private Collection <ClassifierReport> IVA_Classify(VisionImage image, Roi roi, string classifierFilePath)
{
//retval = new VisGrainTypeCollection();
// Create a binary image that will contain the segmented image.
using (VisionImage binaryImage = new VisionImage(ImageType.U8, 7))
{
ParticleClassifierSession vaClassifier = new ParticleClassifierSession();
bool fileExists = System.IO.File.Exists(classifierFilePath);
vaClassifier.ReadFile(classifierFilePath);
// Segments the image.
Functions.IVA_Classification_Segmentation(image, binaryImage, roi, vaClassifier.PreprocessingOptions);
// Get the ROIs of all individual particles.
Collection <Roi> rois = Functions.IVA_Classification_Extract_Particles(image, binaryImage);
// Allocates the classifier reports for all objects in the image.
Collection <ClassifierReport> classifierReports = new Collection <ClassifierReport>();
List <VisGrainType> listGrainType = new List <VisGrainType>();
ListShape = new List <VisRectangleContour>();
// Classifies the object located in the given ROIs.
for (int i = 0; i < rois.Count; ++i)
{
//RectangleContour rect = binaryImage=new VisionImage()
object obj1 = rois[i][0].Shape;
RectangleContour rc = (RectangleContour)rois[i][0].Shape;
VisRectangleContour rect = new VisRectangleContour(rc);
ClassifierReport report = vaClassifier.Classify(image, rois[i]);
VisGrainType grainType = new VisGrainType {
Name = report.BestClassName, ScoreClassification = report.ClassificationScore, ScoreIdentification = report.IdentificationScore
};
listGrainType.Add(grainType);
classifierReports.Add(report);
ListShape.Add(rect);
}
GrainResultsType = new VisGrainTypeCollection();
GrainResultsType.Items = listGrainType;
return(classifierReports);
}
}
public PaletteType ProcessImage_OLD(VisionImage image)
{
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(7, 0);
// Extract Color Plane
using (VisionImage plane = new VisionImage(ImageType.U8, 7))
{
// Extract the red color plane and copy it to the main image.
Algorithms.ExtractColorPlanes(image, ColorMode.Rgb, plane, null, null);
Algorithms.Copy(plane, image);
}
// Filters: Convolution - Applies a linear filter to an image by convolving the image with a filtering kernel.
double[] vaCoefficients = { 1, 2, 4, 2, 1, 2, 4, 8, 4, 2, 4, 8, 16, 8, 4, 2, 4, 8, 4, 2, 1, 2, 4, 2, 1 };
Algorithms.Convolute(image, image, new Kernel(5, 5, vaCoefficients));
// Filters: Convolution - Applies a linear filter to an image by convolving the image with a filtering kernel.
double[] vaCoefficients2 = { -1, -1, -1, -1, 10, -1, -1, -1, -1 };
Algorithms.Convolute(image, image, new Kernel(3, 3, vaCoefficients2));
// Automatic Threshold
Algorithms.AutoThreshold(image, image, 2, ThresholdMethod.Clustering);
// Basic Morphology - Applies morphological transformations to binary images.
int[] vaCoefficients3 = { 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem = new StructuringElement(7, 7, vaCoefficients3);
vaStructElem.Shape = StructuringElementShape.Hexagon;
// Applies morphological transformations
Algorithms.Morphology(image, image, MorphologyMethod.Open, vaStructElem);
// Lookup Table: Equalize
// Calculates the histogram of the image and redistributes pixel values
// accross the desired range to maintain the same pixel value distribution.
Range equalizeRange = new Range(0, 255);
if (image.Type != ImageType.U8)
{
equalizeRange.Maximum = 0;
}
Algorithms.Equalize(image, image, null, equalizeRange, null);
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new RectangleContour using the given values.
//20200715
//RectangleContour vaRect = new RectangleContour(49, 33, 667, 163);
RectangleContour vaRect = new RectangleContour(0, 0, image.Width, image.Height);
roi.Add(vaRect);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath = "C:\\DATA\\#hasilscan\\Particle Classifier.clf";
vaClassifierReports = IVA_Classify(image, roi, vaClassifierFilePath);
VisGrainTypeCollection colType = new VisGrainTypeCollection(vaClassifierReports);
string vaClassifierFilePathSize = "C:\\DATA\\#hasilscan2\\Ukuran Classifier.clf";
vaClassifierReports2 = IVA_ClassifySize(image, roi, vaClassifierFilePathSize);
VisGrainSizeCollection colSize = new VisGrainSizeCollection(vaClassifierReports2);
GrainResults = new VisGrainDataCollection(vaClassifierReports, vaClassifierReports2, ListShape);
roi.Dispose();
// Dispose the IVA_Data structure.
ivaData.Dispose();
// Return the palette type of the final image.
return(PaletteType.Binary);
}
public PaletteType ProcessImage(VisionImage image)
{
//vaParticleReportCalibrated = new ParticleMeasurementsReport();
// Initialize the IVA_Data structure to pass results and coordinate systems.
