internal override void Minimum(Image originalImage, KernelFunction kernelFunction, uint neighborhoodSize, uint x, uint y, Image outputImage)
{
ImageGray <T> castedOriginalImage = (ImageGray <T>)originalImage;
ImageGray <T> castedOutputImage = (ImageGray <T>)outputImage;
castedOutputImage.Gray.Minimum(castedOriginalImage.Gray, kernelFunction, x, y, neighborhoodSize);
}
public override Image Clone()
{
ImageGray <T> clone = (ImageGray <T>)ImageFactory.Create(Width, Height, ColorModel.GRAY, GetDataType());
clone.Gray = (GenericChannel <T>)Gray.Clone();
return(clone);
}
public Image <byte> Detect(Image <byte> image)
{
// Detect faces
var dtor = this.detectors[this.detectorType];
dtor.Detect(image);
// Detect face feature points
Image <byte> gray = new ImageGray(image);
var ator = this.alignmentors[this.alignmentType];
var faceRects = from f in dtor.Results select f.Rect;
ator.Align(gray, faceRects);
// Render the result
int colorIndex = ((int)this.detectorType - 1) * this.Alignmentors.Count() + ((int)this.alignmentType - 1);
using (var render = Renderer.Create(new ImageARGB(image)))
{
results = ator.Results;
foreach (var shape in ator.Results)
{
render.DrawPoints(shape, this.radias, this.colors[colorIndex]);
foundFace = true;
}
return(render.Image);
}
}
private void FaceSwapInTwoImages(
Image <byte> argbImage1,
Image <byte> argbImage2,
Image <byte> facepointSource,
out Image <byte> swapResult1)
{
if (argbImage1 == null || argbImage1.Channels < Color.RgbChannels)
{
throw new ArgumentException("Input image 1 should be a valid color image.");
}
if (argbImage2 == null || argbImage2.Channels < Color.RgbChannels)
{
throw new ArgumentException("Input image 2 should be a valid color image.");
}
// face detection
Image <byte> imgGray1 = new ImageGray(facepointSource); //argbImage1
Stopwatch sw = Stopwatch.StartNew();
Stopwatch sw2 = Stopwatch.StartNew();
var faces1 = detector.Detect(imgGray1);
if (faces1.Length < 1)
{
throw new InvalidOperationException("Fail to detect face in image 1.");
}
// pick two largest faces
var face1 = faces1.OrderByDescending(y => y.Rect.Area).First();
Image <byte> imgGray2 = new ImageGray(argbImage2);
sw.Restart();
var faces2 = detector.Detect(imgGray2);
if (faces2.Length < 1)
{
throw new InvalidOperationException("Fail to detect face in image 2.");
}
var face2 = faces2.OrderByDescending(y => y.Rect.Area).First();
sw.Restart();
var alignmentShape1 = alignmentor.Align(imgGray1, face1.Rect);
sw.Restart();
var alignmentShape2 = alignmentor.Align(imgGray2, face2.Rect);
// create the face point file
FaceSwapper.SaveTestData("points-Source", argbImage1, alignmentShape1);
FaceSwapper.SaveTestData("facepoints-Source", facepointSource, alignmentShape1);
FaceSwapper.SaveTestData("points-target", argbImage2, alignmentShape2);
swapResult1 = argbImage2.Clone();
sw.Restart();
FaceSwapInternal(argbImage1, argbImage2, alignmentShape1, alignmentShape2, swapResult1);
}
internal override GenericImage <T> Add(GenericImage <T> other)
{
ImageGray <T> outcome = (ImageGray <T>)ImageFactory.Create(Width, Height, GetColorModel(), GetDataType());
ImageGray <T> otherGray = (ImageGray <T>)other.ToGray();
outcome.Gray = Gray.Add(otherGray.Gray);
return(outcome);
}
public override void OnInspectorGUI()
{
// Show default inspector property editor
DrawDefaultInspector();
ImageGray ig = (ImageGray)target;
ig.Gray = GUILayout.Toggle(ig.Gray, "Gray");
}
/// <summary>
/// 战斗中，最下方一排的英雄显示赋值
/// </summary>
/// <param name="fightHero"></param>
/// <param name="node"></param>
void SetShowNodeData(FightHero fightHero, UINode node)
{
node.GetRef("Icon").GetComponent <Image>().sprite = GameResSys.Instance.GetCard(fightHero.CreatureData.Icon);
node.GetRef("Name").GetComponent <Text>().text = fightHero.CreatureData.Name;
node.GetRef("Hp").GetComponent <Text>().text = "血";
node.GetRef("HpSlider").GetComponent <Slider>().value = fightHero.CreatureData.HpPercent;
