Dnn (JavaCPP Presets for OpenCV 4.11.0-1.5.12 API)

java.lang.Object
- org.opencv.dnn.Dnn

```
public class Dnn
extends Object
```

Field Summary

Fields
Modifier and Type	Field and 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`

Constructor Summary

Constructors
Constructor and Description

Dnn()

Constructors
Constructor and Description
`Dnn()`

Method Summary

All Methods Static Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and 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 Mat`	`blobFromImagesWithParams(List<Mat> images, Image2BlobParams param)` 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`	`blobFromImageWithParams(Mat image)` Creates 4-dimensional blob from image with given params.
`static Mat`	`blobFromImageWithParams(Mat image, Image2BlobParams param)` 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 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 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`	`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`	`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`	`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`	`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`	`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`	`readNetFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)` Load a network from Intel's Model Optimizer intermediate representation.
`static Net`	`readNetFromModelOptimizer(String xml)` Load a network from Intel's Model Optimizer intermediate representation.
`static Net`	`readNetFromModelOptimizer(String xml, String bin)` Load a network from Intel's Model Optimizer intermediate representation.
`static Net`	`readNetFromONNX(MatOfByte buffer)` Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer.
`static Net`	`readNetFromONNX(String onnxFile)` Reads a network model <a href="https://onnx.ai/">ONNX</a>.
`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`	`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`	`readNetFromTFLite(MatOfByte bufferModel)` Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</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`	`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.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DNN_BACKEND_DEFAULT
```
public static final int DNN_BACKEND_DEFAULT
```
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    Constant Field Values
  - DNN_BACKEND_HALIDE
```
public static final int DNN_BACKEND_HALIDE
```
    See Also:
    
    Constant Field Values
  - DNN_BACKEND_INFERENCE_ENGINE
```
public static final int DNN_BACKEND_INFERENCE_ENGINE
```
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    Constant Field Values
  - DNN_BACKEND_OPENCV
```
public static final int DNN_BACKEND_OPENCV
```
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    Constant Field Values
  - DNN_BACKEND_VKCOM
```
public static final int DNN_BACKEND_VKCOM
```
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    Constant Field Values
  - DNN_BACKEND_CUDA
```
public static final int DNN_BACKEND_CUDA
```
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    Constant Field Values
  - DNN_BACKEND_WEBNN
```
public static final int DNN_BACKEND_WEBNN
```
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    Constant Field Values
  - DNN_BACKEND_TIMVX
```
public static final int DNN_BACKEND_TIMVX
```
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    Constant Field Values
  - DNN_BACKEND_CANN
```
public static final int DNN_BACKEND_CANN
```
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    Constant Field Values
  - DNN_LAYOUT_UNKNOWN
```
public static final int DNN_LAYOUT_UNKNOWN
```
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    Constant Field Values
  - DNN_LAYOUT_ND
```
public static final int DNN_LAYOUT_ND
```
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    Constant Field Values
  - DNN_LAYOUT_NCHW
```
public static final int DNN_LAYOUT_NCHW
```
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    Constant Field Values
  - DNN_LAYOUT_NCDHW
```
public static final int DNN_LAYOUT_NCDHW
```
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    Constant Field Values
  - DNN_LAYOUT_NHWC
```
public static final int DNN_LAYOUT_NHWC
```
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    Constant Field Values
  - DNN_LAYOUT_NDHWC
```
public static final int DNN_LAYOUT_NDHWC
```
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    Constant Field Values
  - DNN_LAYOUT_PLANAR
```
public static final int DNN_LAYOUT_PLANAR
```
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    Constant Field Values
  - DNN_PMODE_NULL
```
public static final int DNN_PMODE_NULL
```
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    Constant Field Values
  - DNN_PMODE_CROP_CENTER
```
public static final int DNN_PMODE_CROP_CENTER
```
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    Constant Field Values
  - DNN_PMODE_LETTERBOX
```
public static final int DNN_PMODE_LETTERBOX
```
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    Constant Field Values
  - SoftNMSMethod_SOFTNMS_LINEAR
```
public static final int SoftNMSMethod_SOFTNMS_LINEAR
```
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    Constant Field Values
  - SoftNMSMethod_SOFTNMS_GAUSSIAN
```
public static final int SoftNMSMethod_SOFTNMS_GAUSSIAN
```
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    Constant Field Values
  - DNN_TARGET_CPU
```
public static final int DNN_TARGET_CPU
```
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    Constant Field Values
  - DNN_TARGET_OPENCL
```
public static final int DNN_TARGET_OPENCL
```
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    Constant Field Values
  - DNN_TARGET_OPENCL_FP16
```
public static final int DNN_TARGET_OPENCL_FP16
```
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    Constant Field Values
  - DNN_TARGET_MYRIAD
```
public static final int DNN_TARGET_MYRIAD
```
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    Constant Field Values
  - DNN_TARGET_VULKAN
```
public static final int DNN_TARGET_VULKAN
```
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    Constant Field Values
  - DNN_TARGET_FPGA
```
public static final int DNN_TARGET_FPGA
```
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    Constant Field Values
  - DNN_TARGET_CUDA
```
public static final int DNN_TARGET_CUDA
```
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    Constant Field Values
  - DNN_TARGET_CUDA_FP16
```
public static final int DNN_TARGET_CUDA_FP16
```
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    Constant Field Values
  - DNN_TARGET_HDDL
```
public static final int DNN_TARGET_HDDL
```
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    Constant Field Values
  - DNN_TARGET_NPU
```
public static final int DNN_TARGET_NPU
```
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    Constant Field Values
  - DNN_TARGET_CPU_FP16
```
public static final int DNN_TARGET_CPU_FP16
```
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    Constant Field Values
- Constructor Detail
  - Dnn
```
public Dnn()
```
- Method Detail
  - getAvailableTargets
```
public static List<Integer> getAvailableTargets(int be)
```
  - readNetFromDarknet
```
public static Net readNetFromDarknet(String cfgFile,
                                     String darknetModel)
```
    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.
  - readNetFromDarknet
```
public static Net readNetFromDarknet(String cfgFile)
```
    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.
  - readNetFromDarknet
```
public static Net readNetFromDarknet(MatOfByte bufferCfg,
                                     MatOfByte bufferModel)
```
    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.
  - readNetFromDarknet
```
public static Net readNetFromDarknet(MatOfByte bufferCfg)
```
    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.
  - readNetFromCaffe
```
public static Net readNetFromCaffe(String prototxt,
                                   String caffeModel)
```
    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.
  - readNetFromCaffe
```
public static Net readNetFromCaffe(String prototxt)
```
    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.
  - readNetFromCaffe
```
public static Net readNetFromCaffe(MatOfByte bufferProto,
                                   MatOfByte bufferModel)
```
    Reads a network model stored in Caffe model in memory.
    
