Fix PnP rotation/translation shape for modern OpenCV/NumPy.

solvePnP returns (3,1); transpose made (1,3), which broke eye depth .dot()
and struct.pack of translation scalars. Flatten to (3,) instead.

Co-authored-by: Cursor <cursoragent@cursor.com>

.github/actions/upload-dated-artifact/action.yaml

@@ -0,0 +1,26 @@
+name: Upload dated artifact
+description: Uploads an artifact with a datetime
+
+inputs:
+  name:
+    description: The name of the artifact
+    required: true
+  path:
+    description: The path to the file/folder to upload
+    required: true
+
+runs:
+  using: composite
+  steps:
+    # https://stackoverflow.com/questions/60942067/get-current-date-and-time-in-github-workflows
+    - name: Get current datetime
+      id: date
+      shell: bash
+      run: echo "::set-output name=date::$(date '+%Y-%m-%d_%H-%M-%S')"
+
+    - name: Upload artifact
+      uses: actions/upload-artifact@v3
+      with:
+        name: ${{ inputs.name }}_${{ steps.date.outputs.date }}
+        path: ${{ inputs.path }}
+        if-no-files-found: error

.github/workflows/create-binaries.yaml

@@ -0,0 +1,87 @@
+name: Package using PyInstaller
+on:
+  push:
+    tags:
+      - v**
+  workflow_dispatch:
+
+jobs:
+  build-windows:
+    env:
+      dist-path: dist/OpenSeeFace
+    runs-on: windows-latest
+    steps:
+      - name: Pull OpenSeeFace
+        uses: actions/checkout@v3
+
+      - name: Create binary
+        shell: cmd
+        run: |
+          choco install vcredist140
+
+          xcopy c:\Windows\System32\msvcp140.dll .
+          xcopy c:\Windows\System32\vcomp140.dll .
+          xcopy c:\Windows\System32\concrt140.dll .
+          xcopy c:\Windows\System32\vccorlib140.dll .
+
+          make_exe.bat
+
+      - name: Move files for uploading
+        shell: bash
+        run: |
+          # Prevents the parent dist/ directory from being included
+          mv dist/facetracker ${{ env.dist-path }}
+          cp -r models ${{ env.dist-path }}
+
+      - name: Upload artifact
+        uses: ./.github/actions/upload-dated-artifact
+        with:
+          name: windows
+          path: dist # Add the parent directory to prevent a tarbomb
+
+  # Files must be packed into a tar in order for file permissions to be preserved
+  build-unix:
+    env:
+      artifact-path: OpenSeeFace
+      tar-path: OpenSeeFace.tar
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - name: linux
+            os: ubuntu-latest
+          - name: mac
+            os: macos-latest
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: Pull OpenSeeFace
+        uses: actions/checkout@v3
+
+      - name: Create binary
+        shell: bash
+        run: |
+          chmod +x make_exe.sh
+          ./make_exe.sh
+
+      - name: Move files for uploading
+        shell: bash
+        run: |
+          # Prevents the parent dist/ directory from being included
+          mv dist/facetracker ${{ env.artifact-path }}
+          cp -r models ${{ env.artifact-path }}
+
+      - name: Fix permissions from PyInstaller
+        shell: bash
+        run: |
+          find ${{ env.artifact-path }} -type f -exec chmod -x {} +
+          chmod +x ${{ env.artifact-path }}/facetracker
+
+      - name: Pack files into tar
+        shell: bash
+        run: tar -cvf ${{ env.tar-path }} ${{ env.artifact-path }}
+
+      - name: Upload artifact
+        uses: ./.github/actions/upload-dated-artifact
+        with:
+          name: ${{ matrix.name }}
+          path: ${{ env.tar-path }}

.gitignore

@@ -5,4 +5,4 @@ Samples/*
 *.zip
 *.7z
 CSharp/*
-OpenSeeDemo/*
+VSeeFace/*

Examples/Animations/JawOpen.anim.meta

@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 697b139e69dcb414498dcd93210620ff
+NativeFormatImporter:
+  externalObjects: {}
+  mainObjectFileID: 0
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Examples/OpenSeeVRMExpression.cs

@@ -6,6 +6,7 @@ namespace OpenSee {
 
 public class OpenSeeVRMExpression : MonoBehaviour
 {
+    public OpenSee openSee;
     public OpenSeeExpression openSeeExpression;
     public VRMBlendShapeProxy vrmBlendShapeProxy;
     public string expressionNeutral = "neutral";
@@ -15,7 +16,6 @@ public class OpenSeeVRMExpression : MonoBehaviour
     public string expressionJoy = "shocked";
     
     private BlendShapeKey lastKey;
-    private string lastExpression = "";
     
     void Start()
     {
@@ -26,27 +26,52 @@ public class OpenSeeVRMExpression : MonoBehaviour
         if (openSeeExpression == null || vrmBlendShapeProxy == null) {
             return;
         }
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Neutral), 0f);
+        float rightEyeOpen = 1f;
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Fun), 0f);
+        float leftEyeOpen = 1f;
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Sorrow), 0f);
+        if (openSee != null) {
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Angry), 0f);
+            var openSeeData = openSee.trackingData;
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Joy), 0f);
+            if (openSeeData == null || openSeeData.Length < 1)
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Blink_R), 0f);
+                return;
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Blink_L), 0f);
+            int idx = -1;
+            int i = 0;
+            foreach (var item in openSeeData) {
+                if (item.id == openSeeExpression.faceId)
+                    idx = i;
+                i++;
+            }
+            if (idx > -1) {
+                rightEyeOpen = openSeeData[idx].rightEyeOpen;
+                leftEyeOpen = openSeeData[idx].leftEyeOpen;
+            }
+        }
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Neutral), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Fun), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Sorrow), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Angry), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Joy), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_R), 0f);
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_L), 0f);
         if (openSeeExpression.expression == expressionFun) {
-            vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Fun), 1f);
+            vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Fun), 1f);
             return;
         } else if (openSeeExpression.expression == expressionSorrow) {
-            vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Sorrow), 1f);
+            vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Sorrow), 1f);
             return;
         } else if (openSeeExpression.expression == expressionAngry) {
-            vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Angry), 1f);
+            vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Angry), 1f);
             return;
         } else if (openSeeExpression.expression == expressionJoy) {
-            vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Joy), 1f);
+            vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Joy), 1f);
             return;
+        } else {
+            /*if (rightEyeOpen < 0.05) {
+                vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Blink_R), 1f);
             }
-        vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Neutral), 1f);
+            if (leftEyeOpen < 0.05) {
+                vrmBlendShapeProxy.ImmediatelySetValue(new BlendShapeKey(BlendShapePreset.Blink_L), 1f);
+            }*/
+        }
+        vrmBlendShapeProxy.ImmediatelySetValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Neutral), 1f);
     }
 }
 

Examples/OpenSeeWebcamInfo.cs

@@ -0,0 +1,270 @@
+using System;
+using System.Diagnostics;
+using System.IO;
+using System.Net;
+using System.Runtime.InteropServices;
+using System.Text;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace OpenSee {
+
+[Serializable]
+public enum OpenSeeWebcamType { Unknown = -1, DirectShow, Blackmagic };
+
+[Serializable]
+public enum OpenSeeWebcamFormat { Any = 0, Unknown = 1, ARGB = 100, XRGB, I420 = 200, NV12, YV12, Y800, YVYU = 300, YUY2, UYVY, HDYC, MJPEG = 400, H264 };
+
+[Serializable]
+public struct OpenSeeWebcamCapability {
+    public int id;
+    
+    // DirectShow
+    public int minCX;
+    public int minCY;
+    public int maxCX;
+    public int maxCY;
+    public int granularityCX;
+    public int granularityCY;
+    public int minInterval;
+    public int maxInterval;
+    public int rating;
+    public OpenSeeWebcamFormat format;
+    
+    // Blackmagic
+    public int bmTimescale;
+    public int bmFrameduration;
+    public int bmModecode;
+}
+
+[Serializable]
+public class OpenSeeWebcam {
+    public OpenSeeWebcamType type = OpenSeeWebcamType.Unknown;
+    public int id;
+    public string name;
+
+    // DirectShow
+    public string path;
+    
+    // Blackmagic
+    public long bmId;
+    public int bmFlags;
+
+    // General
+    public OpenSeeWebcamCapability[] caps;
+    
+    public List<string> prettyCaps = null;
+    
+    private List<Tuple<OpenSeeWebcamCapability, int>> splitCaps = null;
+    
+    bool ge(bool a, bool b) {
+        return a || (!a && !b);
+    }
+
+    private int CompareCaps(OpenSeeWebcamCapability a, OpenSeeWebcamCapability b) {
+        int aRes = a.minCX * a.minCY;
+        int bRes = b.minCX * b.minCY;
+        bool aGoodFps = a.minInterval <= 670000;
+        bool bGoodFps = a.minInterval <= 670000;
+        bool aGoodRes = aRes >= 850000 && aRes <= 2200000;
+        bool bGoodRes = bRes >= 850000 && bRes <= 2200000;
+        if (a.rating < b.rating && ge(aGoodFps, bGoodFps) && ge(aGoodRes, bGoodRes))
+            return -1;
+        if (a.rating > b.rating && ge(bGoodFps, aGoodFps) && ge(bGoodRes, aGoodRes))
+            return 1;
+        if (aRes > bRes && ge(aGoodFps, bGoodFps) && ge(aGoodRes, bGoodRes))
+            return -1;
+        if (aRes < bRes && ge(bGoodFps, aGoodFps) && ge(bGoodRes, aGoodRes))
+            return 1;
+        if (a.minInterval < b.minInterval)
+            return -1;
+        if (a.minInterval > b.minInterval)
+            return 1;
+        return 0;
+    }
+    
+    private string GetPrettyCapability(OpenSeeWebcamCapability cap) {
+        float fps = 10000000f / (float)cap.minInterval;
+        string prettyCap = cap.minCX + "x" + cap.minCY + ", " + fps.ToString("0.##") + "fps (" + cap.format.ToString() + ")";
+        return prettyCap;
+    }
+    
+    public List<string> GetPrettyCapabilities() {
+        if (splitCaps != null && prettyCaps != null)
+            return prettyCaps;
+        
+        splitCaps = new List<Tuple<OpenSeeWebcamCapability, int>>();
+        
+        for (int i = 0; i < caps.Length; i++) {
+            if (caps[i].minCX == caps[i].maxCX && caps[i].minCY == caps[i].maxCY)
+                splitCaps.Add(new Tuple<OpenSeeWebcamCapability, int>(caps[i], i));
+            else {
+                OpenSeeWebcamCapability min = caps[i];
+                OpenSeeWebcamCapability max = caps[i];
+                min.maxCX = min.minCX;
+                min.maxCY = min.minCY;
+                max.minCX = max.maxCX;
+                max.minCY = max.maxCY;
+                splitCaps.Add(new Tuple<OpenSeeWebcamCapability, int>(min, i));
+                splitCaps.Add(new Tuple<OpenSeeWebcamCapability, int>(max, i));
+            }
+        }
+        
+        splitCaps.Sort((a, b) => CompareCaps(a.Item1, b.Item1));
+        
+        prettyCaps = new List<string>();
+        if (type == OpenSeeWebcamType.DirectShow)
+            prettyCaps.Add("Default settings");
+        foreach (var cap in splitCaps) {
+            prettyCaps.Add(GetPrettyCapability(cap.Item1));
+        }
+        
+        return prettyCaps;
+    }
+    
+    public OpenSeeWebcamCapability GetCapabilityByPrettyIndex(int index) {
+        int dshowOffset = 0;
+        if (type == OpenSeeWebcamType.DirectShow) {
+            if (index == 0) {
+                OpenSeeWebcamCapability cap = new OpenSeeWebcamCapability();
+                cap.id = -1;
+                cap.format = OpenSeeWebcamFormat.Any;
+                cap.minInterval = 10000000 / 60;
+                return cap;
+            }
+            dshowOffset = 1;
+        }
+        
+        if (splitCaps == null || index >= splitCaps.Count + dshowOffset || index < 0)
+            throw new InvalidOperationException("Invalid capability index.");
+        
+        return caps[splitCaps[index - dshowOffset].Item2];
+    }
+}
+
+[DefaultExecutionOrder(-50)]
+public class OpenSeeWebcamInfo : MonoBehaviour {
+    #region DllImport
+    // DirectShow
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "create_capture")]
+	private static extern System.IntPtr create_capture_x64();
+
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json_length")]
+	private static extern int get_json_length_x64(System.IntPtr cap);
+
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json")]
+	private static extern void get_json_x64(System.IntPtr cap, [Out] StringBuilder namebuffer, int bufferlength);
+    
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroy_capture")]
+	private static extern void destroy_capture_x64(System.IntPtr cap);
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "create_capture")]
+	private static extern System.IntPtr create_capture_x86();
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json_length")]
+	private static extern int get_json_length_x86(System.IntPtr cap);
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json")]
+	private static extern void get_json_x86(System.IntPtr cap, [Out] StringBuilder namebuffer, int bufferlength);
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroy_capture")]
+	private static extern void destroy_capture_x86(System.IntPtr cap);
+
+    // Blackmagic
+    [DllImport("libminibmcapture64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json_length")]
+	private static extern int bm_get_json_length_x64();
+
+    [DllImport("libminibmcapture64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json")]
+	private static extern void bm_get_json_x64([Out] StringBuilder namebuffer, int bufferlength);
+
+    [DllImport("libminibmcapture32", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json_length")]
+	private static extern int bm_get_json_length_x86();
+
+    [DllImport("libminibmcapture32", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_json")]
+	private static extern void bm_get_json_x86([Out] StringBuilder namebuffer, int bufferlength);
+    #endregion
+    
+    [Serializable]
+    public class OpenSeeWebcamList {
+        public List<OpenSeeWebcam> list;
+    }
+
+    public List<OpenSeeWebcam> cameras;
+    public bool dumpJson = false;
+    
+    private static bool dumpJsonStatic = false;
+    
+    static private string ListCameraDetails_x64() {
+        System.IntPtr cap = create_capture_x64();
+		int length = get_json_length_x64(cap);
+        StringBuilder buffer = new StringBuilder(length);
+        get_json_x64(cap, buffer, length);
+        destroy_capture_x64(cap);
+		return buffer.ToString();
+    }
+
+    static private string ListCameraDetails_x86() {
+        System.IntPtr cap = create_capture_x86();
+		int length = get_json_length_x86(cap);
+        StringBuilder buffer = new StringBuilder(length);
+        get_json_x86(cap, buffer, length);
+        destroy_capture_x86(cap);
+		return buffer.ToString();
+    }
+
+    static private string ListBlackMagicDetails_x64() {
+		int length = bm_get_json_length_x64();
+        StringBuilder buffer = new StringBuilder(length);
+        bm_get_json_x64(buffer, length);
+		return buffer.ToString();
+    }
+
+    static private string ListBlackMagicDetails_x86() {
+		int length = bm_get_json_length_x86();
+        StringBuilder buffer = new StringBuilder(length);
+        bm_get_json_x86(buffer, length);
+		return buffer.ToString();
+    }
+
+    static public List<OpenSeeWebcam> ListCameraDetails(bool includeBlackMagic) {
+        string jsonData = null;
+        string bmJsonData = null;
+        if (Environment.Is64BitProcess) {
+            jsonData = ListCameraDetails_x64();
+            if (includeBlackMagic)
+                bmJsonData = ListBlackMagicDetails_x64();
+        } else {
+            jsonData = ListCameraDetails_x86();
+            if (includeBlackMagic)
+                bmJsonData = ListBlackMagicDetails_x86();
+        }
+        if (dumpJsonStatic) {
+            UnityEngine.Debug.Log("Camera JSON: " + jsonData);
+            if (includeBlackMagic)
+                UnityEngine.Debug.Log("Blackmagic JSON: " + bmJsonData);
+        }
+        List<OpenSeeWebcam> details = JsonUtility.FromJson<OpenSeeWebcamList>("{\"list\":" + jsonData + "}").list;
+        foreach (var cam in details)
+            cam.type = OpenSeeWebcamType.DirectShow;
+        if (includeBlackMagic) {
+            List<OpenSeeWebcam> bmDetails = JsonUtility.FromJson<OpenSeeWebcamList>("{\"list\":" + bmJsonData + "}").list;
+            foreach (var cam in bmDetails)
+                cam.type = OpenSeeWebcamType.Blackmagic;
+            details.AddRange(bmDetails);
+        }
+        return details;
+    }
+    
+    void Start() {
+        Initialize(false);
+    }
+    
+    public void Initialize(bool includeBlackMagic) {
+        dumpJsonStatic = dumpJson;
+        cameras = ListCameraDetails(includeBlackMagic);
+        foreach (var camera in cameras)
+            camera.GetPrettyCapabilities();
+    }
+}
+}

