/** * Encodes an image into a memory buffer. * * The function imencode compresses the image and stores it in the memory buffer that is resized to fit the * result. See cv::imwrite for the list of supported formats and flags description. * * param ext File extension that defines the output format. * param img Image to be written. * param buf Output buffer resized to fit the compressed image. * return automatically generated */ public static bool imencode(string ext, Mat img, MatOfByte buf) { if (img != null) { img.ThrowIfDisposed(); } if (buf != null) { buf.ThrowIfDisposed(); } Mat buf_mat = buf; return(imgcodecs_Imgcodecs_imencode_11(ext, img.nativeObj, buf_mat.nativeObj)); }
// // C++: static Net cv::dnn::Net::readFromModelOptimizer(vector_uchar bufferModelConfig, vector_uchar bufferWeights) // /** * Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR). * param bufferModelConfig buffer with model's configuration. * param bufferWeights buffer with model's trained weights. * return Net object. */ public static Net readFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights) { if (bufferModelConfig != null) { bufferModelConfig.ThrowIfDisposed(); } if (bufferWeights != null) { bufferWeights.ThrowIfDisposed(); } Mat bufferModelConfig_mat = bufferModelConfig; Mat bufferWeights_mat = bufferWeights; return(new Net(dnn_Net_readFromModelOptimizer_11(bufferModelConfig_mat.nativeObj, bufferWeights_mat.nativeObj))); }
//javadoc: readNetFromTensorflow(bufferModel) public static Net readNetFromTensorflow(MatOfByte bufferModel) { if (bufferModel != null) { bufferModel.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat bufferModel_mat = bufferModel; Net retVal = new Net(dnn_Dnn_readNetFromTensorflow_13(bufferModel_mat.nativeObj)); return(retVal); #else return(null); #endif }
//javadoc: readNetFromDarknet(bufferCfg) public static Net readNetFromDarknet(MatOfByte bufferCfg) { if (bufferCfg != null) { bufferCfg.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat bufferCfg_mat = bufferCfg; Net retVal = new Net(dnn_Dnn_readNetFromDarknet_13(bufferCfg_mat.nativeObj)); return(retVal); #else return(null); #endif }
// // C++: bool cv::imencode(String ext, Mat img, vector_uchar& buf, vector_int _params = std::vector<int>()) // /** * Encodes an image into a memory buffer. * * The function imencode compresses the image and stores it in the memory buffer that is resized to fit the * result. See cv::imwrite for the list of supported formats and flags description. * * param ext File extension that defines the output format. * param img Image to be written. * param buf Output buffer resized to fit the compressed image. * param _params automatically generated * return automatically generated */ public static bool imencode(string ext, Mat img, MatOfByte buf, MatOfInt _params) { if (img != null) { img.ThrowIfDisposed(); } if (buf != null) { buf.ThrowIfDisposed(); } if (_params != null) { _params.ThrowIfDisposed(); } Mat buf_mat = buf; Mat _params_mat = _params; return(imgcodecs_Imgcodecs_imencode_10(ext, img.nativeObj, buf_mat.nativeObj, _params_mat.nativeObj)); }
//javadoc: imencode(ext, img, buf) public static bool imencode(string ext, Mat img, MatOfByte buf) { if (img != null) { img.ThrowIfDisposed(); } if (buf != null) { buf.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat buf_mat = buf; bool retVal = imgcodecs_Imgcodecs_imencode_11(ext, img.nativeObj, buf_mat.nativeObj); return(retVal); #else return(false); #endif }
// // C++: void cv::drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_DMatch matches1to2, Mat& outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_char matchesMask = std::vector<char>(), DrawMatchesFlags flags = DrawMatchesFlags::DEFAULT) // //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor, singlePointColor, matchesMask) public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask) { if (img1 != null) { img1.ThrowIfDisposed(); } if (keypoints1 != null) { keypoints1.ThrowIfDisposed(); } if (img2 != null) { img2.ThrowIfDisposed(); } if (keypoints2 != null) { keypoints2.ThrowIfDisposed(); } if (matches1to2 != null) { matches1to2.ThrowIfDisposed(); } if (outImg != null) { outImg.ThrowIfDisposed(); } if (matchesMask != null) { matchesMask.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat keypoints1_mat = keypoints1; Mat keypoints2_mat = keypoints2; Mat matches1to2_mat = matches1to2; Mat matchesMask_mat = matchesMask; features2d_Features2d_drawMatches_10(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj, matchColor.val[0], matchColor.val[1], matchColor.val[2], matchColor.val[3], singlePointColor.val[0], singlePointColor.val[1], singlePointColor.val[2], singlePointColor.val[3], matchesMask_mat.nativeObj); return; #else return; #endif }
