ImageSharp Sample
This commit is contained in:
Sheil Kumar
Родитель
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Коммит
34316af986
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@ -104,7 +104,10 @@ These advanced samples show how to use various binding and evaluation features i
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- **[PyTorch Data Analysis](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: The tutorial shows how to solve a classification task with a neural network using the PyTorch library, export the model to ONNX format and deploy the model with the Windows Machine Learning application that can run on any Windows device.
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- **[PyTorch Image Classification](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: The tutorial shows how to train an image classification neural network model using PyTorch, export the model to the ONNX format, and deploy it in a Windows Machine Learning application running locally on your Windows device.
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- **[YoloV4 Object Detection](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: This tutorial shows how to build a UWP C# app that uses the YOLOv4 model to detect objects in video streams.
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### Interop with other external Image Processing Libraries
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- **[OpenCV Interop](Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/OpenCVInterop)**: This sample demonstrates how to interop between [Windows ML](https://docs.microsoft.com/en-us/windows/ai/windows-ml/) and [OpenCV](https://github.com/opencv/opencv).
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- **[ImageSharp Interop](Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSHarpInterop)**: This sample demonstrates how to interop between [Windows ML](https://docs.microsoft.com/en-us/windows/ai/windows-ml/) and [ImageSharp](https://github.com/SixLabors/ImageSharp).
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## Developer Tools
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@ -31,6 +31,9 @@ namespace WinMLSamplesGallery
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case "OpenCVInterop":
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SampleFrame.Navigate(typeof(Samples.OpenCVInterop));
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break;
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case "ImageSharpInterop":
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SampleFrame.Navigate(typeof(Samples.ImageSharpInterop));
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break;
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}
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if (sampleMetadata.Docs.Count > 0)
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DocsHeader.Visibility = Visibility.Visible;
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@ -36,14 +36,24 @@
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]
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},
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{
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"Title": "OpenCV Interop",
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"DescriptionShort": "The sample uses Windows ML to classify images that have been denoised using OpenCV.",
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"Title": "OpenCV",
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"DescriptionShort": "The sample uses Windows ML to classify images that have been denoised natively using OpenCV.",
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"Description": "This sample demonstrates interop between Windows ML and OpenCV. The sample classifes images that have been denoised using OpenCV's medianBlur using the SqueezeNet model on Windows ML. Choose an image to get started.",
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"Icon": "\uE155",
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"Tag": "OpenCVInterop",
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"XAMLGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/OpenCVInterop/OpenCVInterop.xaml",
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"CSharpGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/OpenCVInterop/OpenCVInterop.xaml.cs",
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"Docs": []
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},
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{
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"Title": "ImageSharp",
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"DescriptionShort": "The sample uses Windows ML to classify images that have been loaded by the managed ImageSharp library.",
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"Description": "The sample uses Windows ML to classify images that have been rotated by the managed ImageSharp library. Choose an image to get started.",
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"Icon": "\uE155",
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"Tag": "ImageSharpInterop",
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"XAMLGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/ImageSharpInterop.xaml",
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"CSharpGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/ImageSharpInterop.xaml.cs",
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"Docs": []
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}
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]
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}
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@ -0,0 +1,49 @@
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using System;
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using System.Linq;
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using System.Runtime.InteropServices.WindowsRuntime;
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using SixLabors.ImageSharp;
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using SixLabors.ImageSharp.Processing;
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using SixLabors.ImageSharp.PixelFormats;
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using SixLabors.ImageSharp.Advanced;
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using SixLabors.ImageSharp.Memory;
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using Microsoft.AI.MachineLearning;
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using Windows.Graphics.Imaging;
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namespace ImageSharpExtensrions
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{
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public static class ImageExtensions
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{
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public static Windows.Storage.Streams.IBuffer AsBuffer<TPixel>(this Image<TPixel> img) where TPixel : unmanaged, IPixel<TPixel>
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{
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var memoryGroup = img.GetPixelMemoryGroup();
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var memory = memoryGroup.ToArray()[0];
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var pixelData = System.Runtime.InteropServices.MemoryMarshal.AsBytes(memory.Span).ToArray(); // Can we get rid of this?
