private void initFundMatrix() // initialize fundamental matrix
{
Point2f[] leftMatchPoints =
{
new Point2f(54, 31), //1,1
new Point2f(116, 98), //2,2
new Point2f(180, 162), //3,3
new Point2f(242, 229), //4,4
new Point2f(304, 294), //5,5
new Point2f(367, 361), //6,6
new Point2f(304, 31), //5,1
new Point2f(367, 97), //6,2
new Point2f(428, 162), //7,3
};
Point2f[] rightMatchPoints =
{
new Point2f(30, 31), //1,1
new Point2f(92, 98), //2,2
new Point2f(156, 162), //3,3
new Point2f(217, 229), //4,4
new Point2f(281, 294), //5,5
new Point2f(343, 361), //6,6
new Point2f(280, 31), //5,1
new Point2f(343, 97), //6,2
new Point2f(404, 162), //7,3
};
InputArray leftInput = InputArray.Create(leftMatchPoints);
InputArray rightInput = InputArray.Create(rightMatchPoints);
this.fundMatrix = Cv2.FindFundamentalMat(leftInput, rightInput, FundamentalMatMethod.Point8);
}
public static Dictionary <string, double> CalculateMatrix(IPattern from, IPattern to)
{
Point2f[] from_ = new Point2f[from.Points.Count];
Point2f[] to_ = new Point2f[to.Points.Count];
int i = 0;
from.Points.ForEach(pt =>
{
from_[i++] = new Point2f((float)pt.X, (float)pt.Y);
});
i = 0;
to.Points.ForEach(pt =>
{
to_[i++] = new Point2f((float)pt.X, (float)pt.Y);
});
Mat inliers = new Mat();
Mat affineMatrix = Cv2.EstimateAffine2D(InputArray.Create(from_), InputArray.Create(to_), inliers, RobustEstimationAlgorithms.RANSAC, 300.0, 2000, 0.99, 10);
PrintMatAsync(inliers);
return(TransformToDict(affineMatrix));
}
public static Mat GetHomography(Mat mMain, Mat mSecondary)
{
KeyPoint[] keypoints = null;
KeyPoint[] keypoints2 = null;
using (SIFT sIFT = SIFT.Create(1000))
{
using (Mat mat = new Mat())
{
using (Mat mat2 = new Mat())
{
sIFT.DetectAndCompute(mMain, new Mat(), out keypoints, mat);
sIFT.DetectAndCompute(mSecondary, new Mat(), out keypoints2, mat2);
FlannBasedMatcher flannBasedMatcher = new FlannBasedMatcher();
DMatch[] array = new DMatch[0];
array = flannBasedMatcher.Match(mat, mat2);
List <Point2f> list = new List <Point2f>();
List <Point2f> list2 = new List <Point2f>();
for (int i = 0; i < array.Length; i++)
{
list.Add(keypoints[array[i].QueryIdx].Pt);
list2.Add(keypoints2[array[i].TrainIdx].Pt);
}
return(Cv2.FindHomography(InputArray.Create(list2), InputArray.Create(list), HomographyMethods.Ransac));
}
}
}
}
public Angles GetAnglesAndPoints(Mat <Point2d> points, int width, int height)
{
using (var objPtsMat = InputArray.Create(Model_points, MatType.CV_32FC3)) //new Mat(objPts.Count, 1, MatType.CV_32FC3, objPts))
using (var imgPtsMat = InputArray.Create(points, MatType.CV_32FC2)) //new Mat(imgPts.Length, 1, MatType.CV_32FC2, imgPts))
using (var cameraMatrixMat = Mat.Eye(3, 3, MatType.CV_64FC1))
using (var distMat = Mat.Zeros(5, 0, MatType.CV_64FC1))
using (var rvecMat = new Mat())
using (var tvecMat = new Mat())
{
Cv2.SolvePnP(objPtsMat, imgPtsMat, cameraMatrixMat, distMat, rvecMat, tvecMat);
using (Mat resultPoints = new Mat())
{
Cv2.ProjectPoints(objPtsMat, rvecMat, tvecMat, cameraMatrixMat, distMat, resultPoints);
}
// 根据旋转矩阵求解坐标旋转角
double theta_x = Math.Atan2((float)rvecMat.At <double>(2, 1), (float)rvecMat.At <double>(2, 2));
double theta_y = Math.Atan2((float)-rvecMat.At <double>(2, 0),
Math.Sqrt((float)((rvecMat.At <double>(2, 1) * rvecMat.At <double>(2, 1)) + ((float)rvecMat.At <double>(2, 2) * rvecMat.At <double>(2, 2)))));
double theta_z = Math.Atan2((float)rvecMat.At <double>(1, 0), (float)rvecMat.At <double>(0, 0));
// 将弧度转为角度
Angles angles = new Angles();
angles.Roll = theta_x * (180 / Math.PI);
angles.Pitch = theta_y * (180 / Math.PI);
angles.Yaw = theta_z * (180 / Math.PI);
// 将映射的点的坐标保存下来