// // C++: Net cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>()) // //javadoc: readNet(framework, bufferModel, bufferConfig) public static Net readNet(string framework, MatOfByte bufferModel, MatOfByte bufferConfig) { if (bufferModel != null) { bufferModel.ThrowIfDisposed(); } if (bufferConfig != null) { bufferConfig.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat bufferModel_mat = bufferModel; Mat bufferConfig_mat = bufferConfig; Net retVal = new Net(dnn_Dnn_readNet_10(framework, bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj)); return(retVal); #else return(null); #endif }
//javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err) public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err) { if (prevImg != null) { prevImg.ThrowIfDisposed(); } if (nextImg != null) { nextImg.ThrowIfDisposed(); } if (prevPts != null) { prevPts.ThrowIfDisposed(); } if (nextPts != null) { nextPts.ThrowIfDisposed(); } if (status != null) { status.ThrowIfDisposed(); } if (err != null) { err.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat prevPts_mat = prevPts; Mat nextPts_mat = nextPts; Mat status_mat = status; Mat err_mat = err; video_Video_calcOpticalFlowPyrLK_15(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj); return; #else return; #endif }
//javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err, winSize, maxLevel, criteria) public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria) { if (prevImg != null) { prevImg.ThrowIfDisposed(); } if (nextImg != null) { nextImg.ThrowIfDisposed(); } if (prevPts != null) { prevPts.ThrowIfDisposed(); } if (nextPts != null) { nextPts.ThrowIfDisposed(); } if (status != null) { status.ThrowIfDisposed(); } if (err != null) { err.ThrowIfDisposed(); } #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER Mat prevPts_mat = prevPts; Mat nextPts_mat = nextPts; Mat status_mat = status; Mat err_mat = err; video_Video_calcOpticalFlowPyrLK_12(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon); return; #else return; #endif }
/** * Draws the found matches of keypoints from two images. * * param img1 First source image. * param keypoints1 Keypoints from the first source image. * param img2 Second source image. * param keypoints2 Keypoints from the second source image. * param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] * has a corresponding point in keypoints2[matches[i]] . * param outImg Output image. Its content depends on the flags value defining what is drawn in the * output image. See possible flags bit values below. * param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) * , the color is generated randomly. * param singlePointColor Color of single keypoints (circles), which means that keypoints do not * have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. * param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are * drawn. * DrawMatchesFlags. * * This function draws matches of keypoints from two images in the output image. Match is a line * connecting two keypoints (circles). See cv::DrawMatchesFlags. */ public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask) { if (img1 != null) { img1.ThrowIfDisposed(); } if (keypoints1 != null) { keypoints1.ThrowIfDisposed(); } if (img2 != null) { img2.ThrowIfDisposed(); } if (keypoints2 != null) { keypoints2.ThrowIfDisposed(); } if (matches1to2 != null) { matches1to2.ThrowIfDisposed(); } if (outImg != null) { outImg.ThrowIfDisposed(); } if (matchesMask != null) { matchesMask.ThrowIfDisposed(); } Mat keypoints1_mat = keypoints1; Mat keypoints2_mat = keypoints2; Mat matches1to2_mat = matches1to2; Mat matchesMask_mat = matchesMask; features2d_Features2d_drawMatches_11(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj, matchColor.val[0], matchColor.val[1], matchColor.val[2], matchColor.val[3], singlePointColor.val[0], singlePointColor.val[1], singlePointColor.val[2], singlePointColor.val[3], matchesMask_mat.nativeObj); }