Class Model

  • Direct Known Subclasses:
    ClassificationModel, DetectionModel, KeypointsModel, SegmentationModel

    public class Model
    extends Net
    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 Detail

      • Model

        protected Model​(long addr)
      • Model

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

        public Model​(java.lang.String model,
                     java.lang.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​(java.lang.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.
    • Method Detail

      • __fromPtr__

        public static Model __fromPtr__​(long addr)
      • 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
      • 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​(double scale)
        Set scalefactor value for frame.
        Parameters:
        scale - Multiplier for frame values.
        Returns:
        automatically generated
      • 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)
        Set input size for frame.
        Parameters:
        width - New input width.
        height - New input height. Note: If shape of the new blob less than 0, then frame size not change.
        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
      • predict

        public void predict​(Mat frame,
                            java.util.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
      • 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) )
      • finalize

        protected void finalize()
                         throws java.lang.Throwable
        Overrides:
        finalize in class Net
        Throws:
        java.lang.Throwable