// outarray类型的resultpoints如何转存到list中？
return(angles);
}
}
private void FindAndDrawHomo()
{
using (var template = new Mat("Images\\Circle_Template.bmp", ImreadModes.Color))
{
using (var surf = SURF.Create(1000))
{
using (var templateDescriptors = new Mat())
{
surf.DetectAndCompute(template, null, out KeyPoint[] templateKeyPoints, templateDescriptors);
using (var image = new Mat("Images\\Circle.bmp", ImreadModes.Color))
{
using (var imageDescriptors = new Mat())
{
surf.DetectAndCompute(image, null, out KeyPoint[] imageKeyPoints, imageDescriptors);
using (var matcher = new BFMatcher())
{
var matches = matcher.Match(imageDescriptors, templateDescriptors);
var goodMatches = matches;//.Where(m => m.Distance < 0.2).ToArray();
using (var srcPoints = InputArray.Create(goodMatches.Select(m => templateKeyPoints[m.TrainIdx].Pt)))
{
using (var dstPoints = InputArray.Create(goodMatches.Select(m => imageKeyPoints[m.QueryIdx].Pt)))
{
using (var h**o = Cv2.FindHomography(srcPoints, dstPoints, HomographyMethods.Rho))
{
////using (var overlay = image.Overlay())
////{
//// DrawBox(template, h**o, overlay);
//// this.Result.Source = overlay.ToBitmapSource();
////}
using (var tmp = image.Overlay())
{
Cv2.BitwiseNot(template, template);
Cv2.WarpPerspective(template, tmp, h**o, tmp.Size());
using (var overlay = tmp.Overlay())
{
for (var r = 0; r < tmp.Rows; r++)
{
for (var c = 0; c < tmp.Cols; c++)
{
overlay.Set(r, c,
tmp.At <int>(r, c) == 0
? new Vec4b(0, 0, 0, 0)
: new Vec4b(0, 0, 255, 150));
}
}
this.Result.Source = overlay.ToBitmapSource();
}
}
}
}
}
}
}
}
}
}
}
}
/// <summary>
/// Srcに対してTargetに合うよう射影変換を適用する
/// </summary>
public void FitSrcToTarget()
{
// 使用する特徴点の量だけベクトル用意
int size = SelectedMatched.Count();
var getPtsSrc = new Vec2f[size];
var getPtsTarget = new Vec2f[size];
// SrcとTarget画像の対応する特徴点の座標を取得し、ベクトル配列に格納していく。
int count = 0;
foreach (var item in SelectedMatched)
{
var ptSrc = KeyPtsSrc[item.QueryIdx].Pt;
var ptTarget = KeyPtsTarget[item.TrainIdx].Pt;
getPtsSrc[count][0] = ptSrc.X;
getPtsSrc[count][1] = ptSrc.Y;
getPtsTarget[count][0] = ptTarget.X;
getPtsTarget[count][1] = ptTarget.Y;
count++;
}
// SrcをTargetにあわせこむ変換行列homを取得する。ロバスト推定法はRANZAC。
var hom = Cv2.FindHomography(
InputArray.Create(getPtsSrc),
InputArray.Create(getPtsTarget),
HomographyMethods.Ransac);
// 行列homを用いてSrcに射影変換を適用する。
WarpedSrcMat = new Mat();
Cv2.WarpPerspective(
SrcMat, WarpedSrcMat, hom,
new OpenCvSharp.Size(TargetMat.Width, TargetMat.Height));
}
public override float Predict(ModelDataSet input)
{
if (TrainedModel == null)
{
throw new Exception("Must initialize the model before calling");
}
lock (TrainedModel)
{
// cache for reuse
if (PredictInput == null)
{
PredictInput = new float[input.Features()];
}
for (int i = 0; i < PredictInput.Length; i++)
{
PredictInput[i] = input.Feature(i);
}
using (var arr = InputArray.Create <float>(PredictInput))
{
return(TrainedModel.Predict(arr));
}
}
}
public void AddScalar()
{
using Mat src = new Mat(2, 2, MatType.CV_8UC1, new byte[] { 1, 2, 3, 4 });
using Mat dst = new Mat();
Cv2.Add(new Scalar(10), src, dst);
Assert.Equal(MatType.CV_8UC1, dst.Type());
Assert.Equal(2, dst.Rows);
Assert.Equal(2, dst.Cols);
Assert.Equal(11, dst.At <byte>(0, 0));
Assert.Equal(12, dst.At <byte>(0, 1));
Assert.Equal(13, dst.At <byte>(1, 0));
Assert.Equal(14, dst.At <byte>(1, 1));
Cv2.Add(src, new Scalar(10), dst);
Assert.Equal(11, dst.At <byte>(0, 0));
Assert.Equal(12, dst.At <byte>(0, 1));
Assert.Equal(13, dst.At <byte>(1, 0));
Assert.Equal(14, dst.At <byte>(1, 1));
using var inputArray = InputArray.Create(10.0);
Cv2.Add(src, inputArray, dst);
Assert.Equal(11, dst.At <byte>(0, 0));
