private void button_Click(object sender, EventArgs e)
{
Stream stream = null;
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.InitialDirectory = "c:\\";
openFileDialog.Filter = "txt files (*.txt)|*.txt|All files (*.*)|*.*";
openFileDialog.FilterIndex = 2;
openFileDialog.RestoreDirectory = true;
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
try
{
if ((stream = openFileDialog.OpenFile()) != null)
{
using (stream)
{
string pathName = openFileDialog.FileName;
Bitmap image = new Bitmap(pathName);
pictureBox1.Image = image.Resize(pictureBox1.Width, pictureBox1.Height);
Mask mask = new Mask(new int[,] { { 1, 1, 1 }, { 1, -8, 1 }, { 1, 1, 1 } });
image = LaplacianSharper.SharpImage(image, mask);
pictureBox2.Image = image.Resize(pictureBox2.Width, pictureBox2.Height);
}
}
}
catch (Exception ex)
{
MessageBox.Show("Error: Could not read file from disk. Original error: " + ex.Message);
}
}
}
private void button_Click(object sender, EventArgs e)
{
Stream stream = null;
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.InitialDirectory = "c:\\";
openFileDialog.Filter = "txt files (*.txt)|*.txt|All files (*.*)|*.*";
openFileDialog.FilterIndex = 2;
openFileDialog.RestoreDirectory = true;
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
try
{
if ((stream = openFileDialog.OpenFile()) != null)
{
using (stream)
{
string pathName = openFileDialog.FileName;
Bitmap image = new Bitmap(pathName);
pictureBox1.Image = image.Resize(pictureBox1.Width, pictureBox1.Height);
Histogram histogram1 = new Histogram();
histogram1.SetIntensities(image);
histogram1.DrawHistogram(chart1);
chart1.Visible = true;
this.Controls.Add(chart1);
Histogram histogram2 = new Histogram();
image = histogram1.BalanceImage(image);
pictureBox2.Image = image.Resize(pictureBox2.Width, pictureBox2.Height);
histogram2.SetIntensities(image);
histogram2.DrawHistogram(chart2);
chart2.Visible = true;
this.Controls.Add(chart2);
}
}
}
catch (Exception ex)
{
MessageBox.Show("Error: Could not read file from disk. Original error: " + ex.Message);
}
}
}
public void ResizeDown()
{
Image image = new Bitmap(500, 500);
var newImage = image.Resize(100, 200);
Assert.AreEqual(newImage.Width, 100);
Assert.AreEqual(newImage.Height, 200);
}
public void ChooseImage(int id)
{
var filePaths = Directory.GetFiles(string.Format(@"C:\Users\Dimitar\Pictures\FilterIT\{0}\", User.Identity.Name), "*.png");
var img = new Bitmap(filePaths[id]);
int height = (int)(img.Height / ((float)img.Width / 500f));
var filteredImages = img.Resize(500, height).ApplyFilters();
Session["Image"] = img;
Session["ImageName"] = Path.GetFileName(filePaths[id]);
Session["FilteredImages"] = filteredImages;
}
public static bool Resize(this string sourcePath, string destinationPath, Size size)
{
if (File.Exists(sourcePath))
{
using (Image image = new Bitmap(sourcePath))
{
using (Image resized = image.Resize(size))
{
resized.Save(destinationPath, ImageFormat.Jpeg);
}
}
return true;
}
return false;
}
public string GetThumbnails(HttpPostedFileBase fileUpload)
{
var stream = fileUpload.InputStream;
Bitmap img = new Bitmap(stream);
if(User.Identity.IsAuthenticated)
SaveImage(fileUpload.FileName, img);
int height = (int)(img.Height / ((float)img.Width / 500f));
var filteredImages = img.Resize(500, height).ApplyFilters();
Session["Image"] = img;
Session["ImageName"] = fileUpload.FileName;
Session["FilteredImages"] = filteredImages;
return filteredImages;
}
private static Bitmap CreateLastPositionImage(Bitmap source)
{
try
{
var img = new Bitmap(source.Width, source.Width);
var cropRect = new Rectangle(0, 0, source.Width, source.Width);
using (var sourceImage = source)
{
using (var croppedImage = sourceImage.Clone(cropRect, sourceImage.PixelFormat))
{
using (var tb = new TextureBrush(croppedImage))
{
using (var g = Graphics.FromImage(img))
{
g.FillEllipse(tb, 0, 0, source.Width, source.Width);
g.DrawEllipse(
new Pen(Color.FromArgb(86, 86, 86), 6) { Alignment = PenAlignment.Inset }, 0, 0,
source.Width, source.Width);
}
}
}
}
return img.Resize(24, 24).Grayscale();
}
catch (Exception ex)
{
Global.Logger.AddItem(new LogItem(ex));
}
return null;
}
/// <summary>
/// This method shows how to evaluate a trained image classification model, with
/// explicitly created feature vectors.
