private Image <Bgr, Byte> knn()
{
int K = 10;
int trainSampleCount = 150;
int sigma = 60;
#region Generate the training data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));
Matrix <float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix <float> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
Matrix <float> results, neighborResponses;
results = new Matrix <float>(sample.Rows, 1);
neighborResponses = new Matrix <float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
//using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K)) {
using (KNearest knn = new KNearest()) {
bool trained = knn.Train(trainData, trainClasses, null, false, K, false);
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
//Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++)
{
if (neighborResponses.Data[0, k] == response)
{
accuracy++;
}
}
// highlight the pixel depending on the accuracy (or confidence)
//img[i, j] =
//response == 1 ?
// (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
// (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
img[i, j] =
response == 1 ? (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 30, 30)) :
response == 2 ? (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(30, 90, 30)) :
(accuracy > 5 ? new Bgr(0, 0, 90) : new Bgr(30, 30, 90));
}
}
knn.Save(@"D:\Play Data\KNN训练数据");
}
// display the original training samples
for (int i = 0; i < (trainSampleCount / 3); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), new Bgr(100, 100, 255), -1);
}
return(img);
}
public void TestKNearest()
{
int K = 10;
int trainSampleCount = 100;
#region Generate the training data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);
Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
Matrix<float> sample = new Matrix<float>(1, 2);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
{
//TODO: find out when knn.save will be implemented
//knn.Save("knn.xml");
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++)
{
if (neighborResponses.Data[0, k] == response)
accuracy++;
}
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] =
response == 1 ?
(accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 40, 0)) :
(accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(40, 90, 0));
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}
}
public String DoOCR_ReturnString(KNearest kNearest, Mat src)
{
String RtnString = null;
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
OpenCvSharp.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.");
return(null);
}
//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 OpenCvSharp.Point(boundingRect.X, boundingRect.Y),
new OpenCvSharp.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 OpenCvSharp.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);
RtnString = RtnString + detectedClass.ToString(CultureInfo.InvariantCulture);
contourIndex = hierarchyIndexes[contourIndex].Next;
}
return(RtnString);
}
public void TestKNearest()
{
int K = 10;
int trainSampleCount = 100;
#region Generate the training data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <float> trainClasses = new Matrix <float>(trainSampleCount, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
Matrix <float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix <float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix <float> results, neighborResponses;
results = new Matrix <float>(sample.Rows, 1);
neighborResponses = new Matrix <float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);
using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
{
//TODO: find out when knn.save will be implemented
//knn.Save("knn.xml");
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = j;
sample.Data[0, 1] = i;
// estimates the response and get the neighbors' labels
float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
int accuracy = 0;
// compute the number of neighbors representing the majority
for (int k = 0; k < K; k++)
{
if (neighborResponses.Data[0, k] == response)
{
accuracy++;
}
}
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] =
response == 1 ?
(accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 40, 0)) :
(accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(40, 90, 0));
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}
}
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();
}