Assert.Equal(12, dst.At <byte>(0, 1));
Assert.Equal(13, dst.At <byte>(1, 0));
Assert.Equal(14, dst.At <byte>(1, 1));
}
public static void MatchImpact()
{
Bitmap screen = new Bitmap("ImpactTest.png");
//Bitmap cropped = CompassSensor.Crop(screen, screen.Width - 400, 0, screen.Width - 100, 300);
Mat screenwhole = BitmapConverter.ToMat(screen);
Mat brightHSV = screenwhole.CvtColor(ColorConversionCodes.BGR2HSV);
Mat redMask = brightHSV.InRange(InputArray.Create(new int[] { 0, 250, 200 }), InputArray.Create(new int[] { 5, 256, 256 }))
+ brightHSV.InRange(InputArray.Create(new int[] { 175, 250, 200 }), InputArray.Create(new int[] { 180, 256, 256 }));
Mat darkAreas = new Mat();
screenwhole.CopyTo(darkAreas, redMask);
Mat red = darkAreas.Split()[2];
red.SaveImage("impacttemplateraw.png");
Mat template = new Mat("res3/impacttemplate.png", ImreadModes.GrayScale);
Mat result = new Mat(red.Size(), red.Type());
Cv2.MatchTemplate(red, template, result, TemplateMatchModes.CCoeffNormed);
Window w2 = new Window(red);
Window w3 = new Window(result);
Cv2.Threshold(result, result, 0.4, 1.0, ThresholdTypes.Tozero);
Window w4 = new Window(result);
Window w1 = new Window(screenwhole);
}
/// <summary>
/// 把输入的图片变成灰度图，锐化一次会写入一次图片
/// </summary>
/// <param name="input_img">原尺寸下分割出的文字</param>
/// <param name="x">横坐标值</param>
/// <param name="y">纵坐标值</param>
/// <param name="width">向右</param>
/// <param name="height">向下</param>
/// <returns></returns>
public static string Seg_img_to_text(Mat input_img, double x, double y, int width, int height)
{
//x *= input_img.Width; y *= input_img.Height; //0.09 0.262
//width = 71; height = 32;
/*--缓存，好蠢的方式。但是Pix的LoadFromMemory里的byte是什么鬼--*/
string imgPath = @"D:\XD\1-dis\pic\cache\seged_image.jpg";
using (InputArray kernel = InputArray.Create <double>(new double[3, 3] {
{ -1, -1, -1 }, { -1, 9, -1 }, { -1, -1, -1 }
}))
using (Mat dst = new Mat(input_img, new OpenCvSharp.Rect((int)x, (int)y, width, height)))
{
//不准的话就画外接矩形
//https://www.itdaan.com/tw/46038a31b3020fce189dd3f30699ac0e
if (dst.Channels() != 1)
{
Cv2.CvtColor(dst, dst, ColorConversionCodes.BGR2GRAY);
}
//OTSU = Cv2.Threshold(dst, dst, 200, 255, ThresholdTypes.Otsu);
//Cv2.Resize(dst, dst, new Size(dst.Width * 5, dst.Height * 5), 0, 0, InterpolationFlags.Cubic);
//Cv2.Filter2D(dst, dst, dst.Depth(), kernel, new Point(-1, -1), 0);
//Cv2.Threshold(dst, dst, 125, 255, ThresholdTypes.Binary);
Cv2.ImWrite(imgPath, dst);
}
string strResult = ImageToText(imgPath);
if (string.IsNullOrEmpty(strResult))
{
strResult = "无法识别";
}
strResult = strResult.Replace(" ", ""); //删除中间的空格。为什么识别中文的时候会有空格？？
return(strResult);
}
private static (Mat transformedImage, List <Point2f> transformedPoints) SimilarityTransform(int outputWidth,
int outputHeight, IList <Point2f> face, Mat img)
{
var s60 = (float)Math.Sin(60 * Math.PI / 180.0);
var c60 = (float)Math.Cos(60 * Math.PI / 180.0);
Point2f[] ToEquilateral(Point2f a, Point2f b)
{
var dx = a.X - b.X;
var dy = a.Y - b.Y;
return(new[] { a, b, new Point2f(c60 * dx - s60 * dy + b.X, s60 * dx + c60 * dy + b.Y) });
}
var eyeCornersSource = ToEquilateral(face[36], face[45]);
var eyeCornersDest = ToEquilateral(
new Point2f(0.3F * outputWidth, outputHeight / 3f),
new Point2f(0.7F * outputWidth, outputHeight / 3f));
using (var inputArray = InputArray.Create(eyeCornersSource))
using (var array = InputArray.Create(eyeCornersDest))
using (var transform = Cv2.EstimateRigidTransform(inputArray, array, false))
{
var transformedImage = Mat.Zeros(outputWidth, outputHeight, MatType.CV_32FC3).ToMat();
Cv2.WarpAffine(img, transformedImage, transform, transformedImage.Size());
