private void button1_Click_2(object sender, RoutedEventArgs e)
{
//以下修正
MotionDataHandler handler;
string path;
if (openMotionData(out handler, out path))
{
CvMat resultMat = null;
int length = handler.FrameCount;
IEnumerable <CvMat> depthImages;
Utility.LoadImages(handler.GetDepthImagePaths(), out depthImages);
foreach (CvMat depthMat in depthImages)
{
CvSize depthUserSize = new CvSize(depthMat.Cols, depthMat.Rows);
CvEx.InitCvMat(ref resultMat, depthMat, MatrixType.U8C3);
resultMat.Zero();
double avgDepth = depthMat.Select(v => v.Val0).Where(v => v != 0).Average();
double pDepth = CvEx.Get2DSubPixel(depthMat, new CvPoint2D32f(_undistortion.CameraStruct.PrincipalX, _undistortion.CameraStruct.PrincipalY), 0) ?? 0;
List <double>[] diffs = Enumerable.Range(0, depthUserSize.Width).Select(x => new List <double>()).ToArray();
unsafe
{
short *depthArr = depthMat.DataInt16;
for (int y = 0; y < depthUserSize.Height; y++)
{
int offset = y * depthUserSize.Width;
for (int x = 0; x < depthUserSize.Width - 1; x++)
{
short l = depthArr[offset + x];
short r = depthArr[offset + x + 1];
if (l != 0 && r != 0)
{
double ll = Math.Log(l);
double rl = Math.Log(r);
diffs[x].Add(ll - rl);
}
}
}
}
double[] median = diffs.Select(x => x.Count > 0 ? CalcEx.GetMedian(x) : 0).ToArray();
double max = median.Select(x => Math.Abs(x)).Max();
for (int x = 0; x < depthUserSize.Width; x++)
{
resultMat.DrawLine(new CvPoint(x, 0), new CvPoint(x, resultMat.Rows), new CvScalar(Math.Max(median[x] / max * 255, 0), Math.Max(-median[x] / max * 255, 0), 0));
}
resultMat.PutText(avgDepth.ToString("0.00000"), new CvPoint(0, 20), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
resultMat.PutText(pDepth.ToString("0.00000"), new CvPoint(0, 40), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
putImage(resultMat, PixelFormats.Rgb24);
}
}
}
/// <summary>
/// Jointから絶対座標を引くDictionary二つの間の距離を求めます
/// </summary>
/// <param name="jointWithAbsPosition1">骨格ごとの絶対座標1</param>
/// <param name="jointWithAbsPosition2">骨格ごとの絶対座標2</param>
/// <param name="mirrored">一方の骨格を左右反転した結果で距離を求めるかどうか</param>
/// <returns></returns>
static double getDistance(IDictionary <JointType, CvPoint3D64f> jointWithAbsPosition1, IDictionary <JointType, CvPoint3D64f> jointWithAbsPosition2, bool mirrored)
{
IEnumerable <JointType> joints2Alt = jointWithAbsPosition2.Keys.Select(j => mirrored ? CalcEx.GetMirroredJoint(j) : j);
List <JointType> intersect = jointWithAbsPosition1.Keys.Intersect(joints2Alt).ToList();
List <double> distanceSqList = new List <double>();
foreach (JointType joint in intersect)
{
JointType jointAlt = mirrored ? CalcEx.GetMirroredJoint(joint) : joint;
distanceSqList.Add(CvEx.GetDistanceSq(jointWithAbsPosition1[joint], jointWithAbsPosition2[jointAlt]));
}
// 中央値の平方根
return(Math.Sqrt(CalcEx.GetMedian(distanceSqList)));
}
/// <summary>
/// 統計情報を計算し格納する
/// z = 0.904をデフォルトとする、このとき中央値から65%を網羅できる
/// </summary>
/// <param name="z">標準正規分布表のZ</param>
public void CalcMedianBoneRange(double z = 0.904)
{
foreach (Bone bone in this.bones)
{
List <double> data = this.boneLengthSqLog[bone];
int skip = (int)(data.Count() * 0.3);
int take = data.Count() - skip * 2;
data.Sort();
// 上下30%を削除
data = data.Skip(skip).Take(take).ToList();
double median = CalcEx.GetMedian(data);
double average = data.Average();
