public void TestBackAndForthConversionWithAlphaNoInterleaveNoOffset()
{
IHostEnvironment env = new MLContext();
const int imageHeight = 100;
var 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).Transform(cropped);
IDataView backToBitmaps = new VectorToImageConvertingTransformer(env, "ImageRestored", imageHeight, imageWidth, "ImagePixels",
ImagePixelExtractingEstimator.ColorBits.All).Transform(pixels);
var fname = nameof(TestBackAndForthConversionWithAlphaNoInterleaveNoOffset) + "_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);
}
}
}
}
Done();
}
public void TestGreyscaleTransformImages()
{
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
return;
}
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.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);
IDataView grey = new ImageGrayscalingTransformer(env, ("ImageGrey", "ImageCropped")).Transform(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.GetRowCursorForAllColumns())
{
var bitmapGetter = cursor.GetGetter <Bitmap>(grey.Schema["ImageGrey"]);
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();
}
public static CommonOutputs.TransformOutput ImageLoader(IHostEnvironment env, ImageLoadingTransformer.Options input)
{
var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImageLoaderTransform", input);
var xf = ImageLoadingTransformer.Create(h, input, input.Data);
return(new CommonOutputs.TransformOutput()
{
Model = new TransformModelImpl(h, xf, input.Data),
OutputData = xf
});
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowTransformCifarSavedModel()
{
var modelLocation = "cifar_saved_model";
var mlContext = new MLContext(seed: 1, conc: 1);
var tensorFlowModel = TensorFlowUtils.LoadTensorFlowModel(mlContext, modelLocation);
var schema = tensorFlowModel.GetInputSchema();
Assert.True(schema.TryGetColumnIndex("Input", out int column));
var type = (VectorType)schema[column].Type;
var imageHeight = type.Dimensions[0];
var imageWidth = type.Dimensions[1];
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = mlContext.Data.ReadFromTextFile(dataFile, columns: new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
);
var images = new ImageLoadingTransformer(mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(mlContext, "ImageCropped", imageWidth, imageHeight, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(mlContext, "Input", "ImageCropped", interleave: true).Transform(cropped);
IDataView trans = new TensorFlowTransformer(mlContext, tensorFlowModel, "Output", "Input").Transform(pixels);
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursorForAllColumns())
{
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(4, numRows);
}
}
public void TensorFlowTransformObjectDetectionTest()
{
var modelLocation = @"C:\models\TensorFlow\ssd_mobilenet_v1_coco_2018_01_28\frozen_inference_graph.pb";
var mlContext = new MLContext(seed: 1, conc: 1);
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var images = new ImageLoadingTransformer(mlContext, imageFolder, ("ImageReal", "ImagePath")).Transform(data);
var cropped = new ImageResizingTransformer(mlContext, "ImageCropped", 32, 32, "ImageReal").Transform(images);
var pixels = new ImagePixelExtractingTransformer(mlContext, "image_tensor", "ImageCropped", asFloat: false).Transform(cropped);
var tf = new TensorFlowTransformer(mlContext, modelLocation,
new[] { "detection_boxes", "detection_scores", "num_detections", "detection_classes" }, new[] { "image_tensor" }).Transform(pixels);
tf.Schema.TryGetColumnIndex("image_tensor", out int input);
tf.Schema.TryGetColumnIndex("detection_boxes", out int boxes);
tf.Schema.TryGetColumnIndex("detection_scores", out int scores);
tf.Schema.TryGetColumnIndex("num_detections", out int num);
tf.Schema.TryGetColumnIndex("detection_classes", out int classes);
using (var curs = tf.GetRowCursor(tf.Schema["image_tensor"], tf.Schema["detection_boxes"], tf.Schema["detection_scores"], tf.Schema["detection_classes"], tf.Schema["num_detections"]))
{
var getInput = curs.GetGetter <VBuffer <byte> >(input);
var getBoxes = curs.GetGetter <VBuffer <float> >(boxes);
var getScores = curs.GetGetter <VBuffer <float> >(scores);
var getNum = curs.GetGetter <VBuffer <float> >(num);
var getClasses = curs.GetGetter <VBuffer <float> >(classes);
var buffer = default(VBuffer <float>);
var inputBuffer = default(VBuffer <byte>);
while (curs.MoveNext())
{
getInput(ref inputBuffer);
getBoxes(ref buffer);
getScores(ref buffer);
getNum(ref buffer);
getClasses(ref buffer);
}
}
}
public void TestSaveImages()
{
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
return;
}
var env = new MLContext();
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", 100, 100, "ImageReal", ImageResizingEstimator.ResizingKind.IsoPad).Transform(images);
using (var cursor = cropped.GetRowCursorForAllColumns())
{
var pathGetter = cursor.GetGetter <ReadOnlyMemory <char> >(cropped.Schema["ImagePath"]);
ReadOnlyMemory <char> path = default;
var bitmapCropGetter = cursor.GetGetter <Bitmap>(cropped.Schema["ImageCropped"]);
Bitmap bitmap = default;
while (cursor.MoveNext())
{
pathGetter(ref path);
bitmapCropGetter(ref bitmap);
Assert.NotNull(bitmap);
var fileToSave = GetOutputPath(Path.GetFileNameWithoutExtension(path.ToString()) + ".cropped.jpg");
bitmap.Save(fileToSave, System.Drawing.Imaging.ImageFormat.Jpeg);
}
}
Done();
}
public Mapper(ImageLoadingTransformer parent, DataViewSchema inputSchema)
: base(parent.Host.Register(nameof(Mapper)), parent, inputSchema)
{
_imageType = new ImageType();
_parent = parent;
}
public Mapper(ImageLoadingTransformer parent, DataViewSchema inputSchema, bool type)
: base(parent.Host.Register(nameof(Mapper)), parent, inputSchema)
{
_type = type;
_parent = parent;
}
public void TestBackAndForthConversionWithoutAlphaNoInterleave()
{
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.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", ImagePixelExtractingEstimator.ColorBits.Rgb, false, 2f / 255, 127.5f).Transform(cropped);
IDataView backToBitmaps = new VectorToImageTransform(env, new VectorToImageTransform.Arguments()
{
InterleaveArgb = false,
Offset = -1f,
Scale = 255f / 2,
Columns = new VectorToImageTransform.Column[1] {
new VectorToImageTransform.Column()
{
Name = "ImageRestored", Source = "ImagePixels", ImageHeight = imageHeight, ImageWidth = imageWidth, ContainsAlpha = false
}
}
}, 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);
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();
}