using (var src = InputArray.Create(face))
using (var dst = new Mat())
{
Cv2.Transform(src, dst, transform);
var transformedPoints = new Point2f[dst.Rows * dst.Cols];
dst.GetArray(0, 0, transformedPoints);
return(transformedImage, transformedPoints.ToList());
}
}
}
public override void Apply(Mat input)
{
_start = DateTime.Now;
Input = input;
OpenCvSharp.Cv2.Canny(InputArray.Create(Input), Output, _minThreshold, _minThreshold * 2);
base.Apply(input);
}
public static double MSE(Bitmap reference, Bitmap compare)
{
InputArray referenceArray = InputArray.Create(BitmapConverter.ToMat(reference));
InputArray compareArray = InputArray.Create(BitmapConverter.ToMat(compare));
Scalar scalar = QualityMSE.Compute(referenceArray, compareArray, null);
return((scalar[0] + scalar[1] + scalar[2]) / 3);
}
static void Main(string[] args)
{
using (var sourceMat = new Mat("source.jpg"))
using (var destinationMat = new Mat("destination.jpg"))
using (var hc = new CascadeClassifier("HaarCascade.xml"))
using (var facemark = FacemarkLBF.Create())
{
Console.WriteLine("Face detection starting..");
var sourceFaceRects = hc.DetectMultiScale(sourceMat);
if (sourceFaceRects == null || sourceFaceRects.Length == 0)
{
Console.WriteLine($"Source image: No faces detected.");
return;
}
Console.WriteLine($"Source image: detected {sourceFaceRects.Length} faces.");
var destFaceRects = hc.DetectMultiScale(destinationMat);
if (destFaceRects == null || destFaceRects.Length == 0)
{
Console.WriteLine($"Destination image: No faces detected.");
return;
}
Console.WriteLine($"Destination image: detected {destFaceRects.Length} faces.");
facemark.LoadModel("lbfmodel.yaml");
using (var sourceInput = InputArray.Create(sourceFaceRects))
using (var destInput = InputArray.Create(destFaceRects))
{
facemark.Fit(sourceMat, sourceInput, out Point2f[][] sourceLandmarks);
var sourcePoints = sourceLandmarks[0];
facemark.Fit(destinationMat, destInput, out Point2f[][] destLandmarks);
var destPoints = destLandmarks[0];
var triangles = destPoints.Take(60).GetDelaunayTriangles();
var warps = triangles.GetWarps(sourcePoints.Take(60), destPoints.Take(60));
using (var warpedMat = sourceMat.ApplyWarps(destinationMat.Width, destinationMat.Height, warps))
using (var mask = new Mat(destinationMat.Size(), MatType.CV_8UC3))
using (var result = new Mat(destinationMat.Size(), MatType.CV_8UC3))
{
mask.SetTo(0);
var convexHull = Cv2.ConvexHull(destPoints).Select(s => new Point(s.X, s.Y));
Cv2.FillConvexPoly(mask, convexHull, Scalar.White);
var rect = Cv2.BoundingRect(convexHull);
var center = new Point(rect.X + rect.Width / 2, rect.Y + rect.Height / 2);
Cv2.SeamlessClone(warpedMat, destinationMat, mask, center, result, SeamlessCloneMethods.NormalClone);
var blured = result.MedianBlur(5);
blured.SaveImage("result.png");
}
}
}
Console.WriteLine("Done");
}
public override bool run()
{
InputArray m = InputArray.Create <double>(matrix, MatType.CV_64FC1);
double d1, d2, d3, d4, d5, d6;
evaluateMatrix(m.GetMat(), out d1, out d2, out d3, out d4, out d5, out d6);
dst = src.WarpAffine(m, src.Size(), InterpolationFlags.Lanczos4, BorderTypes.Constant);
return(true);
}
public static double GetMaxDifference(Mat a, Mat b)
{
var resultMat = new Mat();
Cv2.Absdiff(InputArray.Create(a), InputArray.Create(b), resultMat);
resultMat.MinMaxLoc(out double minVal, out double maxVal);
return(maxVal);
}
public void NormVecb()
{
var vec = new Vec3b(10, 20, 30);
using var ia = InputArray.Create(vec);
var norm = Cv2.Norm(ia, NormTypes.L1);
Assert.Equal(60, norm);
}
/// <summary>
/// Try to match (part of) a large green circle on the screen.