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var buffer = pixelData.AsBuffer();
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return buffer;
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}
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public static ITensor AsTensor<TPixel>(this Image<TPixel> img) where TPixel : unmanaged, IPixel<TPixel>
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{
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var buffer = img.AsBuffer();
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var shape = new long[] { 1, buffer.Length };
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var tensor = TensorUInt8Bit.CreateFromBuffer(shape, buffer);
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return tensor;
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}
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public static SoftwareBitmap AsSoftwareBitmap<TPixel>(this Image<TPixel> img) where TPixel : unmanaged, IPixel<TPixel>
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{
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var buffer = img.AsBuffer();
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var format = BitmapPixelFormat.Unknown;
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if (typeof(TPixel) == typeof(Bgra32))
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{
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format = BitmapPixelFormat.Bgra8;
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}
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var softwareBitmap = SoftwareBitmap.CreateCopyFromBuffer(buffer, BitmapPixelFormat.Bgra8, img.Width, img.Height);
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return softwareBitmap;
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}
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}
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}
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@ -0,0 +1,136 @@
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<Page
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x:Class="WinMLSamplesGallery.Samples.ImageSharpInterop"
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xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
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xmlns:d="http://schemas.microsoft.com/expression/blend/2008"
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xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
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xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
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xmlns:local_controls="using:WinMLSamplesGallery.Controls"
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xmlns:local_samples="using:WinMLSamplesGallery.Samples"
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mc:Ignorable="d"
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Background="{ThemeResource ApplicationPageBackgroundThemeBrush}">
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<Page.Resources>
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<DataTemplate x:Key="ImageTemplate" x:DataType="local_controls:Thumbnail">
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<Grid Width="200" Height="204">
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<Image Stretch="UniformToFill" Source="{x:Bind ImageUri}" HorizontalAlignment="Center" VerticalAlignment="Center" Height="204"/>
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</Grid>
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</DataTemplate>
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<DataTemplate x:Name="InferenceResultsTemplate" x:DataType="local_controls:Prediction">
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<StackPanel Orientation="Horizontal">
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<TextBlock Width="414"
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FontSize="14"
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Foreground="Black"
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Padding="0,1,1,1"
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Typography.Capitals="AllSmallCaps"
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Typography.StylisticSet4="True"
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TextTrimming="CharacterEllipsis">
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<Run Text="[" />
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<Run Text="{Binding Index}" />
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<Run Text="] " />
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<Run Text="{Binding Name}" />
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</TextBlock>
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<TextBlock Width="120"
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FontSize="14"
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Foreground="Black"
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Padding="0,1,1,1"
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Typography.Capitals="AllSmallCaps"
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Typography.StylisticSet4="True">
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<Run Text="p =" />
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<Run Text="{Binding Probability}" />
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</TextBlock>
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</StackPanel>
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</DataTemplate>
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<DataTemplate x:Name="AllModelsTemplate" x:DataType="local_samples:ClassifierViewModel">
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<Grid Background="#e6e6e6" BorderBrush="#12bef6" BorderThickness="1">
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<TextBlock FontSize="14" Text="{x:Bind Title}"
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Typography.Capitals="AllSmallCaps"
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Typography.StylisticSet4="True"
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VerticalAlignment="Top"
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Padding="10,2,10,2"
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MinWidth="136"
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/>
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</Grid>
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</DataTemplate>
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</Page.Resources>
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<Grid>
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<ScrollViewer
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ZoomMode="Disabled"
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IsVerticalScrollChainingEnabled="True"
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HorizontalScrollMode="Enabled" HorizontalScrollBarVisibility="Disabled"
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VerticalScrollMode="Enabled" VerticalScrollBarVisibility="Visible">
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<Grid>
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<Grid.RowDefinitions>
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<RowDefinition Height="*" />
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<RowDefinition Height="Auto" />
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<RowDefinition Height="*" />
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</Grid.RowDefinitions>
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<StackPanel Grid.Row="0" Orientation="Horizontal" Padding="0,10,0,0">
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<StackPanel Orientation="Vertical">
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<StackPanel Orientation="Horizontal" HorizontalAlignment="Left">
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<Button FontFamily="Segoe MDL2 Assets" Content="" Width="97" Height="50" HorizontalAlignment="Left" Click="OpenButton_Clicked" />
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<Grid Padding="5,0,0,0">
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<ComboBox x:Name="DeviceComboBox" SelectedIndex="0" Background="LightGray" PlaceholderText="Device" Height="50" Width="97"
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SelectionChanged="DeviceComboBox_SelectionChanged">
