private Mat GetHoughLines(Mat imgGrayWB, bool DispB = false)
{
Mat imgGrayWstdI = _01_ReverseBW(imgGrayWB);
List <LineSegmentPoint> segStdLst = Cv2.HoughLinesP(imgGrayWstdI, 1, Math.PI / 180, 20, 10, 10).ToList();
// HoughLinesP(Mat image, double rho, double theta, int threshold, double minLineLength,double maxLineGap)
Mat dstImg = new Mat(imgGrayWB.Size(), MatType.CV_8UC1, Scalar.White);
foreach (LineSegmentPoint s in segStdLst)
{
dstImg.Line(s.P1, s.P2, Scalar.Black, 1, LineTypes.AntiAlias, 0);
}
if (DispB)
{
using (new Window("SourceGray", WindowMode.KeepRatio, imgGrayWB))
using (new Window("DetectedFrame", WindowMode.KeepRatio, dstImg)){ Cv2.WaitKey(); }
}
return(dstImg);
}
static void Main(string[] args)
{
var reader = new AnnotationReader(new DirectoryInfo(@"C:\Library\정올 2017\Source\MPIIGaze\Data\Original\"));
reader.Read();
NoteData ordered = reader.Datas.OrderByDescending(x => x.OnScreenPoint.X).First();
Console.WriteLine($"MaxX: {ordered}");
ordered = reader.Datas.OrderByDescending(x => x.OnScreenPoint.Y).First();
Console.WriteLine($"MaxY: {ordered}");
while (true)
{
Console.Write(">>> ");
var cmd = Console.ReadLine();
switch (cmd)
{
case "show":
bool br = false;
bool skip = false;
bool framebyframe = false;
foreach (var d in reader.Datas)
{
if (br)
{
break;
}
d.ImShow(false);
int sleep = 500;
if (skip)
{
sleep = 1;
}
if (framebyframe)
{
sleep = 0;
}
char c = (char)Cv2.WaitKey(sleep);
switch (c)
{
case ' ':
framebyframe = !framebyframe;
break;
case 's':
skip = !skip;
break;
case 'e':
br = true;
break;
}
}
break;
case "save":
int startInd = -1;
try
{
Console.Write("ind?>>> ");
startInd = Convert.ToInt32(Console.ReadLine());
}
catch
{
}
if (startInd > -1 && startInd < reader.Datas.Count)
{
DirectoryInfo di = new DirectoryInfo(System.IO.Path.Combine(Environment.CurrentDirectory, "save"));
DirectoryInfo diRight = new DirectoryInfo(Path.Combine(di.FullName, "right"));
DirectoryInfo diLeft = new DirectoryInfo(Path.Combine(di.FullName, "left"));
if (!di.Exists)
{
di.Create();
}
else
{
Console.WriteLine("already did");
}
if (!diRight.Exists)
{
diRight.Create();
}
if (!diLeft.Exists)
{
diLeft.Create();
}
List <CascadeClassifier> cascades = new List <CascadeClassifier>();
for (int i = 0; i < Environment.ProcessorCount; i++)
{
cascades.Add(new CascadeClassifier("haarcascade_eye.xml"));
}
object countLocker = new object();
int count = 0;
Parallel.For(startInd, reader.Datas.Count, new ParallelOptions()
{
MaxDegreeOfParallelism = Environment.ProcessorCount
}, (i) =>
{
CascadeClassifier filter;
NoteData d;
int id;
lock (countLocker)
{
count++;
filter = cascades[count % cascades.Count];
id = count - 1 + startInd;
d = reader.Datas[id];
}
using (Mat frame = Cv2.ImRead(d.File.FullName))
{
var rects = filter.DetectMultiScale(frame, 1.2, 3, HaarDetectionType.ScaleImage, new Size(30, 30), new Size(308, 308));
if (d.GetEye(rects, true) != null && d.GetEye(rects, false) != null)
{
d.Save(diLeft, true, frame, rects);
d.Save(diRight, false, frame, rects);
}
}
Console.WriteLine($"Extracted({id})[{count}/{reader.Datas.Count - startInd}] {d}");
});
}
break;
}
}
}
private void th_grab(int height = 0, int width = 0, int snap_wait = 500)
{
try
{
// Set the acquisition mode to free running continuous acquisition when the camera is opened.
camera.CameraOpened += Configuration.AcquireContinuous;
// Open the connection to the camera device.
camera.Open();
if (width == 0 || width > camera.Parameters[PLCamera.Width].GetMaximum())
{
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
}
else if (width < camera.Parameters[PLCamera.Width].GetMinimum())
{
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMinimum());
}
else
{
camera.Parameters[PLCamera.Width].SetValue(width);
}
if (height == 0 || width > camera.Parameters[PLCamera.Height].GetMaximum())
{
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
}
else if (height < camera.Parameters[PLCamera.Height].GetMinimum())
{
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMinimum());
}
else
{
camera.Parameters[PLCamera.Height].SetValue(height);
}
camera.Parameters[PLCamera.CenterX].SetValue(true);
camera.Parameters[PLCamera.CenterY].SetValue(true);
camera.StreamGrabber.Start();
if (saveTracked)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".avi";
videoWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveOrigin)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".origin.avi";
originWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveHisto)
{
var expected = new OpenCvSharp.Size(256, 300);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".histo.avi";
histoWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveHeatmap)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".heatmap.avi";
heatmapWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, true);
}
while (grabbing)
{
camera.Parameters[PLCamera.Gain].SetValue(valueGain);
camera.Parameters[PLCamera.ExposureTime].SetValue(valueExpTime);
IGrabResult grabResult = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (grabResult)
{
if (grabResult.GrabSucceeded)
{
// convert image from basler IImage to OpenCV Mat
Mat img = convertIImage2Mat(grabResult);
// convert image from BayerBG to RGB
Cv2.CvtColor(img, img, ColorConversionCodes.BayerBG2GRAY);
Cv2.Resize(img, img, new OpenCvSharp.Size(1920, 1374), 0, 0, InterpolationFlags.Linear);
Mat histo = new Mat();
Mat heatmap = new Mat();
Mat dst = img.Clone();
// Apply Histogram
histo = cvProcess.histogram(dst);
// Apply ColorMap
Cv2.ApplyColorMap(dst, heatmap, ColormapTypes.Rainbow);
// Apply Background map subtraction
Cv2.Subtract(dst, -5, dst);
if (saveOrigin)
{
originWriter.Write(img);
}
// Create Tracked Image
dst = Iso11146(img, dst);
Cv2.Resize(dst, dst, new OpenCvSharp.Size(1920, 1374), 0, 0, InterpolationFlags.Linear);
if (saveTracked)
{
videoWriter.Write(dst);
}
if (saveHisto)
{
histoWriter.Write(histo);
}
if (saveHeatmap)
{
heatmapWriter.Write(heatmap);
}
// resize image to fit the imageBox
Cv2.Resize(dst, dst, new OpenCvSharp.Size(960, 687), 0, 0, InterpolationFlags.Linear);
Cv2.Resize(heatmap, heatmap, new OpenCvSharp.Size(256, 183), 0, 0, InterpolationFlags.Linear);
Cv2.Rectangle(dst, new OpenCvSharp.Rect(axis_x, axis_y, axis_scale, axis_scale), Scalar.White, 1);
// display images
BitmapToImageSource(dst);
BitmapHistoToImageSource(histo);
BitmapHeatmapToImageSource(heatmap);
}
else
{
System.Windows.MessageBox.Show("Error: {0} {1}" + grabResult.ErrorCode, grabResult.ErrorDescription);
}
}
count++;
if (count > 500)
{
count = 0;
tracking = false;
}
Thread.Sleep(snap_wait);
}
videoWriter.Release();
originWriter.Release();
histoWriter.Release();
heatmapWriter.Release();
camera.StreamGrabber.Stop();
camera.Close();
}
catch (Exception exception)
{
if (camera.IsOpen)
{
camera.Close();
}
System.Windows.MessageBox.Show("Exception: {0}" + exception.Message);
}
}
// close the opencv window
public void OnDestroy()
{
Cv2.DestroyAllWindows();
}
protected override NetResult[] BeginDetect(Bitmap img, float minProbability = 0.3F, string[] labelsFilters = null)
{
//Extract width and height from config file
ExtractValueFromConfig("width", out int widthBlob);
ExtractValueFromConfig("height", out int heightBlob);
using (Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(img))
{
//Create the blob
var blob = CvDnn.BlobFromImage(mat, Scale, size: new OpenCvSharp.Size(widthBlob, heightBlob), crop: false);
//Set blob of a default layer
network.SetInput(blob);
//Get all out layers
string[] outLayers = network.GetUnconnectedOutLayersNames();
//Initialize all blobs for the all out layers
Mat[] result = new Mat[outLayers.Length];
for (int i = 0; i < result.Length; i++)
{
result[i] = new Mat();
}
///Execute all out layers
network.Forward(result, outLayers);
List <NetResult> netResults = new List <NetResult>();
foreach (var item in result)
{
for (int i = 0; i < item.Rows; i++)
{
//Get the max loc and max of the col range by prefix result
