void DoProcess()
{
if (!(image.Value is OpenCVForUnityPlayMakerActions.Mat))
{
LogError("image is not initialized. Add Action \"newMat\".");
return;
}
OpenCVForUnity.CoreModule.Mat wrapped_image = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Mat, OpenCVForUnity.CoreModule.Mat>(image);
if (!(er_filter1.Value is OpenCVForUnityPlayMakerActions.ERFilter))
{
LogError("er_filter1 is not initialized. Add Action \"newERFilter\".");
return;
}
OpenCVForUnity.TextModule.ERFilter wrapped_er_filter1 = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ERFilter, OpenCVForUnity.TextModule.ERFilter>(er_filter1);
if (!(er_filter2.Value is OpenCVForUnityPlayMakerActions.ERFilter))
{
LogError("er_filter2 is not initialized. Add Action \"newERFilter\".");
return;
}
OpenCVForUnity.TextModule.ERFilter wrapped_er_filter2 = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ERFilter, OpenCVForUnity.TextModule.ERFilter>(er_filter2);
if (!(groups_rects.Value is OpenCVForUnityPlayMakerActions.MatOfRect))
{
LogError("groups_rects is not initialized. Add Action \"newMatOfRect\".");
return;
}
OpenCVForUnity.CoreModule.MatOfRect wrapped_groups_rects = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.MatOfRect, OpenCVForUnity.CoreModule.MatOfRect>(groups_rects);
OpenCVForUnity.TextModule.Text.detectRegions(wrapped_image, wrapped_er_filter1, wrapped_er_filter2, wrapped_groups_rects, method.Value, filename.Value);
}
//javadoc: detectRegions(image, er_filter1, er_filter2, groups_rects)
public static void detectRegions(Mat image, ERFilter er_filter1, ERFilter er_filter2, MatOfRect groups_rects)
{
if (image != null)
{
image.ThrowIfDisposed();
}
if (er_filter1 != null)
{
er_filter1.ThrowIfDisposed();
}
if (er_filter2 != null)
{
er_filter2.ThrowIfDisposed();
}
if (groups_rects != null)
{
groups_rects.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat groups_rects_mat = groups_rects;
text_Text_detectRegions_13(image.nativeObj, er_filter1.getNativeObjAddr(), er_filter2.getNativeObjAddr(), groups_rects_mat.nativeObj);
return;
#else
return;
#endif
}
void DoProcess()
{
if (!(image.Value is OpenCVForUnityPlayMakerActions.Mat))
{
LogError("image is not initialized. Add Action \"newMat\".");
return;
}
OpenCVForUnity.CoreModule.Mat wrapped_image = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Mat, OpenCVForUnity.CoreModule.Mat>(image);
if (!(er_filter1.Value is OpenCVForUnityPlayMakerActions.ERFilter))
{
LogError("er_filter1 is not initialized. Add Action \"newERFilter\".");
return;
}
OpenCVForUnity.TextModule.ERFilter wrapped_er_filter1 = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ERFilter, OpenCVForUnity.TextModule.ERFilter>(er_filter1);
if (!(er_filter2.Value is OpenCVForUnityPlayMakerActions.ERFilter))
{
LogError("er_filter2 is not initialized. Add Action \"newERFilter\".");
return;
}
OpenCVForUnity.TextModule.ERFilter wrapped_er_filter2 = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ERFilter, OpenCVForUnity.TextModule.ERFilter>(er_filter2);
List <OpenCVForUnity.CoreModule.MatOfPoint> wrapped_regions = new List <OpenCVForUnity.CoreModule.MatOfPoint>();
OpenCVForUnityPlayMakerActionsUtils.ConvertFsmArrayToList <OpenCVForUnityPlayMakerActions.MatOfPoint, OpenCVForUnity.CoreModule.MatOfPoint>(regions, wrapped_regions);
OpenCVForUnity.TextModule.Text.detectRegions(wrapped_image, wrapped_er_filter1, wrapped_er_filter2, wrapped_regions);
OpenCVForUnityPlayMakerActionsUtils.ConvertListToFsmArray <OpenCVForUnity.CoreModule.MatOfPoint, OpenCVForUnityPlayMakerActions.MatOfPoint>(wrapped_regions, regions);
}
//javadoc: createERFilterNM1(filename, thresholdDelta, minArea, maxArea, minProbability)
public static ERFilter createERFilterNM1(string filename, int thresholdDelta, float minArea, float maxArea, float minProbability)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ERFilter retVal = ERFilter.__fromPtr__(text_Text_createERFilterNM1_12(filename, thresholdDelta, minArea, maxArea, minProbability));
return(retVal);
#else
return(null);
#endif
}
//javadoc: createERFilterNM2(filename)
public static ERFilter createERFilterNM2(string filename)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ERFilter retVal = ERFilter.__fromPtr__(text_Text_createERFilterNM2_11(filename));
return(retVal);
#else
return(null);
#endif
}
//
// C++: void cv::text::detectRegions(Mat image, Ptr_ERFilter er_filter1, Ptr_ERFilter er_filter2, vector_vector_Point& regions)
//
/**
* Converts MSER contours (vector<Point>) to ERStat regions.