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<TextBlock Text="CPU" FontSize="18" Typography.Capitals="AllSmallCaps" Typography.StylisticSet4="True"/>
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<TextBlock Text="DML" FontSize="18" Typography.Capitals="AllSmallCaps" Typography.StylisticSet4="True"/>
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</ComboBox>
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</Grid>
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</StackPanel>
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<GridView
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x:Name="BasicGridView"
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ItemTemplate="{StaticResource ImageTemplate}"
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IsItemClickEnabled="True"
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SelectionChanged="SampleInputsGridView_SelectionChanged"
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SelectionMode="Single"
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Padding="0,6,0,0"
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HorizontalAlignment="Center">
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<GridView.ItemsPanel>
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<ItemsPanelTemplate>
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<StackPanel Orientation="Vertical" />
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</ItemsPanelTemplate>
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</GridView.ItemsPanel>
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<GridView.Items>
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<local_controls:Thumbnail ImageUri="ms-appx:///InputData/kitten.png" />
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</GridView.Items>
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</GridView>
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</StackPanel>
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<Slider x:Name="RotationSlider" IsEnabled="False" ValueChanged="RotationSlider_ValueChanged" Orientation="Vertical" TickFrequency="60" TickPlacement="Outside" Maximum="360" Minimum="0"/>
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<Border BorderBrush="LightGray" Padding="5,0,0,0">
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<Image x:Name="InputImage" Stretch="UniformToFill" Height="260" HorizontalAlignment="Center"/>
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</Border>
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</StackPanel>
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<StackPanel Orientation="Horizontal" Grid.Row="2"
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Padding="0,7,0,0">
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<ListView
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x:Name="InferenceResults"
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HorizontalAlignment="Stretch"
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Padding="0,2,0,0"
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ItemTemplate="{StaticResource InferenceResultsTemplate}"
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IsItemClickEnabled="False"
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SingleSelectionFollowsFocus="False">
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<ListView.ItemContainerStyle>
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<Style TargetType="ListViewItem">
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<Setter Property="Margin" Value="1,1,1,1"/>
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<Setter Property="MinHeight" Value="0"/>
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</Style>
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</ListView.ItemContainerStyle>
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<ListView.ItemsPanel>
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<ItemsPanelTemplate>
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<ItemsWrapGrid x:Name="MaxItemsWrapGrid" Orientation="Vertical" HorizontalAlignment="Stretch"/>
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</ItemsPanelTemplate>
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</ListView.ItemsPanel>
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</ListView>
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<local_controls:PerformanceMonitor x:Name="PerformanceMetricsMonitor"/>
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</StackPanel>
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</Grid>
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</ScrollViewer>
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</Grid>
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</Page>
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@ -0,0 +1,259 @@
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using Microsoft.AI.MachineLearning;
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using Microsoft.UI.Xaml;
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using Microsoft.UI.Xaml.Controls;
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using Microsoft.UI.Xaml.Data;
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using Microsoft.UI.Xaml.Media;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Runtime.InteropServices.WindowsRuntime;
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using Windows.Foundation;
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using Windows.Foundation.Collections;
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using Windows.Foundation.Metadata;
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using Windows.Graphics.Imaging;
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using Windows.Media;
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using Windows.Storage;
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using Windows.UI;
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using WinMLSamplesGallery.Common;
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using WinMLSamplesGallery.Controls;
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using SixLabors.ImageSharp;
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using SixLabors.ImageSharp.PixelFormats;
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using ImageSharpExtensrions;
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using SixLabors.ImageSharp.Processing;
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namespace WinMLSamplesGallery.Samples
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{
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/// <summary>
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/// An empty page that can be used on its own or navigated to within a Frame.
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/// </summary>
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public sealed partial class ImageSharpInterop : Page
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{
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const long BatchSize = 1;
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const long TopK = 10;
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const long Height = 224;
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const long Width = 224;
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const long Channels = 4;
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private LearningModelSession _inferenceSession;
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private LearningModelSession _tensorizationSession;
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private LearningModelSession _postProcessingSession;
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private static Dictionary<long, string> _imagenetLabels;
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private Image<Bgra32> CurrentImage { get; set; }
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#pragma warning disable CA1416 // Validate platform compatibility
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private LearningModelDeviceKind SelectedDeviceKind
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{
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get
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{
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return (DeviceComboBox.SelectedIndex == 0) ?