/// </summary>
public static List<float> EvaluateImageInputUsingFeatureVector()
{
List<float> outputs = null;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Classification\ResNet
// along with a sample image file named "zebra.jpg".
Environment.CurrentDirectory = initialDirectory;
using (var model = new IEvaluateModelManagedF())
{
model.CreateNetwork(string.Format("modelPath=\"{0}\"", resnetModelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != resNetImageSize * resNetImageSize * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(resNetImageSize, resNetImageSize, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { {inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("EvaluateImageInputUsingFeatureVector: Outcome = {0}", max);
}
catch (CNTKException ex)
{
OnCNTKException(ex);
}
catch (Exception ex)
{
OnGeneralException(ex);
}
return outputs;
}
public void Resize()
{
using (Bitmap TestObject = new Bitmap(@"..\..\Data\Image\Lenna.jpg"))
{
using (Bitmap Image = Assert.Do<Bitmap>(() => TestObject.Resize(50, Utilities.Media.Image.ExtensionMethods.BitmapExtensions.Quality.Low, @".\Testing\LennaResize.jpg")))
{
Assert.NotNull(Image);
Assert.Equal(50,Image.Width);
Assert.Equal(50, Image.Height);
}
}
}
/// <summary>
/// This method shows how to evaluate a trained image classification model
/// </summary>
public static void EvaluateImageClassificationModel()
{
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
// The model is assumed to be located at: <CNTK>\Examples\Image\Miscellaneous\ImageNet\ResNet
// along with a sample image file named "zebra.jpg".
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Image\Miscellaneous\ImageNet\ResNet");
Environment.CurrentDirectory = initialDirectory;
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
string modelFilePath = Path.Combine(workingDirectory, "ResNet_18.model");
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
string imageFileName = Path.Combine(workingDirectory, "zebra.jpg");
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { {inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
// Retrieve the outcome index (so we can compare it with the expected index)
var max = outputs.Select((value, index) => new { Value = value, Index = index })
.Aggregate((a, b) => (a.Value > b.Value) ? a : b)
.Index;
Console.WriteLine("Outcome: {0}", max);
}
catch (CNTKException ex)
{
Console.WriteLine("Error: {0}\nNative CallStack: {1}\n Inner Exception: {2}", ex.Message, ex.NativeCallStack, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
}
}
/// <summary>
/// Makes a scaled version of an image using BrawlLib's texture converter.
/// </summary>
public static Bitmap Resize(Bitmap orig, Size resizeTo)
{
return orig.Resize(resizeTo.Width, resizeTo.Height);
}
public async Task<string[]> EvaluateCustomDNN(string imageUrl)
{
string domainBaseDirectory = AppDomain.CurrentDomain.BaseDirectory;
string workingDirectory = Environment.CurrentDirectory;
try
{
// This example requires the RestNet_18 model.
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
int numThreads = 1;
List<float> outputs;
using (var model = new IEvaluateModelManagedF())
{
// initialize the evaluation engine
// TODO: initializing the evaluation engine should be one only once at the beginning
var initParams = string.Format("numCPUThreads={0}", numThreads);
model.Init(initParams);
// load the model
string modelFilePath = Path.Combine(domainBaseDirectory, @"CNTK\Models\ResNet_18.model");
var modelOption = string.Format("modelPath=\"{0}\"", modelFilePath);
model.CreateNetwork(modelOption, deviceId: -1);
// Prepare input value in the appropriate structure and size
var inDims = model.GetNodeDimensions(NodeGroup.Input);
if (inDims.First().Value != 224 * 224 * 3)
{
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
}
// Transform the image
System.Net.Http.HttpClient httpClient = new HttpClient();
Stream imageStream = await httpClient.GetStreamAsync(imageUrl);
Bitmap bmp = new Bitmap(Bitmap.FromStream(imageStream));
var resized = bmp.Resize(224, 224, true);
var resizedCHW = resized.ParallelExtractCHW();
var inputs = new Dictionary<string, List<float>>() { { inDims.First().Key, resizedCHW } };
// We can call the evaluate method and get back the results (single layer output)...
