🤖 AIによる機械翻訳（非公式） — これは OpenCV 4.13.0 公式リファレンス（英語）を AI (Claude) で自動翻訳したものです。訳に誤りを含む場合があります。正確な情報は 公式英語版（原文） を参照してください。
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クラス Class Description org.opencv.aruco.EstimateParameters Use Board::matchImagePoints and cv::solvePnP SEE: PatternPositionType, solvePnP()
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フィールド Field Description org.opencv.core.CvType.CV_USRTYPE1 please useCvType.CV_16F
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メソッド Method Description org.opencv.aruco.Aruco.calibrateCameraAruco(List<Mat>, Mat, Mat, Board, Size, Mat, Mat, List<Mat>, List<Mat>, int, TermCriteria) Use Board::matchImagePoints and cv::solvePnPorg.opencv.aruco.Aruco.calibrateCameraArucoExtended(List<Mat>, Mat, Mat, Board, Size, Mat, Mat, List<Mat>, List<Mat>, Mat, Mat, Mat, int, TermCriteria) Use Board::matchImagePoints and cv::solvePnPorg.opencv.aruco.Aruco.calibrateCameraCharuco(List<Mat>, List<Mat>, CharucoBoard, Size, Mat, Mat, List<Mat>, List<Mat>, int, TermCriteria) Use CharucoBoard::matchImagePoints and cv::solvePnPorg.opencv.aruco.Aruco.calibrateCameraCharucoExtended(List<Mat>, List<Mat>, CharucoBoard, Size, Mat, Mat, List<Mat>, List<Mat>, Mat, Mat, Mat, int, TermCriteria) Use CharucoBoard::matchImagePoints and cv::solvePnPorg.opencv.aruco.Aruco.detectCharucoDiamond(Mat, List<Mat>, Mat, float, List<Mat>, Mat, Mat, Mat, Dictionary) Use CharucoDetector::detectDiamondsorg.opencv.aruco.Aruco.detectMarkers(Mat, Dictionary, List<Mat>, Mat, DetectorParameters, List<Mat>) Use class ArucoDetector::detectMarkersorg.opencv.aruco.Aruco.drawCharucoDiamond(Dictionary, int[], int, int, Mat, int, int) Use CharucoBoard::generateImage()org.opencv.aruco.Aruco.drawPlanarBoard(Board, Size, Mat, int, int) Use Board::generateImageorg.opencv.aruco.Aruco.estimatePoseBoard(List<Mat>, Mat, Board, Mat, Mat, Mat, Mat, boolean) Use Board::matchImagePoints and cv::solvePnPorg.opencv.aruco.Aruco.estimatePoseCharucoBoard(Mat, Mat, CharucoBoard, Mat, Mat, Mat, Mat, boolean) Use CharucoBoard::matchImagePoints and cv::solvePnP SEE: use cv::drawFrameAxes to get world coordinate system axis for object pointsorg.opencv.aruco.Aruco.estimatePoseSingleMarkers(List<Mat>, float, Mat, Mat, Mat, Mat, Mat, EstimateParameters) Use cv::solvePnPorg.opencv.aruco.Aruco.getBoardObjectAndImagePoints(Board, List<Mat>, Mat, Mat, Mat) Use Board::matchImagePointsorg.opencv.aruco.Aruco.interpolateCornersCharuco(List<Mat>, Mat, Mat, CharucoBoard, Mat, Mat, Mat, Mat, int) Use CharucoDetector::detectBoardorg.opencv.aruco.Aruco.refineDetectedMarkers(Mat, Board, List<Mat>, Mat, List<Mat>, Mat, Mat, float, float, boolean, Mat, DetectorParameters) Use class ArucoDetector::refineDetectedMarkersorg.opencv.aruco.Aruco.testCharucoCornersCollinear(CharucoBoard, Mat) Use CharucoBoard::checkCharucoCornersCollinearorg.opencv.core.Core.convertFp16(Mat, Mat) Use Mat::convertTo with CV_16F instead.org.opencv.core.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- 現在の4.1 TBBリリースでは未サポート。将来サポートされる可能性がある。OpenMP- 現在のチーム内における、呼び出し元スレッドのスレッド番号。Concurrency- 現在のコンテキストが実行されている仮想プロセッサのID(マスタースレッドでは0、その他では一意の番号となるが、必ずしも1,2,3,...とは限らない)。GCD- システムが呼び出すスレッドのID。並列領域内では決して0を返さない。C=- 現在の並列タスクのインデックス。SEE: setNumThreads, getNumThreads
org.opencv.dnn.Dnn.getInferenceEngineBackendType() org.opencv.dnn.Dnn.setInferenceEngineBackendType(String) org.opencv.dnn.Layer.run(List<Mat>, List<Mat>, List<Mat>) This method will be removed in the future release.org.opencv.dnn.Net.getLayer(String) Use int getLayerId(const String &layer)org.opencv.imgproc.Imgproc.linearPolar(Mat, Mat, Point, double, int) This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) 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.logPolar(Mat, Mat, Point, double, int) This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); 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.text.Text.loadOCRHMMClassifierCNN(String) use loadOCRHMMClassifier insteadorg.opencv.text.Text.loadOCRHMMClassifierNM(String) loadOCRHMMClassifier instead