Cv2.MinMaxLoc(item.Row[i].ColRange(Prefix, item.Cols), out double min, out double max, out OpenCvSharp.Point minLoc, out OpenCvSharp.Point maxLoc);
//Validate the min probability
if (max >= minProbability)
{
//The label is the max Loc
string label = Labels[maxLoc.X];
if (labelsFilters != null)
{
if (!labelsFilters.Contains(label))
{
continue;
}
}
//The probability is the max value
double probability = max;
//Center BoundingBox X is the 0 index result
int centerX = Convert.ToInt32(item.At <float>(i, 0) * (float)mat.Width);
//Center BoundingBox X is the 1 index result
int centerY = Convert.ToInt32(item.At <float>(i, 1) * (float)mat.Height);
//Width BoundingBox is the 2 index result
int width = Convert.ToInt32(item.At <float>(i, 2) * (float)mat.Width);
//Height BoundingBox is the 2 index result
int height = Convert.ToInt32(item.At <float>(i, 3) * (float)mat.Height);
//Build NetResult
netResults.Add(NetResult.Build(centerX, centerY, width, height, label, probability));
}
}
}
return(netResults.ToArray());
}
}
static void Main()
{
var img = Cv2.ImRead(_input);
Catalog = CatalogUtil.ReadCatalogItems(_catalogPath);
var fileTuples = new List <(string input, string output)> {
(_input, _output)
};
string modelFile = _modelPath;
using (var graph = new TFGraph())
{
var model = File.ReadAllBytes(modelFile);
graph.Import(new TFBuffer(model));
using (var session = new TFSession(graph))
{
foreach (var tuple in fileTuples)
{
var tensor = ImageUtil.CreateTensorFromImageFile(tuple.input, TFDataType.UInt8);
var runner = session.GetRunner();
runner
.AddInput(graph["image_tensor"][0], tensor)
.Fetch(
graph["detection_boxes"][0],
graph["detection_scores"][0],
graph["detection_classes"][0],
graph["num_detections"][0]);
Stopwatch sw = new Stopwatch();
sw.Start();
var output = runner.Run();
sw.Stop();
Console.WriteLine($"Runtime:{sw.ElapsedMilliseconds} ms");
var boxes = (float[, , ])output[0].GetValue(jagged: false);
var scores = (float[, ])output[1].GetValue(jagged: false);
var classes = (float[, ])output[2].GetValue(jagged: false);
var num = (float[])output[3].GetValue(jagged: false);
#region show image
for (int i = 0; i < boxes.GetLength(1); i++)
{
if (scores[0, i] > 0.5)
{
var idx = Convert.ToInt32(classes[0, i]);
var x1 = (int)(boxes[0, i, 1] * img.Width);
var y1 = (int)(boxes[0, i, 0] * img.Height);
var x2 = (int)(boxes[0, i, 3] * img.Width);
var y2 = (int)(boxes[0, i, 2] * img.Height);
var catalog = Catalog.First(x => x.Id == idx);
string label = $"{(catalog == null ? idx.ToString() : catalog.DisplayName)}: {scores[0, i] * 100:0.00}%";
Console.WriteLine($"{label} {x1} {y1} {x2} {y2}");
Cv2.Rectangle(img, new Rect(x1, y1, x2 - x1, y2 - y1), Scalar.Red, 2);
int baseline;
var textSize = Cv2.GetTextSize(label, HersheyFonts.HersheyTriplex, 0.5, 1, out baseline);
textSize.Height = textSize.Height + baseline / 2;
var y = y1 - textSize.Height < 0 ? y1 + textSize.Height : y1;
Cv2.Rectangle(img, new Rect(x1, y - textSize.Height, textSize.Width, textSize.Height + baseline / 2), Scalar.Red, Cv2.FILLED);
Cv2.PutText(img, label, new Point(x1, y), HersheyFonts.HersheyTriplex, 0.5, Scalar.Black);
}
}
#endregion
}
}
}
using (new Window("image", img))
{
Cv2.WaitKey();
}
}
}
// Simple example of canny edge detect
void ProcessImage(Mat _image)
{
Cv2.Flip(_image, _image, FlipMode.X);
Cv2.Canny(_image, cannyImage, 100, 100);
var grayImage = new Mat();
Cv2.CvtColor(_image, grayImage, ColorConversionCodes.BGR2GRAY);
Cv2.EqualizeHist(grayImage, grayImage);
var face_cascade = new CascadeClassifier();
face_cascade.Load(Application.dataPath + "/Plugins/Classifiers/haarcascade_frontalface_default.xml");
var eye_cascade = new CascadeClassifier();
eye_cascade.Load(Application.dataPath + "/Plugins/Classifiers/haarcascade_eye_tree_eyeglasses.xml");
var mouth_cascade = new CascadeClassifier();
mouth_cascade.Load(Application.dataPath + "/Plugins/Classifiers/haarcascade_mcs_mouth.xml");
//Debug.Log(" ");
var faces = face_cascade.DetectMultiScale(
image: grayImage,
scaleFactor: 1.3,
minNeighbors: 5,
flags: HaarDetectionType.DoRoughSearch | HaarDetectionType.ScaleImage,
minSize: new Size(100, 100)
);
//Bounds meshRendererBounds = GetComponentInChildren<MeshRenderer>().bounds;
//Vector3 meshRendererCenter = meshRendererBounds.center;
//Vector3 maxBound = meshRendererBounds.max;
//Vector3 minBound = meshRendererBounds.min;
//OpenCvSharp.Rect rect = new OpenCvSharp.Rect((int)meshRendererCenter.x + 350,(int)meshRendererCenter.y + 50, 600,600);
var global_rectangle_color = Scalar.FromRgb(0, 0, 255);
//Cv2.Rectangle(_image, rect, global_rectangle_color, 3);
//Console.WriteLine("Detected faces: {0}", faces.Length);
//Debug.Log(faces.Length);
//var rnd = new System.Random();
var face_count = 0;
var mouth_count = 0;
var eye_count = 0;
foreach (var faceRect in faces)
{
var detectedFaceImage = new Mat(_image, faceRect);
//Cv2.ImShow(string.Format("Face {0}", face_count), detectedFaceImage);
//Cv2.WaitKey(1); // do events
var facec_rectangle_color = Scalar.FromRgb(255, 0, 0);
face = faceRect;
Cv2.Rectangle(_image, faceRect, facec_rectangle_color, 3);
rectFront = new OpenCvSharp.Rect(faceRect.X + faceRect.Width / 2 - 50, faceRect.Y + 50, 100, 50);
//Cv2.Rectangle(_image, rectFront, global_rectangle_color, 3);
var detectedFaceGrayImage = new Mat();
Cv2.CvtColor(detectedFaceImage, detectedFaceGrayImage, ColorConversionCodes.BGRA2GRAY);
var eyes = eye_cascade.DetectMultiScale(
image: grayImage,
scaleFactor: 1.3,
minNeighbors: 5,
flags: HaarDetectionType.DoRoughSearch | HaarDetectionType.ScaleImage,
minSize: new Size(50, 50)
);
foreach (var eyeRect in eyes)
{
var detectedEyeImage = new Mat(_image, eyeRect);
//Cv2.ImShow(string.Format("Face {0}", eye_count), detectedEyeImage);
//Cv2.WaitKey(1); // do events
var eye_rectangle_color = Scalar.FromRgb(0, 255, 0);
//Cv2.Rectangle(_image, eyeRect, eye_rectangle_color, 3);
if (eye_count == 1)
{
// Par rapport à la position de l'oeil gauche
rectEyeLeft = new OpenCvSharp.Rect(eyeRect.X + 75, eyeRect.Y + 100, 25, 25);
//Cv2.Rectangle(_image, rectEyeLeft, global_rectangle_color, 3);
}
else
{
// Par rapport à la position de l'oeil droit
rectEyeRight = new OpenCvSharp.Rect(eyeRect.X, eyeRect.Y + 100, 25, 25);
//Cv2.Rectangle(_image, rectEyeRight, global_rectangle_color, 3);
}
var detectedEyeGrayImage = new Mat();
Cv2.CvtColor(detectedEyeImage, detectedEyeGrayImage, ColorConversionCodes.BGRA2GRAY);
eye_count++;
}
var mouth = mouth_cascade.DetectMultiScale(
image: grayImage,
scaleFactor: 1.3,
minNeighbors: 5,
flags: HaarDetectionType.DoRoughSearch | HaarDetectionType.ScaleImage,
minSize: new Size(50, 50)
);
foreach (var m in mouth)
{
var detectedEarImage = new Mat(_image, m);
//Cv2.ImShow(string.Format("Face {0}", eye_count), detectedEyeImage);
//Cv2.WaitKey(1); // do events
if (m.Y > eyes[0].Y && Mathf.Abs(m.Y - eyes[0].Y) > 100)
{
//Debug.Log("mouth height :");
//Debug.Log(m.Height);
var eye_rectangle_color = Scalar.FromRgb(0, 255, 0);
Cv2.Rectangle(_image, m, eye_rectangle_color, 3);
lipHeight = (float)m.Height / (float)face.Height;
}
var detectedEyeGrayImage = new Mat();
Cv2.CvtColor(detectedEarImage, detectedEyeGrayImage, ColorConversionCodes.BGRA2GRAY);
mouth_count++;
}
face_count++;
}
}
static void Main()
{
#region parameter
var image = Path.Combine(Location, "kite.jpg");
var cfg = Path.Combine(Location, Cfg);
var model = Path.Combine(Location, Weight);
const float threshold = 0.5f; //for confidence
const float nmsThreshold = 0.3f; //threshold for nms
#endregion
//get image
var org = new Mat(image);
//setting blob, size can be:320/416/608
//opencv blob setting can check here https://github.com/opencv/opencv/tree/master/samples/dnn#object-detection
var blob = CvDnn.BlobFromImage(org, 1.0 / 255, new Size(416, 416), new Scalar(), true, false);
//load model and config, if you got error: "separator_index < line.size()", check your cfg file, must be something wrong.