node.GetRef("Mp").GetComponent <Text>().text = "气";
node.GetRef("MpSlider").GetComponent <Slider>().value = fightHero.CreatureData.MpPercent;
ImageGray ig = node.GetRef("Icon").GetComponent <ImageGray>();
ig.Gray = fightHero.CreatureData.HpPercent <= 0;
}
/// <summary>
/// Detect faces in the given image
/// </summary>
/// <param name="image">Image object</param>
public void Detect(Image <byte> image)
{
if (image == null)
{
throw new ArgumentNullException("image", "Invalid parameter.");
}
#if CALL_ADVANCED_DETECTION
//
// You can fine control the detection behavior by providing more information, like
// whether to use color information, the face area in the image, the minimum and maximum face size,
// whether to use post-filter (post filter can efficiently prune false positives),
// the search step (in pixel) between neighboring sliding windows (in both horizontal and vertical directions).
//
// The following code shows how to call the advanced Detect method
//
// Assume the input is a color image, convert it to grayscale.
ImageGray gray = new ImageGray(image);
// The minimum detectable face size is given by Detector.MinResolution. Small faces below that resolution
// will not be included in the detection result. For convenince, we specify (0,0) here.
//
// The maximum detectable face size is given by Detector.MaxResolution. Large faces beyond that resolution
// will not be included in the detection result. For convenince, we specify image size here.
//
// The step ratio specifies the gaps (in pixel) between neighboring sliding windows (a sliding window defines
// a search area) in both horiztonal and vertical directions. For example, if the window size is 40x40, given
// a step ratio of 0.1, the gaps will be 4 pixels.
// A smaller ratio probably will improve the recall, but takes more time. We use 0.1 as a default value.
//
// The post filter flag indicates whether to apply post filter over the raw detection result. By default, we
// turn on this flag to efficiently remove false positives.
//
// The search region parameter defines the local area to search the faces. By default, we search the entire image.
//
// If the color image is provided, the detector will employ the color information to search the faces.
// For example, it will first label the possible skin region and then search faces on those regions only, which
// can greatly improve the detection speed.
this.faces = this.detector.Detect(gray, Size.Empty, gray.Size, 0.1f, true, gray.Region, image);
#else
// If the input is grayscale image, the Detect method will search the face over the entire
// image region based on the local patterns.
// If the input is a color image (RGB or ARGB), internally the Detect method will
// convert it to grayscale, and employ color information to search the face area.
this.faces = this.detector.Detect(image);
#endif
}
void OnGetRewardReturnToStage(object p1, object p2)
{
//update Node
int nodeId = (int)p2;
Transform nodeTrans = _nodeUIs[nodeId];
ImageGray ig = nodeTrans.gameObject.AddComponent <ImageGray>();
ig.Gray = true;
FightHero hero = (FightHero)p1;
Transform t = nodeTrans.GetComponent <UINode>().GetRef("HeroRoot");
_fightHeroNodes[hero.Id].transform.SetParent(t);
SetPathPassed(hero.LastNodeId, hero.NowNodeId, ((HeroData)hero.CreatureData).TheColor);
}
/// <summary>
/// Detect faces in the given image
/// </summary>
/// <param name="image">Image object</param>
public void Detect(Image<byte> image)
{
if (image == null) throw new ArgumentNullException("image", "Invalid parameter.");
#if CALL_ADVANCED_DETECTION
//
// You can fine control the detection behavior by providing more information, like
// whether to use color information, the face area in the image, the minimum and maximum face size,
// whether to use post-filter (post filter can efficiently prune false positives),
// the search step (in pixel) between neighboring sliding windows (in both horizontal and vertical directions).