IVA_Data ivaData = new IVA_Data(15, 0);
// Image Buffer: Push
Functions.IVA_PushBuffer(ivaData, image, 0);
// Operators: NOR Image
Algorithms.Nor(image, Functions.IVA_GetBuffer(ivaData, 0), image);
// Extract Color Plane
using (VisionImage plane = new VisionImage(ImageType.U8, 7))
{
// Extract the red color plane and copy it to the main image.
Algorithms.ExtractColorPlanes(image, ColorMode.Rgb, plane, null, null);
Algorithms.Copy(plane, image);
}
// Thresholds an image into 2 classes by using local thresholding.
LocalThresholdOptions vaLocalThresholdOptions = new LocalThresholdOptions();
vaLocalThresholdOptions.DeviationWeight = 1;
vaLocalThresholdOptions.Method = LocalThresholdMethod.BackgroundCorrection;
vaLocalThresholdOptions.ParticleType = ParticleType.Dark;
vaLocalThresholdOptions.ReplaceValue = 1;
vaLocalThresholdOptions.WindowHeight = 35;
vaLocalThresholdOptions.WindowWidth = 35;
Algorithms.LocalThreshold(image, image, vaLocalThresholdOptions);
// Basic Morphology - Applies morphological transformations to binary images.
int[] vaCoefficients = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem = new StructuringElement(3, 3, vaCoefficients);
vaStructElem.Shape = StructuringElementShape.Square;
// Applies morphological transformations
Algorithms.Morphology(image, image, MorphologyMethod.GradientOut, vaStructElem);
// Advanced Morphology: Fill Holes
Algorithms.FillHoles(image, image, Connectivity.Connectivity8);
// Advanced Morphology: Remove Objects
int[] vaCoefficients2 = { 1, 1, 1, 1, 1, 1, 1, 1, 1 };
StructuringElement vaStructElem2 = new StructuringElement(3, 3, vaCoefficients2);
vaStructElem.Shape = StructuringElementShape.Hexagon;
// Filters particles based on their size.
Algorithms.RemoveParticle(image, image, 6, SizeToKeep.KeepLarge, Connectivity.Connectivity8, vaStructElem2);
// Advanced Morphology: Remove Border Objects - Eliminates particles touching the border of the image.
Algorithms.RejectBorder(image, image, Connectivity.Connectivity8);
// Lookup Table: Equalize
// Calculates the histogram of the image and redistributes pixel values
// accross the desired range to maintain the same pixel value distribution.
Range equalizeRange = new Range(0, 255);
if (image.Type != ImageType.U8)
{
equalizeRange.Maximum = 0;
}
Algorithms.Equalize(image, image, null, equalizeRange, null);
// Image Buffer: Push
Functions.IVA_PushBuffer(ivaData, image, 1);
// Particle Analysis - Computes the number of particles detected in a binary image and
// returns the requested measurements about the particles.
Collection <MeasurementType> vaPixelMeasurements = new Collection <MeasurementType>(new MeasurementType[] { MeasurementType.BoundingRectLeft, MeasurementType.BoundingRectTop, MeasurementType.BoundingRectRight, MeasurementType.BoundingRectBottom, MeasurementType.MaxFeretDiameter });
Collection <MeasurementType> vaCalibratedMeasurements = new Collection <MeasurementType>(new MeasurementType[] {});
//IVA_Particle(image, Connectivity.Connectivity4, null, null, null, 10, out vaParticleReport, out vaParticleReportCalibrated);
IVA_Particle(image, Connectivity.Connectivity4, vaPixelMeasurements, vaCalibratedMeasurements, ivaData, 10, out vaParticleReport, out vaParticleReportCalibrated);
// Image Buffer: Pop
Algorithms.Copy(Functions.IVA_GetBuffer(ivaData, 1), image);
// Creates a new, empty region of interest.
Roi roi = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect = new RectangleContour(0, 0, image.Width, image.Height);
roi.Add(vaRect);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath = "C:\\DATA\\#hasilscan3\\Particle Classifier.clf";
vaClassifierReports = IVA_Classify(image, roi, vaClassifierFilePath);
VisGrainTypeCollection colType = new VisGrainTypeCollection(vaClassifierReports);
roi.Dispose();
// Image Buffer: Pop
Algorithms.Copy(Functions.IVA_GetBuffer(ivaData, 1), image);
// Creates a new, empty region of interest.
Roi roi2 = new Roi();
// Creates a new RectangleContour using the given values.