var net = CvDnn.ReadNetFromDarknet(cfg, model);
#region set preferable
net.SetPreferableBackend(3);
/*
* 0:DNN_BACKEND_DEFAULT
* 1:DNN_BACKEND_HALIDE
* 2:DNN_BACKEND_INFERENCE_ENGINE
* 3:DNN_BACKEND_OPENCV
*/
net.SetPreferableTarget(0);
/*
* 0:DNN_TARGET_CPU
* 1:DNN_TARGET_OPENCL
* 2:DNN_TARGET_OPENCL_FP16
* 3:DNN_TARGET_MYRIAD
* 4:DNN_TARGET_FPGA
*/
#endregion
//input data
net.SetInput(blob);
//get output layer name
var outNames = net.GetUnconnectedOutLayersNames();
//create mats for output layer
var outs = outNames.Select(_ => new Mat()).ToArray();
#region forward model
Stopwatch sw = new Stopwatch();
sw.Start();
net.Forward(outs, outNames);
sw.Stop();
Console.WriteLine($"Runtime:{sw.ElapsedMilliseconds} ms");
#endregion
//get result from all output
GetResult(outs, org, threshold, nmsThreshold);
using (new Window("died.tw", org))
{
Cv2.WaitKey();
}
}
public SURFSample()
{
// cvExtractSURF
// call cv::initModule_nonfree() before using SURF/SIFT.
Cv2.InitModule_NonFree();
using (IplImage obj = Cv.LoadImage(FilePath.Image.SurfBox, LoadMode.GrayScale))
using (IplImage image = Cv.LoadImage(FilePath.Image.SurfBoxinscene, LoadMode.GrayScale))
using (IplImage objColor = Cv.CreateImage(obj.Size, BitDepth.U8, 3))
using (IplImage correspond = Cv.CreateImage(new CvSize(image.Width, obj.Height + image.Height), BitDepth.U8, 1))
{
Cv.CvtColor(obj, objColor, ColorConversion.GrayToBgr);
Cv.SetImageROI(correspond, new CvRect(0, 0, obj.Width, obj.Height));
Cv.Copy(obj, correspond);
Cv.SetImageROI(correspond, new CvRect(0, obj.Height, correspond.Width, correspond.Height));
Cv.Copy(image, correspond);
Cv.ResetImageROI(correspond);
CvSURFPoint[] objectKeypoints, imageKeypoints;
float[][] objectDescriptors, imageDescriptors;
Stopwatch watch = Stopwatch.StartNew();
{
CvSURFParams param = new CvSURFParams(500, true);
Cv.ExtractSURF(obj, null, out objectKeypoints, out objectDescriptors, param);
Console.WriteLine("Object Descriptors: {0}", objectDescriptors.Length);
Cv.ExtractSURF(image, null, out imageKeypoints, out imageDescriptors, param);
Console.WriteLine("Image Descriptors: {0}", imageDescriptors.Length);
}
watch.Stop();
Console.WriteLine("Extraction time = {0}ms", watch.ElapsedMilliseconds);
watch.Reset();
watch.Start();
CvPoint[] srcCorners = new CvPoint[4]
{
new CvPoint(0, 0), new CvPoint(obj.Width, 0), new CvPoint(obj.Width, obj.Height), new CvPoint(0, obj.Height)
};
CvPoint[] dstCorners = LocatePlanarObject(objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors, srcCorners);
if (dstCorners != null)
{
for (int i = 0; i < 4; i++)
{
CvPoint r1 = dstCorners[i % 4];
CvPoint r2 = dstCorners[(i + 1) % 4];
Cv.Line(correspond, new CvPoint(r1.X, r1.Y + obj.Height), new CvPoint(r2.X, r2.Y + obj.Height), CvColor.White);
}
}
// 対応点同士を線で引く
int[] ptPairs = FindPairs(objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors);
for (int i = 0; i < ptPairs.Length; i += 2)
{
CvSURFPoint r1 = objectKeypoints[ptPairs[i]];
CvSURFPoint r2 = imageKeypoints[ptPairs[i + 1]];
Cv.Line(correspond, r1.Pt, new CvPoint(Cv.Round(r2.Pt.X), Cv.Round(r2.Pt.Y + obj.Height)), CvColor.White);
}
// 特徴点の場所に円を描く
for (int i = 0; i < objectKeypoints.Length; i++)
{
CvSURFPoint r = objectKeypoints[i];
CvPoint center = new CvPoint(Cv.Round(r.Pt.X), Cv.Round(r.Pt.Y));
int radius = Cv.Round(r.Size * (1.2 / 9.0) * 2);
Cv.Circle(objColor, center, radius, CvColor.Red, 1, LineType.AntiAlias, 0);
}
watch.Stop();
Console.WriteLine("Drawing time = {0}ms", watch.ElapsedMilliseconds);
using (CvWindow windowObject = new CvWindow("Object", WindowMode.AutoSize))
using (CvWindow windowCorrespond = new CvWindow("Object Correspond", WindowMode.AutoSize))
{
windowObject.ShowImage(correspond);
windowCorrespond.ShowImage(objColor);
Cv.WaitKey(0);
}
}
}
private void _ClearFrame(Mat Img, int sf = 4, bool DispB = false)
{
if (DispB)
{
using (new Window("Before", WindowMode.KeepRatio, Img)){ Cv2.WaitKey(0); }
}
int W = Img.Width, H = Img.Height;
byte b128 = (byte)128, b255 = (byte)255;
Queue <int> Que = new Queue <int>();
unsafe {
byte *S0 = Img.DataPointer;
int rc;
for (int r = 0; r < H; r++)
{
if (r < sf || r >= H - sf)
{
for (int c = 0; c < W; c++)
{
rc = r * W + c;
if (S0[rc] < b128)
{
S0[rc] = b255;
if (r > 0 && S0[rc - W] < b128)
{
Que.Enqueue(rc - W);
}
if (r < H - 1 && S0[rc + W] < b128)
{
Que.Enqueue(rc + W);
}
if (c > 0 && S0[rc - 1] < b128)
{
Que.Enqueue(rc - 1);
}
if (c < W - 1 && S0[rc + 1] < b128)
{
Que.Enqueue(rc + 1);
}
}
}
}
else
{
for (int c = 0; c < sf; c++)
{
rc = r * W + c;
if (S0[rc] < b128)
{
S0[rc] = b255;
if (r > 0 && S0[rc - W] < b128)
{
Que.Enqueue(rc - W);
}
if (r < H - 1 && S0[rc + W] < b128)
{
Que.Enqueue(rc + W);
}
if (c > 0 && S0[rc - 1] < b128)
{
Que.Enqueue(rc - 1);
}
if (c < W - 1 && S0[rc + 1] < b128)
{
Que.Enqueue(rc + 1);
}
}
}
for (int c = W - sf; c < W; c++)
{
rc = r * W + c;
if (S0[rc] < b128)
{
S0[rc] = b255;
if (r > 0 && S0[rc - W] < b128)
{
Que.Enqueue(rc - W);
}
if (r < H - 1 && S0[rc + W] < b128)
{
Que.Enqueue(rc + W);
}
if (c > 0 && S0[rc - 1] < b128)
{
Que.Enqueue(rc - 1);
}
if (c < W - 1 && S0[rc + 1] < b128)
{
Que.Enqueue(rc + 1);
}
}
}
}
}
while (Que.Count > 0)
{
rc = Que.Dequeue();
*(S0 + rc) = b255;
int r = rc / W, c = rc % W;
if (S0[rc] < b128)
{
S0[rc] = b255;
if (r > 0 && S0[rc - W] < b128)
{
Que.Enqueue(rc - W);
}
if (r < H - 1 && S0[rc + W] < b128)
{
Que.Enqueue(rc + W);
}
if (c > 0 && S0[rc - 1] < b128)
{
Que.Enqueue(rc - 1);
}
if (c < W - 1 && S0[rc + 1] < b128)
{
Que.Enqueue(rc + 1);
}
}
}
if (DispB)
{
using (new Window("After", WindowMode.KeepRatio, Img)){ Cv2.WaitKey(0); }
}
}
}
/// <summary>
/// Get result form all output
/// </summary>
/// <param name="output"></param>
/// <param name="image"></param>
/// <param name="threshold"></param>
/// <param name="nmsThreshold">threshold for nms</param>
/// <param name="nms">Enable Non-maximum suppression or not</param>
private static void GetResult(IEnumerable <Mat> output, Mat image, float threshold, float nmsThreshold, bool nms = true)
{
//for nms
var classIds = new List <int>();
var confidences = new List <float>();
var probabilities = new List <float>();
var boxes = new List <Rect2d>();
var w = image.Width;
var h = image.Height;
/*
* YOLO3 COCO trainval output
* 0 1 : center 2 3 : w/h
* 4 : confidence 5 ~ 84 : class probability
*/
const int prefix = 5; //skip 0~4
foreach (var prob in output)
{
for (var i = 0; i < prob.Rows; i++)
{
var confidence = prob.At <float>(i, 4);
if (confidence > threshold)
{
//get classes probability
Cv2.MinMaxLoc(prob.Row[i].ColRange(prefix, prob.Cols), out _, out Point max);
var classes = max.X;
var probability = prob.At <float>(i, classes + prefix);
if (probability > threshold) //more accuracy, you can cancel it
{