    Parameters:
    
    bufferProto - buffer containing the content of the .prototxt file
    
    bufferModel - buffer containing the content of the .caffemodel file
    
    Returns:
    
    Net object.
  - readNetFromCaffe
```
public static Net readNetFromCaffe(MatOfByte bufferProto)
```
    Reads a network model stored in Caffe model in memory.
    
    Parameters:
    
    bufferProto - buffer containing the content of the .prototxt file
    
    Returns:
    
    Net object.
  - readNetFromTensorflow
```
public static Net readNetFromTensorflow(String model,
                                        String config)
```
    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
    
    config - 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.
  - readNetFromTensorflow
```
public static Net readNetFromTensorflow(String model)
```
    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.
  - readNetFromTensorflow
```
public static Net readNetFromTensorflow(MatOfByte bufferModel,
                                        MatOfByte bufferConfig)
```
    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
    
    bufferConfig - buffer containing the content of the pbtxt file
    
    Returns:
    
    Net object.
  - readNetFromTensorflow
```
public static Net readNetFromTensorflow(MatOfByte bufferModel)
```
    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.
  - readNetFromTFLite
```
public static Net readNetFromTFLite(String model)
```
    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.
  - readNetFromTFLite
```
public static Net readNetFromTFLite(MatOfByte bufferModel)
```
    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.
  - readNetFromTorch
```
public 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.
    
    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.
  - readNetFromTorch
```
public static Net readNetFromTorch(String model,
                                   boolean isBinary)
```
    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.
  - readNetFromTorch
```
public static Net readNetFromTorch(String model)
```
    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.
  - readNet
```
public static Net readNet(String model,
                          String config,
                          String framework)
```
    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 | *.onnx (OpenVINO, 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 (OpenVINO, 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 and config arguments does not matter.
  - readNet
```
public static Net readNet(String model,
                          String config)
```
    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 | *.onnx (OpenVINO, 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 (OpenVINO, 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 and config arguments does not matter.
  - readNet
```
public static Net readNet(String model)
```
    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 | *.onnx (OpenVINO, 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 (OpenVINO, 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 and config arguments does not matter.
  - readNet
```
public static Net readNet(String framework,
                          MatOfByte bufferModel,
                          MatOfByte bufferConfig)
```
    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
    
    bufferConfig - A buffer with a content of text file contains network configuration.
    
    Returns:
    
    Net object.
  - readNet
```
public static Net readNet(String framework,
                          MatOfByte bufferModel)
```
    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.
  - readTorchBlob
```
public static Mat readTorchBlob(String filename,
                                boolean isBinary)
```
    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
    
    isBinary - automatically generated
    
    Returns:
    
    automatically generated
  - readTorchBlob
```
public static Mat readTorchBlob(String filename)
```
    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
  - readNetFromModelOptimizer
```
public static Net readNetFromModelOptimizer(String xml,
                                            String bin)
```
    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.
  - readNetFromModelOptimizer
```
public static Net readNetFromModelOptimizer(String xml)
```
    Load a network from Intel's Model Optimizer intermediate representation.
    
    Parameters:
    
    xml - XML configuration file with network's topology.
    
    Returns:
    
    Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.
  - readNetFromModelOptimizer
```
public static Net readNetFromModelOptimizer(MatOfByte bufferModelConfig,
                                            MatOfByte bufferWeights)
```
    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.
  - readNetFromONNX
```
public static Net readNetFromONNX(String onnxFile)
```
    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.
  - readNetFromONNX
```
public static Net readNetFromONNX(MatOfByte buffer)
```
    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
```
public static Mat readTensorFromONNX(String path)
```
    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 crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values.
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB 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
    
    ddepth - Depth of output blob. Choose CV_32F or CV_8U. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean 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 crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values.
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB 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 if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean 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 crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values.