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LearningModelDeviceKind.Cpu :
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LearningModelDeviceKind.DirectXHighPerformance;
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}
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}
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#pragma warning restore CA1416 // Validate platform compatibility
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public ImageSharpInterop()
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{
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this.InitializeComponent();
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_imagenetLabels = LoadLabels("ms-appx:///InputData/sysnet.txt");
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var tensorizationModel = TensorizationModels.BasicTensorization(Height, Width, BatchSize, Channels, Height, Width, "nearest");
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_tensorizationSession = CreateLearningModelSession(tensorizationModel, SelectedDeviceKind);
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_inferenceSession = CreateLearningModelSession("ms-appx:///Models/squeezenet1.1-7.onnx");
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_postProcessingSession = CreateLearningModelSession(TensorizationModels.SoftMaxThenTopK(TopK));
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}
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#pragma warning disable CA1416 // Validate platform compatibility
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private (IEnumerable<string>, IReadOnlyList<float>) Classify(Image<Bgra32> image, float angle)
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{
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long start, stop;
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PerformanceMetricsMonitor.ClearLog();
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// Tensorize
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start = HighResolutionClock.UtcNow();
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image.Mutate(ctx => ctx.Rotate(angle));
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var resizeOptions = new ResizeOptions()
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{
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Mode = ResizeMode.Crop,
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Size = new SixLabors.ImageSharp.Size((int)Width, (int)Height)
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};
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image.Mutate(ctx => ctx.Resize(resizeOptions));
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object input = image.AsTensor();
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var tensorizationResults = Evaluate(_tensorizationSession, input);
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object tensorizedOutput = tensorizationResults.Outputs.First().Value;
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stop = HighResolutionClock.UtcNow();
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var tensorizeDuration = HighResolutionClock.DurationInMs(start, stop);
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// Inference
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start = HighResolutionClock.UtcNow();
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var inferenceResults = Evaluate(_inferenceSession, tensorizedOutput);
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var inferenceOutput = inferenceResults.Outputs.First().Value;
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stop = HighResolutionClock.UtcNow();
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var inferenceDuration = HighResolutionClock.DurationInMs(start, stop);
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// PostProcess
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start = HighResolutionClock.UtcNow();
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var postProcessedOutputs = Evaluate(_postProcessingSession, inferenceOutput);
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var topKValues = (TensorFloat)postProcessedOutputs.Outputs["TopKValues"];
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var topKIndices = (TensorInt64Bit)postProcessedOutputs.Outputs["TopKIndices"];
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// Return results
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var probabilities = topKValues.GetAsVectorView();
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var indices = topKIndices.GetAsVectorView();
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var labels = indices.Select((index) => _imagenetLabels[index]);
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stop = HighResolutionClock.UtcNow();
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var postProcessDuration = HighResolutionClock.DurationInMs(start, stop);
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PerformanceMetricsMonitor.Log("Tensorize", tensorizeDuration);
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PerformanceMetricsMonitor.Log("Pre-process", 0);
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PerformanceMetricsMonitor.Log("Inference", inferenceDuration);
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PerformanceMetricsMonitor.Log("Post-process", postProcessDuration);
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RenderingHelpers.BindSoftwareBitmapToImageControl(InputImage, image.AsSoftwareBitmap());
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image.Dispose();
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return (labels, probabilities);
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}
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private static LearningModelEvaluationResult Evaluate(LearningModelSession session, object input)
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{
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// Create the binding
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var binding = new LearningModelBinding(session);
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// Create an emoty output, that will keep the output resources on the GPU