double std = Math.Abs(Math.Sqrt(data.Select(d => Math.Pow(d - average, 2)).Sum() / (data.Count() - 1)));
double minLength = median - std * z;
double maxLength = median + std * z;
BoneStatistics bs = new BoneStatistics(minLength, maxLength, median, average, std);
this.boneLengthSqStatistics.Add(bone, bs);
}
}
public static UserSegmentation[] Identification(FrameSequence frameseq, double maxDistance)
{
if (frameseq.Segmentations.Any(seg => seg == null))
{
throw new InvalidOperationException("ユーザトラッキングデータがセグメンテーションされていません");
}
HashSet <Tuple <RecordAndUser, RecordAndUser> > contemporaryList = new HashSet <Tuple <RecordAndUser, RecordAndUser> >();
for (int recordNo = 0; recordNo < frameseq.recordNum; recordNo++)
{
IEnumerable <MotionData> record = frameseq.GetMotionDataSequence(recordNo);
int frameIndex = 0;
foreach (MotionData motionData in record)
{
IList <ulong> users = motionData.bodies.ToList().Select(b => b.TrackingId).ToList();
foreach (var tuple in users.SelectMany(u => users.Select(v => new Tuple <RecordAndUser, RecordAndUser>(new RecordAndUser(recordNo, u), new RecordAndUser(recordNo, v)))))
{
contemporaryList.Add(tuple);
}
frameIndex++;
}
}
DateTime beginTime = frameseq.startTime;
DateTime endTime = frameseq.endTime;
double frequency = frameseq.frameRate;
TimeSpan increment = new TimeSpan((long)(10000000 / frequency));
long totalCount = (endTime.Ticks - beginTime.Ticks) / increment.Ticks;
long totalIndex = 0;
Dictionary <Tuple <RecordAndUser, RecordAndUser>, List <double> > distanceListMatrix = new Dictionary <Tuple <RecordAndUser, RecordAndUser>, List <double> >();
foreach (Frame frame in frameseq.Frames)
{
// 現在の時刻での各レコードの各ユーザの各骨格の絶対座標を求める
Dictionary <ulong, Dictionary <JointType, CvPoint3D64f> >[] absPositions = new Dictionary <ulong, Dictionary <JointType, CvPoint3D64f> > [frameseq.recordNum];
for (int recordNo = 0; recordNo < frameseq.recordNum; recordNo++)
{
Dictionary <ulong, Dictionary <JointType, CvPoint3D64f> > recordUserPositions = new Dictionary <ulong, Dictionary <JointType, CvPoint3D64f> >();
foreach (SerializableBody body in frame.GetBodyList(recordNo))
{
Dictionary <JointType, CvPoint3D64f> userPositions = new Dictionary <JointType, CvPoint3D64f>();
if (body.Joints == null)
{
continue;
}
foreach (var jointPair in body.Joints)
{
CvPoint3D64f posInCamera = jointPair.Value.Position.ToCvPoint3D();
CvPoint3D64f posInWorld = CvEx.ConvertPoint3D(posInCamera, frameseq.ToWorldConversions[recordNo]);
userPositions[jointPair.Key] = posInWorld;
}
recordUserPositions[body.TrackingId] = userPositions;
}
absPositions[recordNo] = recordUserPositions;
}
// 現在の時刻で各レコード間のユーザ間の距離を求める
for (int i = 0; i < frameseq.recordNum; i++)
{
if (absPositions[i] == null)
{
continue;
}
for (int j = i + 1; j < frameseq.recordNum; j++)
{
if (absPositions[j] == null)
{
continue;
}
foreach (var userJoint1 in absPositions[i])
{
RecordAndUser recordUser1 = new RecordAndUser(i, userJoint1.Key);
foreach (var userJoint2 in absPositions[j])
{
RecordAndUser recordUser2 = new RecordAndUser(j, userJoint2.Key);
double distanceNormal = getDistance(userJoint1.Value, userJoint2.Value, false);
double distanceMirrored = getDistance(userJoint1.Value, userJoint2.Value, true);
double distance = Math.Min(distanceNormal, distanceMirrored);