//get center and width/height
var centerX = prob.At <float>(i, 0) * w;
var centerY = prob.At <float>(i, 1) * h;
var width = prob.At <float>(i, 2) * w;
var height = prob.At <float>(i, 3) * h;
if (!nms)
{
// draw result (if don't use NMSBoxes)
Draw(image, classes, confidence, probability, centerX, centerY, width, height);
continue;
}
//put data to list for NMSBoxes
classIds.Add(classes);
confidences.Add(confidence);
probabilities.Add(probability);
boxes.Add(new Rect2d(centerX, centerY, width, height));
}
}
}
}
if (!nms)
{
return;
}
//using non-maximum suppression to reduce overlapping low confidence box
CvDnn.NMSBoxes(boxes, confidences, threshold, nmsThreshold, out int[] indices);
Console.WriteLine($"NMSBoxes drop {confidences.Count - indices.Length} overlapping result.");
foreach (var i in indices)
{
var box = boxes[i];
Draw(image, classIds[i], confidences[i], probabilities[i], box.X, box.Y, box.Width, box.Height);
}
}
public int[] sdkFrameRecg_SolverV3(Mat imgWhiteB, byte thrVal, bool DispB = false)
{
//___PrintTimeSpan("start");
Mat imgBlackW = _01_ReverseBW(imgWhiteB);
Point2d[] Q4 = null;
stdImg = _01_DetectGrid4(imgWhiteB, imgBlackW, thrVal, out Q4, DispB: false); //Binarization: background white, object black
//___PrintTimeSpan("_01_DetectGrid4");
if (Q4 == null)
{
return(null);
}
if (DispB)
{
using (new Window("_0A_StandardFrame", WindowMode.KeepRatio, stdImg)){ Cv2.WaitKey(0); }
}
Mat stdImg2 = GetHoughLines(stdImg);
//___PrintTimeSpan("GetHoughLines");
Point2d[] Q16 = _0_DetectGrid16(stdImg2, Q4, DispB: false); //Frame distortion adjustment
//___PrintTimeSpan("_0_DetectGrid16");
Point2d[] Q100 = _0_DetectGrid100Ex(stdImg2, Q16, DispB: false);
//___PrintTimeSpan("_0_DetectGrid100Ex");
int[] SDK8 = _0_digitRecognition100(stdImg, Q100, DispB: false);//true); //Result display
//___PrintTimeSpan("_0_digitRecognition100");
return(SDK8); //stdImg;
}
private Point2d[] _0_DetectGrid100Ex(Mat stdImg, Point2d[] Q16, bool DispB)
{
double a = 1.0 / 3.0, b = 2.0 / 3.0;
var Q100 = new Point2d[100];
for (int k = 0; k < 16; k++)
{
Q100[(k / 4 * 30) + (k % 4) * 3] = Q16[k];
}
for (int r = 0; r <= 10; r += 3)
{
for (int c = 0; c < 9; c += 3)
{
int rc = r * 10 + c;
Q100[rc + 1] = _2_Get_InterPolation(Q100[rc], Q100[rc + 3], a);
Q100[rc + 2] = _2_Get_InterPolation(Q100[rc], Q100[rc + 3], b);
}
}
for (int c = 0; c <= 9; c += 3)
{
for (int r = 0; r < 9; r += 3)
{
int rc = (r) * 10 + c;
Q100[rc + 10] = _2_Get_InterPolation(Q100[rc], Q100[rc + 30], a);
Q100[rc + 20] = _2_Get_InterPolation(Q100[rc], Q100[rc + 30], b);
}
}
Mat Q100Mat = stdImg.CvtColor(ColorConversionCodes.GRAY2BGR); //####
foreach (var P in Q100.Where(p => p.X > 0))
{
Q100Mat.Circle((Point)P, 3, Scalar.Green, 2); //####
}
double evMax = 0.0;
for (int loop = 0; loop < 10; loop++) //####
{
double ev = 0.0;
for (int bk = 0; bk < 9; bk++)
{
int rc0 = (bk / 3) * 30 + (bk % 3) * 3;
ev += AdjustGrid81(stdImg, Q100, rc0 + 1, rc0 + 0, rc0 + 3, rc0 + 31, rc0 + 30, rc0 + 33);
ev += AdjustGrid81(stdImg, Q100, rc0 + 2, rc0 + 0, rc0 + 3, rc0 + 32, rc0 + 30, rc0 + 33);
ev += AdjustGrid81(stdImg, Q100, rc0 + 10, rc0 + 0, rc0 + 30, rc0 + 13, rc0 + 3, rc0 + 33);
ev += AdjustGrid81(stdImg, Q100, rc0 + 20, rc0 + 0, rc0 + 30, rc0 + 23, rc0 + 3, rc0 + 33);
}
//WriteLine("### AdjustGrid81 ev:{0} evMax:{1}", ev, evMax );
if (ev <= evMax)
{
break;
}
evMax = ev;
}
if (DispB)
{
foreach (var P in Q100.Where(p => p.X > 0))
{
Q100Mat.Circle((Point)P, 5, Scalar.Red, 2); //####
}
using (new Window("_0_DetectGrid100Ex", WindowMode.KeepRatio, Q100Mat)){ Cv2.WaitKey(0); } //####
}
return(Q100);
}
public int[] _01B_DetectDigits100(Mat frame2, Point2d[] Q100, bool DispBres = false)
{
Point[] Q4W = new Point[4];
for (int r = 1; r < 9; r++)
{
if ((r % 3) == 0)
{
continue;
}
int r0 = r / 3 * 3;
for (int c = 1; c < 9; c++)
{
if ((c % 3) == 0)
{
continue;
}
int c0 = c / 3 * 3;
Q100[r * 10 + c] = crossPoint(Q100[r * 10 + c0], Q100[r * 10 + c0 + 3], Q100[r0 * 10 + c], Q100[(r0 + 3) * 10 + c]);
}
}
int[] SB81 = new int[81];
for (int rc = 0; rc < 81; rc++)
{
int r = rc / 9, c = rc % 9, rcA = r * 10 + c;
Q4W[0] = (Point)Q100[rcA]; Q4W[1] = (Point)Q100[rcA + 1]; Q4W[2] = (Point)Q100[rcA + 11]; Q4W[3] = (Point)Q100[rcA + 10];
//Inverse projection transformation
Mat reaRecog9 = _2B_SDK_ProjectivTransformInv(frame2, Q4W, DispB: false); //##@@2
//Sudoku digits recognition
int Q = _2B_DigitRecognition(reaRecog9, rc0: rc, resDispB: true); //$$$$$$$$
SB81[rc] = Q;
if (folderName != null && Q == 99)
{
if (!Directory.Exists(folderName))
{
Directory.CreateDirectory(folderName);
}
string fName = folderName + "/" + DateTime.Now.ToString("yyMMdd_HHmmssfff") + ".jpg";
Cv2.ImWrite(fName, reaRecog9);
}
/*
* if(DispBres || Q==99){
* using( new Window("DDigit rc:"+rc,WindowMode.KeepRatio,reaRecog9) ){ Cv2.WaitKey(0); }
*
* }
*/
}
if (DispBres)
{
string st = "SDK_ImageRecogEx2 ";
for (int rc = 0; rc < 81; rc++)
{
int sb = SB81[rc];
if (sb == 10)
{
st += " .";
}
else if (sb == 0 || sb > 10)
{
st += " X";
}
else
{
st += " " + SB81[rc];
}
if ((rc % 9) == 8)
{
WriteLine(st); st = "SDK_ImageRecogEx2 ";
}
}
WriteLine();
}
return(SB81);
}
private void button1_Click(object sender, EventArgs e)
{
int imgWidth = Convert.ToInt32(textBox1.Text);
int blankPx = Convert.ToInt32(textBox2.Text);
Mat srcImage1 = new Mat();
Mat blankImg = new Mat(new OpenCvSharp.Size(imgWidth, blankPx), MatType.CV_8UC3,
new Scalar(textBox3.BackColor.B, textBox3.BackColor.G, textBox3.BackColor.R));
Mat board;
if (!Directory.Exists("Output"))
{
Directory.CreateDirectory("Output");
}
CommonOpenFileDialog dialog = new CommonOpenFileDialog();
dialog.IsFolderPicker = true;
if (dialog.ShowDialog() == CommonFileDialogResult.Ok)
{
try
{
string[] dirs = Directory.GetDirectories(dialog.FileName);
int Count = 0;
foreach (var XX in dirs)
{
DirectoryInfo di = new DirectoryInfo(XX);
board = new Mat();
toolStripProgressBar1.Maximum = dirs.Length;
int total = dirs.Length;
var items = di.GetFiles("*.*").Where(s => s.Name.EndsWith(".jpg") || s.Name.EndsWith(".png") || s.Name.EndsWith(".bmp"));
foreach (var item in items)
{
Application.DoEvents();
Invoke((MethodInvoker)(() => toolStripStatusLabel1.Text = $"작업목록: {di.Name}"));
//Console.WriteLine(item.Name);
srcImage1 = Cv2.ImRead(item.FullName);
double ratio = imgWidth / (double)srcImage1.Width;
Cv2.Resize(srcImage1, srcImage1, new OpenCvSharp.Size(0, 0), ratio, ratio, InterpolationFlags.Area);
if (board.Width != imgWidth)
{
Cv2.VConcat(srcImage1, blankImg, board);
}
else
{
Cv2.VConcat(srcImage1, blankImg, srcImage1);
Cv2.VConcat(board, srcImage1, board);
}
srcImage1.Dispose();
Cv2.ImWrite($"Output\\{di.Name}.png", board);
}
toolStripProgressBar1.Value = ++Count;
board.Dispose();
}
}
catch (Exception ex)
{