/// </summary>
public CircleSegment FindCorona()
{
// see the Experiments for how this works
Bitmap cropped = CompassSensor.Crop(screen.bitmap,
screen.bitmap.Width * 1 / 3,
screen.bitmap.Height * 1 / 3,
screen.bitmap.Width * 2 / 3,
screen.bitmap.Height * 2 / 3);
Mat screenwhole = BitmapConverter.ToMat(cropped);
Point2f ShipPointerOffset = new Point2f(0, 0);
try
{
ShipPointerOffset = FindShipPointer(IsolateYellow(screenwhole));
}
catch (Exception)
{
// If we can't find the ship pointer (it's hard to see it against the sun) then use the middle of the screen.
}
// erase the vivid areas, otherwise the blur subtraction turns yellow near red to green
Mat brightHSV = screenwhole.CvtColor(ColorConversionCodes.BGR2HSV);
Mat darkAreasMask = brightHSV.InRange(InputArray.Create(new int[] { 0, 0, 0 }), InputArray.Create(new int[] { 180, 255, 180 }));
Mat darkAreas = new Mat();
screenwhole.CopyTo(darkAreas, darkAreasMask);
Mat screenblur = darkAreas - darkAreas.Blur(new OpenCvSharp.Size(10, 10));
Mat sourceHSV = screenblur.CvtColor(ColorConversionCodes.BGR2HSV);
Mat mask = sourceHSV.InRange(InputArray.Create(new int[] { 35, 204, 20 }), InputArray.Create(new int[] { 90, 255, 255 }));
Mat sourceHSVFiltered = new Mat();
sourceHSV.CopyTo(sourceHSVFiltered, mask);
Mat sourceGrey = sourceHSVFiltered.Split()[2].InRange(32, 256);
LineSegmentPoint[] result = sourceGrey.HoughLinesP(1, 3.1415 / 180, 5, 10, 2);
List <Point2d> points = new List <Point2d>();
foreach (var line in result)
{
points.Add(line.P1);
points.Add(line.P2);
}
if (points.Count < 8)
{
throw new ArgumentException("Not enough points in corona circle");
}
CircleSegment c = ComputeCircle(points);
sourceGrey.Line(c.Center, ShipPointerOffset, 255);
c.Center -= ShipPointerOffset; // adjust for camera movement by taking ship pointer offset
sourceGrey.Circle(c.Center, (int)c.Radius, 255);
debugWindow.Image = BitmapConverter.ToBitmap(sourceGrey);
return(c);
}
private async Task CaptureLoop(CancellationToken cancelToken)
{
byte[] dilateArray =
{ 1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1, };
Texture2D stencilViewTexture = new Texture2D(width, height);
Texture2D rgbViewTexture = new Texture2D(width, height);
Texture2D inpaintViewTexture = new Texture2D(width, height);
HumanStencil_Image.texture = stencilViewTexture;
RGB_Image.texture = rgbViewTexture;
Inpaint_Image.texture = inpaintViewTexture;
while (!cancelToken.IsCancellationRequested)
{
await Task.Delay(10);
if (RGB_Texture == null || Stencil_Texture == null)
{
continue;
}
using (Mat stencilMat = OpenCvSharp.Unity.TextureToMat(Stencil_Texture))
using (Mat rgbMat = OpenCvSharp.Unity.TextureToMat(RGB_Texture))
using (Mat inpaintMat = new Mat())
{
#region stencil texture
Cv2.CvtColor(stencilMat, stencilMat, ColorConversionCodes.BGR2GRAY);
Cv2.Dilate(stencilMat, stencilMat, InputArray.Create(dilateArray));
Cv2.Resize(stencilMat, stencilMat, new OpenCvSharp.Size(width, height));
stencilViewTexture = OpenCvSharp.Unity.MatToTexture(stencilMat, stencilViewTexture);
#endregion
#region rgb texture
Cv2.Resize(rgbMat, rgbMat, new OpenCvSharp.Size(width, height));
Cv2.Flip(rgbMat, rgbMat, FlipMode.Y);
rgbViewTexture = OpenCvSharp.Unity.MatToTexture(rgbMat, rgbViewTexture);
#endregion
#region inpaint
Cv2.Inpaint(rgbMat, stencilMat, inpaintMat, 3, InpaintMethod.NS);
inpaintViewTexture = OpenCvSharp.Unity.MatToTexture(inpaintMat, inpaintViewTexture);
#endregion
stencilMat.Dispose();
rgbMat.Dispose();
inpaintMat.Dispose();
}
}
}
/// <summary>
/// Filter the given image to select certain yellow hues (returned as grayscale)
/// </summary>
public static Mat IsolateYellow(Mat source)
{
Mat sourceHSV = source.CvtColor(ColorConversionCodes.BGR2HSV);
