public SimpleBVHTree(GroupingMethod gm, int num_per_group, string name)
: base(name)
{
box = new BBox();
grouping = gm;
n = num_per_group;
}
public static object CreateSalesReportForAllCategoriesAndAllSalespeople(
DateTime startDate, DateTime endDate,
GroupingMethod groupBy, SortMethod sortBy,
bool includeReturnedOrders = true, bool includeUnshippedOrders = false)
{
// Imaginary report-generating code goes here
return(new object());
}
public static object CreateSalesReport(
DateTime startDate, DateTime endDate,
List <int> categoryIDsToInclude, List <int> salespersonIDsToInclude,
GroupingMethod groupBy, SortMethod sortBy,
bool includeReturnedOrders = true, bool includeUnshippedOrders = false)
{
// Imaginary report-generating code goes here
return(new object());
}
/// <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);
ContribInvoke.CvERGrouping(
iaImage, iaChannels,
erstatsHandle.AddrOfPinnedObject(), erstatPtrs.Length,
regionGroups, groupsBoxes,
groupMethods,
s, minProbability);
erstatsHandle.Free();
return(groupsBoxes.ToArray());
}
}
internal static extern void CvERGrouping(
IntPtr image, IntPtr channels,
IntPtr regions, int count,
IntPtr groups, IntPtr groupRects,
GroupingMethod method, IntPtr fileName, float minProbability);
public ICanSetSortBy GroupBy(GroupingMethod groupBy)
{
_groupBy = groupBy;
return(this);
}
internal static extern void CvERGrouping(
IntPtr image, IntPtr channels,
IntPtr regions, int count,
IntPtr groups, IntPtr groupRects,
GroupingMethod method, IntPtr fileName, float minProbability);
static private void Grouping(GroupingMethod groupingMethod, int groupsize)
{
Function.ConsoleWriteLine("Grouping:" + groupsize);
switch (groupingMethod)
{
case GroupingMethod.Seperate:
//组数
GroupVariable.AnnotatorGroups = new IList <Annotator> [Variable.NumberOfAnnotationsPerSentence / groupsize];
for (int i = 0; i < GroupVariable.AnnotatorGroups.Length; ++i)
{
GroupVariable.AnnotatorGroups[i] = new List <Annotator>();
}
//每组人数
Variable.NumberOfAnnotationsPerSentenceAfterGrouping = groupsize;
foreach (Sentence sentence in Variable.Sentences)
{
//这个sentence在每组中的标注
sentence.AnnotaitonGroups = new AnnotationGroup[GroupVariable.AnnotatorGroups.Length];
for (int i = 0; i < GroupVariable.AnnotatorGroups.Length; ++i)
{
sentence.AnnotaitonGroups[i] = new AnnotationGroup();
}
int n = 0;
foreach (Annotator annotator in Variable.Annotators)
{
if (Variable.Data[annotator].ContainsKey(sentence))
{
foreach (Annotation annotation in Variable.Data[annotator][sentence])
{
sentence.AnnotaitonGroups[n / groupsize].AnnotatorAnnotationDic.Add(annotator, annotation); //每人只有一个标注(手动修改标注文件，去掉一人同时标几句的情况)
if (!GroupVariable.AnnotatorGroups[n / groupsize].Contains(annotator))
{
GroupVariable.AnnotatorGroups[n / groupsize].Add(annotator);
}
++n;
}
}
}
}
break;
case GroupingMethod.Overlap: //太慢，废弃
int[] temp = new int[groupsize];
IList <Annotator[]> list = new List <Annotator[]>();
getCombination(ref list, Variable.Annotators, Variable.Annotators.Count, groupsize, temp, groupsize); //全匹配
GroupVariable.AnnotatorGroups = new IList <Annotator> [list.Count];
for (int i = 0; i < list.Count; ++i)
{
GroupVariable.AnnotatorGroups[i] = new List <Annotator>();
foreach (Annotator annotator in list[i])
{
GroupVariable.AnnotatorGroups[i].Add(annotator);
}
}
Variable.NumberOfAnnotationsPerSentenceAfterGrouping = groupsize;
foreach (Sentence sentence in Variable.Sentences)
{
sentence.AnnotaitonGroups = new AnnotationGroup[GroupVariable.AnnotatorGroups.Length];
for (int i = 0; i < GroupVariable.AnnotatorGroups.Length; ++i)
{
sentence.AnnotaitonGroups[i] = new AnnotationGroup();
foreach (Annotator annotator in GroupVariable.AnnotatorGroups[i])
{
foreach (Annotation annotation in Variable.Data[annotator][sentence])
{
sentence.AnnotaitonGroups[i].AnnotatorAnnotationDic.Add(annotator, annotation);
}
}
}
}
break;
}
}
/// <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);
CvERGrouping(
iaImage, iaChannels,
erstatsHandle.AddrOfPinnedObject(), erstatPtrs.Length,
regionGroups, groupsBoxes,
groupMethods,
s, minProbability);
erstatsHandle.Free();
return groupsBoxes.ToArray();
}
}
