin framework of trained model
and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
REF: readNetFromTorch or REF: readNetFromDarknet. An order of
model
andconfig
arguments does not matter.
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readNet
Read deep learning network represented in one of the supported formats. This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.- Parameters:
framework
- Name of origin framework.bufferModel
- A buffer with a content of binary file with weightsbufferConfig
- A buffer with a content of text file contains network configuration.- Returns:
- Net object.
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readNet
Read deep learning network represented in one of the supported formats. This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.- Parameters:
framework
- Name of origin framework.bufferModel
- A buffer with a content of binary file with weights- Returns:
- Net object.
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readTorchBlob
Loads blob which was serialized as torch.Tensor object of Torch7 framework. WARNING: This function has the same limitations as readNetFromTorch().- Parameters:
filename
- automatically generatedisBinary
- automatically generated- Returns:
- automatically generated
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readTorchBlob
Loads blob which was serialized as torch.Tensor object of Torch7 framework. WARNING: This function has the same limitations as readNetFromTorch().- Parameters:
filename
- automatically generated- Returns:
- automatically generated
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readNetFromModelOptimizer
Load a network from Intel's Model Optimizer intermediate representation.- Parameters:
xml
- XML configuration file with network's topology.bin
- Binary file with trained weights.- Returns:
- Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.
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readNetFromModelOptimizer
Load a network from Intel's Model Optimizer intermediate representation.- Parameters:
bufferModelConfig
- Buffer contains XML configuration with network's topology.bufferWeights
- Buffer contains binary data with trained weights.- Returns:
- Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.
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readNetFromONNX
Reads a network model <a href="https://onnx.ai/">ONNX</a>.- Parameters:
onnxFile
- path to the .onnx file with text description of the network architecture.- Returns:
- Network object that ready to do forward, throw an exception in failure cases.
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readNetFromONNX
Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer.- Parameters:
buffer
- in-memory buffer that stores the ONNX model bytes.- Returns:
- Network object that ready to do forward, throw an exception in failure cases.
-
readTensorFromONNX
Creates blob from .pb file.- Parameters:
path
- to the .pb file with input tensor.- Returns:
- Mat.
-
blobFromImage
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values.size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or notddepth
- Depth of output blob. Choose CV_32F or CV_8U. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values.size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or not ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB)Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values.size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values.size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values.size
- spatial size for output image to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels).scalefactor
- multiplier forimages
values. to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImage
Creates 4-dimensional blob from image. Optionally resizes and cropsimage
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
image
- input image (with 1-, 3- or 4-channels). to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or notddepth
- Depth of output blob. Choose CV_32F or CV_8U. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or not ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB)Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values.swapRB
- flag which indicates that swap first and last channels in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output image to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels). to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true.scalefactor
- multiplier forimages
values. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImages
Creates 4-dimensional blob from series of images. Optionally resizes and cropsimages
from center, subtractmean
values, scales values byscalefactor
, swap Blue and Red channels.- Parameters:
images
- input images (all with 1-, 3- or 4-channels). to be in (mean-R, mean-G, mean-B) order ifimage
has BGR ordering andswapRB
is true. in 3-channel image is necessary. ifcrop
is true, input image is resized so one side after resize is equal to corresponding dimension insize
and another one is equal or larger. Then, crop from the center is performed. Ifcrop
is false, direct resize without cropping and preserving aspect ratio is performed.- Returns:
- 4-dimensional Mat with NCHW dimensions order.
Note:
The order and usage of
scalefactor
andmean
are (input - mean) * scalefactor.
-
blobFromImageWithParams
Creates 4-dimensional blob from image with given params. This function is an extension of REF: blobFromImage to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.- Parameters:
image
- input image (all with 1-, 3- or 4-channels).param
- struct of Image2BlobParams, contains all parameters needed by processing of image to blob.- Returns:
- 4-dimensional Mat.
-
blobFromImageWithParams
Creates 4-dimensional blob from image with given params. This function is an extension of REF: blobFromImage to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.- Parameters:
image
- input image (all with 1-, 3- or 4-channels).- Returns:
- 4-dimensional Mat.
-
blobFromImageWithParams
-
blobFromImageWithParams
-
blobFromImagesWithParams
Creates 4-dimensional blob from series of images with given params. This function is an extension of REF: blobFromImages to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.- Parameters:
images
- input image (all with 1-, 3- or 4-channels).param
- struct of Image2BlobParams, contains all parameters needed by processing of image to blob.- Returns:
- 4-dimensional Mat.
-
blobFromImagesWithParams
Creates 4-dimensional blob from series of images with given params. This function is an extension of REF: blobFromImages to meet more image preprocess needs. Given input image and preprocessing parameters, and function outputs the blob.- Parameters:
images
- input image (all with 1-, 3- or 4-channels).- Returns:
- 4-dimensional Mat.