Console.WriteLine("Error: " + ex.Message);
}
}
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
List <Mat> grayImages = new List <Mat>();
List <Mat> unwarpedImages = new List <Mat>();
List <Mat> filtered = new List <Mat>();
List <Mat> processedSquares = new List <Mat>();
List <Mat> trainingData = new List <Mat>();
string[] imgNames =
{
"7_a.jpg",
"7_b.jpg",
"1_a.jpg",
"1_b.jpg",
"2_a.jpg",
"2_b.jpg",
"3_a.jpg",
"3_b.jpg",
"4_a.jpg",
"4_b.jpg",
"5_a.jpg",
"5_b.jpg",
"6_a.jpg",
"6_b.jpg",
"8_a.jpg",
"8_b.jpg",
"9_a.jpg",
"9_b.jpg"
};
var imganalyzer = new SudokuFinder();
var gray = new Mat();
List <int> intList = new List <int>();
foreach (var s in imgNames)
{
var src = Cv2.ImRead(@".\Resources\" + s);
imganalyzer.FindSudoku(src);
var data = imganalyzer.ProcessPuzzle(imganalyzer.unwarpedSudoku);
grayImages.Add(src);
filtered.Add(imganalyzer.unwarpedSudoku.Clone());
processedSquares.Add(concatPUzzle(data));
trainingData.AddRange(data);
int x = Int32.Parse(s[0].ToString());
var myArray = new List <int>();
for (int i = 0; i < 81; i++)
{
myArray.Add(x);
}
intList.AddRange(myArray);
}
intList.Add(0);
List <double[]> TrainginData = new List <double[]>();
int size = 0;
foreach (var d in trainingData)
{
size = d.Size().Height *d.Size().Height;
byte[] managedArray = new byte[size];
Marshal.Copy(d.Data, managedArray, 0, size);
TrainginData.Add(Array.ConvertAll(managedArray, c => c != 0 ? (double)1 : 0));
}
TrainginData.Add(new double[size]);
var saved = imganalyzer.trainSVM(TrainginData.ToArray(), intList.ToArray());
double sizeb = ((float)saved.Count()) / 1000000.0;
//Test
imganalyzer = new SudokuFinder(saved);
var src2 = Cv2.ImRead(@".\Resources\2.jpg");
imganalyzer.FindSudoku(src2);
var data2 = imganalyzer.ProcessPuzzle(imganalyzer.unwarpedSudoku);
var ans2 = imganalyzer.OCR(data2);
Cv2.ImShow("Source", imganalyzer.unwarpedSudoku);
Cv2.ImShow("newbox", src2);
Cv2.ImShow("data2", concatPUzzle(data2));
Application.Run();
Console.ReadKey();
}
public static Bitmap CaptureScreenArea(Rectangle captureArea)
{
var bmp = new Bitmap(captureArea.Width, captureArea.Height, PixelFormat.Format32bppArgb);
var g = Graphics.FromImage(bmp);
g.CopyFromScreen(captureArea.Left, captureArea.Top, 0, 0, bmp.Size, CopyPixelOperation.SourceCopy);
var src = OpenCvSharp.Extensions.BitmapConverter.ToMat(bmp);
var vertices = new List <List <Point> >
{
new List <Point>()
{
new Point(10, 500)
},
new List <Point>()
{
new Point(10, 300)
},
new List <Point>()
{
new Point(300, 200)
},
new List <Point>()
{
new Point(500, 200)
},
new List <Point>()
{
new Point(700, 300)
},
new List <Point>()
{
new Point(700, 500)
}
};
//src = src.CvtColor(ColorConversionCodes.RGB2RGBA);
src.FillPoly(vertices, Scalar.Pink);
src.AdjustROI(200, 200, 200, 200);
src = src.CvtColor(ColorConversionCodes.BGR2GRAY);
src = src.Canny(200, 300);
src = src.GaussianBlur(new OpenCvSharp.Size(5, 5), 0);
var lines = src.HoughLinesP(1, Math.PI / 180, 50, 30, 15);
var dst = src.EmptyClone();
foreach (var line in lines)
{
Cv2.Line(dst, line.P1, line.P2, Scalar.Cyan, 2);
}
return(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst));
//var mask = src.EmptyClone();
//Cv2.BitwiseAnd(src, mask, OutputArray.Create(src));
//var dst = new Mat();
//src.CopyTo(dst);
//src = src.GaussianBlur(new OpenCvSharp.Size(7, 9),0);
//src = src.CvtColor(ColorConversionCodes.BGR2GRAY);
//src = src.Canny(50, 75);
//var channels=src.Split();
//var lines = src.HoughLinesP(1, Math.PI / 180, 50);
//var Blank = new Mat(src.Rows, src.Cols, MatType.CV_8UC3, new Scalar(0, 0, 0));
//foreach (var line in lines)
//{
// //Cv2.Line(src, line.P1, line.P2, new Scalar(0, 0, 0), 2, LineTypes.AntiAlias);
// Cv2.Line(dst, line.P1, line.P2, new Scalar(255, 255, 255), 2, LineTypes.AntiAlias);
//}
//src = src.AdjustROI(100, 640, 100, 700);
}
public void Run()
{
Mat img = Cv2.ImRead(FilePath.Lenna, LoadMode.GrayScale);
// expand input image to optimal size
Mat padded = new Mat();
int m = Cv2.GetOptimalDFTSize(img.Rows);
int n = Cv2.GetOptimalDFTSize(img.Cols); // on the border add zero values
Cv2.CopyMakeBorder(img, padded, 0, m - img.Rows, 0, n - img.Cols, BorderType.Constant, Scalar.All(0));
// Add to the expanded another plane with zeros
Mat paddedF32 = new Mat();
padded.ConvertTo(paddedF32, MatType.CV_32F);
Mat[] planes = { paddedF32, Mat.Zeros(padded.Size(), MatType.CV_32F) };
Mat complex = new Mat();
Cv2.Merge(planes, complex);
// this way the result may fit in the source matrix
Mat dft = new Mat();
Cv2.Dft(complex, dft);
// compute the magnitude and switch to logarithmic scale
// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
Mat[] dftPlanes;
Cv2.Split(dft, out dftPlanes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
// planes[0] = magnitude
Mat magnitude = new Mat();
Cv2.Magnitude(dftPlanes[0], dftPlanes[1], magnitude);
magnitude += Scalar.All(1); // switch to logarithmic scale
Cv2.Log(magnitude, magnitude);
// crop the spectrum, if it has an odd number of rows or columns
Mat spectrum = magnitude[
new Rect(0, 0, magnitude.Cols & -2, magnitude.Rows & -2)];
// rearrange the quadrants of Fourier image so that the origin is at the image center
int cx = spectrum.Cols / 2;
int cy = spectrum.Rows / 2;
Mat q0 = new Mat(spectrum, new Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant
Mat q1 = new Mat(spectrum, new Rect(cx, 0, cx, cy)); // Top-Right
Mat q2 = new Mat(spectrum, new Rect(0, cy, cx, cy)); // Bottom-Left
Mat q3 = new Mat(spectrum, new Rect(cx, cy, cx, cy)); // Bottom-Right
// swap quadrants (Top-Left with Bottom-Right)
Mat tmp = new Mat();
q0.CopyTo(tmp);
q3.CopyTo(q0);
tmp.CopyTo(q3);
// swap quadrant (Top-Right with Bottom-Left)
q1.CopyTo(tmp);
q2.CopyTo(q1);
tmp.CopyTo(q2);
// Transform the matrix with float values into a
Cv2.Normalize(spectrum, spectrum, 0, 1, NormType.MinMax);
// Show the result
Cv2.ImShow("Input Image", img);
Cv2.ImShow("Spectrum Magnitude", spectrum);
// calculating the idft
Mat inverseTransform = new Mat();
Cv2.Dft(dft, inverseTransform, DftFlag2.Inverse | DftFlag2.RealOutput);
Cv2.Normalize(inverseTransform, inverseTransform, 0, 1, NormType.MinMax);
Cv2.ImShow("Reconstructed by Inverse DFT", inverseTransform);
Cv2.WaitKey();
}
public void DoOCR(KNearest kNearest, string path)
{
var src = Cv2.ImRead(path);
Cv2.ImShow("Source", src);
var gray = new Mat();
Cv2.CvtColor(src, gray, ColorConversionCodes.BGRA2GRAY);
var threshImage = new Mat();
Cv2.Threshold(gray, threshImage, Thresh, ThresholdMaxVal, ThresholdTypes.BinaryInv); // Threshold to find contour
Point[][] contours;
HierarchyIndex[] hierarchyIndexes;
Cv2.FindContours(
threshImage,
out contours,
out hierarchyIndexes,
mode: RetrievalModes.CComp,
method: ContourApproximationModes.ApproxSimple);