Examples/OpenSeeWebcamInfo.cs.meta

@@ -0,0 +1,11 @@
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+guid: 2e9598f1218283b418c7e72604be0335
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: -50
+  icon: {instanceID: 0}
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+  assetBundleVariant: 

Licenses/DShowCapture.txt

@@ -0,0 +1,505 @@
+Modified by Emiliana_vt
+https://github.com/emilianavt/libdshowcapture
+
+                  GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 2.1, February 1999
+
+ Copyright (C) 1991, 1999 Free Software Foundation, Inc.
+ 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+[This is the first released version of the Lesser GPL.  It also counts
+ as the successor of the GNU Library Public License, version 2, hence
+ the version number 2.1.]
+
+                            Preamble
+
+  The licenses for most software are designed to take away your
+freedom to share and change it.  By contrast, the GNU General Public
+Licenses are intended to guarantee your freedom to share and change
+free software--to make sure the software is free for all its users.
+
+  This license, the Lesser General Public License, applies to some
+specially designated software packages--typically libraries--of the
+Free Software Foundation and other authors who decide to use it.  You
+can use it too, but we suggest you first think carefully about whether
+this license or the ordinary General Public License is the better
+strategy to use in any particular case, based on the explanations below.
+
+  When we speak of free software, we are referring to freedom of use,
+not price.  Our General Public Licenses are designed to make sure that
+you have the freedom to distribute copies of free software (and charge
+for this service if you wish); that you receive source code or can get
+it if you want it; that you can change the software and use pieces of
+it in new free programs; and that you are informed that you can do
+these things.
+
+  To protect your rights, we need to make restrictions that forbid
+distributors to deny you these rights or to ask you to surrender these
+rights.  These restrictions translate to certain responsibilities for
+you if you distribute copies of the library or if you modify it.
+
+  For example, if you distribute copies of the library, whether gratis
+or for a fee, you must give the recipients all the rights that we gave
+you.  You must make sure that they, too, receive or can get the source
+code.  If you link other code with the library, you must provide
+complete object files to the recipients, so that they can relink them
+with the library after making changes to the library and recompiling
+it.  And you must show them these terms so they know their rights.
+
+  We protect your rights with a two-step method: (1) we copyright the
+library, and (2) we offer you this license, which gives you legal
+permission to copy, distribute and/or modify the library.
+
+  To protect each distributor, we want to make it very clear that
+there is no warranty for the free library.  Also, if the library is
+modified by someone else and passed on, the recipients should know
+that what they have is not the original version, so that the original
+author's reputation will not be affected by problems that might be
+introduced by others.
+
+  Finally, software patents pose a constant threat to the existence of
+any free program.  We wish to make sure that a company cannot
+effectively restrict the users of a free program by obtaining a
+restrictive license from a patent holder.  Therefore, we insist that
+any patent license obtained for a version of the library must be
+consistent with the full freedom of use specified in this license.
+
+  Most GNU software, including some libraries, is covered by the
+ordinary GNU General Public License.  This license, the GNU Lesser
+General Public License, applies to certain designated libraries, and
+is quite different from the ordinary General Public License.  We use
+this license for certain libraries in order to permit linking those
+libraries into non-free programs.
+
+  When a program is linked with a library, whether statically or using
+a shared library, the combination of the two is legally speaking a
+combined work, a derivative of the original library.  The ordinary
+General Public License therefore permits such linking only if the
+entire combination fits its criteria of freedom.  The Lesser General
+Public License permits more lax criteria for linking other code with
+the library.
+
+  We call this license the "Lesser" General Public License because it
+does Less to protect the user's freedom than the ordinary General
+Public License.  It also provides other free software developers Less
+of an advantage over competing non-free programs.  These disadvantages
+are the reason we use the ordinary General Public License for many
+libraries.  However, the Lesser license provides advantages in certain
+special circumstances.
+
+  For example, on rare occasions, there may be a special need to
+encourage the widest possible use of a certain library, so that it becomes
+a de-facto standard.  To achieve this, non-free programs must be
+allowed to use the library.  A more frequent case is that a free
+library does the same job as widely used non-free libraries.  In this
+case, there is little to gain by limiting the free library to free
+software only, so we use the Lesser General Public License.
+
+  In other cases, permission to use a particular library in non-free
+programs enables a greater number of people to use a large body of
+free software.  For example, permission to use the GNU C Library in
+non-free programs enables many more people to use the whole GNU
+operating system, as well as its variant, the GNU/Linux operating
+system.
+
+  Although the Lesser General Public License is Less protective of the
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+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright {yyyy} {name of copyright owner}
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   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.

Licenses/libminibmcapture.txt

@@ -0,0 +1,19 @@
+zlib License
+
+libminibmcapture (C) 2020 noorus
+
+This software is provided 'as-is', without any express or implied
+warranty.  In no event will the authors be held liable for any damages
+arising from the use of this software.
+
+Permission is granted to anyone to use this software for any purpose,
+including commercial applications, and to alter it and redistribute it
+freely, subject to the following restrictions:
+
+1. The origin of this software must not be misrepresented; you must not
+   claim that you wrote the original software. If you use this software
+   in a product, an acknowledgment in the product documentation would be
+   appreciated but is not required.
+2. Altered source versions must be plainly marked as such, and must not be
+   misrepresented as being the original software.
+3. This notice may not be removed or altered from any source distribution.

Licenses/scikit-image.txt

@@ -0,0 +1,81 @@
+Copyright (C) 2019, the scikit-image team
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ 1. Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+ 2. Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in
+    the documentation and/or other materials provided with the
+    distribution.
+ 3. Neither the name of skimage nor the names of its contributors may be
+    used to endorse or promote products derived from this software without
+    specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+skimage/_shared/version_requirements.py:_check_version
+
+    Copyright (c) 2013 The IPython Development Team
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright notice, this
+      list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above copyright notice,
+      this list of conditions and the following disclaimer in the documentation
+      and/or other materials provided with the distribution.
+
+    * Neither the name of the copyright holder nor the names of its
+      contributors may be used to endorse or promote products derived from
+      this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+    FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+    OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+skimage/_shared/version_requirements.py:is_installed:
+
+    Original Copyright (C) 2009-2011 Pierre Raybaut
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal in the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+
+    The above copyright notice and this permission notice shall be
+    included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+    LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+    OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+    WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,20 +1,30 @@
+![OSF.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/OSF.png)
+
 # Overview
 
+**Note**: This is a tracking library, **not** a stand-alone avatar puppeteering program. I'm also working on [VSeeFace](https://www.vseeface.icu/), which allows animating [VRM](https://vrm.dev/en/how_to_make_vrm/) and [VSFAvatar](https://www.youtube.com/watch?v=jhQ8DF87I5I) 3D models by using OpenSeeFace tracking. [VTube Studio](https://denchisoft.com/) uses OpenSeeFace for webcam based tracking to animate Live2D models. A renderer for the Godot engine can be found [here](https://github.com/virtual-puppet-project/vpuppr).
+
 This project implements a facial landmark detection model based on MobileNetV3.
 
 As Pytorch 1.3 CPU inference speed on Windows is very low, the model was converted to ONNX format. Using [onnxruntime](https://github.com/microsoft/onnxruntime) it can run at 30 - 60 fps tracking a single face. There are four models, with different speed to tracking quality trade-offs.
 
 If anyone is curious, the name is a silly pun on the open seas and seeing faces. There's no deeper meaning.
 
-Thanks to [@Virtual_Deat](https://twitter.com/Virtual_Deat) and [@ENiwatori](https://twitter.com/eniwatori) for helping me test things. Additional thanks to [@ArgamaWitch](https://twitter.com/ArgamaWitch) for further testing.
+An up to date sample video can be found [here](https://www.youtube.com/watch?v=AaNap_ud_3I&vq=hd1080), showing the default tracking model's performance under different noise and light levels.
 
