org.opencv.dnn

## Class Dnn

- 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_DEFAULT`
    `static int` | `DNN_BACKEND_HALIDE`
    `static int` | `DNN_BACKEND_INFERENCE_ENGINE`
    `static int` | `DNN_BACKEND_OPENCV`
    `static int` | `DNN_TARGET_CPU`
    `static int` | `DNN_TARGET_FPGA`
    `static int` | `DNN_TARGET_MYRIAD`
    `static int` | `DNN_TARGET_OPENCL`
    `static int` | `DNN_TARGET_OPENCL_FP16`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `Dnn()`

  - ### Method Summary

    All Methods Static Methods Concrete 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 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)`
    `static void` | `NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)`
    `static void` | `NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)`
    `static void` | `NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)` Performs non maximum suppression given boxes and corresponding scores.
    `static void` | `NMSBoxes(MatOfRect 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(MatOfRect 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` | `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(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` | `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` | `resetMyriadDevice()` Release a Myriad device (binded by OpenCV).
    `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` | `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_TARGET_CPU

          public static final int DNN_TARGET_CPU

      See Also:
        Constant Field Values

    - #### DNN_TARGET_OPENCL

          public static final int DNN_TARGET_OPENCL

      See Also:
        Constant Field Values

    - #### DNN_TARGET_OPENCL_FP16

          public static final int DNN_TARGET_OPENCL_FP16

      See Also:
        Constant Field Values

    - #### DNN_TARGET_MYRIAD

          public static final int DNN_TARGET_MYRIAD

      See Also:
        Constant Field Values

    - #### DNN_TARGET_FPGA

          public static final int DNN_TARGET_FPGA

      See Also:
        Constant Field Values

    - #### DNN_BACKEND_DEFAULT

          public static final int DNN_BACKEND_DEFAULT

      See Also:
        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

      See Also:
        Constant Field Values

    - #### DNN_BACKEND_OPENCV

          public static final int DNN_BACKEND_OPENCV

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### Dnn

          public Dnn()

  - ### Method Detail

    - #### 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).
        `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 `image` 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.

    - #### 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).
        `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 `image` 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.

    - #### 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).
        `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 `image` 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.

    - #### 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).
        `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 `image` 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.

    - #### 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).
        `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 `image` 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.

    - #### 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). to be in (mean-R, mean-G, mean-B) order if `image` has BGR ordering and `swapRB` is true.
        `scalefactor` - multiplier for `image` 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### readTensorFromONNX

          public static Mat readTensorFromONNX(String path)

      Creates blob from .pb file.
      Parameters:
        `path` - to the .pb file with input tensor.
      Returns:
        Mat.

    - #### 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

    - #### 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.

    - #### 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` (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` 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` (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` 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` (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` and `config` arguments does not matter.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### 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.

    - #### getInferenceEngineVPUType

          public static String getInferenceEngineVPUType()

      Returns Inference Engine VPU type. See values of `CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*` macros.
      Returns:
        automatically generated

    - #### NMSBoxes

          public static void NMSBoxes(MatOfRect 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 `&gt;0`, keep at most `top_k` picked indices.

    - #### NMSBoxes

          public static void NMSBoxes(MatOfRect 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(MatOfRect 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.

    - #### NMSBoxes

          public static void NMSBoxes(MatOfRect2d bboxes,
                                      MatOfFloat scores,
                                      float score_threshold,
                                      float nms_threshold,
                                      MatOfInt indices,
                                      float eta,
                                      int top_k)

    - #### NMSBoxes

          public static void NMSBoxes(MatOfRect2d bboxes,
                                      MatOfFloat scores,
                                      float score_threshold,
                                      float nms_threshold,
                                      MatOfInt indices,
                                      float eta)

    - #### NMSBoxes

          public static void NMSBoxes(MatOfRect2d bboxes,
                                      MatOfFloat scores,
                                      float score_threshold,
                                      float nms_threshold,
                                      MatOfInt indices)

    - #### 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)

    - #### 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).

    - #### 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.

    - #### 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.

Generated on Thu Jul 25 2019 21:39:44 UTC / OpenCV 3.4.7