Tuple <RecordAndUser, RecordAndUser> key = new Tuple <RecordAndUser, RecordAndUser>(recordUser1, recordUser2);
List <double> distanceList;
if (!distanceListMatrix.TryGetValue(key, out distanceList))
{
distanceListMatrix[key] = distanceList = new List <double>();
}
distanceList.Add(distance);
}
}
}
}
totalIndex++;
}
// 中央値で集計して小さい順に並べる
Dictionary <Tuple <RecordAndUser, RecordAndUser>, double> distanceMatrix = distanceListMatrix.ToDictionary(p => p.Key, p => CalcEx.GetMedian(p.Value));
List <Tuple <RecordAndUser, RecordAndUser, double> > neighborList = (
from x in distanceMatrix
orderby x.Value
select new Tuple <RecordAndUser, RecordAndUser, double>(x.Key.Item1, x.Key.Item2, x.Value)
).ToList();
IdentificationSet <RecordAndUser> identificationSet = new IdentificationSet <RecordAndUser>();
// 同一判定をする
foreach (var neighbor in neighborList)
{
if (neighbor.Item3 > maxDistance)
{
identificationSet.Add(neighbor.Item1);
identificationSet.Add(neighbor.Item2);
continue;
}
IList <RecordAndUser> recordUsers1 = identificationSet.GetEquivalentElements(neighbor.Item1);
IList <RecordAndUser> recordUsers2 = identificationSet.GetEquivalentElements(neighbor.Item2);
// 同フレーム内にいるか判定
bool contemporary = (
from ru1 in recordUsers1
from ru2 in recordUsers2
select new Tuple <RecordAndUser, RecordAndUser>(ru1, ru2)).Any(pair => contemporaryList.Contains(pair));
if (!contemporary)
{
// 同フレーム内にいなければ同一視
identificationSet.MakeEquivalent(neighbor.Item1, neighbor.Item2);
}
}
// 番号を圧縮
identificationSet.CompactIdentificationNumber();
// 新しいセグメンテーション番号を与える
UserSegmentation[] ret = Enumerable.Range(0, frameseq.recordNum).Select(i => new UserSegmentation()).ToArray();
for (int recordNo = 0; recordNo < frameseq.recordNum; recordNo++)
{
foreach (var pair in frameseq.Segmentations[recordNo].Conversions)
{
int frameIndex = pair.Key;
Dictionary <ulong, int> newConversions = new Dictionary <ulong, int>();
foreach (var conv in pair.Value)
{
int ident = identificationSet.ConvertToIdentificationNumber(new RecordAndUser(recordNo, conv.Key));
newConversions[conv.Key] = ident;
}
ret[recordNo].Conversions[frameIndex] = newConversions;
}
ret[recordNo].fixNumUsers();
}
return(ret);
}
private void buttonScalingScore_Click(object sender, RoutedEventArgs e)
{
int cols, rows;
double horizLength, vertLength;
if (!parseChessboardParameters(out cols, out rows, out horizLength, out vertLength))
{
return;
}
// 以下改造
MotionDataHandler handler;
string path;
if (openMotionData(out handler, out path))
{
CvMat displayMat1 = null;
CvMat displayMat3 = null;
CvMat displayMat4 = null;
CvMat gray = null;
int length = handler.FrameCount;
if (length == 0)
{
return;
}
CvSize boardSize = new CvSize(cols, rows);
CvSize imageSize = new CvSize();
List <Tuple <double, double> > pairs = new List <Tuple <double, double> >();
CvPoint2D32f[] lastCorners = null;
IEnumerable <CvMat> colorImages, depthImages;
Utility.LoadImages(handler.GetColorImagePaths(), out colorImages);
Utility.LoadImages(handler.GetDepthImagePaths(), out depthImages);
var images = colorImages.Zip(depthImages, (first, second) => Tuple.Create(first, second));
foreach (Tuple <CvMat, CvMat> imagePair in images)
{
CvMat imageMat = imagePair.Item1;
CvMat depthMat = imagePair.Item2;
if (displayMat4 == null)
{
displayMat4 = CvEx.InitCvMat(imageMat);