-[Unity sample video](https://twitter.com/emiliana_vt/status/1210622149314203648) | [Sample video](https://www.youtube.com/watch?v=AOPHiAp9DBE) | [Sample video](https://www.youtube.com/watch?v=-cBSuHGdBWQ) | [Model comparison](https://www.youtube.com/watch?v=yMcJszUy7FA)
+# Tracking quality
 
-These sample videos use older versions of the tracking models.
+Since the landmarks used by OpenSeeFace are a bit different from those used by other approaches (they are close to iBUG 68, with two less points in the mouth corners and quasi-3D face contours instead of face contours that follow the visible outline) it is hard to numerically compare its accuracy to that of other approaches found commonly in scientific literature. The tracking performance is also more optimized for making landmarks that are useful for animating an avatar than for exactly fitting the face image. For example, as long as the eye landmarks show whether the eyes are opened or closed, even if their location is somewhat off, they can still be useful for this purpose.
+
+From general observation, OpenSeeFace performs well in adverse conditions (low light, high noise, low resolution) and keeps tracking faces through a very wide range of head poses with relatively high stability of landmark positions. Compared to MediaPipe, OpenSeeFace landmarks remain more stable in challenging conditions and it accurately represents a wider range of mouth poses. However, tracking of the eye region can be less accurate.
+
+I ran OpenSeeFace on a sample clip from the video presentation for [3D Face Reconstruction with Dense Landmarks](https://microsoft.github.io/DenseLandmarks/) by Wood et al. to compare it to MediaPipe and their approach. You can watch the result [here](https://www.vseeface.icu/assets/media/OSFMediaPipe3DFR.mp4).
 
 # Usage
 
-The face tracking itself is done by the `facetracker.py` Python 3.7 script. It is a commandline program, so you should start it manually from cmd or write a batch file to start it.
+A sample Unity project for VRM based avatar animation can be found [here](https://github.com/emilianavt/OpenSeeFaceSample).
+
+The face tracking itself is done by the `facetracker.py` Python 3.7 script. It is a commandline program, so you should start it manually from cmd or write a batch file to start it. If you downloaded a release and are on Windows, you can run the `facetracker.exe` inside the `Binary` folder without having Python installed. You can also use the `run.bat` inside the `Binary` folder for a basic demonstration of the tracker.
 
 The script will perform the tracking on webcam input or video file and send the tracking data over UDP. This design also allows tracking to be done on a separate PC from the one who uses the tracking information. This can be useful to enhance performance and to avoid accidentially revealing camera footage.
 
@@ -28,6 +38,8 @@ A simple demonstration can be achieved by creating a new scene in Unity, adding
 
     python facetracker.py --visualize 3 --pnp-points 1 --max-threads 4 -c video.mp4
 
+__Note__: If dependencies were installed using [poetry](https://python-poetry.org/), the commands have to be executed from a `poetry shell` or have to be prefixed with `poetry run`.
+
 This way the tracking script will output its own tracking visualization while also demonstrating the transmission of tracking data to Unity.
 
 The included `OpenSeeLauncher` component allows starting the face tracker program from Unity. It is designed to work with the pyinstaller created executable distributed in the binary release bundles. It provides three public API functions:
@@ -79,15 +91,34 @@ To save both the trained model and the captured training data, type in a filenam
 
 # Models
 
-Four pretrained models are included. Using the `--model` switch, it is possible to select them for tracking. The given fps values are for running the model on a single face video on a single CPU core. Lowering the frame rate would reduce CPU usage by a corresponding degree.
+Four pretrained face landmark models are included. Using the `--model` switch, it is possible to select them for tracking. The given fps values are for running the model on a single face video on a single CPU core. Lowering the frame rate would reduce CPU usage by a corresponding degree.
 
+* Model **-1**: This model is for running on toasters, so it's a very very fast and very low accuracy model. (213fps without gaze tracking)
 * Model **0**: This is a very fast, low accuracy model. (68fps)
-* Model **1**: This is a slightly slower model with better accuracy based on MobileNetV3. (59fps)
+* Model **1**: This is a slightly slower model with better accuracy. (59fps)
-* Model **2**: This is a slower model with good accuracy based on ShuffleNetV2. (50fps)
+* Model **2**: This is a slower model with good accuracy. (50fps)
-* Model **3** (default): This is the slowest and highest accuracy model. It is based on MobileNetV3. (44fps)
+* Model **3** (default): This is the slowest and highest accuracy model. (44fps)
 
 FPS measurements are from running on one core of my CPU.
 
+Pytorch weights for use with `model.py` can be found [here](https://mega.nz/file/vvYXlYQT#h7FpEg4tmOCJNxjpsDEw0JomJIkVGKwrt4OUV0RNDDU). Some unoptimized ONNX models can be found [here](https://github.com/emilianavt/OpenSeeFace/issues/48).
+
+# Results
+
+## Landmarks
+
+![Results1.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results1.png)
+
+![Results2.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results2.png)
+
+More samples: [Results3.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results3.png), [Results4.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/Results4.png)
+
+## Face detection
+
+The landmark model is quite robust with respect to the size and orientation of the faces, so the custom face detection model gets away with rougher bounding boxes than other approaches. It has a favorable speed to accuracy ratio for the purposes of this project.
+
+![EmiFace.png](https://raw.githubusercontent.com/emilianavt/OpenSeeFace/master/Images/EmiFace.png)
+
 # Release builds
 
 The builds in the release section of this repository contain a `facetracker.exe` inside a `Binary` folder that was built using `pyinstaller` and contains all required dependencies.
@@ -98,10 +129,25 @@ When distributing it, you should also distribute the `Licenses` folder along wit
 
 The release builds contain a custom build of ONNX Runtime without telemetry.
 
+# Dependencies (Python 3.6 - 3.9)
+
+* ONNX Runtime
+* OpenCV
+* Pillow
+* Numpy
+
+The required libraries can be installed using pip:
+
+     pip install onnxruntime opencv-python pillow numpy
+
+Alternatively poetry can be used to 
+install all dependencies for this project in a separate virtual env:
+
+     poetry install
+
 # Dependencies
 
-* Python 3.7
+* onnxruntime
-* ONNX Runtime
 * OpenCV
 * Pillow
 * Numpy
@@ -135,6 +181,17 @@ Additional training has been done on the WFLW dataset after reducing it to 66 po
 
 For the training the gaze and blink detection model, the [MPIIGaze](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/) dataset was used. Additionally, around 125000 synthetic eyes generated with [UnityEyes](https://www.cl.cam.ac.uk/research/rainbow/projects/unityeyes/) were used during training.
 
+It should be noted that additional custom data was also used during the training process and that the reference landmarks from the original datasets have been modified in certain ways to address various issues. It is likely not possible to reproduce these models with just the original LS3D-W and WFLW datasets, however the additional data is not redistributable.
+
+The heatmap regression based face detection model was trained on random 224x224 crops from the WIDER FACE dataset.
+
+	@inproceedings{yang2016wider,
+	  Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+	  Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+	  Title = {WIDER FACE: A Face Detection Benchmark},
+	  Year = {2016}
+    }
+
 ## Algorithm
 
 The algorithm is inspired by:
@@ -147,7 +204,7 @@ The algorithm is inspired by:
 
 The MobileNetV3 code was taken from [here](https://github.com/rwightman/gen-efficientnet-pytorch).
 
-For all training after the first model, a modified version of [Adaptive Wing Loss](https://github.com/tankrant/Adaptive-Wing-Loss) was used.
+For all training a modified version of [Adaptive Wing Loss](https://github.com/tankrant/Adaptive-Wing-Loss) was used.
 
 * [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://arxiv.org/abs/1904.07399) by Xinyao Wang, Liefeng Bo, Li Fuxin
 
@@ -164,6 +221,18 @@ Face detection is done using a custom heatmap regression based face detection mo
 
 RetinaFace detection is based on [this](https://github.com/biubug6/Pytorch_Retinaface) implementation. The pretrained model was modified to remove unnecessary landmark detection and converted to ONNX format for a resolution of 640x640.
 
+# Thanks!
+
+Many thanks to everyone who helped me test things!
+
+* [@Virtual_Deat](https://twitter.com/Virtual_Deat), who also inspired me to start working on this.
+* [@ENiwatori](https://twitter.com/eniwatori) and family.
+* [@ArgamaWitch](https://twitter.com/ArgamaWitch)
+* [@AngelVayuu](https://twitter.com/AngelVayuu)
+* [@DapperlyYours](https://twitter.com/DapperlyYours)
+* [@comdost_art](https://twitter.com/comdost_art)
+* [@Ponoki_Chan](https://twitter.com/Ponoki_Chan)
+
 # License
 
 The code and models are distributed under the BSD 2-clause license. 