//
// The following code shows how to call the advanced Detect method
//
// Assume the input is a color image, convert it to grayscale.
ImageGray gray = new ImageGray(image);
// The minimum detectable face size is given by Detector.MinResolution. Small faces below that resolution
// will not be included in the detection result. For convenince, we specify (0,0) here.
//
// The maximum detectable face size is given by Detector.MaxResolution. Large faces beyond that resolution
// will not be included in the detection result. For convenince, we specify image size here.
//
// The step ratio specifies the gaps (in pixel) between neighboring sliding windows (a sliding window defines
// a search area) in both horiztonal and vertical directions. For example, if the window size is 40x40, given
// a step ratio of 0.1, the gaps will be 4 pixels.
// A smaller ratio probably will improve the recall, but takes more time. We use 0.1 as a default value.
//
// The post filter flag indicates whether to apply post filter over the raw detection result. By default, we
// turn on this flag to efficiently remove false positives.
//
// The search region parameter defines the local area to search the faces. By default, we search the entire image.
//
// If the color image is provided, the detector will employ the color information to search the faces.
// For example, it will first label the possible skin region and then search faces on those regions only, which
// can greatly improve the detection speed.
this.faces = this.detector.Detect(gray, Size.Empty, gray.Size, 0.1f, true, gray.Region, image);
#else
// If the input is grayscale image, the Detect method will search the face over the entire
// image region based on the local patterns.
// If the input is a color image (RGB or ARGB), internally the Detect method will
// convert it to grayscale, and employ color information to search the face area.
this.faces = this.detector.Detect(image);
#endif
}
void OnGetSafeReturnToStage(object p1, object p2)
{
FightHero hero = (FightHero)p1;
//
Transform nodeTrans = _nodeUIs[hero.LastNodeId];
ImageGray ig = nodeTrans.gameObject.AddComponent <ImageGray>();
ig.Gray = true;
//
nodeTrans = _nodeUIs[hero.NowNodeId];
UINode node = nodeTrans.GetComponent <UINode>();
Transform t = node.GetRef("HeroRoot");
_fightHeroNodes[hero.Id].transform.SetParent(t);
SetPathPassed(hero.LastNodeId, hero.NowNodeId, ((HeroData)hero.CreatureData).TheColor);
if (node.GetRef("HeroRoot").childCount > 1)
{
node.GetRef("All").gameObject.SetActive(true);
Dragable dragable = node.GetRef("All").gameObject.GetComponent <Dragable>();
if (dragable == null)
{
dragable = node.GetRef("All").gameObject.AddComponent <Dragable>();
}
dragable.HasTail = true;
dragable.TailColor = Color.white;
dragable.TailSprite = GameResSys.Instance.GetMask(GameConstants.CommonDragTail);
dragable.TailWidth = 30;
dragable.ActionId = hero.NowNodeId;
dragable.Canv = _stageCanvas;
dragable.OnDragStart = i =>
{
_dragFrom = DragFrom.All;
_dragId = hero.NowNodeId;
};
}
}
void OnEnemyAttack(object p1, object p2)
{
FightHero hero = (FightHero)p1;
int damage = (int)p2;
UINode node = _heroNodes[hero.Id].GetComponent <UINode>();
SetHeroData(hero, node);
node.GetRef("Selected").gameObject.SetActive(false);