var outDims = model.GetNodeDimensions(NodeGroup.Output);
outputs = model.Evaluate(inputs, outDims.First().Key);
}
List<string> o = new List<string>();
foreach (float f in outputs)
{
o.Add(f.ToString());
}
return o.ToArray<string>();
}
catch (Exception ex)
{
return new string[] { string.Format("domainBase directory {0}, workingDirectory {1}, exception details: {2}.", domainBaseDirectory, workingDirectory, ex.ToString()) };
}
}
protected override void GetImage(out Bitmap bac)
{
if (TEXT.IndexOf("Blade") != -1 || TEXT.IndexOf("Swashplate") != -1)
{
double time = Game.TIME;
double angle = time;
if (TEXT.IndexOf("Blade") != -1)
{
double limit = UpgradeInfo.DataBase.BladeUpgradeValues[INDEX];
if (limit != double.MaxValue)
{
angle %= 2.0 * limit;
if (angle > limit) angle = 2.0 * limit - angle;
angle -= 0.5 * limit;
}
angle *= 2.0;
}
else if (TEXT.IndexOf("Swashplate") != -1)
{
angle = 0.5 * Math.Cos(Math.PI * time / UpgradeInfo.DataBase.SwashplateUpgradeValues[INDEX]) * Math.PI;
}
Bitmap bmp = Pod_Frame.Pod.GenerateImage(angle, Math.PI * Game.TIME);
BITMAP.New(out bac,50, 50, Color.FromArgb(0, 0, 128));
bac.Paste(bmp, bac.Half() - bmp.Half(), ImagePasteMode.Transparent);
bac = bac.Resize(2.0);
BitmapData data_bac = bac.GetBitmapData();
DrawLOCKED(data_bac);
bac.UnlockBits(data_bac);
}
else
{
Bitmap bmp; base.GetImage(out bmp);
BITMAP.New(out bac,bmp.Width, bmp.Height, Color.FromArgb(0, 0, 128));
bac.Paste(bmp, new Point(0, 0), ImagePasteMode.Transparent);
}
}
protected override void GetImage(out Bitmap bmp)
{
bmp=ITEM[ITEM_EQUIPPED].GET_IMAGE();bmp=bmp.Resize(GetSize());
BitmapData data_bmp = bmp.GetBitmapData();
DrawLOCKED(data_bmp);
bmp.UnlockBits(data_bmp);
}
public void ImportFromBitmap(Bitmap bitmap, CompressionMethod compression = CompressionMethod.RLE)
{
if (bitmap.Width <= 32 || bitmap.Height <= 32) { bitmap = bitmap.Resize(64, 64); }
width = bitmap.Width;
height = bitmap.Height;
int planeCount = 4;// Image.GetPixelFormatSize(bitmap.PixelFormat) / 8;
if (bitmap.Width < 8 && bitmap.Height < 8) { planeCount = 1; }
BitmapData bmpdata = bitmap.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb);
byte[] iB = new byte[4 * bmpdata.Width * bmpdata.Height];
Marshal.Copy(bmpdata.Scan0, iB, 0, bmpdata.Stride * bmpdata.Height);
bitmap.UnlockBits(bmpdata);
Parallel.For(0, planeCount,
i =>
//for (int i = 0; i < planeCount; i++)
{
var plane = new Plane(i);
plane.Data = iB.ToList().Every(planeCount, i).ToArray();
plane.Compress(planeCount > 1 ? compression : CompressionMethod.None);
planes.Add(plane);
}
);
switch (planeCount)
{
case 1:
planeFormat = PlaneFormat.Format1planeARGB;
break;
case 3:
planeFormat = PlaneFormat.Format4planeXRGB;
break;
case 4:
planeFormat = PlaneFormat.Format4planeARGB;
break;
}
}
private static Bitmap CreateSidebarImage(Bitmap source)
{
return source.Resize(46, 46);
}
public ActionResult Upload(string qqfile)
{
try
{
var stream = Request.InputStream;
if (Request.Files.Count > 0)
{
HttpPostedFileBase postedFile = Request.Files[0];
stream = postedFile.InputStream;
}
string fileUrl = _logoManager.GetPath("img" + DateTime.Now.Ticks + ".jpg");
var image = new Bitmap(stream);
image = image.Resize(150, 1024);
image.SaveJpeg(Server.MapPath(fileUrl), 90);
return Json(new { imageUrl = fileUrl });
}
catch (Exception ex)
{
return Json(new { imageUrl = _logoManager.GetPath(String.Empty) });
}
}
private static Bitmap GetPortrait(string path)
{
var portrait = new Bitmap(path);
portrait = portrait.Clone(new Rectangle(10, 12, 103, 100), PixelFormat.Format24bppRgb);
portrait = portrait.Resize(new Size(50, 50), false) as Bitmap;
return portrait;
}
private static Bitmap CreateGankImage(Bitmap source)
{
return source.Resize(24, 24);
}
public void ResizeWithWidthIsGreaterThanImageWidthThrowsArgumentOutOfRangeException(int invalidWidth)
{
// Arrange
var image = new Bitmap(1, 1);
// Act
// Assert
Assert.Throws<ArgumentOutOfRangeException>(() => image.Resize(invalidWidth));
}
//
// The example shows
// - how to load model.
// - how to prepare input data for a signle sample, a batch of samples in dense format.
// - how to prepare input and output data map
// - how to evaluate a model
// - how to retrieve evaluation result and retrieve output data in dense format.