Source/ThunderSVMWrapper.cpp

@@ -0,0 +1,222 @@
+// ThunderSVMWrapper.cpp : Defines the exported functions for the DLL.
+//
+
+#include "pch.h"
+#include "framework.h"
+#include "ThunderSVMWrapper.h"
+#include "thundersvm/thundersvm.h"
+#include "thundersvm/model/svmmodel.h"
+#include "thundersvm/model/svc.h"
+#include <cmath>
+
+using namespace std;
+
+extern "C" void __cdecl print_log(const char *str) {
+    return;
+}
+
+struct problem {
+    int r;
+    int c;
+    DataSet::node2d x;
+    vector<double> y;
+    DataSet dataset;
+};
+
+struct SVMWrapper {
+    SVC *model;
+    int cols;
+    int classes;
+    double *means;
+    double *sdevs;
+    SvmParam param;
+    struct problem problem;
+};
+
+typedef struct svm_node svmnode;
+
+void make_svmnode_array(DataSet::node2d &dataset, float features[], int rows, int cols, double *means, double *sdevs, int rescale) {
+    double row_factor = 1.0 / (double)rows;
+    if (rescale) {
+        for (int c = 0; c < cols; c++) {
+            means[c] = 0;
+            sdevs[c] = 0;
+        }
+        for (int r = 0; r < rows; r++) {
+            for (int c = 0; c < cols; c++) {
+                means[c] += (double)features[r * cols + c];
+            }
+        }
+        for (int c = 0; c < cols; c++) {
+            means[c] *= row_factor;
+        }
+        for (int r = 0; r < rows; r++) {
+            for (int c = 0; c < cols; c++) {
+                double sq_diff = ((double)features[r * cols + c] - means[c]);
+                sq_diff *= sq_diff;
+                sdevs[c] += sq_diff;
+            }
+        }
+        for (int c = 0; c < cols; c++) {
+            sdevs[c] *= row_factor;
+            sdevs[c] = sqrt(sdevs[c]);
+            sdevs[c] = 1.0 / sdevs[c];
+        }
+    }
+
+    for (int r = 0; r < rows; r++) {
+        dataset[r] = vector<DataSet::node>(cols);
+        int idx = 0;
+        for (int c = 0; c < cols; c++) {
+            dataset[r][c].index = c + 1;
+            dataset[r][c].value = (float)((features[r * cols + c] - means[c]) * sdevs[c]);
+        }
+    }
+}
+
+void make_problem(struct problem &problem, float features[], float labels[], int rows, int cols, double *means, double *sdevs) {
+    problem.r = rows;
+    problem.c = cols;
+    problem.y = vector<double>(rows);
+    for (int i = 0; i < rows; i++)
+        problem.y[i] = (double)labels[i];
+    problem.x = DataSet::node2d(rows);
+    make_svmnode_array(problem.x, features, rows, cols, means, sdevs, 1);
+    problem.dataset = DataSet(problem.x, cols, problem.y);
+}
+
+void destroy_problem(struct problem &problem) {
+    problem.y.clear();
+    problem.x.clear();
+}
+
+extern "C" {
+    __declspec(dllexport) void *__cdecl trainModel(float features[], float labels[], float weights[], int rows, int cols, int classes, int probability, float C) {
+        if (!(rows > 0 && cols > 0))
+            return NULL;
+
+        //svm_set_print_string_function(print_log);
+
+        struct SVMWrapper *sw = new SVMWrapper();
+        sw->cols = cols;
+        sw->classes = classes;
+        sw->means = new double[cols];
+        sw->sdevs = new double[cols];
+
+        sw->param.svm_type = SvmParam::C_SVC;
+        sw->param.kernel_type = SvmParam::RBF;
+        sw->param.degree = 3;
+        sw->param.gamma = 1.0 / (double)cols;
+        sw->param.coef0 = 0;
+        sw->param.nu = 0.5;
+        sw->param.C = C;
+        sw->param.epsilon = 0.001;
+        sw->param.p = 0.1;
+        sw->param.probability = probability;
+        sw->param.nr_weight = 0;
+        sw->param.weight_label = NULL;
+        sw->param.weight = NULL;
+
+        if (weights != NULL) {
+            sw->param.nr_weight = classes;
+            sw->param.weight_label = new int[classes];
+            sw->param.weight = new double[classes];
+            for (int i = 0; i < classes; i++) {
+                sw->param.weight_label[i] = i;
+                sw->param.weight[i] = (double)weights[i];
+            }
+        }
+
+        make_problem(sw->problem, features, labels, rows, cols, sw->means, sw->sdevs);
+        sw->model = new SVC();//svm_train(sw->problem, &sw->param);
+        sw->model->train(sw->problem.dataset, sw->param);
+
+        if (weights != NULL) {
+            delete sw->param.weight_label;
+            delete sw->param.weight;
+        }
+
+        return sw;
+    }
+
+    __declspec(dllexport) void __cdecl predict(void *ptr, float features[], float predictions[], double probabilities[], int rows) {
+        struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
+        int cols = sw->cols;
+
+        DataSet::node2d instances = DataSet::node2d(rows);
+        make_svmnode_array(instances, features, rows, sw->cols, sw->means, sw->sdevs, 0);
+        vector<double> predicted = sw->model->predict(instances, -1);
+        instances.clear();
+        for (int r = 0; r < rows; r++)
+            predictions[r] = (float)predicted[r];
+        if (probabilities != NULL && sw->param.probability) {
+            vector<float> probs = sw->model->get_prob_predict();
+            for (int r = 0; r < rows; r++)
+                for (int c = 0; c < sw->classes; c++)
+                    probabilities[c + r * sw->classes] = probs[c + r * sw->classes];
+        }
+        predicted.clear();
+
+        return;
+    }
+
+    __declspec(dllexport) void *__cdecl loadModel(char *filename, int cols, int classes, double means[], double sdevs[]) {
+        struct SVMWrapper *sw = new SVMWrapper();
+        sw->cols = cols;
+        sw->model = new SVC();
+        sw->model->load_from_file(filename);
+        sw->classes = classes;
+        sw->means = new double[cols];
+        sw->sdevs = new double[cols];
+        for (int c = 0; c < cols; c++) {
+            sw->means[c] = means[c];
+            sw->sdevs[c] = sdevs[c];
+        }
+        return sw;
+    }
+
+    __declspec(dllexport) void *__cdecl loadModelString(char *modelString, int cols, int classes, double means[], double sdevs[]) {
+        struct SVMWrapper *sw = new SVMWrapper();
+        sw->cols = cols;
+        sw->model = new SVC();
+        sw->model->load_from_string(modelString);
+        sw->classes = classes;
+        sw->means = new double[cols];
+        sw->sdevs = new double[cols];
+        for (int c = 0; c < cols; c++) {
+            sw->means[c] = means[c];
+            sw->sdevs[c] = sdevs[c];
+        }
+        return sw;
+    }
+
+    __declspec(dllexport) void __cdecl getScales(void *ptr, double means[], double sdevs[]) {
+        struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
+        int cols = sw->cols;
+        for (int c = 0; c < cols; c++) {
+            means[c] = sw->means[c];
+            sdevs[c] = sw->sdevs[c];
+        }
+    }
+
+    __declspec(dllexport) const char *__cdecl saveModelString(void *ptr) {
+        struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
+        string model = sw->model->save_to_string();
+        return _strdup(model.c_str());
+    }
+
+    __declspec(dllexport) int __cdecl saveModel(void *ptr, char *filename) {
+        struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
+        sw->model->save_to_file(string(filename));
+        return 1;
+    }
+
+    __declspec(dllexport) void __cdecl destroyModel(void *ptr) {
+        struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
+        delete sw->means;
+        delete sw->sdevs;
+        delete sw->model;
+        destroy_problem(sw->problem);
+        delete sw;
+    }
+}

Unity/OpenSee.cs

@@ -24,6 +24,8 @@ public class OpenSee : MonoBehaviour {
     [Tooltip("This contains the actual tracking data")]
     public OpenSeeData[] trackingData = null;
     
+    public bool listening { get; private set; } = false;
+    
     [HideInInspector]
     public float maxFit3DError = 100f;
 
@@ -39,9 +41,9 @@ public class OpenSee : MonoBehaviour {
         public float rightEyeOpen;
         [Tooltip("This field tells you how likely it is that the left eye is open.")]
         public float leftEyeOpen;
-        [Tooltip("This field tells you how likely it is that the right eye is open.")]
+        [Tooltip("This field contains the rotation of the right eyeball.")]
         public Quaternion rightGaze;
-        [Tooltip("This field tells you how likely it is that the left eye is open.")]
+        [Tooltip("This field contains the rotation of the left eyeball.")]
         public Quaternion leftGaze;
         [Tooltip("This field tells you if 3D points have been successfully estimated from the 2D points. If this is false, do not rely on pose or 3D data.")]
         public bool got3DPoints;
@@ -211,6 +213,7 @@ public class OpenSee : MonoBehaviour {
 
     void performReception() {
         EndPoint senderRemote = new IPEndPoint(IPAddress.Any, 0);
+        listening = true;
         while (!stopReception) {
             try {
                 int receivedBytes = socket.ReceiveFrom(buffer, SocketFlags.None, ref senderRemote);

Unity/OpenSeeExpression.cs

@@ -13,6 +13,18 @@ public class OpenSeeExpression : MonoBehaviour
     [Header("Settings")]
     [Tooltip("This is the source of face tracking data. If it is not set, any OpenSee component on the current object is used.")]
     public OpenSee openSee;
+    [Tooltip("When enabled ThunderSVM is used instead of LibSVM, which is usually faster, but may cause issues on some systems.")]
+    public bool useThunderSVM = false;
+    [Header("Simple mode")]
+    [Tooltip("When enabled, some simple heuristics will be used to detect expressions, rather than an SVM model. Outputs one of: neutral, fun, angry, surprise")]
+    public bool simpleMode = false;
+    [Tooltip("This smoothing factor is applied to the features used for simple expression detection.")]
+    [Range(0f, 1f)]
+    public float simpleSmoothing = 0.6f;
+    [Tooltip("This smoothing factor is applied to the features used for simple expression detection.")]
+    [Range(0f, 5f)]
+    public float simpleSensitivity = 1f;
+    [Header("Calibration")]
     [Tooltip("This specifies which expression calibration data should be collected for.")]
     public string calibrationExpression = "neutral";
     [Tooltip("This specifies the id of the face for which data should be collected and predictions should be made. Face ids depend only on the order of first detection and locations of the faces.")]
@@ -50,7 +62,7 @@ public class OpenSeeExpression : MonoBehaviour
     [Tooltip("These settings can be used to train an expression detection model on a subset of points.")]
     public PointSelection pointSelection;
     [Header("Information")]
-    [Tooltip("This is the of expressions for which calibration data was collected. The maximum number is 10.")]
+    [Tooltip("This is the number of expressions for which calibration data was collected. The maximum number is 10.")]
     public int expressionNumber = 0;
     [Tooltip("This is the percentage of necessary training data collected for the current expression.")]
     public float percentRecorded = 0.0f;
@@ -72,6 +84,44 @@ public class OpenSeeExpression : MonoBehaviour
     [Tooltip("This shows any accuracy warnings that resulted from the last training run.")]
     public string[] warnings = null;
     
+    private float lastMouthCorner = 0f;
+    private float lastEyebrows = 0f;
+    private bool hadFun = false;
+    private bool hadAngry = false;
+    private bool hadSurprised = false;
+    float AdjustThreshold(bool active) {
+        if (active)
+            return 0.8f;
+        else
+            return 1f;
+    }
+    void ThresholdDetection() {
+        lastMouthCorner = lastMouthCorner * simpleSmoothing + (openSeeData.features.MouthCornerUpDownLeft + openSeeData.features.MouthCornerUpDownRight) * 0.5f * (1f - simpleSmoothing);
+        lastEyebrows = lastEyebrows * simpleSmoothing + (openSeeData.features.EyebrowUpDownLeft + openSeeData.features.EyebrowUpDownRight) * 0.5f * (1f - simpleSmoothing);
+        if (lastMouthCorner * simpleSensitivity < -0.2f * AdjustThreshold(hadFun)) {
+            expression = "fun";
+            hadFun = true;
+            hadSurprised = false;
+            hadAngry = false;
+        } else if (lastEyebrows * simpleSensitivity > 0.2f * AdjustThreshold(hadSurprised)) {
+            expression = "surprise";
+            hadFun = false;
+            hadSurprised = true;
+            hadAngry = false;
+        } else if (lastEyebrows * simpleSensitivity < -0.25f * AdjustThreshold(hadAngry) && lastMouthCorner * simpleSensitivity > -0.3f * (2f - AdjustThreshold(hadAngry))) {
+            expression = "angry";
+            hadFun = false;
+            hadSurprised = false;
+            hadAngry = true;
+        } else {
+            expression = "neutral";
+            hadFun = false;
+            hadSurprised = false;
+            hadAngry = false;
+        }
+        expressionTime = Time.time;
+    }
+
     [Serializable]
     public class PointSelection {
         [Tooltip("When enabled, the points of the face contour will be used to determine the expression.")]
@@ -104,6 +154,7 @@ public class OpenSeeExpression : MonoBehaviour
         private string[] classLabels = null;
         private int[] indices = null;
         private PointSelection pointSelection;
+        private bool thunderSVM = false;
         //private bool newModel = false;
 
         static public void LoadSerialized(byte[] modelBytes, out Dictionary<string, List<float[]>> expressions, out SVMModel model, out string[] classLabels, out int[] indices, ref PointSelection pointSelection) {
@@ -115,6 +166,9 @@ public class OpenSeeExpression : MonoBehaviour
                 oser = formatter.Deserialize(gzipStream) as OpenSeeExpressionRepresentation;
             }
             expressions = oser.expressions;
+            /*if (oser.thunderSVM)
+                model = new ThunderSVMModel(oser.modelBytes);
+            else*/
             model = new SVMModel(oser.modelBytes);
             classLabels = oser.classLabels;
             indices = oser.indices;
@@ -137,6 +191,7 @@ public class OpenSeeExpression : MonoBehaviour
             oser.classLabels = classLabels;
             oser.indices = indices;
             oser.pointSelection = pointSelection;
+            oser.thunderSVM = model is ThunderSVMModel;
             //oser.newModel = true;
 
             IFormatter formatter = new BinaryFormatter();
@@ -309,6 +364,9 @@ public class OpenSeeExpression : MonoBehaviour
         }
         ResetInfo();
         expressions = new Dictionary<string, List<float[]>>();
+        if (useThunderSVM)
+            model = new ThunderSVMModel();
+        else
             model = new SVMModel();
         rnd = new System.Random();
     }
@@ -363,7 +421,10 @@ public class OpenSeeExpression : MonoBehaviour
             return false;
         }
         modelReady = false;
-        //model = new SVMModel();
+        if (model is SVMModel && useThunderSVM)
+            model = new ThunderSVMModel();
+        else if (model is ThunderSVMModel && !useThunderSVM)
+            model = new SVMModel();
         SelectPoints();
         List<string> keys = new List<string>();
         List<string> accuracyWarnings = new List<string>();
@@ -377,13 +438,13 @@ public class OpenSeeExpression : MonoBehaviour
                 samples += list.Count;
             } else {
                 if (list != null && list.Count > 20) {
-                    Debug.Log("[Training warning] Expression " + key + " has little data and might be inaccurate.");
+                    Debug.Log("[Training warning] Expression " + key + " has little data and might be inaccurate. (" + list.Count + ")");
-                    accuracyWarnings.Add("Expression " + key + " has little data and might be inaccurate.");
+                    accuracyWarnings.Add("Expression " + key + " has little data and might be inaccurate. (" + list.Count + ")");
                     samples += list.Count;
                     keys.Add(key);
                 } else {
-                    Debug.Log("[Training warning] Skipping expression " + key + " due to lack of collected data.");
+                    Debug.Log("[Training warning] Skipping expression " + key + " due to lack of collected data. (" + list.Count + ")");
-                    accuracyWarnings.Add("Skipping expression " + key + " due to lack of collected data.");
+                    accuracyWarnings.Add("Skipping expression " + key + " due to lack of collected data. (" + list.Count + ")");
                     continue;
                 }
             }
@@ -401,11 +462,16 @@ public class OpenSeeExpression : MonoBehaviour
         if (weightMap != null) {
             weights = new float[classLabels.Length];
             for (int i = 0; i < classLabels.Length; i++) {
+                if (weightMap.ContainsKey(classLabels[i]))
                     weights[i] = weightMap[classLabels[i]];
+                else
+                    weights[i] = 1f;
                 Debug.Log("[Training info] Adding weight " + weights[i] + " for " + classLabels[i] + ".");
             }
         }
         