}
imageSize = new CvSize(imageMat.Cols, imageMat.Rows);
CvPoint2D32f[] corners;
int count;
CvEx.InitCvMat(ref gray, imageMat, MatrixType.U8C1);
imageMat.CvtColor(gray, ColorConversion.RgbToGray);
if (gray.FindChessboardCorners(boardSize, out corners, out count, ChessboardFlag.AdaptiveThresh))
{
CvEx.CloneCvMat(ref displayMat1, imageMat);
CvTermCriteria criteria = new CvTermCriteria(50, 0.01);
gray.FindCornerSubPix(corners, count, new CvSize(3, 3), new CvSize(-1, -1), criteria);
CvPoint3D32f?[] cornerPoints = new CvPoint3D32f?[corners.Length];
for (int j = 0; j < corners.Length; j++)
{
CvPoint2D32f corner = corners[j];
double? value = CalcEx.BilateralFilterDepthMatSinglePixel(corner, depthMat, 100, 4, 9);
if (value.HasValue)
{
cornerPoints[j] = new CvPoint3D32f(corner.X, corner.Y, value.Value);
}
}
for (int x = 0; x < cols; x++)
{
for (int y = 0; y < rows; y++)
{
if (!cornerPoints[x + y * cols].HasValue)
{
continue;
}
CvPoint3D32f point1 = cornerPoints[x + y * cols].Value;
CvPoint3D64f undistortPoint1 = this.UndistortionData.GetRealFromScreenPos(point1, imageSize);
foreach (var offset in new[] { new { X = 1, Y = 0, D = horizLength }, new { X = 0, Y = 1, D = vertLength } })
{
int dx = x + offset.X;
int dy = y + offset.Y;
if (dx >= cols || dy >= rows)
{
continue;
}
if (!cornerPoints[dx + dy * cols].HasValue)
{
continue;
}
CvPoint3D32f point2 = cornerPoints[dx + dy * cols].Value;
CvPoint3D64f undistortPoint2 = this.UndistortionData.GetRealFromScreenPos(point2, imageSize);
double distance = Math.Sqrt(CvEx.GetDistanceSq(undistortPoint1, undistortPoint2));
double scale = distance / offset.D;
CvColor color = CalcEx.HSVtoRGB(Math.Max(0, Math.Min(300, scale * 600 - 450)), scale, 2 - scale);
displayMat4.DrawLine((int)point1.X, (int)point1.Y, (int)point2.X, (int)point2.Y, new CvScalar(color.R, color.G, color.B), 1, LineType.AntiAlias);
pairs.Add(new Tuple <double, double>(distance, offset.D));
}
}
}
CvEx.DrawChessboardCornerFrame(displayMat1, boardSize, corners, new CvScalar(64, 128, 64));
displayMat1.DrawChessboardCorners(boardSize, corners, true);
lastCorners = corners;
putImage(displayMat1, PixelFormats.Rgb24);
}
else
{
CvEx.CloneCvMat(ref displayMat3, imageMat);
putImage(displayMat3, PixelFormats.Rgb24);
}
}
CvMat displayMat2 = CvEx.InitCvMat(displayMat1);
displayMat1.Undistort2(displayMat2, this.UndistortionData.CameraStruct.CreateCvMat(), this.UndistortionData.DistortStruct.CreateCvMat(true));
if (lastCorners != null)
{
drawUndistortedCornerFrame(displayMat2, lastCorners, boardSize);
}
displayMat2.PutText(string.Format("Min: {0}", pairs.Min(x => x.Item1 / x.Item2)), new CvPoint(20, 20), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
displayMat2.PutText(string.Format("Max: {0}", pairs.Max(x => x.Item1 / x.Item2)), new CvPoint(20, 40), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
displayMat2.PutText(string.Format("Avg: {0}", pairs.Average(x => x.Item1 / x.Item2)), new CvPoint(20, 60), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
displayMat2.PutText(string.Format("Med: {0}", CalcEx.GetMedian(pairs.Select(x => x.Item1 / x.Item2).ToList())), new CvPoint(20, 80), new CvFont(FontFace.HersheyPlain, 1, 1), new CvScalar(255, 255, 255));
putImage(displayMat4, PixelFormats.Rgb24);
displayLabels();
}
}