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// It will be chained into a the post processing on the GPU as well
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var output = TensorFloat.Create();
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// Bind inputs and outputs
|
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// For squeezenet these evaluate to "data", and "squeezenet0_flatten0_reshape0"
|
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string inputName = session.Model.InputFeatures[0].Name;
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string outputName = session.Model.OutputFeatures[0].Name;
|
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binding.Bind(inputName, input);
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var outputBindProperties = new PropertySet();
|
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outputBindProperties.Add("DisableTensorCpuSync", PropertyValue.CreateBoolean(true));
|
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binding.Bind(outputName, output, outputBindProperties);
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||||
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// Evaluate
|
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return session.Evaluate(binding, "");
|
||||
}
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||||
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||||
private LearningModelSession CreateLearningModelSession(string modelPath)
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||||
{
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||||
var model = CreateLearningModel(modelPath);
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||||
var session = CreateLearningModelSession(model);
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||||
return session;
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||||
}
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||||
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||||
private LearningModelSession CreateLearningModelSession(LearningModel model, Nullable<LearningModelDeviceKind> kind = null)
|
||||
{
|
||||
var device = new LearningModelDevice(kind ?? SelectedDeviceKind);
|
||||
var options = new LearningModelSessionOptions()
|
||||
{
|
||||
CloseModelOnSessionCreation = true // Close the model to prevent extra memory usage
|
||||
};
|
||||
var session = new LearningModelSession(model, device, options);
|
||||
return session;
|
||||
}
|
||||
|
||||
private static LearningModel CreateLearningModel(string modelPath)
|
||||
{
|
||||
var uri = new Uri(modelPath);
|
||||
var file = StorageFile.GetFileFromApplicationUriAsync(uri).GetAwaiter().GetResult();
|
||||
return LearningModel.LoadFromStorageFileAsync(file).GetAwaiter().GetResult();
|
||||
}
|
||||
#pragma warning restore CA1416 // Validate platform compatibility
|
||||
|
||||
private static Dictionary<long, string> LoadLabels(string csvFile)
|
||||
{
|
||||
var file = StorageFile.GetFileFromApplicationUriAsync(new Uri(csvFile)).GetAwaiter().GetResult();
|
||||
var text = Windows.Storage.FileIO.ReadTextAsync(file).GetAwaiter().GetResult();
|
||||
var labels = new Dictionary<long, string>();
|
||||
var records = text.Split(Environment.NewLine);
|
||||
foreach (var record in records)
|
||||
{
|
||||
var fields = record.Split(",", 2);
|
||||
if (fields.Length == 2)
|
||||
{
|
||||
var index = long.Parse(fields[0]);
|
||||
labels[index] = fields[1];
|
||||
}
|
||||
}
|
||||
return labels;
|
||||
}
|
||||
|
||||
private void TryPerformInference(bool reloadImages = true)
|
||||
{
|
||||
if (CurrentImage != null)
|
||||
{
|
||||
// Classify
|
||||
var angle = (float)RotationSlider.Value;
|
||||
var (labels, probabilities) = Classify(CurrentImage.Clone(), angle);
|
||||
|
||||
// Render the classification and probabilities
|
||||
RenderInferenceResults(labels, probabilities);
|
||||
}
|
||||
}
|
||||
|
||||
private void RenderInferenceResults(IEnumerable<string> labels, IReadOnlyList<float> probabilities)
|
||||
{
|
||||
var indices = Enumerable.Range(1, probabilities.Count);
|
||||
var zippedResults = indices.Zip(labels.Zip(probabilities));
|
||||
var results = zippedResults.Select(
|
||||
(zippedResult) =>
|
||||
new Controls.Prediction {
|
||||
Index = zippedResult.First,
|
||||
Name = zippedResult.Second.First.Trim(new char[] { ',' }),
|
||||
Probability = zippedResult.Second.Second.ToString("E4")
|
||||
});
|
||||
InferenceResults.ItemsSource = results;
|
||||
InferenceResults.SelectedIndex = 0;
|
||||
}
|
||||
|
||||
private void OpenButton_Clicked(object sender, RoutedEventArgs e)
|
||||
{
|
||||
var storageFile = ImageHelper.PickImageFiles();
|
||||
if (storageFile != null)
|
||||
{
|
||||
BasicGridView.SelectedItem = null;
|
||||
SetCurrentImage(storageFile.Path);
|
||||
TryPerformInference();
|
||||
}
|
||||
}
|
||||
|
||||
private void SampleInputsGridView_SelectionChanged(object sender, SelectionChangedEventArgs e)
|
||||
{
|
||||
var gridView = (GridView)sender;
|
||||
var thumbnail = (Thumbnail)gridView.SelectedItem;
|
||||
if (thumbnail != null)
|
||||
{
|
||||
var file = StorageFile.GetFileFromApplicationUriAsync(new Uri(thumbnail.ImageUri)).GetAwaiter().GetResult();
|
||||
SetCurrentImage(file.Path);
|
||||
TryPerformInference();
|
||||
}
|
||||
}
|
||||
|
||||
private void SetCurrentImage(string path)
|
||||
{
|
||||
if (CurrentImage != null) CurrentImage.Dispose();
|
||||
|
||||
RotationSlider.IsEnabled = true;
|
||||
|
||||
CurrentImage = SixLabors.ImageSharp.Image.Load<Bgra32>(path);
|
||||
RenderingHelpers.BindSoftwareBitmapToImageControl(InputImage, CurrentImage.AsSoftwareBitmap());
|
||||
}
|
||||
|
||||
private void DeviceComboBox_SelectionChanged(object sender, SelectionChangedEventArgs e)
|
||||
{
|
||||
TryPerformInference();
|
||||
}
|
||||
|
||||
private void RotationSlider_ValueChanged(object sender, Microsoft.UI.Xaml.Controls.Primitives.RangeBaseValueChangedEventArgs e)
|
||||
{
|
||||
TryPerformInference();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
# WinML Samples Gallery: ImageSharp Interop
|
||||
This sample demonstrates how to interop between [Windows ML](https://docs.microsoft.com/en-us/windows/ai/windows-ml/) and [ImageSharp](https://docs.sixlabors.com/articles/imagesharp/index.html).