if (contours.Length == 0)
{
throw new NotSupportedException("Couldn't find any object in the image.");
}
//Create input sample by contour finding and cropping
var dst = new Mat(src.Rows, src.Cols, MatType.CV_8UC3, Scalar.All(0));
var contourIndex = 0;
while ((contourIndex >= 0))
{
var contour = contours[contourIndex];
var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contour
Cv2.Rectangle(src,
new Point(boundingRect.X, boundingRect.Y),
new Point(boundingRect.X + boundingRect.Width, boundingRect.Y + boundingRect.Height),
new Scalar(0, 0, 255),
2);
var roi = new Mat(threshImage, boundingRect); //Crop the image
var resizedImage = new Mat();
var resizedImageFloat = new Mat();
Cv2.Resize(roi, resizedImage, new Size(10, 10)); //resize to 10X10
resizedImage.ConvertTo(resizedImageFloat, MatType.CV_32FC1); //convert to float
var result = resizedImageFloat.Reshape(1, 1);
var results = new Mat();
var neighborResponses = new Mat();
var dists = new Mat();
var detectedClass = (int)kNearest.FindNearest(result, 1, results, neighborResponses, dists);
//Console.WriteLine("DetectedClass: {0}", detectedClass);
//Cv2.ImShow("roi", roi);
//Cv.WaitKey(0);
//Cv2.ImWrite(string.Format("det_{0}_{1}.png",detectedClass, contourIndex), roi);
Cv2.PutText(
dst,
detectedClass.ToString(CultureInfo.InvariantCulture),
new Point(boundingRect.X, boundingRect.Y + boundingRect.Height),
0,
1,
new Scalar(0, 255, 0),
2);
contourIndex = hierarchyIndexes[contourIndex].Next;
}
Cv2.ImShow("Segmented Source", src);
Cv2.ImShow("Detected", dst);
Cv2.ImWrite("dest.jpg", dst);
Cv2.WaitKey();
}
/// <summary>
/// https://github.com/Itseez/opencv_extra/blob/master/learning_opencv_v2/ch9_watershed.cpp
/// </summary>
private static void watershedExample()
{
var src = new Mat(@"..\..\Images\corridor.jpg", ImreadModes.AnyDepth | ImreadModes.AnyColor);
var srcCopy = new Mat();
src.CopyTo(srcCopy);
var markerMask = new Mat();
Cv2.CvtColor(srcCopy, markerMask, ColorConversionCodes.BGRA2GRAY);
var imgGray = new Mat();
Cv2.CvtColor(markerMask, imgGray, ColorConversionCodes.GRAY2BGR);
markerMask = new Mat(markerMask.Size(), markerMask.Type(), s: Scalar.All(0));
var sourceWindow = new Window("Source (Select areas by mouse and then press space)")
{
Image = srcCopy
};
var previousPoint = new Point(-1, -1);
sourceWindow.OnMouseCallback += (@event, x, y, flags) =>
{
if (x < 0 || x >= srcCopy.Cols || y < 0 || y >= srcCopy.Rows)
{
return;
}
if (@event == MouseEvent.LButtonUp || !flags.HasFlag(MouseEvent.FlagLButton))
{
previousPoint = new Point(-1, -1);
}
else if (@event == MouseEvent.LButtonDown)
{
previousPoint = new Point(x, y);
}
else if (@event == MouseEvent.MouseMove && flags.HasFlag(MouseEvent.FlagLButton))
{
var pt = new Point(x, y);
if (previousPoint.X < 0)
{
previousPoint = pt;
}
Cv2.Line(img: markerMask, pt1: previousPoint, pt2: pt, color: Scalar.All(255), thickness: 5);
Cv2.Line(img: srcCopy, pt1: previousPoint, pt2: pt, color: Scalar.All(255), thickness: 5);
previousPoint = pt;
sourceWindow.Image = srcCopy;
}
};
var rnd = new Random();
for (; ;)
{
var key = Cv2.WaitKey(0);
if ((char)key == 27) // ESC
{
break;
}
if ((char)key == 'r') // Reset
{
markerMask = new Mat(markerMask.Size(), markerMask.Type(), s: Scalar.All(0));
src.CopyTo(srcCopy);
sourceWindow.Image = srcCopy;
}
if ((char)key == 'w' || (char)key == ' ') // Apply watershed
{
Point[][] contours; //vector<vector<Point>> contours;
HierarchyIndex[] hierarchyIndexes; //vector<Vec4i> hierarchy;
Cv2.FindContours(
markerMask,
out contours,
out hierarchyIndexes,
mode: RetrievalModes.CComp,
method: ContourApproximationModes.ApproxSimple);
if (contours.Length == 0)
{
continue;
}
var markers = new Mat(markerMask.Size(), MatType.CV_32S, s: Scalar.All(0));
var componentCount = 0;
var contourIndex = 0;
while ((contourIndex >= 0))
{
Cv2.DrawContours(
markers,
contours,
contourIndex,
color: Scalar.All(componentCount + 1),
thickness: -1,
lineType: LineTypes.Link8,
hierarchy: hierarchyIndexes,
maxLevel: int.MaxValue);
componentCount++;
contourIndex = hierarchyIndexes[contourIndex].Next;
}
if (componentCount == 0)
{
continue;
}
var colorTable = new List <Vec3b>();
for (var i = 0; i < componentCount; i++)
{
var b = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
var g = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
var r = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
colorTable.Add(new Vec3b((byte)b, (byte)g, (byte)r));
}
Cv2.Watershed(src, markers);
var watershedImage = new Mat(markers.Size(), MatType.CV_8UC3);
// paint the watershed image
for (var i = 0; i < markers.Rows; i++)
{
for (var j = 0; j < markers.Cols; j++)
{
var idx = markers.At <int>(i, j);
if (idx == -1)
{
watershedImage.Set(i, j, new Vec3b(255, 255, 255));
}
else if (idx <= 0 || idx > componentCount)
{
watershedImage.Set(i, j, new Vec3b(0, 0, 0));
}
else
{
watershedImage.Set(i, j, colorTable[idx - 1]);
}
}
}
watershedImage = watershedImage * 0.5 + imgGray * 0.5;
Cv2.ImShow("Watershed Transform", watershedImage);
Cv2.WaitKey(1); //do events
}
}
sourceWindow.Dispose();
Cv2.DestroyAllWindows();
src.Dispose();
}
// Display the original video in a opencv window
void UpdateWindow(Mat _image)
{
Cv2.Flip(_image, _image, FlipMode.X);
//Cv2.ImShow("Copy video", _image);
displayCount++;
}
private void ProcImage(ref System.Drawing.Bitmap src, ref System.Drawing.Bitmap maskRef, out System.Drawing.Bitmap dst)
{
dst = null;
Mat srcImg = BitmapConverter.ToMat(src);
Cv2.CvtColor(srcImg, srcImg, ColorConversionCodes.BGRA2BGR);
Mat srcMask = BitmapConverter.ToMat(maskRef);
Cv2.CvtColor(srcMask, srcMask, ColorConversionCodes.BGRA2BGR);
Cv2.ImWrite(@"C:\opencv\ImageProcessing\ImageProcessing\Images\manualmask.jpg", srcMask);
Mat mask = new Mat(new OpenCvSharp.Size(src.Width, src.Height), MatType.CV_8UC1, 0);
for (int i = 0; i < srcMask.Cols; i++)
{
for (int j = 0; j < srcMask.Rows; j++)
{
if (i > srcMask.Cols / 4 && i < srcMask.Cols / 4 * 3 && j > srcMask.Rows / 4 && j < srcMask.Rows / 4 * 3)
{
mask.Set <byte>(j, i, 3);
}
else
{
mask.Set <byte>(j, i, 0);
}
Vec3b intensity = srcMask.Get <Vec3b>(j, i);
if (intensity.Item1 > 40)
{
mask.Set <byte>(j, i, 1);
}
if (intensity.Item2 > 40)
{
mask.Set <byte>(j, i, 0);
}
}
}
//dilate process
//Cv2.Dilate(srcImg, dstImg, new Mat());
Mat bgdModel = new Mat();
Mat fgdModel = new Mat();
OpenCvSharp.Rect r = new OpenCvSharp.Rect(500, 500, (int)Width - 1000, (int)Height - 1000);
Cv2.GrabCut(srcImg, mask, r, bgdModel, fgdModel, 3, GrabCutModes.InitWithMask);
//for (int i = mask.Cols / 2 - 50; i < mask.Cols / 2 + 50; i++)
//{
// for (int j = mask.Rows / 2 - 25; j < mask.Rows / 2 + 75; j++)
// {
// mask.Set<byte>(j, i, 1);
// }
//}
//Cv2.GrabCut(srcImg, mask, r, bgdModel, fgdModel, 1, GrabCutModes.InitWithMask);