//
static void EvaluationWithDenseData(DeviceDescriptor device)
{
const string outputName = "Plus2060_output";
// Load the model.
Function modelFunc = Function.LoadModel("z.model");
// Get output variable based on name
Variable outputVar = modelFunc.Outputs.Where(variable => string.Equals(variable.Name, outputName)).Single();
// Get input variable. The model has only one single input.
// The same way described above for output variable can be used here to get input variable by name.
Variable inputVar = modelFunc.Arguments.Single();
// Get shape data for the input variable
NDShape inputShape = inputVar.Shape;
uint imageWidth = inputShape[0];
uint imageHeight = inputShape[1];
uint imageChannels = inputShape[2];
uint imageSize = inputShape.TotalSize;
var outputDataMap = new Dictionary<Variable, Value>();
// Use case 1: Evaluate with single image
Console.WriteLine("Evaluate single image");
// Image preprocessing to match input requirements of the model.
Bitmap bmp = new Bitmap(Bitmap.FromFile("00000.png"));
var resized = bmp.Resize((int)imageWidth, (int)imageHeight, true);
List<float> resizedCHW = resized.ParallelExtractCHW();
// Create input data map
var inputDataMap = new Dictionary<Variable, Value>();
var inputVal = Value.CreateBatch(inputVar.Shape, resizedCHW, device);
inputDataMap.Add(inputVar, inputVal);
// Create ouput data map. Using null as Value to indicate using system allocated memory.
// Alternatively, create a Value object and add it to the data map.
outputDataMap.Add(outputVar, null);
// Start evaluation on the device
modelFunc.Evaluate(inputDataMap, outputDataMap, device);
// Get evaluate result as dense output
var outputData = new List<List<float>>();
Value outputVal = outputDataMap[outputVar];
outputVal.CopyTo(outputVar.Shape, outputData);
// Use case 2: Evaluate with batch of images
Console.WriteLine("Evaluate batch of images");
var fileList = new List<string>() { "00000.png", "00001.png", "00002.png" };
var seqData = new List<float>();
for (int sampleIndex = 0; sampleIndex < fileList.Count; sampleIndex++)
{
bmp = new Bitmap(Bitmap.FromFile(fileList[sampleIndex++]));
resized = bmp.Resize((int)imageWidth, (int)imageHeight, true);
resizedCHW = resized.ParallelExtractCHW();
// Aadd this sample to the data buffer.
seqData.AddRange(resizedCHW);
}
// Create Value for the batch data.
inputVal = Value.CreateBatch(inputVar.Shape, seqData, device);
// Create input and output data map.
inputDataMap[inputVar] = inputVal;
outputDataMap[outputVar] = null;
// Evaluate the model against the batch input
modelFunc.Evaluate(inputDataMap, outputDataMap, device);
// Retrieve the evaluation result.
outputData = new List<List<float>>();
outputVal = outputDataMap[outputVar];
outputVal.CopyTo(outputVar.Shape, outputData);
// Output result
Console.WriteLine("The number of sequences in the batch: " + outputData.Count);
int seqNo = 0;
uint outputSampleSize = outputVar.Shape.TotalSize;
foreach(var seq in outputData)
{
Console.WriteLine(String.Format("Sequence {0} contains {1} samples.", seqNo++, seq.Count/outputSampleSize));
uint i = 0;
uint sampleNo = 0;
foreach (var element in seq)
{
Console.Write(String.Format(" sample {0}: " + sampleNo));
Console.Write(element);
if (++i % outputSampleSize == 0)
{
Console.WriteLine(".");
sampleNo++;
}
else
Console.WriteLine(",");
}
}
}
static Bitmap Get_Image()
{
Bitmap ans;
using (Bitmap bmp = new Bitmap(FULL))
{
Point p = new Point(0, ((1.0 - RATIO) * bmp.Height).Round());
Size sz = new Size(bmp.Width, bmp.Height - p.Y);
using (Bitmap bac = new Bitmap(EMPTY))
{
BitmapData data_bac = bac.GetBitmapData();
data_bac.Paste(bmp, new Point(0, 0), ImagePasteMode.Transparent, new Rectangle(p, sz));
data_bac.Add_RGB(-(BLACK_VALUE * 100.0).Round());
double healthratio = VALUE / MAXIMUM;
string text = (healthratio * 100.0).ToString("F2") + " %";
Color textcolor = Color.FromArgb(0, 255, 0).Merge(Color.FromArgb(0, 0, 0), BLACK_VALUE);
using (Font font = new Font("微軟正黑體", 14, FontStyle.Bold))
{
data_bac.Paste(text, data_bac.Half(), textcolor, font, StringAlign.Middle, StringRowAlign.Middle);
}
bac.UnlockBits(data_bac);
ans = bac.Resize(SIZE_RATIO);
}
}
return ans;
}