|
||||
|
||||
ImageSharp is a new, fully featured, fully managed, cross-platform, 2D graphics library.
|
||||
|
||||
The demo will run [SqueezeNet](https://github.com/onnx/models/tree/master/vision/classification/squeezenet) image classification in WindowsML and consume images arbitrarily rotated using ImageSharp.
|
||||
|
||||
ImageSharp will be used to load, rotate, resize and crop images.
|
||||
Windows ML will be used to tensorize the image into NCHW format and perform image classification.
|
||||
|
||||
|
||||
<img src="docs/screenshot.png" width="650"/>
|
||||
|
||||
- [Licenses](#licenses)
|
||||
- [Getting Started](#getting-started)
|
||||
- [Feedback]($feedback)
|
||||
- [External Links](#links)
|
||||
|
||||
|
||||
## Licenses
|
||||
See [ThirdPartyNotices.txt](../../../../../ThirdPartyNotices.txt) for relevant license info.
|
||||
|
||||
## Getting Started
|
||||
You can check out the source [here](https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/ImageSharpInterop.xaml.cs).
|
||||
|
||||
## Feedback
|
||||
Please file an issue [here](https://github.com/microsoft/Windows-Machine-Learning/issues/new) if you encounter any issues with this sample.
|
||||
|
||||
## External Links
|
||||
|
||||
- [Windows ML Library (WinML)](https://docs.microsoft.com/en-us/windows/ai/windows-ml/)
|
||||
- [DirectML](https://github.com/microsoft/directml)
|
||||
- [ONNX Model Zoo](https://github.com/onnx/models)
|
||||
- [Windows UI Library (WinUI)](https://docs.microsoft.com/en-us/windows/apps/winui/)
|
||||
Двоичные данные
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/docs/screenshot.png
Normal file
Двоичные данные
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/docs/screenshot.png
Normal file
Двоичный файл не отображается.
|
После Ширина: | Высота: | Размер: 368 KiB |
|
|
@ -36,7 +36,7 @@ In order to build this sample, OpenCV will need to be built and linked into the
|
|||
- Launch the `WinMLSamplesGallery.sln` and build with the same **Architecture** and **Configuration** to see the sample appear.
|
||||
|
||||
|
||||
You can check out the source [here](https://github.com/microsoft/Windows-Machine-Learning/blob/91e493d699df80a633654929418f41bab136ae1d/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/OpenCVImage.cpp#L21).
|
||||
You can check out the source [here](https://github.com/microsoft/Windows-Machine-Learning/blob/6840e7bd312b09ecd9f51127758f5168e4f844b9/Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/OpenCVImage.cpp#L19).
|
||||
|
||||
## Feedback
|
||||
Please file an issue [here](https://github.com/microsoft/Windows-Machine-Learning/issues/new) if you encounter any issues with this sample.