Mat mask = sourceHSV.InRange(InputArray.Create(new int[] { 10, 200, 128 }), InputArray.Create(new int[] { 30, 255, 255 }));
Mat sourceHSVFiltered = new Mat();
sourceHSV.CopyTo(sourceHSVFiltered, mask);
Mat valueChannel = sourceHSVFiltered.Split()[2];
return(valueChannel);
}
public Mat Detect(Mat mat)
{
using (var gray = new Mat())
{
Cv2.CvtColor(InputArray.Create(mat), OutputArray.Create(gray), ColorConversionCodes.BGR2GRAY);
cascadeClassifier.DetectMultiScale(gray, Convert.ToDouble(scaleFactor), minimumNeighbors, 0,
new OpenCvSharp.Size(minSizeWidth, minSizeHeight))
.ToList().ForEach(r => mat.Rectangle(new OpenCvSharp.Rect(r.X, r.Y, r.Width, r.Height), Scalar.Red));
return(mat);
}
}
public static Mat IsolateRed(Mat source)
{
Mat brightHSV = source.CvtColor(ColorConversionCodes.BGR2HSV);
Mat redMask = brightHSV.InRange(InputArray.Create(new int[] { 0, 250, 200 }), InputArray.Create(new int[] { 5, 256, 256 }))
+ brightHSV.InRange(InputArray.Create(new int[] { 175, 250, 200 }), InputArray.Create(new int[] { 180, 256, 256 }));
Mat redAreas = new Mat();
source.CopyTo(redAreas, redMask);
Mat red = redAreas.Split()[2];
return(red);
}
public static int?GetLastFinalBossFrame(VideoCapture capture, float scale, int videoEndFrame,
Action <LoadRemover.ProgressPhase, float> updateProgress, int start = 1, int stepSize = -1)
{
if (stepSize <= 0)
{
stepSize = (int)capture.Fps;
}
capture.Set(VideoCaptureProperties.PosFrames, videoEndFrame);
int maxFrames = (int)(MaxTimeAfterFinalHit * capture.Fps);
for (int i = start; i < maxFrames; i += stepSize)
{
// Start from the end of the video and move backwards to find the last occurrence of the boss icon
capture.Set(VideoCaptureProperties.PosFrames, videoEndFrame - i);
Mat mat = new Mat();
capture.Read(mat);
Cv2.Resize(mat, mat, new Size(mat.Width * scale, mat.Height * scale));
// Crop to the bottom-left corner, since that's where the lives are
mat = mat[(int)(mat.Height * 0.83f), mat.Height, 0, (int)(mat.Width * 0.085f)];
Mat result = new Mat();
Cv2.MatchTemplate(InputArray.Create(mat), InputArray.Create(ImgEndBoss), result,
TemplateMatchModes.SqDiffNormed, InputArray.Create(ImgEndBossMask));
result.MinMaxLoc(out double minVal, out double maxVal, out Point minLoc, out Point maxLoc);
var timespan = TimeSpan.FromSeconds((videoEndFrame - i) / (float)capture.Fps);
if (start == 1)
{
updateProgress.Invoke(LoadRemover.ProgressPhase.Phase_4_EndingTime, (float)i / maxFrames);
}
if (minVal < EndBossIconMatchThreshold)
{
if (stepSize == 1)
{
return((videoEndFrame - i) + 1);
}
else
{
return(GetLastFinalBossFrame(capture, scale, videoEndFrame, updateProgress, i - stepSize, stepSize: 1));
}
}
}
return(null);
}
public List <Point2f> UndistortPoint(List <Point2f> src)
{
if (_init)
{
List <Point2f> dst = new List <Point2f>();
Cv2.UndistortPoints(InputArray.Create(src), OutputArray.Create(dst), _cameraMatrix, _distCoeffs);
return(dst);
}
else
{
return(src);
}
}
private static BossType?IsBossFrame(VideoCapture capture, float scale, float posSeconds)
{
int frameNum = (int)(posSeconds * capture.Fps);
capture.Set(VideoCaptureProperties.PosFrames, frameNum);
Mat mat = new Mat();
capture.Read(mat);
Cv2.Resize(mat, mat, new Size(mat.Width * scale, mat.Height * scale));
// Crop to the bottom-left corner, since that's where the lives are
mat = mat[(int)(mat.Height * 0.83f), mat.Height, 0, (int)(mat.Width * 0.085f)];
float lowestThreshold = float.MaxValue;
BossType?type = null;
for (int i = 0; i < (int)BossType._NumTypes; i++)
{
Mat result = new Mat();
Cv2.MatchTemplate(InputArray.Create(mat), InputArray.Create(ImgBoss[(BossType)i]), result,
TemplateMatchModes.SqDiffNormed, InputArray.Create(ImgBossMask[(BossType)i]));