RectangleContour vaRect2 = new RectangleContour(0, 0, image.Width, image.Height);
roi2.Add(vaRect2);
// Classifies all the objects located in the given ROI.
string vaClassifierFilePath2 = "C:\\DATA\\#hasilscan3\\Ukuran Classifier.clf";
vaClassifierReports2 = IVA_Classify(image, roi2, vaClassifierFilePath2);
VisGrainSizeCollection colSize = new VisGrainSizeCollection(vaClassifierReports2);
GrainResults = new VisGrainDataCollection(vaClassifierReports, vaClassifierReports2, ListShape);
roi2.Dispose();
// Dispose the IVA_Data structure.
ivaData.Dispose();
// Return the palette type of the final image.
return(PaletteType.Binary);
}
public HttpResponseMessage Post()
{
HttpResponseMessage result = null;
var httpRequest = HttpContext.Current.Request;
var form = httpRequest.Form;
VisCalculation calc = new VisCalculation();
//VisCalculationSize calcSize = new VisCalculationSize();
string connectionString = System.Configuration.ConfigurationManager.ConnectionStrings["GrainConnection"].ConnectionString;
if (httpRequest.Files.Count > 0)
{
var docfiles = new List <string>();
string fileName = "";
foreach (string file in httpRequest.Files)
{
var postedFile = httpRequest.Files[file];
//string myFile = Server.MapPath("images") + "\" + FileUpload1.PostedFile.FileName;
fileName = Path.GetFileName(postedFile.FileName);
string foderDate = DateTime.Now.ToString("yyyyMMdd");
var filePath = HttpContext.Current.Server.MapPath("~/images/" + foderDate + "/" + fileName); //postedFile.FileName);
string fullPath = Path.GetDirectoryName(filePath);
if (!Directory.Exists(fullPath))
{
Directory.CreateDirectory(fullPath);
}
postedFile.SaveAs(filePath);
//Image image = Image.FromFile(HttpContext.Current.Server.MapPath("~/images/" + foderDate + "/" + fileName));
FileInformation fileinfo = Algorithms.GetFileInformation(filePath);
VisionImage image = new VisionImage();
image.Type = fileinfo.ImageType;
image.ReadFile(filePath);
//calculation grain type
calc = new VisCalculation(image);
//calculation grain size
//calcSize = new VisCalculationSize(image);
docfiles.Add(filePath);
}
//save to database
string user = "";
double lat = 1000;
double lng = 1000;
if (form.Count > 0)
{
var items = form.AllKeys.SelectMany(form.GetValues, (k, v) => new { key = k, value = v });
foreach (var item in items)
{
//Console.WriteLine("{0} {1}", item.key, item.value);
if (item.key.Equals("USER_ID"))
{
user = item.value;
}
else if (item.key.Equals("LATITUDE"))
{
Double.TryParse(item.value, out lat);
}
else if (item.key.Equals("LONGITUDE"))
{
Double.TryParse(item.value, out lng);
}
}
/*
* int pos = 0;
* string param1 = "";
* string param2 = "";
* string param3 = "";
* foreach (var item in items)
* {
* //Console.WriteLine("{0} {1}", item.key, item.value);
* if (pos == 0)
* {
* //param1 = item.key;
* param1 = item.value;
* }
* else if (pos == 1)
* {
* //param2 = item.key;
* param2 = item.value;
* }
* else if (pos == 2)
* {
* //param3 = item.key;
* param3 = item.value;
* }
* pos = pos + 1;
* } */
//string param1 = "form.Get(0)";
//string param2 = "form.Get(1)";
//string param3 = "form.Get(2)";
//if (!user.Equals("") && lat != 1000 && lng !=1000)
//{
SqlConnection con = new SqlConnection(connectionString);
try
{
con.Open();
string json = JsonConvert.SerializeObject(calc.GrainResults);
string sql = "INSERT INTO G_RESULT (DATE_STAMP, USER_ID, LATITUDE, LONGITUDE, FILE_NAME, RESULT) VALUES('" + DateTime.Now.ToString("yyy-MM-dd HH:mm:ss") + "','" + user + "','" + Convert.ToString(lat) + "','" + Convert.ToString(lng) + "','" + fileName + "','" + json + "');";
//string sql = "INSERT INTO G_TEST1 (PARAM1, PARAM2, PARAM3) VALUES('" + param1 + "','" + param2 + "','" + param3 + "');";
SqlCommand cmd = new SqlCommand(sql, con);
cmd.CommandType = System.Data.CommandType.Text;
int ret = cmd.ExecuteNonQuery();
//msg = "Success";
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
//msg = ex.Message;
}
//}
}
result = Request.CreateResponse(HttpStatusCode.OK, calc.GrainResults);
//result = Request.CreateResponse(HttpStatusCode.OK, user + ", " + Convert.ToString(lat) + ", " + Convert.ToString(lng));
}
else
{
//result = Request.CreateResponse(HttpStatusCode.BadRequest);
VisGrainTypeCollection cols = new VisGrainTypeCollection();
cols.Message = "BadRequest";
//string msg = "";
//cols.Message = msg; // connectionString;
result = Request.CreateResponse(HttpStatusCode.OK, cols);
}
return(result);
}
