Package org.opencv.dnn

Class Model

java.lang.Object
org.opencv.dnn.Model
Direct Known Subclasses:
ClassificationModel, DetectionModel, KeypointsModel, SegmentationModel, TextDetectionModel, TextRecognitionModel
public class Model extends Object
This class is presented high-level API for neural networks. Model allows to set params for preprocessing input image. Model creates net from file with trained weights and config, sets preprocessing input and runs forward pass.

  • Constructor Details

    • Model

      public Model(String model, String config)
      Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
      Parameters:
      model - Binary file contains trained weights.
      config - Text file contains network configuration.

    • Model

      public Model(String model)
      Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
      Parameters:
      model - Binary file contains trained weights.

    • Model

      public Model(Net network)
      Create model from deep learning network.
      Parameters:
      network - Net object.

  • Method Details

    • getNativeObjAddr

      public long getNativeObjAddr()

    • __fromPtr__

      public static Model __fromPtr__(long addr)

    • setInputSize

      public Model setInputSize(Size size)
      Set input size for frame.
      Parameters:
      size - New input size. Note: If shape of the new blob less than 0, then frame size not change.
      Returns:
      automatically generated

    • setInputSize

      public Model setInputSize(int width, int height)
      Parameters:
      width - New input width.
      height - New input height.
      Returns:
      automatically generated

    • setInputMean

      public Model setInputMean(Scalar mean)
      Set mean value for frame.
      Parameters:
      mean - Scalar with mean values which are subtracted from channels.
      Returns:
      automatically generated

    • setInputScale

      public Model setInputScale(Scalar scale)
      Set scalefactor value for frame.
      Parameters:
      scale - Multiplier for frame values.
      Returns:
      automatically generated

    • setInputCrop

      public Model setInputCrop(boolean crop)
      Set flag crop for frame.
      Parameters:
      crop - Flag which indicates whether image will be cropped after resize or not.
      Returns:
      automatically generated

    • setInputSwapRB

      public Model setInputSwapRB(boolean swapRB)
      Set flag swapRB for frame.
      Parameters:
      swapRB - Flag which indicates that swap first and last channels.
      Returns:
      automatically generated

    • setOutputNames

      public Model setOutputNames(List<String> outNames)
      Set output names for frame.
      Parameters:
      outNames - Names for output layers.
      Returns:
      automatically generated

    • setInputParams

      public void setInputParams(double scale, Size size, Scalar mean, boolean swapRB, boolean crop)
      Set preprocessing parameters for frame.
      Parameters:
      size - New input size.
      mean - Scalar with mean values which are subtracted from channels.
      scale - Multiplier for frame values.
      swapRB - Flag which indicates that swap first and last channels.
      crop - Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • setInputParams

      public void setInputParams(double scale, Size size, Scalar mean, boolean swapRB)
      Set preprocessing parameters for frame.
      Parameters:
      size - New input size.
      mean - Scalar with mean values which are subtracted from channels.
      scale - Multiplier for frame values.
      swapRB - Flag which indicates that swap first and last channels. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • setInputParams

      public void setInputParams(double scale, Size size, Scalar mean)
      Set preprocessing parameters for frame.
      Parameters:
      size - New input size.
      mean - Scalar with mean values which are subtracted from channels.
      scale - Multiplier for frame values. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • setInputParams

      public void setInputParams(double scale, Size size)
      Set preprocessing parameters for frame.
      Parameters:
      size - New input size.
      scale - Multiplier for frame values. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • setInputParams

      public void setInputParams(double scale)
      Set preprocessing parameters for frame.
      Parameters:
      scale - Multiplier for frame values. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • setInputParams

      public void setInputParams()
      Set preprocessing parameters for frame. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )

    • predict

      public void predict(Mat frame, List<Mat> outs)
      Given the input frame, create input blob, run net and return the output blobs.
      Parameters:
      outs - Allocated output blobs, which will store results of the computation.
      frame - automatically generated

    • setPreferableBackend

      public Model setPreferableBackend(int backendId)

    • setPreferableTarget

      public Model setPreferableTarget(int targetId)

    • enableWinograd

      public Model enableWinograd(boolean useWinograd)