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@
|
|||
<None Remove="Samples\ImageEffects.xaml" />
|
||||
<None Remove="Samples\ObjectDetector\ObjectDetector.xaml" />
|
||||
<None Remove="Samples\OpenCVInterop\OpenCVInterop.xaml" />
|
||||
<None Remove="Samples\ImageSharpInterop\ImageSharpInterop.xaml" />
|
||||
<None Remove="Video.xaml" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
|
|
@ -73,6 +74,7 @@
|
|||
<PackageReference Include="Microsoft.AI.MachineLearning" Version="1.9.1" />
|
||||
<PackageReference Include="Microsoft.ProjectReunion" Version="0.8.4" />
|
||||
<PackageReference Include="Microsoft.Windows.CsWinRT" Version="1.3.5" />
|
||||
<PackageReference Include="SixLabors.ImageSharp" Version="1.0.4" />
|
||||
<Manifest Include="$(ApplicationManifest)" />
|
||||
</ItemGroup>
|
||||
|
||||
|
|
@ -80,6 +82,7 @@
|
|||
<None Include="Samples\ImageEffects\ImageEffects.xaml.cs" />
|
||||
<None Include="Samples\ImageClassifier\ImageClassifier.xaml.cs" />
|
||||
<None Include="Samples\OpenCVInterop\OpenCVInterop.xaml.cs" />
|
||||
<None Include="Samples\ImageSharpInterop\ImageSharpInterop.xaml.cs" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
|
|
@ -142,6 +145,9 @@
|
|||
<Page Update="Samples\OpenCVInterop\OpenCVInterop.xaml">
|
||||
<Generator>MSBuild:Compile</Generator>
|
||||
</Page>
|
||||
<Page Update="Samples\ImageSharpInterop\ImageSharpInterop.xaml">
|
||||
<Generator>MSBuild:Compile</Generator>
|
||||
</Page>
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ Do Not Translate or Localize
|
|||
This software incorporates third party material from the projects listed below.
|
||||
|
||||
- opencv : https://github.com/opencv/opencv
|
||||
|
||||
- ImageSharp : https://github.com/SixLabors/ImageSharp
|
||||
-------------------------------------------------------------------------------
|
||||
opencv
|
||||
-------------------------------------------------------------------------------
|
||||
|
|
@ -210,3 +210,208 @@ opencv
|
|||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
|
||||
-------------------------------------------------------------------------------
|
||||
ImageSharp
|
||||
-------------------------------------------------------------------------------
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
|
||||
"Licensor" shall mean the copyright owner or entity authorized by
|
||||
the copyright owner that is granting the License.
|
||||
|
||||
"Legal Entity" shall mean the union of the acting entity and all
|
||||
other entities that control, are controlled by, or are under common
|
||||
control with that entity. For the purposes of this definition,
|
||||
"control" means (i) the power, direct or indirect, to cause the
|
||||
direction or management of such entity, whether by contract or
|
||||
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
||||
outstanding shares, or (iii) beneficial ownership of such entity.
|
||||
|
||||
"You" (or "Your") shall mean an individual or Legal Entity
|
||||
exercising permissions granted by this License.
|
||||
|
||||
"Source" form shall mean the preferred form for making modifications,
|
||||
including but not limited to software source code, documentation
|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
"Work" shall mean the work of authorship, whether in Source or
|
||||
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|
||||
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|
||||
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|
||||
|
||||
"Derivative Works" shall mean any work, whether in Source or Object
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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"Contribution" shall mean any work of authorship, including
|
||||
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|
||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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||||
|
||||
"Contributor" shall mean Licensor and any individual or Legal Entity
|
||||
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|
||||
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|
||||
|
||||
2. Grant of Copyright License. Subject to the terms and conditions of
|
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this License, each Contributor hereby grants to You a perpetual,
|
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|
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|
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|
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||||
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
4. Redistribution. You may reproduce and distribute copies of the
|
||||
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|
||||
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|
||||
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|
||||
|
||||
(a) You must give any other recipients of the Work or
|
||||
Derivative Works a copy of this License; and
|
||||
|
||||
(b) You must cause any modified files to carry prominent notices
|
||||
stating that You changed the files; and
|
||||
|
||||
(c) You must retain, in the Source form of any Derivative Works
|
||||
that You distribute, all copyright, patent, trademark, and
|
||||
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|
||||
excluding those notices that do not pertain to any part of
|
||||
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|
||||
|
||||
(d) If the Work includes a "NOTICE" text file as part of its
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
|
||||
You may add Your own copyright statement to Your modifications and
|
||||
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||||
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||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
Notwithstanding the above, nothing herein shall supersede or modify
|
||||
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|
||||
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|
||||
|
||||
6. Trademarks. This License does not grant permission to use the trade
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||||
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|
||||
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||||
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||||
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|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
APPENDIX: How to apply the Apache License to your work.
|
||||
|
||||
To apply the Apache License to your work, attach the following
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||||
|
||||
Copyright (c) Six Labors
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
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you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
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||||
|
||||
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|
||||
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||||
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||||
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||||
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