-
blobFromImagesWithParams
-
blobFromImagesWithParams
-
imagesFromBlob
Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).- Parameters:
blob_
- 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.images_
- array of 2D Mat containing the images extracted from the blob in floating point precision (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
-
shrinkCaffeModel
Convert all weights of Caffe network to half precision floating point.- Parameters:
src
- Path to origin model from Caffe framework contains single precision floating point weights (usually has.caffemodel
extension).dst
- Path to destination model with updated weights.layersTypes
- Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights. Note: Shrinked model has no origin float32 weights so it can't be used in origin Caffe framework anymore. However the structure of data is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe. So the resulting model may be used there.
-
shrinkCaffeModel
Convert all weights of Caffe network to half precision floating point.- Parameters:
src
- Path to origin model from Caffe framework contains single precision floating point weights (usually has.caffemodel
extension).dst
- Path to destination model with updated weights. By default, converts only Convolutional and Fully-Connected layers' weights. Note: Shrinked model has no origin float32 weights so it can't be used in origin Caffe framework anymore. However the structure of data is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe. So the resulting model may be used there.
-
writeTextGraph
Create a text representation for a binary network stored in protocol buffer format.- Parameters:
model
- A path to binary network.output
- A path to output text file to be created. Note: To reduce output file size, trained weights are not included.
-
NMSBoxes
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)Performs non maximum suppression given boxes and corresponding scores.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).top_k
- if>0
, keep at mosttop_k
picked indices.
-
NMSBoxes
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)Performs non maximum suppression given boxes and corresponding scores.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
-
NMSBoxes
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)Performs non maximum suppression given boxes and corresponding scores.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.
-
NMSBoxesRotated
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k) -
NMSBoxesRotated
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta) -
NMSBoxesRotated
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices) -
NMSBoxesBatched
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)Performs batched non maximum suppression on given boxes and corresponding scores across different classes.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.class_ids
- a set of corresponding class ids. Ids are integer and usually start from 0.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).top_k
- if>0
, keep at mosttop_k
picked indices.
-
NMSBoxesBatched
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta)Performs batched non maximum suppression on given boxes and corresponding scores across different classes.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.class_ids
- a set of corresponding class ids. Ids are integer and usually start from 0.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
-
NMSBoxesBatched
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices)Performs batched non maximum suppression on given boxes and corresponding scores across different classes.- Parameters:
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.class_ids
- a set of corresponding class ids. Ids are integer and usually start from 0.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.
-
softNMSBoxes
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma)Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503- Parameters:
bboxes
- a set of bounding boxes to apply Soft NMS.scores
- a set of corresponding confidences.updated_scores
- a set of corresponding updated confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.top_k
- keep at mosttop_k
picked indices.sigma
- parameter of Gaussian weighting. SEE: SoftNMSMethod
-
softNMSBoxes
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k)Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503- Parameters:
bboxes
- a set of bounding boxes to apply Soft NMS.scores
- a set of corresponding confidences.updated_scores
- a set of corresponding updated confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.top_k
- keep at mosttop_k
picked indices. SEE: SoftNMSMethod
-
softNMSBoxes
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices)Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503- Parameters:
bboxes
- a set of bounding boxes to apply Soft NMS.scores
- a set of corresponding confidences.updated_scores
- a set of corresponding updated confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS. SEE: SoftNMSMethod
-
getInferenceEngineBackendType
Deprecated.Returns Inference Engine internal backend API. See values ofCV_DNN_BACKEND_INFERENCE_ENGINE_*
macros.OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE
runtime parameter (environment variable) is ignored since 4.6.0.- Returns:
- automatically generated
-
setInferenceEngineBackendType
Deprecated.Specify Inference Engine internal backend API. See values ofCV_DNN_BACKEND_INFERENCE_ENGINE_*
macros.- Parameters:
newBackendType
- automatically generated- Returns:
- previous value of internal backend API
-
resetMyriadDevice
Release a Myriad device (binded by OpenCV). Single Myriad device cannot be shared across multiple processes which uses Inference Engine's Myriad plugin. -
getInferenceEngineVPUType
Returns Inference Engine VPU type. See values ofCV_DNN_INFERENCE_ENGINE_VPU_TYPE_*
macros.- Returns:
- automatically generated
-
getInferenceEngineCPUType
Returns Inference Engine CPU type. Specify OpenVINO plugin: CPU or ARM.- Returns:
- automatically generated
-
releaseHDDLPlugin
Release a HDDL plugin.
-