result.MinMaxLoc(out double minVal, out double maxVal, out Point minLoc, out Point maxLoc);
if (minVal < EndBossIconMatchThreshold)
{
if (minVal < lowestThreshold) // pick the match with the lowest threshold
{
type = (BossType)i;
lowestThreshold = (float)minVal;
}
}
}
if (type == BossType.Bzzit || type == BossType.Mosquito)
{
capture.Read(mat);
// check bottom line, for mosquito fight it's black
mat = mat[(int)(mat.Height * 0.983f), mat.Height, 0, mat.Width];
if (Util.GetAverageBrightness(mat) < Util.DarknessMaxBrightness)
{
type = BossType.Mosquito;
}
}
return(type);
}
public static void FindTriQuadrant()
{
Bitmap image = (Bitmap)Image.FromFile("res3/supercruisetarget.png");
Mat source = BitmapConverter.ToMat(image);
Mat sourceHSV = source.CvtColor(ColorConversionCodes.BGR2HSV);
/* Paint.Net uses HSV [0..360], [0..100], [0..100].
* OpenCV uses H: 0 - 180, S: 0 - 255, V: 0 - 255
* Paint.NET colors:
* 50 94 100 bright yellow
* 27 93 90 orange
* 24 91 74 brown
* 16 73 25 almost background (low V)
* suggested range [20..55], [80..100], [50..100] (paint.net)
* suggested range [10..27], [200..255], [128..255] (openCV
* */
Mat mask = sourceHSV.InRange(InputArray.Create(new int[] { 10, 200, 128 }), InputArray.Create(new int[] { 27, 255, 255 }));
Mat sourceHSVFiltered = new Mat();
sourceHSV.CopyTo(sourceHSVFiltered, mask);
Window w3 = new Window("yellowfilter", sourceHSVFiltered.CvtColor(ColorConversionCodes.HSV2BGR));
Mat sourceGrey = sourceHSVFiltered.Split()[2]; // Value channel is pretty good as a greyscale conversion
Window w4 = new Window("yellowFilterValue", sourceGrey);
CircleSegment[] circles2 = sourceGrey.HoughCircles(
HoughMethods.Gradient,
dp: 1f, /* resolution scaling factor? full resolution seems to work better */
minDist: 100, /* if we find more than one then we go to the second analysis, the crosshair is probably blue as well*/
param1: 100, /* default was fine after experimentation */
param2: 13, /* required quality factor. 9 finds too many, 14 finds too few */
minRadius: 40,
maxRadius: 47);
foreach (CircleSegment circle in circles2)
{
var quarterCircle = new OpenCvSharp.Point2f(circle.Radius, circle.Radius);
source.Rectangle(circle.Center - quarterCircle, circle.Center + quarterCircle, new Scalar(0, 255, 0));
}
Mat templatepointer = new Mat("res3/squaretarget.png", ImreadModes.GrayScale);
Mat matches = sourceGrey.MatchTemplate(templatepointer, TemplateMatchModes.CCoeffNormed);
Window w6 = new Window("pointer", matches);
OpenCvSharp.Point minloc, maxloc;
matches.MinMaxLoc(out minloc, out maxloc);
source.Rectangle(maxloc, maxloc + new OpenCvSharp.Point(templatepointer.Size().Width, templatepointer.Size().Height), new Scalar(255, 255, 0));
Window w5 = new Window("result", source);
}
public static unsafe Mat NormalizeTextImage(
Mat input,
TextImageHistStats stats,
Size size,
double noiseStdevFactor = 1)
{
var x0 = stats.X0;
var x1 = stats.X1;
using var inverse_ = x1 < x0 ? ~input : null;
using var inverse = inverse_ != null ? (Mat)inverse_ : null;
if (x1 < x0)
{
x0 = 255 - x0;
x1 = 255 - x1;
}
var background =
(byte)Math.Max((int)(x1 - stats.Stdev1 * noiseStdevFactor), 0);
var src = inverse ?? input;
using var mean = src.Blur(size);
var meanIndex = mean.GetUnsafeGenericIndexer <byte>();
var index = src.GetUnsafeGenericIndexer <byte>();
var normalized = new Mat(input.Size(), MatType.CV_8UC1);
normalized.ForEachAsByte((value, position) =>
{
var y = position[0];
var x = position[1];
var invalue = index[y, x];
var mean = meanIndex[y, x];
*value = mean < background ? invalue : background;
});
var k = 255.0 / (background - x0);
var lookup = new byte[256];
for (var i = 0; i < lookup.Length; ++i)
{
lookup[i] = i <= x0 ? (byte)0 : i >= x1 ?