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
    
    swapRB - flag which indicates that swap first and last channels in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImage
```
public static Mat blobFromImage(Mat image,
                                double scalefactor,
                                Size size,
                                Scalar mean)
```
    Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values.
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImage
```
public static Mat blobFromImage(Mat image,
                                double scalefactor,
                                Size size)
```
    Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values.
    
    size - spatial size for output image to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImage
```
public static Mat blobFromImage(Mat image,
                                double scalefactor)
```
    Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    image - input image (with 1-, 3- or 4-channels).
    
    scalefactor - multiplier for images values. to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImage
```
public static Mat blobFromImage(Mat image)
```
    Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, 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 if image has BGR ordering and swapRB is true. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean 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 crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    images - input images (all with 1-, 3- or 4-channels).
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images 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
    
    ddepth - Depth of output blob. Choose CV_32F or CV_8U. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean 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 crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    images - input images (all with 1-, 3- or 4-channels).
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images 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 if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean 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 crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    images - input images (all with 1-, 3- or 4-channels).
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images values.
    
    swapRB - flag which indicates that swap first and last channels in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImages
```
public static Mat blobFromImages(List<Mat> images,
                                 double scalefactor,
                                 Size size,
                                 Scalar mean)
```
    Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
    
    Parameters:
    
    images - input images (all with 1-, 3- or 4-channels).
    
    size - spatial size for output image
    
    mean - scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images values. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImages
```
public static Mat blobFromImages(List<Mat> images,
                                 double scalefactor,
                                 Size size)
```
    Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, 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 if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images values. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImages
```
public static Mat blobFromImages(List<Mat> images,
                                 double scalefactor)
```
    Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, 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 if image has BGR ordering and swapRB is true.
    
    scalefactor - multiplier for images values. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImages
```
public static Mat blobFromImages(List<Mat> images)
```
    Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, 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 if image has BGR ordering and swapRB is true. in 3-channel image is necessary. if crop is true, input image is resized so one side after resize is equal to corresponding dimension in size and another one is equal or larger. Then, crop from the center is performed. If crop 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 and mean are (input - mean) * scalefactor.
  - blobFromImageWithParams
```
public static Mat blobFromImageWithParams(Mat image,
                                          Image2BlobParams param)
```
    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
```
public static Mat blobFromImageWithParams(Mat image)
```
    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
```
public static void blobFromImageWithParams(Mat image,
                                           Mat blob,
                                           Image2BlobParams param)
```
  - blobFromImageWithParams
```
public static void blobFromImageWithParams(Mat image,
                                           Mat blob)
```
  - blobFromImagesWithParams
```
public static Mat blobFromImagesWithParams(List<Mat> images,
                                           Image2BlobParams param)
```
    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
```
public static Mat blobFromImagesWithParams(List<Mat> images)
```
    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
```
public static void blobFromImagesWithParams(List<Mat> images,
                                            Mat blob,
                                            Image2BlobParams param)
```
  - blobFromImagesWithParams
```
public static void blobFromImagesWithParams(List<Mat> images,
                                            Mat blob)
```
  - imagesFromBlob
```
public 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>).
    