*
* param image Source image CV_8UC1 from which the MSERs where extracted.
*
*
* param regions Output where the ERStat regions are stored.
*
* It takes as input the contours provided by the OpenCV MSER feature detector and returns as output
* two vectors of ERStats. This is because MSER() output contains both MSER+ and MSER- regions in a
* single vector<Point>, the function separates them in two different vectors (this is as if the
* ERStats where extracted from two different channels).
*
* An example of MSERsToERStats in use can be found in the text detection webcam_demo:
* <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
* param er_filter1 automatically generated
* param er_filter2 automatically generated
*/
public static void detectRegions(Mat image, ERFilter er_filter1, ERFilter er_filter2, List <MatOfPoint> regions)
{
if (image != null)
{
image.ThrowIfDisposed();
}
if (er_filter1 != null)
{
er_filter1.ThrowIfDisposed();
}
if (er_filter2 != null)
{
er_filter2.ThrowIfDisposed();
}
Mat regions_mat = new Mat();
text_Text_detectRegions_14(image.nativeObj, er_filter1.getNativeObjAddr(), er_filter2.getNativeObjAddr(), regions_mat.nativeObj);
Converters.Mat_to_vector_vector_Point(regions_mat, regions);
regions_mat.release();
}
/**
* Extracts text regions from image.
*
* param image Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
* param er_filter1 Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
* param er_filter2 Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
* param groups_rects Output list of rectangle blocks with text
*/
public static void detectRegions(Mat image, ERFilter er_filter1, ERFilter er_filter2, MatOfRect groups_rects)
{
if (image != null)
{
image.ThrowIfDisposed();
}
if (er_filter1 != null)
{
er_filter1.ThrowIfDisposed();
}
if (er_filter2 != null)
{
er_filter2.ThrowIfDisposed();
}
if (groups_rects != null)
{
groups_rects.ThrowIfDisposed();
}
Mat groups_rects_mat = groups_rects;
text_Text_detectRegions_13(image.nativeObj, er_filter1.getNativeObjAddr(), er_filter2.getNativeObjAddr(), groups_rects_mat.nativeObj);
}
//
// C++: void cv::text::detectRegions(Mat image, Ptr_ERFilter er_filter1, Ptr_ERFilter er_filter2, vector_Rect& groups_rects, int method = ERGROUPING_ORIENTATION_HORIZ, String filename = String(), float minProbability = (float)0.5)
//
/**
* Extracts text regions from image.
*
* param image Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
* param er_filter1 Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
* param er_filter2 Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
* param groups_rects Output list of rectangle blocks with text
* param method Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.
* param filename The XML or YAML file with the classifier model (e.g. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ERGROUPING_ORIENTATION_ANY.
* param minProbability The minimum probability for accepting a group. Only to use when grouping method is ERGROUPING_ORIENTATION_ANY.
