public void TestBackAndForthConversionWithDifferentOrder()
{
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
const int imageWidth = 130;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.String, 0),
new TextLoader.Column("Name", DataKind.String, 1),
}
}, new MultiFileSource(dataFile));
var images = new ImageLoadingTransformer(env, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(env, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(env, "ImagePixels", "ImageCropped", ImagePixelExtractingEstimator.ColorBits.All, orderOfExtraction: ImagePixelExtractingEstimator.ColorsOrder.ABRG).Transform(cropped);
IDataView backToBitmaps = new VectorToImageConvertingTransformer(env, "ImageRestored", imageHeight, imageWidth, "ImagePixels",
ImagePixelExtractingEstimator.ColorBits.All, orderOfColors: ImagePixelExtractingEstimator.ColorsOrder.ABRG).Transform(pixels);
var fname = nameof(TestBackAndForthConversionWithDifferentOrder) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(backToBitmaps));
backToBitmaps = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
using (var cursor = backToBitmaps.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageRestored"]);
Bitmap restoredBitmap = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageCropped"]);
Bitmap croppedBitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref restoredBitmap);
Assert.NotNull(restoredBitmap);
bitmapCropGetter(ref croppedBitmap);
Assert.NotNull(croppedBitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var c = croppedBitmap.GetPixel(x, y);
var r = restoredBitmap.GetPixel(x, y);
if (c != r)
{
Assert.False(true);
}
Assert.True(c == r);
}
}
}
}
Done();
}
public void TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset()
{
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
const int imageWidth = 130;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Arguments()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var images = new ImageLoadingTransformer(env, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(env, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(env, "ImagePixels", "ImageCropped").Transform(cropped);
IDataView backToBitmaps = new VectorToImageTransform(env, new VectorToImageTransform.Arguments()
{
InterleaveArgb = false,
Columns = new VectorToImageTransform.Column[1] {
new VectorToImageTransform.Column()
{
Name = "ImageRestored", Source = "ImagePixels", ImageHeight = imageHeight, ImageWidth = imageWidth, ContainsAlpha = false
}
}
}, pixels);
var fname = nameof(TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(backToBitmaps));
backToBitmaps = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
backToBitmaps.Schema.TryGetColumnIndex("ImageRestored", out int bitmapColumn);
backToBitmaps.Schema.TryGetColumnIndex("ImageCropped", out int cropBitmapColumn);
using (var cursor = backToBitmaps.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(bitmapColumn);
Bitmap restoredBitmap = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(cropBitmapColumn);
Bitmap croppedBitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref restoredBitmap);
Assert.NotNull(restoredBitmap);
bitmapCropGetter(ref croppedBitmap);
Assert.NotNull(croppedBitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var c = croppedBitmap.GetPixel(x, y);
var r = restoredBitmap.GetPixel(x, y);
Assert.True(c.R == r.R && c.G == r.G && c.B == r.B);
}
}
}
Done();
}
}
public void TestGreyscaleTransformImages()
{
IHostEnvironment env = new MLContext();
var imageHeight = 150;
var imageWidth = 100;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Name = "ImageCropped", Source = "ImageReal", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
IDataView grey = ImageGrayscaleTransform.Create(env, new ImageGrayscaleTransform.Arguments()
{
Column = new ImageGrayscaleTransform.Column[1] {
new ImageGrayscaleTransform.Column()
{
Name = "ImageGrey", Source = "ImageCropped"
}
}
}, cropped);
var fname = nameof(TestGreyscaleTransformImages) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(grey));
grey = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
grey.Schema.TryGetColumnIndex("ImageGrey", out int greyColumn);
using (var cursor = grey.GetRowCursor((x) => true))
{
var bitmapGetter = cursor.GetGetter <Bitmap>(greyColumn);
Bitmap bitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref bitmap);
Assert.NotNull(bitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var pixel = bitmap.GetPixel(x, y);
// greyscale image has same values for R,G and B
Assert.True(pixel.R == pixel.G && pixel.G == pixel.B);
}
}
}
}
Done();
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowTransformCifarSavedModel()
{
var model_location = "cifar_saved_model";
var env = new MLContext();
var tensorFlowModel = TensorFlowUtils.LoadTensorFlowModel(env, model_location);
var schema = tensorFlowModel.GetInputSchema();
Assert.True(schema.TryGetColumnIndex("Input", out int column));
var type = (VectorType)schema.GetColumnType(column);
var imageHeight = type.Dimensions[0];
var imageWidth = type.Dimensions[1];
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(env, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "Input", UseAlpha = false, InterleaveArgb = true
}
}
}, cropped);
IDataView trans = TensorFlowTransform.Create(env, pixels, tensorFlowModel, new[] { "Output" }, new[] { "Input" });
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursor(col => col == output))
{
var buffer = default(VBuffer <float>);
var getter = cursor.GetGetter <VBuffer <float> >(output);
var numRows = 0;
while (cursor.MoveNext())
{
getter(ref buffer);
Assert.Equal(10, buffer.Length);
numRows += 1;
}
Assert.Equal(3, numRows);
}
}
public void TestBackAndForthConversionWithoutAlphaNoInterleave()
{
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
return;
}
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
const int imageWidth = 130;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(env, new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("ImagePath", DataKind.String, 0),
new TextLoader.Column("Name", DataKind.String, 1),
}
}, new MultiFileSource(dataFile));
var images = new ImageLoadingTransformer(env, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(env, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(env, "ImagePixels", "ImageCropped", scaleImage: 2f / 19, offsetImage: 30).Transform(cropped);
IDataView backToBitmaps = new VectorToImageConvertingTransformer(env, "ImageRestored", imageHeight, imageWidth, "ImagePixels",
scaleImage: 19 / 2f, offsetImage: -30).Transform(pixels);
var fname = nameof(TestBackAndForthConversionWithoutAlphaNoInterleave) + "_model.zip";
var fh = env.CreateOutputFile(fname);
using (var ch = env.Start("save"))
TrainUtils.SaveModel(env, ch, fh, null, new RoleMappedData(backToBitmaps));
backToBitmaps = ModelFileUtils.LoadPipeline(env, fh.OpenReadStream(), new MultiFileSource(dataFile));
DeleteOutputPath(fname);
using (var cursor = backToBitmaps.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageRestored"]);
Bitmap restoredBitmap = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(backToBitmaps.Schema["ImageCropped"]);
Bitmap croppedBitmap = default;
while (cursor.MoveNext())
{
bitmapGetter(ref restoredBitmap);
Assert.NotNull(restoredBitmap);
bitmapCropGetter(ref croppedBitmap);
Assert.NotNull(croppedBitmap);
for (int x = 0; x < imageWidth; x++)
{
for (int y = 0; y < imageHeight; y++)
{
var c = croppedBitmap.GetPixel(x, y);
var r = restoredBitmap.GetPixel(x, y);
Assert.True(c.R == r.R && c.G == r.G && c.B == r.B);
}
}
}
}
Done();
}