    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
```
public static void shrinkCaffeModel(String src,
                                    String dst,
                                    List<String> layersTypes)
```
    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
```
public static void shrinkCaffeModel(String src,
                                    String dst)
```
    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
```
public static void writeTextGraph(String model,
                                  String output)
```
    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 most top_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 most top_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 most top_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 most top_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
public static String getInferenceEngineBackendType()
```
    Deprecated.
    
    Returns Inference Engine internal backend API. See values of CV_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
public static String setInferenceEngineBackendType(String newBackendType)
```
    Deprecated.
    
    Specify Inference Engine internal backend API. See values of CV_DNN_BACKEND_INFERENCE_ENGINE_* macros.
    
    Parameters:
    
    newBackendType - automatically generated
    
    Returns:
    
    previous value of internal backend API
  - resetMyriadDevice
```
public static void 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
```
public static String getInferenceEngineVPUType()
```
    Returns Inference Engine VPU type. See values of CV_DNN_INFERENCE_ENGINE_VPU_TYPE_* macros.
    
    Returns:
    
    automatically generated
  - getInferenceEngineCPUType
```
public static String getInferenceEngineCPUType()
```
    Returns Inference Engine CPU type. Specify OpenVINO plugin: CPU or ARM.
    
    Returns:
    
    automatically generated
  - releaseHDDLPlugin
```
public static void releaseHDDLPlugin()
```
    Release a HDDL plugin.

Class Dnn

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DNN_BACKEND_DEFAULT

DNN_BACKEND_HALIDE

DNN_BACKEND_INFERENCE_ENGINE

DNN_BACKEND_OPENCV

DNN_BACKEND_VKCOM

DNN_BACKEND_CUDA

DNN_BACKEND_WEBNN

DNN_BACKEND_TIMVX

DNN_BACKEND_CANN

DNN_LAYOUT_UNKNOWN

DNN_LAYOUT_ND

DNN_LAYOUT_NCHW

DNN_LAYOUT_NCDHW

DNN_LAYOUT_NHWC

DNN_LAYOUT_NDHWC

DNN_LAYOUT_PLANAR

DNN_PMODE_NULL

DNN_PMODE_CROP_CENTER

DNN_PMODE_LETTERBOX

SoftNMSMethod_SOFTNMS_LINEAR

SoftNMSMethod_SOFTNMS_GAUSSIAN

DNN_TARGET_CPU

DNN_TARGET_OPENCL

DNN_TARGET_OPENCL_FP16

DNN_TARGET_MYRIAD

DNN_TARGET_VULKAN

DNN_TARGET_FPGA

DNN_TARGET_CUDA

DNN_TARGET_CUDA_FP16

DNN_TARGET_HDDL

DNN_TARGET_NPU

DNN_TARGET_CPU_FP16

Constructor Detail

Dnn

Method Detail

getAvailableTargets

readNetFromDarknet

readNetFromDarknet

readNetFromDarknet

readNetFromDarknet

readNetFromCaffe

readNetFromCaffe

readNetFromCaffe

readNetFromCaffe

readNetFromTensorflow

readNetFromTensorflow

readNetFromTensorflow

readNetFromTensorflow

readNetFromTFLite

readNetFromTFLite

readNetFromTorch

readNetFromTorch

readNetFromTorch

readNet

readNet

readNet

readNet

readNet

readTorchBlob

readTorchBlob

readNetFromModelOptimizer

readNetFromModelOptimizer

readNetFromModelOptimizer

readNetFromONNX

readNetFromONNX

readTensorFromONNX

blobFromImage

blobFromImage

blobFromImage

blobFromImage

blobFromImage

blobFromImage

blobFromImage

blobFromImages