+        Debug.Log("[Training info] Preparing trainig data.");
+        
         int train_split = maxSamples * 3 / 4;
         int test_split = maxSamples - train_split;
         int rows_train = classes * train_split;
@@ -455,6 +521,7 @@ public class OpenSeeExpression : MonoBehaviour
                 i_test++;
             }
         }
+        Debug.Log("[Training info] Preparation complete.");
         int probability = 0;
         if (enableProbabilityTraining)
             probability = 1;
@@ -474,17 +541,24 @@ public class OpenSeeExpression : MonoBehaviour
         }
         warnings = accuracyWarnings.ToArray();
         modelReady = true;
+        Debug.Log("[Training info] Trained model.");
         return true;
     }
 
     public bool PredictExpression() {
-        if (openSee == null || !(modelReady && model.Ready())) {
+        if (openSee == null)
+            return false;
+        if (!simpleMode && !(modelReady && model.Ready())) {
             ResetInfo();
             return false;
         }
         openSeeData = openSee.GetOpenSeeData(faceId);
         if (openSeeData == null || openSeeData.time <= lastCapture)
             return false;
+        if (simpleMode) {
+            ThresholdDetection();
+            return true;
+        }
         float[] faceData = GetData(openSeeData);
         float[] predictionData = new float[cols];
         for (int i = 0; i < cols; i++)
@@ -632,7 +706,7 @@ public class OpenSeeExpression : MonoBehaviour
             SaveToFile();
         if (!predict)
             expression = "";
-        else if (modelReady)
+        else if (simpleMode || modelReady)
             PredictExpression();
         else
             predict = false;

Unity/OpenSeeIKTarget.cs

@@ -2,15 +2,24 @@
 
 namespace OpenSee {
 
+[DefaultExecutionOrder(-1)]
 public class OpenSeeIKTarget : MonoBehaviour
 {
     [Header("Settings")]
     [Tooltip("This is the source of face tracking data.")]
     public OpenSee openSee;
+    [Tooltip("When this is set to another game object, its kinematic interpolation component will get updated from values of this.")]
+    public OpenSeeKinematicInterpolation kinematicInterpolation;
+    [Tooltip("When set, the this object will follow the specified transform instead and automatically recalibrate based on it.")]
+    public Transform externalTarget = null;
+    [Tooltip("This can be used to skip the first few tracking frames.")]
+    public int skipFirst = 5;
     [Tooltip("When set to true, the next rotation and translation values will be set as zero rotation and zero translation.")]
     public bool calibrate = true;
     [Tooltip("This sets which face id to look for in the OpenSee data.")]
     public int faceId = 0;
+    [Tooltip("When enabled, the IK target will not move up and down.")]
+    public bool preventVerticalMotion = false;
     [Tooltip("When this is enabled, rotations and movement are mirrored.")]
     public bool mirrorMotion = false;
     [Tooltip("Often, the translation vector's scale is too high. Setting this somewhere between 0.05 to 0.3 seems stabilize things, but it also reduces the range of motion.")]
@@ -26,13 +35,34 @@ public class OpenSeeIKTarget : MonoBehaviour
     public bool driftBack = false;
     [Tooltip("This sets the strength of the drifting effect. At 0 there is now drifting, while the IK target will be static at 1.")]
     public float driftFactor = 0.005f;
+    [Tooltip("This sets the minimum angle difference between two poses after which the pose will be considered an outlier.")]
+    [Range(0, 360)]
+    public float outlierThresholdAngle = 35f;
+    [Tooltip("This sets the number of seconds for which angle outliers are ignored before being accepted. Setting this to zero will disable outlier skipping.")]
+    public float outlierSkipPeriod = 0.4f;
+    [Tooltip("This sets the minimum distance difference between two poses after which the pose will be considered an outlier.")]
+    public float outlierThresholdDistance = 2f;
+    [Tooltip("This sets the number of seconds for which distance outliers are ignored before being accepted. Once this period elapses, automatic recalibration is triggered. Setting this to zero will disable outlier skipping.")]
+    public float outlierSkipPeriodDistance = 0.4f;
+    [Tooltip("If set to true, persistent positional outliers will cause automatic recalibration.")]
+    public bool outlierRecalibrate = false;
+    [Tooltip("If set to true, recalibration caused by positional outliers will keep the current position and rotation of the head.")]
+    public bool outlierKeepOffset = false;
     [Header("Information")]
+    [Tooltip("This is the number of received tracking frames.")]
+    public int trackingFrames = 0;
     [Tooltip("This is the current rotation calibration value as euler angles.")]
     public Vector3 rotationOffset = new Vector3(0f, 0f, -90f);
     [Tooltip("This is the current translation calibration.")]
     public Vector3 translationOffset = new Vector3(0f, 0f, 0f);
     [Tooltip("This is the average number of interpolated frames per received tracking data.")]
     public float averageInterpolations = 0f;
+    [Tooltip("This is the number of outlier skipped tracking data frames so far.")]
+    public int outlierSkips = 0;
+    [Tooltip("This is the number of distance outlier skipped tracking data frames so far.")]
+    public int outlierSkipsDistance = 0;
+    [Tooltip("This is the number of times a recalibration was triggered due to positional outliers.")]
+    public int outlierCalibrations = 0;
 
     private double updated = 0.0f;
     private Quaternion dR = Quaternion.Euler(0f, 0f, -90f);
@@ -47,17 +77,56 @@ public class OpenSeeIKTarget : MonoBehaviour
     private Vector3 currentT;
     private bool lastMirror = false;
 
+    private bool gotAccepted = false;
+    private Quaternion lastAccepted;
+    private bool skipped = false;
+    private float skipStartTime = -1f;
+    
+    private bool gotAcceptedPosition = false;
+    private Vector3 lastAcceptedPosition;
+    private bool skippedPosition = false;
+    private float skipStartTimePosition = -1f;
+    
+    private Transform lastExternalTarget = null;
+    
     public void Calibrate() {
         calibrate = true;
     }
     
+    public static Vector3 PrettyEuler(Vector3 last, Vector3 current) {
+        // Ew, but how would you do componentwise filtering/interpolation with quaternions?
+        if (Mathf.Abs(current.x - last.x) > Mathf.Abs((current.x - 360f) - last.x))
+            current.x -= 360f;
+        else if (Mathf.Abs(current.x - last.x) > Mathf.Abs((current.x + 360f) - last.x))
+            current.x += 360f;
+        if (Mathf.Abs(current.y - last.y) > Mathf.Abs((current.y - 360f) - last.y))
+            current.y -= 360f;
+        else if (Mathf.Abs(current.y - last.y) > Mathf.Abs((current.y + 360f) - last.y))
+            current.x += 360f;
+        if (Mathf.Abs(current.z - last.z) > Mathf.Abs((current.z - 360f) - last.z))
+            current.z -= 360f;
+        else if (Mathf.Abs(current.z - last.z) > Mathf.Abs((current.z + 360f) - last.z))
+            current.z += 360f;
+        return current;
+    }
+    
+    Vector3 FilterPos(Vector3 a, Vector3 b) {
+        return Vector3.Lerp(a, b, 1f - smooth);
+    }
+    
+    Quaternion FilterRot(Quaternion a, Quaternion b) {
+        return Quaternion.Lerp(a, b, 1f - smooth);
+    }
+
     void Interpolate() {
         if (!interpolate || interpolateState < 2)
             return;
-        float t = Mathf.Clamp((float)interpolationCount / avgInterps, 0f, 0.985f);
+        float t = Mathf.Clamp((float)interpolationCount / avgInterps, 0f, 1.5f);
-        transform.localPosition = Vector3.Lerp(lastT, currentT, t);
+        transform.localPosition = Vector3.SlerpUnclamped(lastT, currentT, t);
-        transform.localRotation = Quaternion.Lerp(lastR, currentR, t);
+        transform.localRotation = Quaternion.SlerpUnclamped(lastR, currentR, t);
         interpolationCount++;
+        //transform.localPosition = new Vector3(0.3f * ((Time.time/3f) % 3f) - 0.5f, 0.3f * Mathf.Sin(Mathf.Rad2Deg * ((Time.time/5f) % 3f)), 0f);
+        //transform.localRotation = Quaternion.identity * Quaternion.AngleAxis(20f * Mathf.Sin(Mathf.Rad2Deg * ((Time.time/5f) % 3f)) - 10f, Vector3.up);
     }
 
     Quaternion MirrorQuaternion(Quaternion q) {
@@ -70,20 +139,100 @@ public class OpenSeeIKTarget : MonoBehaviour
 
     void RunUpdate() {
         var openSeeData = openSee.GetOpenSeeData(faceId);
+        Quaternion convertedQuaternion;
+        Vector3 t;
+        Vector3 convertedTranslation;
+        
+        if (externalTarget == null) {
             if (openSeeData == null || openSeeData.fit3DError > openSee.maxFit3DError)
                 return;
             if (openSeeData.time > updated) {
                 updated = openSeeData.time;
+                trackingFrames++;
+                if (trackingFrames < skipFirst)
+                    return;
             } else {
                 Interpolate();
                 return;
             }
             
-        Quaternion convertedQuaternion = new Quaternion(-openSeeData.rawQuaternion.y, -openSeeData.rawQuaternion.x, openSeeData.rawQuaternion.z, openSeeData.rawQuaternion.w);
+            convertedQuaternion = new Quaternion(-openSeeData.rawQuaternion.y, -openSeeData.rawQuaternion.x, openSeeData.rawQuaternion.z, openSeeData.rawQuaternion.w);
-        Vector3 t = openSeeData.translation;
+            t = openSeeData.translation;
             t.x = -t.x;
             t.z = -t.z;
-        Vector3 convertedTranslation = new Vector3(t.x, t.y, t.z);
+            convertedTranslation = new Vector3(t.x, t.y, t.z);
+        } else {
+            convertedQuaternion = externalTarget.localRotation;
+            convertedTranslation = externalTarget.localPosition;
+            t = convertedTranslation;
+        }
+        
+        // Check for angular outliers
+        if (gotAccepted) {
+            float angularDifference = Quaternion.Angle(lastAccepted, convertedQuaternion);
+            if (angularDifference >= outlierThresholdAngle) {
+                if (!skipped) {
+                    if (outlierSkipPeriod >= 0.0000001f) {
+                        skipStartTime = Time.time;
+                        skipped = true;
+                    }
+                } else {
+                    if (Time.time > skipStartTime + outlierSkipPeriod) {
+                        lastAccepted = convertedQuaternion;
+                        skipped = false;
+                    }
+                }
+            } else {
+                skipped = false;
+                lastAccepted = convertedQuaternion;
+            }
+        } else {
+            lastAccepted = convertedQuaternion;
+            skipped = false;
+        }
+        gotAccepted = true;
+        if (skipped) {
+            outlierSkips += 1;
+            return;
+        }
+        
+        // Check for positional outliers
+        bool keepOffset = false;
+        if (gotAcceptedPosition) {
+            float distance = Vector3.Distance(t, lastAcceptedPosition);
+            if (distance > outlierThresholdDistance) {
+                if (!skippedPosition) {
+                    if (outlierSkipPeriodDistance >= 0.0000001f) {
+                        skipStartTimePosition = Time.time;
+                        skippedPosition = true;
+                    }
+                } else {
+                    if (Time.time > skipStartTimePosition + outlierSkipPeriodDistance) {
+                        lastAcceptedPosition = t;
+                        skippedPosition = false;
+                        if (outlierRecalibrate) {
+                            calibrate = true;
+                            keepOffset = outlierKeepOffset;
+                            outlierCalibrations += 1;
+                        }
+                    }
+                }
+            } else {
+                skippedPosition = false;
+                lastAcceptedPosition = t;
+            }
+        } else {
+            skippedPosition = false;
+            lastAcceptedPosition = t;
+        }
+        gotAcceptedPosition = true;
+        if (skippedPosition) {
+            outlierSkipsDistance += 1;
+            return;
+        }
+        
+        calibrate = calibrate || (externalTarget != lastExternalTarget);
+        lastExternalTarget = externalTarget;
 
