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takuya-takeuchitakuya-takeuchi
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1.3.0.5
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‎FaceRecognitionDotNet.sln‎

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@@ -43,6 +43,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "HeadPoseTraining", "tools\H
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "HeadPoseEstimationDemo", "examples\HeadPoseEstimationDemo\HeadPoseEstimationDemo.csproj", "{8540B212-5605-4402-BAE4-BEFEBAAD9F97}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "BenchmarkEndToEnd", "examples\BenchmarkEndToEnd\BenchmarkEndToEnd.csproj", "{F26A2088-EB8B-43F0-8F78-9A2BCE514367}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
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{8540B212-5605-4402-BAE4-BEFEBAAD9F97}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{8540B212-5605-4402-BAE4-BEFEBAAD9F97}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{8540B212-5605-4402-BAE4-BEFEBAAD9F97}.Release|Any CPU.Build.0 = Release|Any CPU
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{F26A2088-EB8B-43F0-8F78-9A2BCE514367}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{F26A2088-EB8B-43F0-8F78-9A2BCE514367}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{F26A2088-EB8B-43F0-8F78-9A2BCE514367}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{F26A2088-EB8B-43F0-8F78-9A2BCE514367}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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{13DDB1E7-EF71-48BA-A1E7-365C94783709} = {FEEAC07F-70D7-4C12-B92C-153CEE0F2539}
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{BB6A1F98-DEF9-475C-A69A-82FE518D1E9E} = {8C8838E0-B002-426F-9B25-4C1F65A6D33D}
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{8540B212-5605-4402-BAE4-BEFEBAAD9F97} = {FEEAC07F-70D7-4C12-B92C-153CEE0F2539}
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{F26A2088-EB8B-43F0-8F78-9A2BCE514367} = {FEEAC07F-70D7-4C12-B92C-153CEE0F2539}
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {4D44C572-D749-4A76-A199-8C598A08AE8A}

‎examples/BenchmarkEndToEnd/BenchmarkEndToEnd.csproj‎

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.0</TargetFramework>
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<Authors>Takuya Takeuchi</Authors>
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<Description>Example of FaceRecognitionDotNet</Description>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.Extensions.CommandLineUtils" Version="1.1.1" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\src\FaceRecognitionDotNet\FaceRecognitionDotNet.csproj" />
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</ItemGroup>
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</Project>

‎examples/BenchmarkEndToEnd/Program.cs‎

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/*
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* This sample program is ported by C# from https://github.com/ageitgey/face_recognition/blob/master/examples/benchmark.py.
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*/
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using System;
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using System.Diagnostics;
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using System.IO;
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using System.Linq;
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using FaceRecognitionDotNet;
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using Microsoft.Extensions.CommandLineUtils;
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namespace BenchmarkEndToEnd
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{
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internal class Program
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{
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#region Fields
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private static FaceRecognition _FaceRecognition;
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private static bool _UseCnn = false;
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#endregion
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#region Methods
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private static void Main(string[] args)
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{
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var app = new CommandLineApplication(false);
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app.Name = nameof(BenchmarkEndToEnd);
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app.Description = "The program for measure face encoding performance";
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app.HelpOption("-h|--help");
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var modelsOption = app.Option("-m|--model", "model files directory path", CommandOptionType.SingleValue);
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var cnnOption = app.Option("-c|--cnn", "use cnn", CommandOptionType.NoValue);
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app.OnExecute(() =>
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{
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if (!modelsOption.HasValue())
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{
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app.ShowHelp();
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return -1;
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}
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var directory = modelsOption.Value();
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if (!Directory.Exists(directory))
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{
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app.ShowHelp();
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return -1;
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}
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_UseCnn = cnnOption.HasValue();
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_FaceRecognition = FaceRecognition.Create(directory);
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var testImages = new[]
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{
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"obama-240p.jpg",
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"obama-480p.jpg",
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"obama-720p.jpg",
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"obama-1080p.jpg"
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};
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Console.WriteLine("Benchmarks");
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Console.WriteLine();
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foreach (var image in testImages)
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{
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var size = image.Split('-')[1].Split('.')[0];
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Console.WriteLine($"Timings at {size}:");
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var faceLocations = RunTest(image, SetupLocateFaces, TestEndToEnd);
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Console.WriteLine($" - Face locations, landmark, encoding, distance: {faceLocations.Item1:F4}s ({faceLocations.Item2:F2} fps)");
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Console.WriteLine();
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}
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return 0;
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});
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app.Execute(args);
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}
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#region Helpers
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private static Tuple<double, double> RunTest<T>(string path, Func<string, T> setup, Action<T> test, int iterationsPerTest = 5, int testsToRun = 10, bool useCnn = false)
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{
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var image = setup(path);
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var iteration = new Func<double>(() =>
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{
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var sw = new Stopwatch();
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sw.Start();
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for (var count = 0; count < iterationsPerTest; count++)
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test(image);
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sw.Stop();
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return sw.ElapsedMilliseconds;
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});
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var fastestExecution = Enumerable.Repeat(0, testsToRun).Select(i => iteration()).Min();
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var executionTime = fastestExecution / 1000 / iterationsPerTest;
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var fps = 1.0 / executionTime;
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(image as IDisposable)?.Dispose();
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return new Tuple<double, double>(executionTime, fps);
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}
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private static Image SetupLocateFaces(string path)
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{
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return FaceRecognition.LoadImageFile(path);
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}
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private static void TestEndToEnd(Image image)
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{
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var model = _UseCnn ? Model.Cnn : Model.Hog;
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var faceLocations = _FaceRecognition.FaceLocations(image, model: model);
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var faceLocationCount = faceLocations.Count();
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var faceLandmarks = _FaceRecognition.FaceLandmark(image, faceLocations, model: model);
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var faceLandmarkCount = faceLandmarks.Count();
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var encoding = _FaceRecognition.FaceEncodings(image, faceLocations, model: model);
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var faceEncodingCount = encoding.Count();
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// it could do matching for 1 time
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foreach (var faceEncoding in encoding)
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FaceRecognition.FaceDistance(faceEncoding, faceEncoding);
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foreach (var faceEncoding in encoding)
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faceEncoding.Dispose();
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}
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#endregion
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#endregion
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}
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}