*/
public static void detectRegions(Mat image, ERFilter er_filter1, ERFilter er_filter2, MatOfRect groups_rects, int method, string filename, float minProbability)
{
if (image != null)
{
image.ThrowIfDisposed();
}
if (er_filter1 != null)
{
er_filter1.ThrowIfDisposed();
}
if (er_filter2 != null)
{
er_filter2.ThrowIfDisposed();
}
if (groups_rects != null)
{
groups_rects.ThrowIfDisposed();
}
Mat groups_rects_mat = groups_rects;
text_Text_detectRegions_10(image.nativeObj, er_filter1.getNativeObjAddr(), er_filter2.getNativeObjAddr(), groups_rects_mat.nativeObj, method, filename, minProbability);
}
//
// C++: void cv::text::detectRegions(Mat image, Ptr_ERFilter er_filter1, Ptr_ERFilter er_filter2, vector_vector_Point& regions)
//
//javadoc: detectRegions(image, er_filter1, er_filter2, regions)
public static void detectRegions(Mat image, ERFilter er_filter1, ERFilter er_filter2, List <MatOfPoint> regions)
{
if (image != null)
{
image.ThrowIfDisposed();
}
if (er_filter1 != null)
{
er_filter1.ThrowIfDisposed();
}
if (er_filter2 != null)
{
er_filter2.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat regions_mat = new Mat();
text_Text_detectRegions_14(image.nativeObj, er_filter1.getNativeObjAddr(), er_filter2.getNativeObjAddr(), regions_mat.nativeObj);
Converters.Mat_to_vector_vector_Point(regions_mat, regions);
regions_mat.release();
return;
#else
return;
#endif
}
//
// C++: Ptr_ERFilter cv::text::createERFilterNM1(Ptr_ERFilter_Callback cb, int thresholdDelta = 1, float minArea = (float)0.00025, float maxArea = (float)0.13, float minProbability = (float)0.4, bool nonMaxSuppression = true, float minProbabilityDiff = (float)0.1)
//
// Unknown type 'Ptr_ERFilter_Callback' (I), skipping the function
//
// C++: Ptr_ERFilter cv::text::createERFilterNM1(String filename, int thresholdDelta = 1, float minArea = (float)0.00025, float maxArea = (float)0.13, float minProbability = (float)0.4, bool nonMaxSuppression = true, float minProbabilityDiff = (float)0.1)
//
/**
* Reads an Extremal Region Filter for the 1st stage classifier of N&M algorithm
* from the provided path e.g. /path/to/cpp/trained_classifierNM1.xml
*
* param filename automatically generated
* param thresholdDelta automatically generated
* param minArea automatically generated
* param maxArea automatically generated
* param minProbability automatically generated
* param nonMaxSuppression automatically generated
* param minProbabilityDiff automatically generated
* return automatically generated
*/
public static ERFilter createERFilterNM1(string filename, int thresholdDelta, float minArea, float maxArea, float minProbability, bool nonMaxSuppression, float minProbabilityDiff)
{
return(ERFilter.__fromPtr__(text_Text_createERFilterNM1_10(filename, thresholdDelta, minArea, maxArea, minProbability, nonMaxSuppression, minProbabilityDiff)));
}
/**
* Reads an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
* from the provided path e.g. /path/to/cpp/trained_classifierNM2.xml
*
* param filename automatically generated
* return automatically generated
*/
public static ERFilter createERFilterNM2(string filename)
{
return(ERFilter.__fromPtr__(text_Text_createERFilterNM2_11(filename)));
}
//
// C++: Ptr_ERFilter cv::text::createERFilterNM2(Ptr_ERFilter_Callback cb, float minProbability = (float)0.3)
//
// Unknown type 'Ptr_ERFilter_Callback' (I), skipping the function
//
// C++: Ptr_ERFilter cv::text::createERFilterNM2(String filename, float minProbability = (float)0.3)
//
/**
* Reads an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
* from the provided path e.g. /path/to/cpp/trained_classifierNM2.xml
*
* param filename automatically generated
* param minProbability automatically generated
* return automatically generated
*/
public static ERFilter createERFilterNM2(string filename, float minProbability)
{
return(ERFilter.__fromPtr__(text_Text_createERFilterNM2_10(filename, minProbability)));
}
/**
* Reads an Extremal Region Filter for the 1st stage classifier of N&M algorithm
* from the provided path e.g. /path/to/cpp/trained_classifierNM1.xml
*
* param filename automatically generated
* param thresholdDelta automatically generated
* return automatically generated
*/
public static ERFilter createERFilterNM1(string filename, int thresholdDelta)
{
return(ERFilter.__fromPtr__(text_Text_createERFilterNM1_15(filename, thresholdDelta)));
}
/**
* Reads an Extremal Region Filter for the 1st stage classifier of N&M algorithm
* from the provided path e.g. /path/to/cpp/trained_classifierNM1.xml
*
* param filename automatically generated
* param thresholdDelta automatically generated
* param minArea automatically generated
* param maxArea automatically generated
* return automatically generated
*/
public static ERFilter createERFilterNM1(string filename, int thresholdDelta, float minArea, float maxArea)
{
return(ERFilter.__fromPtr__(text_Text_createERFilterNM1_13(filename, thresholdDelta, minArea, maxArea)));
}
public ERFilter(OpenCVForUnity.TextModule.ERFilter nativeObj) : base(nativeObj)
{
}