(byte)255 : (byte)Math.Min((i - x0) * k, 255);
}
using var lut = InputArray.Create(lookup);
return(normalized.LUT(lut));
}
// filtriramo vrhove zato što znamo da prsti imaju oštar vrh
private IEnumerable <DefectVertex> FilterVerticesWithAngle(IEnumerable <DefectVertex> vertices, int maxAngle)
{
return(vertices.Where(x =>
{
var vert1 = Cv2.Norm(InputArray.Create(x.d1.Subtract(x.d2).ToVec2i()));
var vert2 = Cv2.Norm(InputArray.Create(x.point.Subtract(x.d1).ToVec2i()));
var vert3 = Cv2.Norm(InputArray.Create(x.point.Subtract(x.d2).ToVec2i()));
var deg = Math.Acos((vert2 * vert2 + vert3 * vert3 - vert1 * vert1) / (2 * vert2 * vert3)) *
(180 / Math.PI);
return deg < maxAngle;
}));
}
public override void Apply(Mat input)
{
_start = DateTime.Now;
Input = input;
if (IsActive)
{
OpenCvSharp.Cv2.CvtColor(InputArray.Create(input), OutputArray.Create(Output), ColorConversionCodes.BGR2HSV);
}
else
{
OpenCvSharp.Cv2.CopyTo(input, Output);
}
base.Apply(input);
}
public static void MatchCorona()
{
Bitmap screen = new Bitmap("Screenshot_0028.bmp");
Bitmap cropped = CompassSensor.Crop(screen, screen.Width * 1 / 3, screen.Height * 1 / 3, screen.Width * 2 / 3, screen.Height * 2 / 3);
Mat screenwhole = BitmapConverter.ToMat(cropped);
// erase the vivid areas, otherwise the blur subtraction turns yellow near red to green
Mat brightHSV = screenwhole.CvtColor(ColorConversionCodes.BGR2HSV);
Mat darkAreasMask = brightHSV.InRange(InputArray.Create(new int[] { 0, 0, 0 }), InputArray.Create(new int[] { 180, 255, 180 }));
Mat darkAreas = new Mat();
screenwhole.CopyTo(darkAreas, darkAreasMask);
Mat screenblur = darkAreas - darkAreas.Blur(new OpenCvSharp.Size(10, 10));
Window w3 = new Window(screenblur);
//screenblur.SaveImage("sharplines.png");
Mat sourceHSV = screenblur.CvtColor(ColorConversionCodes.BGR2HSV);
/* Paint.Net uses HSV [0..360], [0..100], [0..100].
* OpenCV uses H: 0 - 180, S: 0 - 255, V: 0 - 255
* Paint.NET colors:
* 73 100 18 brightest part of green edge
* 72 98 9 very dark green
* suggested range [70..180], [80..100], [8..100] (paint.net)
* suggested range [35..90], [204..255], [20..255] (openCV)
* */
Mat mask = sourceHSV.InRange(InputArray.Create(new int[] { 35, 204, 20 }), InputArray.Create(new int[] { 90, 255, 255 }));
Mat sourceHSVFiltered = new Mat();
sourceHSV.CopyTo(sourceHSVFiltered, mask);
Window w5 = new Window("yellowfilter", sourceHSVFiltered.CvtColor(ColorConversionCodes.HSV2BGR));
Mat sourceGrey = sourceHSVFiltered.Split()[2].InRange(32, 256); // Value channel is pretty good as a greyscale conversion
Window w6 = new Window("yellowFilterValue", sourceGrey);
LineSegmentPoint[] result = sourceGrey.HoughLinesP(1, 3.1415 / 180, 5, 10, 2);
List <Point2d> points = new List <Point2d>();
foreach (var line in result)
{
points.Add(line.P1);
points.Add(line.P2);
darkAreas.Line(line.P1, line.P2, new Scalar(255, 0, 255));
}
CircleSegment c = CruiseSensor.ComputeCircle(points);
darkAreas.Circle(c.Center, (int)c.Radius, new Scalar(255, 255, 0));
Window w9 = new Window("final", darkAreas);
}