‎examples/BenchmarkEndToEnd/README.md‎

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# Benchmark
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This example measures performance of calculating for face encodings.
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This sample program is ported by C# from https://github.com/ageitgey/face_recognition/blob/master/examples/benchmark.py.
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## How to use?
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## 1. Preparation
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This sample requires test image and model files.
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## 2. Build
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1. Open command prompt and change to &lt;Benchmark_dir&gt;
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1. Type the following command
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````
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$ dotnet remove reference ../../src/FaceRecognitionDotNet\FaceRecognitionDotNet.csproj
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$ dotnet add package FaceRecognitionDotNet
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$ dotnet build -c Release
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````
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2. Copy ***DlibDotNet.dll***, ***DlibDotNet.Native.dll*** and ***DlibDotNet.Dnn.dll*** to output directory; &lt;Benchmark_dir&gt;\bin\Release\netcoreapp2.0.
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* if you use FaceRecognitionDotNet with CUDA, you must copy also cuda libraries.
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## 3. Run
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1. Open command prompt and change to &lt;Benchmark_dir&gt;
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1. Type the following sample command
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````
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$ dotnet run -c Release -- "-m=models"
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Benchmarks
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Timings at 240p:
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- Face locations: 0.0268s (37.31 fps)
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- Face landmarks: 0.0014s (714.29 fps)
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- Encode face (inc. landmarks): 0.0210s (47.62 fps)
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- End-to-end: 0.0484s (20.66 fps)
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Timings at 480p:
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- Face locations: 0.1068s (9.36 fps)
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- Face landmarks: 0.0014s (714.29 fps)
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- Encode face (inc. landmarks): 0.0202s (49.50 fps)
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- End-to-end: 0.1308s (7.65 fps)
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Timings at 720p:
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- Face locations: 0.2416s (4.14 fps)
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- Face landmarks: 0.0014s (714.29 fps)
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- Encode face (inc. landmarks): 0.0206s (48.54 fps)
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- End-to-end: 0.2700s (3.70 fps)
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Timings at 1080p:
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- Face locations: 0.5430s (1.84 fps)
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- Face landmarks: 0.0016s (625.00 fps)
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- Encode face (inc. landmarks): 0.0206s (48.54 fps)
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- End-to-end: 0.5774s (1.73 fps)
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````
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## 4. Parameters
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This program support the following argument and option.
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### Argument
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|Argument|Description|
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|:---|:---|
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|-m\|--model|Directory path includes model files|
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|-c\|--cnn|Use Cnn|
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## 5. Other
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### Why is Encode face too slow?
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The reason ***face_recognition*** can achieve high performance is using ***Intel Math Kernel Library***.
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If you can use Intel Math Kernel Library, you can build ***DlibDotNet.Native.Dnn*** by linking Intel Math Kernel Library.

‎examples/BenchmarkEndToEnd/obama-1080p.jpg‎

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‎examples/BenchmarkEndToEnd/obama-240p.jpg‎

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‎examples/BenchmarkEndToEnd/obama-480p.jpg‎

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‎examples/BenchmarkEndToEnd/obama-720p.jpg‎

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‎nuget/.gitignore‎

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tmp

‎nuget/ExtractNupkgToArtifacts.ps1‎

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#***************************************
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#Arguments
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#%1: Version of Release (1.2.3.0)
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#***************************************
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Param([Parameter(
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Mandatory=$True,
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Position = 1
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)][string]
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$Version
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)
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$PublishTargets = @{ "FaceRecognitionDotNet"="cpu";
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"FaceRecognitionDotNet.CUDA92"="cuda-92";
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"FaceRecognitionDotNet.CUDA100"="cuda-100";
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"FaceRecognitionDotNet.CUDA101"="cuda-101";
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"FaceRecognitionDotNet.CUDA102"="cuda-102";
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"FaceRecognitionDotNet.CUDA110"="cuda-110";
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"FaceRecognitionDotNet.CUDA111"="cuda-111";
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"FaceRecognitionDotNet.CUDA112"="cuda-112";
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"FaceRecognitionDotNet.MKL"="mkl";
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}
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$Token = $env:FaceRecognitionDotNetNugetToken
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if ([string]::IsNullOrWhitespace($Token))
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{
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Write-Host "nuget token is missing" -ForegroundColor Red
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exit
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}
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# Precheck whether all package is present
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foreach ($key in $PublishTargets.keys)
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{
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$value = $PublishTargets[$key]
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$Package = Join-Path $PSScriptRoot "${key}.${Version}.nupkg"
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if (!(Test-Path ${Package}))
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{
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Write-Host "${Package} is missing" -ForegroundColor Red
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exit
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}
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Expand-Archive -Path "${Package}" -DestinationPath tmp
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$runtime = Join-Path tmp runtimes
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$artifacts = Join-Path artifacts ${value} | `
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Join-Path -ChildPath runtimes
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Copy-Item "${runtime}/*" "${artifacts}" -Recurse -Force
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Remove-Item tmp -Recurse -Force
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}

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