Package org.opencv.dnn

Class Model

    • Field Detail

      • nativeObj

        protected final long nativeObj

    • Constructor Detail

      • Model

        protected Model​(long addr)

      • 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.

      • Model

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

    • Method Detail

      • 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

      • 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,
                    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

      • setPreferableBackend

        public Model setPreferableBackend​(int backendId)

      • setPreferableTarget

        public Model setPreferableTarget​(int targetId)

      • enableWinograd

        public Model enableWinograd​(boolean useWinograd)

      • finalize

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