/// <summary>
/// Find groups of Extremal Regions that are organized as text blocks.
/// </summary>
/// <param name="image">The image where ER grouping is to be perform on</param>
/// <param name="channels">Array of single channel images from which the regions were extracted</param>
/// <param name="erstats">Vector of ER’s retrieved from the ERFilter algorithm from each channel</param>
/// <param name="groupingTrainedFileName">The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml)</param>
/// <param name="minProbability">The minimum probability for accepting a group.</param>
/// <param name="groupMethods">The grouping methods</param>
/// <returns>The output of the algorithm that indicates the text regions</returns>
public static System.Drawing.Rectangle[] ERGrouping(IInputArray image, IInputArrayOfArrays channels, VectorOfERStat[] erstats, GroupingMethod groupMethods = GroupingMethod.OrientationHoriz, String groupingTrainedFileName = null, float minProbability = 0.5f)
{
IntPtr[] erstatPtrs = new IntPtr[erstats.Length];
for (int i = 0; i < erstatPtrs.Length; i++)
{
erstatPtrs[i] = erstats[i].Ptr;
}
using (VectorOfVectorOfPoint regionGroups = new VectorOfVectorOfPoint())
using (VectorOfRect groupsBoxes = new VectorOfRect())
using (InputArray iaImage = image.GetInputArray())
using (InputArray iaChannels = channels.GetInputArray())
using (CvString s = (groupingTrainedFileName == null ? new CvString() : new CvString(groupingTrainedFileName)))
{
GCHandle erstatsHandle = GCHandle.Alloc(erstatPtrs, GCHandleType.Pinned);
TextInvoke.cveERGrouping(
iaImage, iaChannels,
erstatsHandle.AddrOfPinnedObject(), erstatPtrs.Length,
regionGroups, groupsBoxes,
groupMethods,
s, minProbability);
erstatsHandle.Free();
return(groupsBoxes.ToArray());
}
}
/// <summary>
/// Release all the unmanaged memory associate with this ERFilter
/// </summary>
protected override void DisposeObject()
{
if (_ptr != IntPtr.Zero)
{
TextInvoke.cveERFilterRelease(ref _ptr, ref _sharedPtr);
}
}
/// <summary>
/// Release all the unmanaged memory associate with this TextDetector
/// </summary>
protected override void DisposeObject()
{
if (_sharedPtr != IntPtr.Zero)
{
TextInvoke.cveTextDetectorCNNRelease(ref _sharedPtr);
_ptr = IntPtr.Zero;
}
}
/// <summary>
/// Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm
/// </summary>
/// <param name="classifierFileName">The file name of the classifier</param>
/// <param name="thresholdDelta">Threshold step in subsequent thresholds when extracting the component tree.</param>
/// <param name="minArea">The minimum area (% of image size) allowed for retreived ER’s.</param>
/// <param name="maxArea">The maximum area (% of image size) allowed for retreived ER’s.</param>
/// <param name="minProbability">The minimum probability P(er|character) allowed for retreived ER’s.</param>
/// <param name="nonMaxSuppression">Whenever non-maximum suppression is done over the branch probabilities.</param>
/// <param name="minProbabilityDiff">The minimum probability difference between local maxima and local minima ERs.</param>
public ERFilterNM1(
String classifierFileName,
int thresholdDelta = 1,
float minArea = 0.00025f,
float maxArea = 0.13f,
float minProbability = 0.4f,
bool nonMaxSuppression = true,
float minProbabilityDiff = 0.1f)
{
using (CvString s = new CvString(classifierFileName))
_ptr = TextInvoke.cveERFilterNM1Create(s, thresholdDelta, minArea, maxArea, minProbability, nonMaxSuppression, minProbabilityDiff, ref _sharedPtr);
}
/// <summary>
/// Creates an instance of the TextDetectorCNN class using the provided parameters.
/// </summary>
/// <param name="modelArchFilename">The relative or absolute path to the prototxt file describing the classifiers architecture.</param>
/// <param name="modelWeightsFilename">The relative or absolute path to the file containing the pretrained weights of the model in caffe-binary form.</param>
/// <param name="detectionSizes">A list of sizes for multi-scale detection. The values[(300,300),(700,500),(700,300),(700,700),(1600,1600)] are recommended to achieve the best quality.</param>
public TextDetectorCNN(String modelArchFilename, String modelWeightsFilename, Size[] detectionSizes = null)
{
using (CvString csModelArchFilename = new CvString(modelArchFilename))
using (CvString csModelWeightsFilename = new CvString(modelWeightsFilename))
{
if (detectionSizes == null)
{
_ptr = TextInvoke.cveTextDetectorCNNCreate(csModelArchFilename, csModelWeightsFilename,
ref _sharedPtr);
}
else
{
using (VectorOfSize vs = new VectorOfSize(detectionSizes))
_ptr = TextInvoke.cveTextDetectorCNNCreate2(csModelArchFilename, csModelWeightsFilename, vs,
ref _sharedPtr);
}
}
}
/// <summary>
/// Find bounding boxes of text words given an input image.
/// </summary>
/// <param name="inputImage">An image expected to be a CV_U8C3 of any size</param>
/// <returns>The text regions found.</returns>
public TextRegion[] Detect(IInputArray inputImage)
{
using (InputArray iaImage = inputImage.GetInputArray())
using (VectorOfRect vr = new VectorOfRect())
using (VectorOfFloat vf = new VectorOfFloat())
{
TextInvoke.cveTextDetectorCNNDetect(_ptr, iaImage, vr, vf);
Rectangle[] bboxes = vr.ToArray();
float[] confidents = vf.ToArray();
TextRegion[] regions = new TextRegion[bboxes.Length];
for (int i = 0; i < regions.Length; i++)
{
TextRegion tr = new TextRegion();
tr.BBox = bboxes[i];
tr.Confident = confidents[i];
regions[i] = tr;
}
return(regions);
}
}
/// <summary>
/// Takes image on input and returns the selected regions in a vector of ERStat only distinctive ERs which correspond to characters are selected by a sequential classifier
/// </summary>
/// <param name="image">Sinle channel image CV_8UC1</param>
/// <param name="regions">Output for the 1st stage and Input/Output for the 2nd. The selected Extremal Regions are stored here.</param>
public void Run(IInputArray image, VectorOfERStat regions)
{
using (InputArray iaImage = image.GetInputArray())
TextInvoke.cveERFilterRun(_ptr, iaImage, regions);
}
/// <summary>
/// Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
/// </summary>
/// <param name="classifierFileName">The file name of the classifier</param>
/// <param name="minProbability">The minimum probability P(er|character) allowed for retreived ER’s.</param>
public ERFilterNM2(String classifierFileName, float minProbability = 0.3f)
{
using (CvString s = new CvString(classifierFileName))
_ptr = TextInvoke.cveERFilterNM2Create(s, minProbability, ref _sharedPtr);
}
