/**
* 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);
}