ShowHurt(node, damage);
if (hero.CreatureData.Hp.Value <= 0)
{
ImageGray imageGray = node.GetRef("Icon").gameObject.GetComponent <ImageGray>();
if (imageGray == null)
{
imageGray = node.GetRef("Icon").gameObject.AddComponent <ImageGray>();
}
imageGray.Gray = true;
}
ShowItemDes(string.Empty, null);
}
void OnFightWinReturnStage(object p1, object p2)
{
//update Node
int nodeId = (int)p2;
ImageGray ig = _nodeUIs[nodeId].gameObject.AddComponent <ImageGray>();
ig.Gray = true;
Dictionary <int, FightHero> heros = (Dictionary <int, FightHero>)p1;
foreach (KeyValuePair <int, FightHero> pair in heros)
{
int hId = pair.Value.Id;
//change pos
Transform targetTrans = _nodeUIs[pair.Value.NowNodeId].GetComponent <UINode>().GetRef("HeroRoot");
_fightHeroNodes[hId].SetParent(targetTrans);
//update data
UINode showNode = _showHeroNodes[hId].GetComponent <UINode>();
SetShowNodeData(pair.Value, showNode);
Color c = ((HeroData)pair.Value.CreatureData).TheColor;
SetPathPassed(pair.Value.LastNodeId, pair.Value.NowNodeId, c);
}
}
//主执行程序
protected override void RunAct()
{
base.RunAct();
#region 初始化
if (!engineIsnitial)
{
MyEngine = new HDevEngine();
InitialEngine();
}
RuningFinish = false;
base.Result = string.Empty;
base.IsOk = false;
base.isRealOk = false;
//功能块
bFindShapeMode = false;
bGrabPointSetting = false;
if (ImageTestIn == null || !ImageTestIn.IsInitialized())
{
return;
}
int channels = ImageTestIn.CountChannels();
HImage ImageGray;
if (channels == 3)
{
ImageGray = ImageTestIn.Rgb1ToGray();
}
else
{
ImageGray = ImageTestIn.Clone();
}
#endregion
#region 创建模板
if (MCreateShapeModel.hShapeModel == null || !MCreateShapeModel.hShapeModel.IsInitialized() || MCreateShapeModel.createNewModelID)
{
if (!MCreateShapeModel.CreateShapeModelAct(ImageRefIn))
{
Util.Notify("创建模板异常");
if (NotifyExcInfo != null)
{
NotifyExcInfo("创建模板异常");
}
return;
}
}
#endregion
#region 模板匹配
bFindShapeMode = MFindShapeMode.FindShapeModeAct(ImageRefIn, MCreateShapeModel, ImageTestIn);
if (!bFindShapeMode)
{
Util.Notify("查找模板异常");
if (NotifyExcInfo != null)
{
NotifyExcInfo("查找模板异常");
}
return;
}
List <HHomMat2D> homMat2Ds = new List <HHomMat2D>();
homMat2Ds = MFindShapeMode.GetHHomMat2Ds();
if (MFindShapeMode.row == null || MFindShapeMode.row.Length < 1 || homMat2Ds == null)
{
Util.Notify("未找到模板");
if (NotifyExcInfo != null)
{
NotifyExcInfo("未找到模板");
}
base.IsOk = true;
base.isRealOk = true;
return;
}
if (bFindShapeMode)
{
StringBuilder strb = new StringBuilder();
strb.Append("查找结果:" + Environment.NewLine);
int count = MFindShapeMode.row.Length;
for (int i = 0; i < count; i++)
{
HTuple phi;
HOperatorSet.TupleDeg(MFindShapeMode.angle[i], out phi);
strb.Append("第" + (i + 1) + "个:\r\n");
string mes = string.Format(
"Row:{0:F2}\r\n" + "Col:{1:F2}\r\n" + "角度:{2:F2}\r\n" + "得分:{3:F2}"
, MFindShapeMode.row[i].D, MFindShapeMode.column[i].D, phi.D, MFindShapeMode.score[i].D);
strb.Append(mes + Environment.NewLine);
}
if (NotifyExcInfo != null)
{
NotifyExcInfo(strb.ToString());
}
Util.Notify(strb.ToString());
}
#endregion
#region 抓取点
bGrabPointSetting = mGrabPointSetting.setTarget(homMat2Ds);
if (!bGrabPointSetting)
{
Util.Notify("抓取点异常");
if (NotifyExcInfo != null)
{
NotifyExcInfo("抓取点异常");
}
}
else
{
StringBuilder strd = new StringBuilder();