for (int i = 0; i < mask.Cols; i++)
{
for (int j = 0; j < mask.Rows; j++)
{
byte e = mask.Get <byte>(j, i);
if (e == 0 | e == 2)
{
mask.Set <byte>(j, i, 0);
}
else if (e == 1)
{
mask.Set <byte>(j, i, 255);
}
else
{
mask.Set <byte>(j, i, 127);
}
}
}
Mat res = srcImg.Clone();
dst = BitmapConverter.ToBitmap(mask);
}
public static List <System.Drawing.Point> OpenCvFindPic(int left, int top, int width, int height, Bitmap S_bmp, Bitmap P_bmp, int method, out double matchDegree)
{
List <System.Drawing.Point> res = new List <System.Drawing.Point>();
//Mat small = BitmapToMat(P_bmp);
//Mat big = BitmapToMat(S_bmp);
Mat small = OpenCvSharp.Extensions.BitmapConverter.ToMat(P_bmp);
Mat big = OpenCvSharp.Extensions.BitmapConverter.ToMat(S_bmp);
var result = new Mat();
var minLoc = new OpenCvSharp.Point(0, 0);
var maxLoc = new OpenCvSharp.Point(0, 0);
double max = 0, min = 0;
matchDegree = 0;
switch (method)
{
case 0:
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.SqDiff);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
Debug.WriteLine($"SqDiff:{min} {max}");
break;
case 1:
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.SqDiffNormed);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
matchDegree = (1 - min) / 1.0;
Debug.WriteLine($"SqDiffNormed:{min} {max}");
break;
case 2:
// not good for our usage
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.CCorr);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
Debug.WriteLine($"CCorr:{min} {max}");
break;
case 3:
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.CCorrNormed);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
Debug.WriteLine($"CCorrNormed:{min} {max}");
matchDegree = max / 1.0;
break;
case 4:
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.CCoeff);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
Debug.WriteLine($"CCoeff:{min} {max}");
break;
case 5:
default:
Cv2.MatchTemplate(big, small, result, TemplateMatchModes.CCoeffNormed);
Cv2.MinMaxLoc(result, out min, out max, out minLoc, out maxLoc);
matchDegree = (max + 1) / 2.0;
Debug.WriteLine($"CCoeffNormed:{min} {max}");
break;
}
// the sqD lower is good
if (method == 0 || method == 1)
{
res.Add(new System.Drawing.Point(minLoc.X, minLoc.Y));
}
else
{
res.Add(new System.Drawing.Point(maxLoc.X, maxLoc.Y));
}
result.Dispose();
big.Dispose();
small.Dispose();
return(res);
}
private void CreateObjectMask(ref System.Drawing.Bitmap image, out System.Drawing.Bitmap mask)
{
double kThresh = 125;
double hThresh = 55;
double canny1 = 25;
double canny2 = 75;
mask = null;
Mat src = BitmapConverter.ToMat(image);
Mat src_kirsch = BitmapConverter.ToMat(image.KirschFilter());
Mat kirsch_gray = new Mat();
Cv2.CvtColor(src_kirsch, kirsch_gray, ColorConversionCodes.RGB2GRAY);
Cv2.ImWrite(@"C:\temp\kirsch_gray.jpg", kirsch_gray);
Mat kirsch_threshold = new Mat();
Cv2.Threshold(kirsch_gray, kirsch_threshold, kThresh, 255, ThresholdTypes.Binary);
Cv2.ImWrite(@"C:\temp\kirsch_threshold.jpg", kirsch_threshold);
Mat[] contours;
List <OpenCvSharp.Point> hierarchy;
List <Mat> hulls;
Mat morph_element = Cv2.GetStructuringElement(MorphShapes.Ellipse, new OpenCvSharp.Size(2, 2), new OpenCvSharp.Point(1, 1));
#region morphology
Mat kirsch_threshold_copy = new Mat();
kirsch_threshold.CopyTo(kirsch_threshold_copy);
int hullCount = 0, numLoops = 0;
do
{
numLoops++;
Mat kirsch_morph = kirsch_threshold_copy.MorphologyEx(MorphTypes.Gradient, morph_element);
Cv2.ImWrite(@"C:\temp\kirsch_morph" + numLoops + ".jpg", kirsch_morph);
hierarchy = new List <OpenCvSharp.Point>();
Cv2.FindContours(kirsch_morph, out contours, OutputArray.Create(hierarchy),
RetrievalModes.External, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0));
hulls = new List <Mat>();
for (int j = 0; j < contours.Length; j++)
{
Mat hull = new Mat();
Cv2.ConvexHull(contours[j], hull);
hulls.Add(hull);
}
Mat drawing = Mat.Zeros(src.Size(), MatType.CV_8UC1);
Cv2.DrawContours(drawing, hulls, -1, Scalar.White);
Cv2.ImWrite(@"C:\temp\drawing" + numLoops + ".jpg", drawing);
if (hulls.Count != hullCount && numLoops < 100)
{
hullCount = hulls.Count;
kirsch_threshold_copy = drawing;
}
else
{
break;
}
} while (true);
#endregion
if (numLoops >= 100)
{
throw new Exception("Could not find hull");
}
#region bestHull
//try and filter out dust near to stone
double largestArea = hulls.Max(m => Cv2.ContourArea(m));
var bestHulls = hulls.Where(m => Cv2.ContourArea(m) == largestArea).ToList();
Mat hulls_mask = Mat.Zeros(src.Size(), MatType.CV_8UC1);
Cv2.DrawContours(hulls_mask, bestHulls, -1, Scalar.White, -1);
Cv2.ImWrite(@"C:\temp\hulls_mask.jpg", hulls_mask);
//hulls_mask is the convex hull of outline, now look for clefts
Cv2.Threshold(kirsch_gray, kirsch_threshold, hThresh, 255, ThresholdTypes.Binary);
Mat kirsch_mask = Mat.Zeros(kirsch_threshold.Size(), kirsch_threshold.Type());
kirsch_threshold.CopyTo(kirsch_mask, hulls_mask);
Cv2.ImWrite(@"C:\temp\kirsch_mask.jpg", kirsch_mask);
Mat kirsch_mask_canny = new Mat();
Cv2.Canny(kirsch_mask, kirsch_mask_canny, canny1, canny2, 3);
Cv2.ImWrite(@"C:\temp\kirsch_mask_canny.jpg", kirsch_mask_canny);
morph_element = Cv2.GetStructuringElement(MorphShapes.Ellipse, new OpenCvSharp.Size(5, 5), new OpenCvSharp.Point(2, 2));
Mat kirsch_filled = new Mat();
Cv2.Dilate(kirsch_mask_canny, kirsch_filled, morph_element);
Cv2.Dilate(kirsch_filled, kirsch_filled, morph_element);
Cv2.Erode(kirsch_filled, kirsch_filled, morph_element);
Cv2.Erode(kirsch_filled, kirsch_filled, morph_element);
Cv2.ImWrite(@"C:\temp\kirsch_filled.jpg", kirsch_filled);
hierarchy = new List <OpenCvSharp.Point>();;
Cv2.FindContours(kirsch_filled, out contours, OutputArray.Create(hierarchy),
RetrievalModes.External, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0));
#endregion
hulls_mask = Mat.Zeros(src.Size(), MatType.CV_8UC1);
Cv2.DrawContours(hulls_mask, contours, -1, Scalar.White, -1);
Cv2.ImWrite(@"C:\temp\hulls_mask_final.jpg", hulls_mask);
Cv2.Erode(hulls_mask, hulls_mask, morph_element);
Cv2.Erode(hulls_mask, hulls_mask, morph_element);
Mat fImage = new Mat();
src.CopyTo(fImage, hulls_mask);
Cv2.ImWrite(@"C:\temp\fImage.png", fImage);
mask = BitmapConverter.ToBitmap(fImage);
// contrast calculation
double contrast, L, a, b, H, C;
CalculateContrast(ref src, ref hulls_mask, out L, out a, out b, out contrast, out H, out C, ContrastType.RMS);
Console.WriteLine("contrast: {0}, L: {1}, a: {2}, b: {3}, C: {4}, H: {5}", contrast, L, a, b, C, H);
//hulls_mask = null;
CalcHistogram(ref src, ref hulls_mask, out mask);
}
/// <summary>
/// Display image in new window using OpenCV.
/// </summary>
/// <param name="image">
/// Target image in OpenCV matrix format.
/// </param>
/// <param name="title">
/// Window title.
