Deprecated API

Contents

• Deprecated Classes
• Deprecated Methods

Deprecated Classes

Class and Description
org.bytedeco.opencv.opencv_core.CvMat CvMat is now obsolete; consider using Mat instead.
org.bytedeco.opencv.opencv_core.CvMatND consider using cv::Mat instead

Deprecated Methods

Method and Description
org.bytedeco.opencv.opencv_dnn.NormalizeBBoxLayer.acrossSpatial()
org.bytedeco.opencv.global.opencv_stitching.createStitcher(boolean) use Stitcher::create
org.bytedeco.opencv.global.opencv_stitching.createStitcherScans(boolean) use Stitcher::create
org.bytedeco.opencv.global.opencv_video.estimateRigidTransform(GpuMat, GpuMat, boolean)
org.bytedeco.opencv.global.opencv_video.estimateRigidTransform(Mat, Mat, boolean) Use cv::estimateAffine2D, cv::estimateAffinePartial2D instead. If you are using this fuction with images, extract points using cv::calcOpticalFlowPyrLK and then use the estimation fuctions.
org.bytedeco.opencv.global.opencv_video.estimateRigidTransform(UMat, UMat, boolean)
org.bytedeco.opencv.opencv_dnn.Layer.finalize(MatPointerVector, MatVector) Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
org.bytedeco.opencv.opencv_dnn.Layer.finalize(MatVector) Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
org.bytedeco.opencv.opencv_dnn.Layer.forward(MatPointerVector, MatVector, MatVector) Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead
org.bytedeco.opencv.opencv_core.AbstractCvMat.get()
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(double[])
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(int, double[])
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(int, double[], int, int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(int, int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.get(int, int, int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.getByteBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getByteBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getByteBuffer(int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.getDoubleBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getDoubleBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getDoubleBuffer(int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.getFloatBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getFloatBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getFloatBuffer(int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.getIntBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getIntBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getIntBuffer(int)
org.bytedeco.opencv.opencv_core.Program.getPrefix()
org.bytedeco.opencv.opencv_core.Program.getPrefix(BytePointer)
org.bytedeco.opencv.opencv_core.Program.getPrefix(String)
org.bytedeco.opencv.opencv_core.AbstractCvMat.getShortBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getShortBuffer()
org.bytedeco.opencv.opencv_core.AbstractArray.getShortBuffer(int)
org.bytedeco.opencv.global.opencv_core.getThreadNum() Current implementation doesn't corresponding to this documentation. The exact meaning of the return value depends on the threading framework used by OpenCV library: - TBB - Unsupported with current 4.1 TBB release. Maybe will be supported in future. - OpenMP - The thread number, within the current team, of the calling thread. - Concurrency - An ID for the virtual processor that the current context is executing on (0 for master thread and unique number for others, but not necessary 1,2,3,...). - GCD - System calling thread's ID. Never returns 0 inside parallel region. - C= - The index of the current parallel task.
org.opencv.core.Core.getThreadNum()
org.bytedeco.opencv.global.opencv_imgproc.linearPolar(Mat, Mat, Point2f, double, int) This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) \internal Transform the source image using the following transformation (See \ref polar_remaps_reference_image "Polar remaps reference image c)"): \[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\] where \[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\] and \[\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\]
org.opencv.imgproc.Imgproc.linearPolar(Mat, Mat, Point, double, int)
org.bytedeco.opencv.global.opencv_text.loadOCRHMMClassifierCNN(BytePointer) use loadOCRHMMClassifier instead
org.bytedeco.opencv.global.opencv_text.loadOCRHMMClassifierNM(BytePointer) loadOCRHMMClassifier instead
org.bytedeco.opencv.global.opencv_imgproc.logPolar(Mat, Mat, Point2f, double, int) This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); \internal Transform the source image using the following transformation (See \ref polar_remaps_reference_image "Polar remaps reference image d)"): \[\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\] where \[\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\] and \[\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\] The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth.
org.opencv.imgproc.Imgproc.logPolar(Mat, Mat, Point, double, int)
org.bytedeco.opencv.opencv_dnn.PoolingLayer.pad()
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(double...)
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(int, double...)
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(int, double)
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(int, double[], int, int)
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(int, int, double)
org.bytedeco.opencv.opencv_core.AbstractCvMat.put(int, int, int, double)
org.bytedeco.opencv.opencv_core.Program.read(BytePointer, BytePointer)
org.bytedeco.opencv.opencv_core.Program.read(String, String)
org.bytedeco.opencv.opencv_core.AbstractCvMat.reset()
org.opencv.dnn.Layer.run(List<Mat>, List<Mat>, List<Mat>)
org.bytedeco.opencv.opencv_dnn.Layer.run(MatVector, MatVector, MatVector) This method will be removed in the future release.
org.bytedeco.opencv.opencv_dnn.LSTMLayer.setProduceCellOutput()
org.bytedeco.opencv.opencv_dnn.LSTMLayer.setProduceCellOutput(boolean) Use flag use_timestamp_dim in LayerParams. \brief If this flag is set to true then layer will produce c_t as second output. \details Shape of the second output is the same as first output.
org.bytedeco.opencv.opencv_dnn.LSTMLayer.setUseTimstampsDim()
org.bytedeco.opencv.opencv_dnn.LSTMLayer.setUseTimstampsDim(boolean) Use flag produce_cell_output in LayerParams. \brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample. If flag is set to true then shape of input blob will be interpreted as [T, N, [data dims]] where T specifies number of timestamps, N is number of independent streams. In this case each forward() call will iterate through T timestamps and update layer's state T times. If flag is set to false then shape of input blob will be interpreted as [N, [data dims]]. In this case each forward() call will make one iteration and produce one timestamp with shape [N, [out dims]].
org.bytedeco.opencv.opencv_dnn.LSTMLayer.setWeights(Mat, Mat, Mat) Use LayerParams::blobs instead. \brief Set trained weights for LSTM layer. LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights. Let x_t be current input, h_t be current output, c_t be current state. Than current output and current cell state is computed as follows: \begin{eqnarray*} h_t &= o_t \odot tanh(c_t), \\ c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ \end{eqnarray*} where \odot is per-element multiply operation and i_t, f_t, o_t, g_t is internal gates that are computed using learned wights. Gates are computed as follows: \begin{eqnarray*} i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ \end{eqnarray*} where W_{x?}, W_{h?} and b_{?} are learned weights represented as matrices: W_{x?} \in R^{N_h \times N_x}, W_{h?} \in R^{N_h \times N_h}, b_? \in R^{N_h}. For simplicity and performance purposes we use W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] (i.e. W_x is vertical concatenation of W_{x?} ), W_x \in R^{4N_h \times N_x} . The same for W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} and for b = [b_i; b_f, b_o, b_g], b \in R^{4N_h} .
org.bytedeco.opencv.opencv_core.Program.source()
org.bytedeco.opencv.opencv_core.Program.write(BytePointer)
org.bytedeco.opencv.opencv_core.Program.write(String)