         if (calibrate) {
             dR = convertedQuaternion;
@@ -91,6 +240,8 @@ public class OpenSeeIKTarget : MonoBehaviour
             dT = t;
             rotationOffset = new Vector3(dR.eulerAngles.x, dR.eulerAngles.y, dR.eulerAngles.z);
             translationOffset = new Vector3(dT.x, dT.y, dT.z);
+            lastAcceptedPosition = translationOffset;
+            skippedPosition = false;
         }
 
         if (mirrorMotion != lastMirror || (mirrorMotion && calibrate)) {
@@ -98,6 +249,10 @@ public class OpenSeeIKTarget : MonoBehaviour
             dT = MirrorTranslation(dT);
             lastMirror = mirrorMotion;
         }
+        if (calibrate && keepOffset) {
+            dR = Quaternion.Inverse(Quaternion.Inverse(transform.localRotation) * Quaternion.Inverse(dR));
+            dT -= transform.localPosition;
+        }
         calibrate = false;
 
         if (mirrorMotion) {
@@ -110,11 +265,11 @@ public class OpenSeeIKTarget : MonoBehaviour
         interpolationCount = 0;
         averageInterpolations = avgInterps;
         
-        lastT = currentT;
+        lastT = transform.localPosition;
-        lastR = currentR;
+        lastR = transform.localRotation;
         if (interpolateState > 0) {
-            currentT = Vector3.Lerp(currentT, (t - dT) * translationScale, 1f - smooth);
+            currentT = FilterPos(transform.localPosition, (t - dT) * translationScale);
-            currentR = Quaternion.Lerp(transform.localRotation, convertedQuaternion * dR, 1f - smooth);
+            currentR = FilterRot(transform.localRotation, convertedQuaternion * dR);
         } else {
             currentT = (t - dT) * translationScale;
             currentR = convertedQuaternion * dR;
@@ -125,8 +280,22 @@ public class OpenSeeIKTarget : MonoBehaviour
         if (interpolate) {
             Interpolate();
         } else {
-            transform.localPosition = Vector3.Lerp(transform.localPosition, (t - dT) * translationScale, 1f - smooth);
+            transform.localPosition = FilterPos(transform.localPosition, (t - dT) * translationScale);
-            transform.localRotation = Quaternion.Lerp(transform.localRotation, convertedQuaternion * dR, 1f - smooth);
+            transform.localRotation = FilterRot(transform.localRotation, convertedQuaternion * dR);
+        }
+
+       if (preventVerticalMotion) {
+           currentT.y = 0f;
+            Vector3 pos = transform.localPosition;
+            pos.y = 0f;
+            transform.localPosition = pos;
+        }
+
+        if (kinematicInterpolation != null && kinematicInterpolation.gameObject != gameObject) {
+            if (interpolate)
+                kinematicInterpolation.UpdateKI(updated, currentT, currentR);
+            else
+                kinematicInterpolation.UpdateKI(updated, transform.localPosition, transform.localRotation);
         }
         
         if (driftBack) {
@@ -142,6 +311,10 @@ public class OpenSeeIKTarget : MonoBehaviour
        }
     }
     
+    public void SetPreventVerticalMotion(bool v) {
+        preventVerticalMotion = v;
+    }
+
     void FixedUpdate()
     {
         if (fixedUpdate)

Unity/OpenSeeIKTarget.cs.meta

@@ -4,7 +4,7 @@ MonoImporter:
   externalObjects: {}
   serializedVersion: 2
   defaultReferences: []
-  executionOrder: 0
+  executionOrder: -5
   icon: {instanceID: 0}
   userData: 
   assetBundleName: 

Unity/OpenSeeInterpolation.cs

@@ -0,0 +1,232 @@
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace OpenSee {
+    public class InterpolatedMap<T, V, U> where V : InterpolatedValue<U>, new() {
+        private Dictionary<T, V> map = new Dictionary<T, V>();
+        private float clamp = 1f;
+        private float smoothing = 0f;
+        private bool gotDefault = false;
+        private U defaultValue;
+
+        public void Clear() {
+            map.Clear();
+        }
+        
+        public void SetSmoothing(float smoothing) {
+            this.smoothing = smoothing;
+            foreach (var entry in map) {
+                if (entry.Value != null)
+                    entry.Value.SetSmoothing(smoothing);
+            }
+        }
+
+        public void SetClamp(float clamp) {
+            this.clamp = clamp;
+            foreach (var entry in map) {
+                if (entry.Value != null)
+                    entry.Value.SetClamp(clamp);
+            }
+        }
+        
+        void Prepare(T key) {
+            if (!map.ContainsKey(key)) {
+                map.Add(key, new V());
+                map[key].SetSmoothing(smoothing);
+                map[key].SetClamp(clamp);
+            }
+        }
+        
+        public void SetValue(T key, U v) {
+            Prepare(key);
+            map[key].SetValue(v);
+        }
+        
+        public void Store(T key, U v) {
+            Prepare(key);
+            map[key].UpdateValue(v);
+        }
+
+        public void Store(T key, U v, double nowT) {
+            if (!map.ContainsKey(key)) {
+                map.Add(key, new V());
+                map[key].SetSmoothing(smoothing);
+                map[key].SetClamp(clamp);
+            }
+            map[key].UpdateValue(v, nowT);
+        }
+        
+        public U GetDefault() {
+            if (gotDefault)
+                return defaultValue;
+            V tmp = new V();
+            defaultValue = tmp.GetDefault();
+            return defaultValue;
+        }
+        
+        public bool Check(T key) {
+            return map.ContainsKey(key);
+        }
+        
+        public U Get(T key) {
+            if (!map.ContainsKey(key))
+                return GetDefault();
+            return map[key].Interpolate();
+        }
+    }
+
+    abstract public class InterpolatedValue<V> {
+        private float interpolateT = 0f;
+        private float updateT = 0f;
+        private double lastT = 0f;
+        private double currentT = 0f;
+        private float clamp = 1f;
+        private float smoothing = 0f;
+        private int gotData = 0;
+
+        private bool haveDefault = false;
+        private V defaultValue;
+
+        private bool valueSet = false;
+        private V dataValue;
+        private V dataValueLast;
+
+        abstract public V ValueLerpUnclamped(V a, V b, float t);
+        abstract public V GetBuiltinDefault();
+        
+        public void SetDefault(V defaultValue) {
+            this.defaultValue = defaultValue;
+            haveDefault = true;
+        }
+        
+        public void ResetDefault() {
+            haveDefault = false;
+        }
+        
+        public V GetDefault() {
+            if (haveDefault)
+                return defaultValue;
+            return GetBuiltinDefault();
+        }
+        
+        public void SetSmoothing(float smoothing) {
+            this.smoothing = smoothing;
+        }
+
+        public void SetClamp(float clamp) {
+            this.clamp = clamp;
+        }
+        
+        public void SetValue(V v) {
+            dataValueLast = v;
+            dataValue = v;
+            valueSet = true;
+        }
+        
+        public InterpolatedValue(float smoothing = 0f, float clamp = 1f) {
+            this.smoothing = smoothing;
+            this.clamp = clamp;
+        }
+        
+        public void UpdateValue(V v) {
+            UpdateValue(v, Time.realtimeSinceStartup);
+        }
+        
+        public void UpdateValue(V v, double nowT) {
+            if (nowT < 0f)
+                return;
+            lock (this) {
+                if (nowT < currentT || gotData == 0) {
+                    // Reinitialize
+                    gotData = 1;
+                    updateT = Time.time;
+                    lastT = 0f;
+                    currentT = nowT;
+                    dataValueLast = GetDefault();
+                    dataValue = ValueLerpUnclamped(v, dataValueLast, smoothing);
+                } else if (nowT == currentT) {
+                    // Change value
+                    dataValue = ValueLerpUnclamped(v, dataValueLast, smoothing);
+                } else {
+                    // Regular operation
+                    updateT = Time.time;
+                    lastT = currentT;
+                    currentT = nowT;
+                    if (gotData < 2)
+                        gotData++;
+                    dataValueLast = dataValue;
+                    dataValue = ValueLerpUnclamped(v, dataValueLast, smoothing);
+                }
+            }
+        }
+        
+        public V Interpolate() {
+            lock (this) {
+                interpolateT = Time.time;
+                if (gotData < 2) {
+                    if (valueSet)
+                        return dataValue;
+                    else
+                        return GetDefault();
+                }
+                float t = Mathf.Min((interpolateT - updateT) / (float)(currentT - lastT), clamp);
+                return ValueLerpUnclamped(dataValueLast, dataValue, t);
+            }
+        }
+    }
+    
+    public class InterpolatedColor : InterpolatedValue<Color> {
+        public override Color ValueLerpUnclamped(Color a, Color b, float t) {
+            return Color.LerpUnclamped(a, b, t);
+        }
+        public override Color GetBuiltinDefault() {
+            return Color.clear;
+        }
+    }
+    
+    public class InterpolatedQuaternion : InterpolatedValue<Quaternion> {
+        public override Quaternion ValueLerpUnclamped(Quaternion a, Quaternion b, float t) {
+            return Quaternion.SlerpUnclamped(a, b, t);
+        }
+        public override Quaternion GetBuiltinDefault() {
+            return Quaternion.identity;
+        }
+    }
+
+    public class InterpolatedVector2 : InterpolatedValue<Vector2> {
+        public override Vector2 ValueLerpUnclamped(Vector2 a, Vector2 b, float t) {
+            return Vector2.LerpUnclamped(a, b, t);
+        }
+        public override Vector2 GetBuiltinDefault() {
+            return Vector2.zero;
+        }
+    }
+
+    public class InterpolatedVector3 : InterpolatedValue<Vector3> {
+        public override Vector3 ValueLerpUnclamped(Vector3 a, Vector3 b, float t) {
+            return Vector3.LerpUnclamped(a, b, t);
+        }
+        public override Vector3 GetBuiltinDefault() {
+            return Vector3.zero;
+        }
+    }
+
+    public class InterpolatedVector4 : InterpolatedValue<Vector4> {
+        public override Vector4 ValueLerpUnclamped(Vector4 a, Vector4 b, float t) {
+            return Vector4.LerpUnclamped(a, b, t);
+        }
+        public override Vector4 GetBuiltinDefault() {
+            return Vector4.zero;
+        }
+    }
+
+    public class InterpolatedFloat : InterpolatedValue<float> {
+        public override float ValueLerpUnclamped(float a, float b, float t) {
+            return Mathf.LerpUnclamped(a, b, t);
+        }
+        public override float GetBuiltinDefault() {
+            return 0f;
+        }
+    }
+}

Unity/OpenSeeInterpolation.cs.meta

@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: d61e11f1b6900a7419383be1377f191b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/OpenSeeKinematicInterpolation.cs