for (int i = 0; i < MGrabPointSetting.GrabRowTarget.Length; i++)
{
string mes = string.Format(
"第" + (i + 1) + "个抓取点像素坐标:\r\n" +
"Row: {0:F2}\r\n" +
"Col: {1:F2}",
MGrabPointSetting.GrabRowTarget[i].D,
MGrabPointSetting.GrabColTarget[i].D);
strd.Append(mes + Environment.NewLine);
}
Util.Notify(strd.ToString());
if (NotifyExcInfo != null)
{
NotifyExcInfo(strd.ToString());
}
}
#endregion
#region 防呆
if (bFangDai_Enable)
{
bFangDai_Result = MFangDai.FangDai_Act(ImageGray, homMat2Ds);
if (bFangDai_Result)
{
StringBuilder strb = new StringBuilder();
strb.Append("防呆:" + Environment.NewLine);
int count = MFangDai.Area.Length;
for (int i = 0; i < count; i++)
{
string mes = string.Format("第" + (i + 1) + "个:\r\n" +
"面积: {0:F2}"
, MFangDai.Area[i].D);
strb.Append(mes);
strb.Append(Environment.NewLine);
}
if (NotifyExcInfo != null)
{
NotifyExcInfo(strb.ToString());
}
Util.Notify(strb.ToString());
}
else
{
Util.Notify("防呆检测异常");
if (NotifyExcInfo != null)
{
NotifyExcInfo("防呆检测异常");
}
}
}
else
{
bFangDai_Result = true;
}
#endregion
RuningFinish = true;
base.IsOk = bFindShapeMode && bGrabPointSetting && bFangDai_Result;
base.isRealOk = true;
if (ImageGray != null && ImageGray.IsInitialized())
{
ImageGray.Dispose();
}
}
public Image<byte> Detect(Image<byte> image)
{
// Detect faces
var dtor = this.detectors[this.detectorType];
dtor.Detect(image);
// Detect face feature points
Image<byte> gray = new ImageGray(image);
var ator = this.alignmentors[this.alignmentType];
var faceRects = from f in dtor.Results select f.Rect;
ator.Align(gray, faceRects);
// Render the result
int colorIndex = ((int)this.detectorType - 1) * this.Alignmentors.Count() + ((int)this.alignmentType - 1);
using (var render = Renderer.Create(new ImageARGB(image)))
{
results = ator.Results;
foreach (var shape in ator.Results)
{
render.DrawPoints(shape, this.radias, this.colors[colorIndex]);
foundFace = true;
}
return render.Image;
}
}
private async Task <Tuple <Image <byte>, IList <PointF> > > PrepBitmapAsync(SoftwareBitmap bitmap)
{
if (bitmap.PixelHeight % 2 != 0)
{
var resized = new SoftwareBitmap(bitmap.BitmapPixelFormat, bitmap.PixelWidth, bitmap.PixelHeight + 1);
bitmap.CopyTo(resized);
bitmap = resized;
}
Rectangle firstFace;
try
{
var detector = await FaceDetector.CreateAsync();
var formats = FaceDetector.GetSupportedBitmapPixelFormats();
var convertedBitmap = SoftwareBitmap.Convert(bitmap, formats.First());
var detected = await detector.DetectFacesAsync(convertedBitmap);
var faces = detected
.Select(x => x.FaceBox)
.Select(x => new Rectangle((int)x.X, (int)x.X + (int)x.Width, (int)x.Y, (int)x.Y + (int)x.Height));
if (!faces.Any())
{
return(null);
}
firstFace = faces.First();
}
catch (Exception)
{
Debugger.Break();
throw;
}
IList <PointF> points;
var image = ConvertTo.Image.FromSoftwareBitmap(bitmap);
try
{
if (alignmentor == null)
{
using (var stream = ResourceManager.GetStream(ResourceKey.AsmAlignment))
{
alignmentor = FaceAlignmentorFactory.Create(FaceAlignmentType.Asm87Points, stream);
}
}
var grayImage = new ImageGray(image);
points = alignmentor.Align(grayImage, firstFace).ToList();
if (!points.Any())
{
return(null);
}
}
catch (Exception)
{
Debugger.Break();
throw;
}
return(new Tuple <Image <byte>, IList <PointF> >(image, points));
}