/// </param>
public static void DisplayImage(Mat image, string title)
{
Cv2.ImShow(title, image);
}
static void Main(string[] args)
{
Mat src = Cv2.ImRead("OpenCV_Logo.png", ImreadModes.ReducedColor2);
Console.WriteLine(src);
}
static void Main(string[] args)
{
var host = ConfigurationManager.AppSettings["host"];
var username = ConfigurationManager.AppSettings["username"];
var password = ConfigurationManager.AppSettings["password"];
var database = ConfigurationManager.AppSettings["database"];
var connectionString = $"Host={host};Username={username};Password={password};Database={database}";
using (var connection = new NpgsqlConnection(connectionString))
{
connection.Open();
using (var command = new NpgsqlCommand("SELECT name FROM sets", connection))
{
using (var reader = command.ExecuteReader())
{
while (reader.Read())
{
Console.WriteLine(reader.GetString(0));
}
}
}
}
Console.Read();
var barcodeReader = CreateBarcodeReader(BarcodeFormat.ITF, autoRotate: true, tryInverted: true, tryHarder: true);
string barcode = null;
var tesseractEngine = new TesseractEngine("./tessdata", "eng", EngineMode.Default);
var imagePath = args[0];
var sourceImage = new Mat(imagePath, ImreadModes.Color);
ShowImage(sourceImage, "source");
var thresholdedImage = GetRedThresholdedImage(sourceImage);
ShowImage(thresholdedImage, "thresholded");
for (int erodeIterations = 0; erodeIterations <= 4 && barcode == null; erodeIterations++)
{
var contours = GetCountours(thresholdedImage, erodeIterations);
ShowContours(sourceImage, contours, new Scalar(0d, 255d, 0d), "contours " + erodeIterations);
foreach (var contour in contours)
{
if (barcode != null)
{
break;
}
using (var labelImage = GetCorrectedRectangle(sourceImage, contour))
{
var labelSize = labelImage.Size();
//if (contours.Length > 50 && labelSize.Height > 10 && labelSize.Width > 10)
if (labelSize.Height >= 50 && labelSize.Width >= labelSize.Height * 2.5)
{
//resize
while (labelImage.Size().Height < 250)
{
Cv2.Resize(labelImage, labelImage, new OpenCvSharp.Size(0d, 0d), 2d, 2d, InterpolationFlags.Lanczos4);
}
//sharpen
var tempMat = new Mat();
Cv2.GaussianBlur(labelImage, tempMat, new OpenCvSharp.Size(0d, 0d), 3d);
Cv2.AddWeighted(labelImage, 1.5d, tempMat, -0.5d, 0d, labelImage);
ShowImage(labelImage, "label " + contour.Length + " " + new Random().Next());
OCRText(tesseractEngine, labelImage);
using (var barcodeImage = GetBarcodeImage(labelImage))
{
//ShowImage(barcodeImage, "barcode " + new Random().Next());
barcode = barcode ?? DecodeBarcode(barcodeReader, barcodeImage);
}
}
}
}
}
Console.WriteLine("Barcode: " + barcode);
Cv2.WaitKey();
}
public Scalar MeanStdDev(Mat image)
{
Cv2.MeanStdDev(image, out _, out var meanStdDev);
return(meanStdDev);
}
public Mat Iso11146(Mat img, Mat dst)
{
Cv2.Resize(img, img, new OpenCvSharp.Size(960, 687), 0, 0, InterpolationFlags.Linear);
Cv2.Resize(dst, dst, new OpenCvSharp.Size(960, 687), 0, 0, InterpolationFlags.Linear);
OpenCvSharp.Point[][] contours;
HierarchyIndex[] hierarchy;
Cv2.Threshold(img, img, 50, 255, ThresholdTypes.Binary);
Cv2.FindContours(img, out contours, out hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxTC89L1);
foreach (OpenCvSharp.Point[] p in contours)
{
if (Cv2.ContourArea(p) < 1000)
{
continue;
}
Moments moments = Cv2.Moments(p, true);
if (moments.M00 != 0)
{
int cX = (int)(moments.M10 / moments.M00);
int cY = (int)(moments.M01 / moments.M00);
int cX2 = (int)(moments.Mu20 / moments.M00);
int cXY = (int)(moments.Mu11 / moments.M00);
int cY2 = (int)(moments.Mu02 / moments.M00);
double a = Math.Pow(((cX2 + cY2) + 2 * Math.Abs(cXY)), 0.5);
int dX = (int)(2 * Math.Pow(2, 0.5) * Math.Pow(((cX2 + cY2) + 2 * Math.Abs(cXY)), 0.5));
int dY = (int)(2 * Math.Pow(2, 0.5) * Math.Pow(((cX2 + cY2) - 2 * Math.Abs(cXY)), 0.5));
double t;
if ((cX2 - cY2) != 0)
{
t = 2 * cXY / (cX2 - cY2);
}
else
{
t = 0;
}
double theta = 0.5 * Math.Atan(t) * 180;
OpenCvSharp.Point center = new OpenCvSharp.Point(cX, cY);
OpenCvSharp.Size axis = new OpenCvSharp.Size(dX, dY);
Cv2.Circle(dst, cX, cY, 1, Scalar.Black);
if (trackingLD1)
{
if (tracking == false)
{
tracking = true;
if ((cX - (axis_x + axis_scale / 2)) > 10)
{
itc.getTecCurrSetpoint(0, out temp);
itc.setTecCurrSetpoint(temp - 0.005);
}
else if ((cX - (axis_x + axis_scale / 2)) < -10)
{
itc.getTecCurrSetpoint(0, out temp);
itc.setTecCurrSetpoint(temp + 0.005);
}
}
}
if (dX > 0 && dY > 0)
{
Cv2.Ellipse(dst, center, axis, theta, 0, 360, Scalar.White);
}
}
}
return(dst);
}
private Mat CorrectDistortion(Mat imgWhiteB, List <Point2d> PTs, out Point2d[] Q4, bool DispB = false)
{
Center = new Point2d(PTs.Average(P => P.X), PTs.Average(P => P.Y));
List <PointEx> PTda = PTs.ToList().ConvertAll(P => new PointEx(P, Center));
PTda.Sort((A, B) => (int)((A.Angle - B.Angle) * 100000.0));
PTda.AddRange(PTda);
//if(DispB){ for(int k=0; k<PTda.Count(); k++) WriteLine("k:{0} PTda:{1}",k,PTda[k]); }
List <List <PointEx> > PTda4 = _SepareteLins(PTda); //BLTR
if (PTda4 == null || PTda4.Count < 4)
{
Q4 = null; return(null);
}
#if false
if (DispB)
{
Mat resImg = imgWhiteB.CvtColor(ColorConversionCodes.GRAY2BGR); //Gray->Color変換
Scalar[] clrLst = { Scalar.Red, Scalar.Blue, Scalar.Yellow, Scalar.Green };
for (int k = 0; k < 4; k++)
{
var clr = clrLst[k]; //(k%2==0)? Scalar.Red: Scalar.Blue;
PTda4[k].ForEach(Q => resImg.Circle(Q.Pt, 5, clr, 5));
}
using (new Window("◆1◆resImg", WindowMode.KeepRatio, resImg)){ Cv2.WaitKey(0); }
}
#endif
int nSize = 1;
var FRegTop = new FuncApproximation(nSize, PTda4[2], CalculusB: true);
var FRegBtm = new FuncApproximation(nSize, PTda4[0], CalculusB: true);
var FRegLft = new FuncApproximation(nSize, PTda4[1], CalculusB: true);
var FRegRgt = new FuncApproximation(nSize, PTda4[3], CalculusB: true);
Point2d[] PT4 = new Point2d[4];
var FM = new RegFuncUtility(ImgCheck: imgWhiteB);
Point2d P00 = new Point2d(0, 0), PGG = new Point2d(imgWhiteB.Width, imgWhiteB.Height);
PT4[0] = FM.IntersectionPoint(FRegTop, FRegLft, P00, PGG, DispB: false);
PT4[2] = FM.IntersectionPoint(FRegBtm, FRegLft, P00, PGG, DispB: false);
PT4[1] = FM.IntersectionPoint(FRegTop, FRegRgt, P00, PGG, DispB: false);
PT4[3] = FM.IntersectionPoint(FRegBtm, FRegRgt, P00, PGG, DispB: false);
if (DispB)
{
Mat resImg = imgWhiteB.CvtColor(ColorConversionCodes.GRAY2BGR); //Gray->Color
Scalar[] clrLst = { Scalar.Red, Scalar.Blue, Scalar.Yellow, Scalar.Green };
for (int k = 0; k < 4; k++)
{
var clr = clrLst[k]; //(k%2==0)? Scalar.Red: Scalar.Blue;
PTda4[k].ForEach(Q => resImg.Circle((Point)Q.Pt, 5, clr, 5));
}
//using( new Window("#1#resImg",WindowMode.KeepRatio,resImg) ){ Cv2.WaitKey(0); }
for (int k = 0; k < 4; k++)
{
var clr = clrLst[k];
PTda4[k].ForEach(Q => resImg.Circle((Point)PT4[k], 10, clr, 5));
}
using (new Window("#2#resImg", WindowMode.KeepRatio, resImg)){ Cv2.WaitKey(0); }
}
FRegTop.CreateFunction(PT4[0], 0, 32 * 9);
FRegBtm.CreateFunction(PT4[2], 0, 32 * 9);
//FRegLft.CreateFunction(PT4[0],0,32*9);
//FRegRgt.CreateFunction(PT4[2],0,32*9);
Mat MatCor = new Mat(352, 352, MatType.CV_8UC1, Scalar.White);
unsafe {
byte * S = imgWhiteB.DataPointer;
byte * D = MatCor.DataPointer;
int W = imgWhiteB.Width, H = imgWhiteB.Height, W2 = MatCor.Width;
double per = 1.0 / (32 * 9);
for (int x = 0; x <= 32 * 9 + 3; x++)
{
double xd = x;
Point ptT = (Point)FRegTop.EstimateL2Pt(xd);
Point ptB = (Point)FRegBtm.EstimateL2Pt(xd);
for (int y = 0; y <= 32 * 9 + 3; y++)
{
Point pt = (Point)_2_Get_InterPolation(ptT, ptB, per * y);
if (pt.X < 0 || pt.X >= W || pt.Y < 0 || pt.Y >= H)
{
//// using( new Window("#test# imgWhiteB",WindowMode.KeepRatio,imgWhiteB) ){ Cv2.WaitKey(0); }
Q4 = null;
return(null);
}
D[(y + 32) * W2 + x + 32] = S[pt.Y * W + pt.X];
}
}
}
Q4 = new Point2d[4];
Q4[0] = new Point2d(32, 32);
Q4[1] = new Point2d(320, 32);
Q4[2] = new Point2d(32, 320);
Q4[3] = new Point2d(320, 320);
if (DispB)
{
new Window("MatCor", WindowMode.KeepRatio, MatCor);
}
return(MatCor);
}