@@ -0,0 +1,108 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace OpenSee {
+
+public class OpenSeeKinematicInterpolation : MonoBehaviour
+{
+    public float interpolateClamp = 1f;
+    
+    private Vector3 lastPos;
+    private Vector3 lastPosV;
+    private double lastT;
+    
+    private Vector3 currentPos;
+    private Vector3 currentPosV;
+    private double currentT = 0f;
+    
+    private Vector3 lastRot = Vector3.zero;
+    private Vector3 lastRotV;
+    
+    private Vector3 currentRot;
+    private Vector3 currentRotV;
+    
+    private float dT;
+    private Vector3 coeffPosA;
+    private Vector3 coeffPosB;
+    private Vector3 coeffRotA;
+    private Vector3 coeffRotB;
+    
+    private double updateT;
+    private double interpolateT;
+    private int ready = 0;
+    
+    public void UpdateKI(double newT, Vector3 newPos, Quaternion newRot) {
+        Interpolate();
+        if (newT - currentT > 3f)
+            ready = 0;
+
+        currentPos = transform.localPosition;
+        currentRot = OpenSeeIKTarget.PrettyEuler(currentRot, transform.localRotation.eulerAngles);
+        float dTNow = (float)(interpolateT - updateT);
+        if (interpolateT - updateT > 0 || ready < 1) {
+            currentPosV = (currentPos - lastPos) / dT;
+            currentRotV = (currentRot - lastRot) / dT;
+        } else {
+            currentPosV = Vector3.zero;
+            currentRotV = Vector3.zero;
+        }
+
+        lastPos = currentPos;
+        lastPosV = currentPosV;
+        lastRot = currentRot;
+        lastRotV = currentRotV;
+        lastT = currentT;
+        
+        currentPos = newPos;
+        currentRot = OpenSeeIKTarget.PrettyEuler(lastRot, newRot.eulerAngles);
+        currentT = newT;
+        dT = (float)(currentT - lastT);
+        currentPosV = (currentPos - lastPos) / dT;
+        currentRotV = (currentRot - lastRot) / dT;
+
+        float dTPow2 = Mathf.Pow(dT, 2);
+        float dTPow3 = Mathf.Pow(dT, 3);
+        coeffPosA = (- (2f*(currentPosV * dT + 2f * dT * lastPosV - 3f * currentPos + 3f * lastPos)) / dTPow2) / 2f;
+        coeffPosB = ((6f*(currentPosV * dT + dT * lastPosV - 2f * currentPos + 2f * lastPos)) / dTPow3)/6f;
+        coeffRotA = (- (2f*(currentRotV * dT + 2f * dT * lastRotV - 3f * currentRot + 3f * lastRot)) / dTPow2) / 2f;
+        coeffRotB = ((6f*(currentRotV * dT + dT * lastRotV - 2f * currentRot + 2f * lastRot)) / dTPow3)/6f;
+        
+        updateT = Time.time;
+        if (ready <= 2)
+            ready++;
+    }
+    
+    float InterpolateValue(float dTNow, float dTNowPow2, float dTNowPow3, float a, float b, float x1, float x2, float v1, float v2) {
+        return x1 + v1 * dTNow + dTNowPow2 * a + dTNowPow3 * b;
+    }
+    
+    void Interpolate() {
+        interpolateT = Time.time;
+        if (interpolateT == updateT || ready <= 2)
+            return;
+        float dTNow = (float)(interpolateT - updateT);
+        if (dTNow >= dT) {
+            transform.localPosition = Vector3.LerpUnclamped(lastPos, currentPos, Mathf.Min((float)(dTNow / dT), interpolateClamp));
+            transform.localEulerAngles = Vector3.LerpUnclamped(lastRot, currentRot, Mathf.Min((float)(dTNow / dT), interpolateClamp));
+        } else {
+            float dTNowPow2 = Mathf.Pow(dTNow, 2);
+            float dTNowPow3 = Mathf.Pow(dTNow, 3);
+            float x = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffPosA.x, coeffPosB.x, lastPos.x, currentPos.x, lastPosV.x, currentPosV.x);
+            float y = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffPosA.y, coeffPosB.y, lastPos.y, currentPos.y, lastPosV.y, currentPosV.y);
+            float z = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffPosA.z, coeffPosB.z, lastPos.z, currentPos.z, lastPosV.z, currentPosV.z);
+            transform.localPosition = new Vector3(x, y, z);
+            x = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffRotA.x, coeffRotB.x, lastRot.x, currentRot.x, lastRotV.x, currentRotV.x);
+            y = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffRotA.y, coeffRotB.y, lastRot.y, currentRot.y, lastRotV.y, currentRotV.y);
+            z = InterpolateValue(dTNow, dTNowPow2, dTNowPow3, coeffRotA.z, coeffRotB.z, lastRot.z, currentRot.z, lastRotV.z, currentRotV.z);
+            transform.localEulerAngles = new Vector3(x, y, z);
+        }
+    }
+    
+    public void Update() {
+        Interpolate();
+    }
+}
+
+}

Unity/OpenSeeKinematicInterpolation.cs.meta

@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a4112b4be90fc9e4baee9eade65378c3
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: -1
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/OpenSeeLauncher.cs

@@ -13,17 +13,29 @@ namespace OpenSee {
 
 public class OpenSeeLauncher : MonoBehaviour {
     #region DllImport
-    [DllImport("escapi_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "countCaptureDevices")]
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "create_capture")]
-	private static extern int countCaptureDevices_x64();
+	private static extern System.IntPtr create_capture_x64();
 
-	[DllImport("escapi_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "getCaptureDeviceName")]
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_devices")]
-	private static extern void getCaptureDeviceName_x64(int deviceno, [Out] StringBuilder namebuffer, int bufferlength);
+	private static extern int get_devices_x64(System.IntPtr cap);
 
-    [DllImport("escapi_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "countCaptureDevices")]
+	[DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_device")]
-	private static extern int countCaptureDevices_x86();
+	private static extern void get_device_x64(System.IntPtr cap, int deviceno, [Out] StringBuilder namebuffer, int bufferlength);
 
-	[DllImport("escapi_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "getCaptureDeviceName")]
+    [DllImport("dshowcapture_x64", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroy_capture")]
-	private static extern void getCaptureDeviceName_x86(int deviceno, [Out] StringBuilder namebuffer, int bufferlength);
+	private static extern void destroy_capture_x64(System.IntPtr cap);
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "create_capture")]
+	private static extern System.IntPtr create_capture_x86();
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_devices")]
+	private static extern int get_devices_x86(System.IntPtr cap);
+
+	[DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "get_device")]
+	private static extern void get_device_x86(System.IntPtr cap, int deviceno, [Out] StringBuilder namebuffer, int bufferlength);
+
+    [DllImport("dshowcapture_x86", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroy_capture")]
+	private static extern void destroy_capture_x86(System.IntPtr cap);
     #endregion
     
     [Header("Settings")]
@@ -39,15 +51,21 @@ public class OpenSeeLauncher : MonoBehaviour {
     public bool autoListCameras = false;
     [Tooltip("If this is enabled, no output from the tracker will be printed to the debug console. This cannot be changed while the tracker is running.")]
     public bool dontPrint = true;
+    [Tooltip("If enabled, the next available port within 500 starting after the one given on the OpenSee component will be used.")]
+    public bool dynamicPort = false;
     [Tooltip("The path to \"facetracker.exe\".")]
     public string exePath = "facetracker.exe";
     [Tooltip("The path, where the .onnx model files can be found")]
     public string modelPath = "models\\";
     [Tooltip("Additional options that should be passed to the face tracker.")]
-    public List<string> commandlineOptions = new List<string>(new string[] { "--silent", "1", "--max-threads", "4" });
+    public List<string> commandlineOptions = new List<string>(new string[] { "--silent", "1", "--max-threads", "1" });
-    [Tooltip("IL2CPP doesn't support Proocess.Start. When this is enabled, OpenSeeLauncher will create the tracking process by calling the necessary API functions directly through the DLLs. In this case, reading the standard output from the process will not be supported and it will instead be send to the logfiles set in pinvokeStdOut and pinvokeStdErr in the persistent data directory. It will also retrieve the camera list directly through the escapi DLLs, so make sure it is part of your Unity project.")]
+    [Tooltip("This string will be appended at the end of the tracker options without quoting.")]
+    public string extraOptions = "";
+    [Tooltip("If enabled, the tracker commandline option string will be logged with Debug.Log.")]
+    public bool logCommandline = false;
+    [Tooltip("IL2CPP doesn't support Proocess.Start. When this is enabled, OpenSeeLauncher will create the tracking process by calling the necessary API functions directly through the DLLs. In this case, reading the standard output from the process will not be supported and it will instead be send to the logfiles set in pinvokeStdOut and pinvokeStdErr in the persistent data directory. It will also retrieve the camera list directly through the libdshowcapture DLLs, so make sure it is part of your Unity project.")]
     public bool usePinvoke = false;
-    [Tooltip("When this is enabled, even if usePinvoke is disabled, the camera list will be retrieved through escapi DLLs directly, which can be faster. Make sure the DLLs are in your Unity project.")]
+    [Tooltip("When this is enabled, even if usePinvoke is disabled, the camera list will be retrieved through libdshowcapture DLLs directly, which can be faster. Make sure the DLLs are in your Unity project.")]
     public bool usePinvokeListCameras = false;
     [Tooltip("This is the standard output log file's name in the persistent data directory.")]
     public string pinvokeStdOut = "output.txt";
@@ -66,6 +84,30 @@ public class OpenSeeLauncher : MonoBehaviour {
     private System.IntPtr processStdErr = System.IntPtr.Zero;
     private Job job = null;
     
+    public void UnsetOption(string name, bool hasArgument) {
+        int count = 1;
+        if (hasArgument)
+            count++;
+        int found = -1;
+        int i = 0;
+        foreach (string arg in commandlineOptions) {
+            if (arg == name) {
+                found = i;
+                break;
+            }
+            i++;
+        }
+        if (found > -1)
+            commandlineOptions.RemoveRange(found, count);
+    }
+    
+    public void SetOption(string name, string argument) {
+        UnsetOption(name, argument != null);
+        commandlineOptions.Add(name);
+        if (argument != null)
+            commandlineOptions.Add(argument);
+    }
+
     private bool CheckSetup(bool requireTarget) {
         if (openSeeTarget == null)
             openSeeTarget = GetComponent<OpenSee>();
@@ -78,10 +120,10 @@ public class OpenSeeLauncher : MonoBehaviour {
             UnityEngine.Debug.LogError("Facetracker executable cannot be found.");
             return false;
         }
-        if (!Directory.Exists(modelPath)) {
+        /*if (!Directory.Exists(modelPath)) {
             UnityEngine.Debug.LogError("Model directory cannot be found.");
             return false;
-        }
+        }*/
         if (requireTarget) {
             ip = openSeeTarget.listenAddress;
             if (!IPAddress.TryParse(ip, out address)) {
@@ -111,34 +153,38 @@ public class OpenSeeLauncher : MonoBehaviour {
         return true;
     }
     
-    private string[] EscapiListCameras_x64() {
+    private string[] ListCameras_x64() {
 		List<string> cameras = new List<string>();
-		int count = countCaptureDevices_x64();
+        System.IntPtr cap = create_capture_x64();
+		int count = get_devices_x64(cap);
 		for (int i = 0; i < count; i++) {
 			StringBuilder namebuffer = new StringBuilder(2048);
-			getCaptureDeviceName_x64(i, namebuffer, 2048);
+			get_device_x64(cap, i, namebuffer, 2048);
 			cameras.Add(namebuffer.ToString());
 		}
+        destroy_capture_x64(cap);
 		return cameras.ToArray();
     }
     
-    private string[] EscapiListCameras_x86() {
+    private string[] ListCameras_x86() {
 		List<string> cameras = new List<string>();
-		int count = countCaptureDevices_x86();
+        System.IntPtr cap = create_capture_x86();
+		int count = get_devices_x86(cap);
 		for (int i = 0; i < count; i++) {
 			StringBuilder namebuffer = new StringBuilder(2048);
-			getCaptureDeviceName_x86(i, namebuffer, 2048);
+			get_device_x86(cap, i, namebuffer, 2048);
 			cameras.Add(namebuffer.ToString());
 		}
+        destroy_capture_x86(cap);
 		return cameras.ToArray();
     }
     
     public string[] ListCameras() {
         if (usePinvoke || usePinvokeListCameras) {
             if (Environment.Is64BitProcess)
-                return EscapiListCameras_x64();
+                return ListCameras_x64();
             else
-                return EscapiListCameras_x86();
+                return ListCameras_x86();
         }
         
         if (!CheckSetup(false))
@@ -177,6 +223,48 @@ public class OpenSeeLauncher : MonoBehaviour {
         return cameras;
     }
     
+    public float[] Benchmark(int threads) {
+        if (!CheckSetup(false))
+            return null;
+
+        StringBuilder stringBuilder;
+        ProcessStartInfo processStartInfo;
+        Process process;
+
+        stringBuilder = new StringBuilder();
+
+        processStartInfo = new ProcessStartInfo();
+        processStartInfo.CreateNoWindow = true;
+        processStartInfo.RedirectStandardOutput = true;
+        processStartInfo.RedirectStandardInput = true;
+        processStartInfo.RedirectStandardError = true;
+        processStartInfo.UseShellExecute = false;
+        processStartInfo.FileName = exePath;
+        processStartInfo.Arguments = "--benchmark 1 --priority 4 --max-threads " + threads.ToString();
+        
+        process = new Process();
+        process.StartInfo = processStartInfo;
+        process.EnableRaisingEvents = true;
+        process.OutputDataReceived += new DataReceivedEventHandler(delegate(object sender, DataReceivedEventArgs e) {
+            stringBuilder.Append(e.Data);
+            stringBuilder.Append("\n");
+        });
+        process.Start();
+        job.AddProcess(process.Handle);
+        process.BeginOutputReadLine();
+        process.WaitForExit();
+        process.CancelOutputRead();
+        
+        string[] lines = stringBuilder.ToString().Split(new[] { "\r\n", "\r", "\n" }, StringSplitOptions.RemoveEmptyEntries);
+        float[] results = new float[lines.Length];
+        
+        for (int i = 0; i < lines.Length; i++) {
+            results[i] = Single.Parse(lines[i]);
+        }
+        
+        return results;
+    }
+    
     // From: http://csharptest.net/529/how-to-correctly-escape-command-line-arguments-in-c/index.html
     private static string EscapeArguments(params string[] args)
     {
@@ -209,10 +297,32 @@ public class OpenSeeLauncher : MonoBehaviour {
         List<string> arguments = new List<string>();
         arguments.Add("--ip");
         arguments.Add(ip);
+        
+        if (dynamicPort && !openSeeTarget.listening) {
+            System.Net.IPEndPoint[] inUse = System.Net.NetworkInformation.IPGlobalProperties.GetIPGlobalProperties().GetActiveUdpListeners();
+            Array.Sort(inUse, delegate(System.Net.IPEndPoint a, System.Net.IPEndPoint b) { return a.Port.CompareTo(b.Port); });
+            int port = openSeeTarget.listenPort + 1;
+            for (int i = 0; i < inUse.Length; i++) {
+                if (inUse[i].Port < port)
+                    continue;
+                if (inUse[i].Port == port) {
+                    if (port <= openSeeTarget.listenPort + 500 && port <= IPEndPoint.MaxPort) {
+                        port++;
+                        continue;
+                    } else {
+                        return false;
+                    }
+                }
+                break;
+            }
+            openSeeTarget.listenPort = port;
+        }
+
         arguments.Add("--port");
         arguments.Add(openSeeTarget.listenPort.ToString());
         arguments.Add("--model-dir");
         arguments.Add(modelPath);
+        
         arguments.Add("--capture");
         if (videoPath != "" && File.Exists(videoPath))
             arguments.Add(videoPath);
@@ -222,6 +332,11 @@ public class OpenSeeLauncher : MonoBehaviour {
             arguments.Add(argument);
         }
         string argumentString = EscapeArguments(arguments.ToArray());
+        if (extraOptions != "")
+            argumentString = argumentString + " " + extraOptions;
+        
+        if (logCommandline)
+            UnityEngine.Debug.Log("Starting tracker: " + argumentString);
 
         StopTracker();
 

Unity/OpenSeeShowPoints.cs

@@ -7,6 +7,7 @@ namespace OpenSee {
 public class OpenSeeShowPoints : MonoBehaviour {
     public OpenSee openSee = null;
     public int faceId = 0;
+    public bool only30Points = false;
     public bool show3DPoints = true;
     public bool applyTranslation = false;
     public bool applyRotation = false;
@@ -14,17 +15,31 @@ public class OpenSeeShowPoints : MonoBehaviour {
     public float minConfidence = 0.20f;
     public bool showGaze = true;
     public Material material;
+    public bool showLines = false;
+    public float lineWidth = 0.01f;
+    public Material lineMaterial;
+    public bool receiveShadows = false;
+    
     private OpenSee.OpenSeeData openSeeData;
     private GameObject[] gameObjects;
+    private LineRenderer[] lineRenderers;
     private GameObject centerBall;
     private double updated = 0.0;
     private int total = 70;
     
+    private int[] lines = new int[]{/* Contour */ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, -1, /* Eyebrows */ 18, 19, 20, 21, -1, 23, 24, 25, 26, -1, /* Nose */ 28, 29, 30, 33, 32, 33, 34, 35, -1, /* Eye */ 37, 38, 39, 40, 41, 36, /* Eye */ 43, 44, 45, 46, 47, 42, /* Mouth */ 49, 50, 51, 52, 62, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 58, 58, 62};
+    
+    private HashSet<int> pt30Set = new HashSet<int> { 0, 2, 5, 8, 11, 14, 16, 17, 19, 21, 22, 24, 26, 27, 30, 33, 36, 37, 39, 40, 42, 43, 45, 46, 50, 55, 58, 60, 62, 64 };
+    private int[] linesPt30Set = new int[]{/* Contour */ 2, -1, 5, -1, -1, 8, -1, -1, 11, -1, -1, 14, -1, -1, 16, -1, -1, /* Eyebrows */ 19, -1, 21, -1, -1, 24, -1, 26, -1, -1, /* Nose */ 30, -1, -1, 33, -1, -1, -1, -1, -1, /* Eye */ 37, 39, -1, 40, 36, -1, /* Eye */ 43, 45, -1, 46, 42, -1, /* Mouth */ -1, -1, 62, -1, -1, -1, -1, 58, -1, -1, 60, -1, 62, -1, 64, -1, 58, -1, 58, 62};
+
 	void Start () {
         if (openSee == null) {
             openSee = GetComponent<OpenSee>();
         }
         gameObjects = new GameObject[70];
+        lineRenderers = new LineRenderer[68];
+        if (lineMaterial == null)
+            showLines = false;
         if (!showGaze)
             total = 66;
         for (int i = 0; i < total; i++) {
@@ -32,6 +47,7 @@ public class OpenSeeShowPoints : MonoBehaviour {
             gameObjects[i].name = "Point " + (i + 1);
             if (material != null)
                 gameObjects[i].GetComponent<Renderer>().material = material;
+            gameObjects[i].GetComponent<Renderer>().receiveShadows = receiveShadows;
             gameObjects[i].layer = gameObject.layer;
             gameObjects[i].transform.SetParent(transform);
             gameObjects[i].transform.localScale = new Vector3(0.025f, 0.025f, 0.025f);
@@ -39,12 +55,42 @@ public class OpenSeeShowPoints : MonoBehaviour {
                 GameObject cylinder = GameObject.CreatePrimitive(PrimitiveType.Cylinder);
                 if (material != null)
                     cylinder.GetComponent<Renderer>().material = material;
+                cylinder.GetComponent<Renderer>().receiveShadows = receiveShadows;
                 cylinder.layer = gameObject.layer;
                 cylinder.transform.SetParent(gameObjects[i].transform);
                 cylinder.transform.localEulerAngles = new Vector3(90f, 0f, 0f);
                 cylinder.transform.localPosition = new Vector3(0f, 0f, -4f);
                 cylinder.transform.localScale = new Vector3(1f, 4f, 1f);
             }
+            if (only30Points && i < 66 && !pt30Set.Contains(i))
+                gameObjects[i].SetActive(false);
+        }
+        for (int i = 0; i < 68; i++) {
+                if (i == 66) {
+                    GameObject lineGameObject = new GameObject("LineGameObject");
+                    if (only30Points)
+                        lineGameObject.transform.SetParent(gameObjects[50].transform);
+                    else
+                        lineGameObject.transform.SetParent(gameObjects[48].transform);
+                    lineGameObject.layer = gameObject.layer;
+                    lineRenderers[i] = lineGameObject.AddComponent(typeof(LineRenderer)) as LineRenderer;
+                } else if (i == 67) {
+                    GameObject lineGameObject = new GameObject("LineGameObject");
+                    if (only30Points)
+                        lineGameObject.transform.SetParent(gameObjects[55].transform);
+                    else
+                        lineGameObject.transform.SetParent(gameObjects[53].transform);
+                    lineGameObject.layer = gameObject.layer;
+                    lineRenderers[i] = lineGameObject.AddComponent(typeof(LineRenderer)) as LineRenderer;
+                }
+                else
+                    lineRenderers[i] = gameObjects[i].AddComponent(typeof(LineRenderer)) as LineRenderer;
+                lineRenderers[i].useWorldSpace = true;
+                lineRenderers[i].generateLightingData = true;
+                lineRenderers[i].material = lineMaterial;
+                lineRenderers[i].receiveShadows = receiveShadows;
+                lineRenderers[i].widthMultiplier = lineWidth;
+                lineRenderers[i].positionCount = 2;
         }
         centerBall = GameObject.CreatePrimitive(PrimitiveType.Sphere);
         centerBall.name = "Center";
@@ -131,6 +177,7 @@ public class OpenSeeShowPoints : MonoBehaviour {
 
             for (int i = 0; i < 66; i++) {
                 Renderer renderer = gameObjects[i].GetComponent<Renderer>();
+                renderer.receiveShadows = receiveShadows;
                 renderer.material.SetColor("_Color", Color.Lerp(Color.red, Color.green, openSeeData.confidence[i]));
                 float x = openSeeData.points[i].x;
                 float y = -openSeeData.points[i].y;
@@ -141,6 +188,37 @@ public class OpenSeeShowPoints : MonoBehaviour {
                 gameObjects[i].transform.localPosition = position;
             }
         }
+        for (int i = 0; i < 68; i++) {
+            if ((!only30Points && lines[i] == -1) || (only30Points && linesPt30Set[i] == -1))
+                continue;
+            if (!showLines || lineMaterial == null) {
+                lineRenderers[i].enabled = false;
+            } else {
+                int a = i;
+                int b = lines[i];
+                if (only30Points) {
+                    b = linesPt30Set[i];
+                    if (i == 66)
+                        a = 50;
+                    if (i == 67)
+                        a = 55;
+                } else {
+                    if (i == 66)
+                        a = 48;
+                    if (i == 67)
+                        a = 53;
+                }
+                Color color = Color.Lerp(Color.red, Color.green, Mathf.Lerp(0.5f, openSeeData.confidence[a], openSeeData.confidence[b]));
+                lineRenderers[i].enabled = true;
+                lineRenderers[i].widthMultiplier = lineWidth;
+                lineRenderers[i].receiveShadows = receiveShadows;
+                lineRenderers[i].material.SetColor("_Color", color);
+                lineRenderers[i].startColor = color;
+                lineRenderers[i].endColor = color;
+                lineRenderers[i].SetPosition(0, gameObjects[a].transform.position);
+                lineRenderers[i].SetPosition(1, gameObjects[b].transform.position);
+            }
+        }
 	}
 }
 

Unity/Plugins/SVMModel.cs

@@ -6,34 +6,61 @@ using System.Runtime.InteropServices;
 using System.Runtime.Serialization;
 using System.Runtime.Serialization.Formatters.Binary;
 using System.Text;
+using UnityEngine;
 
 namespace OpenSee {
 
 public class SVMModel {
 	#region DllImport
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "trainModel")]
-	private static extern System.IntPtr trainModel(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C);
+	private static extern System.IntPtr trainModelDll(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C);
+    protected virtual System.IntPtr trainModel(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C) {
+        Debug.Log("Training LibSVM");
+        return trainModelDll(features, labels, weights, rows, cols, classes, probability, C);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "predict")]
-	private static extern void predict(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows);
+	private static extern void predictDll(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows);
+    protected virtual void predict(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows) {
+        predictDll(model, features, predictions, probabilities, rows);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "loadModel")]
-	private static extern System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs);
+	private static extern System.IntPtr loadModelDll([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs);
+    protected virtual System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs) {
+        return loadModelDll(filename, cols, classes, means, sdevs);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "loadModelString")]
-	private static extern System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs);
+	private static extern System.IntPtr loadModelStringDll([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs);
+    protected virtual System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs) {
+        Debug.Log("Loading LibSVM");
+        return loadModelStringDll(modelString, cols, classes, means, sdevs);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "getScales")]
-	private static extern void getScales(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs);
+	private static extern void getScalesDll(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs);
+    protected virtual void getScales(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs) {
+        getScalesDll(model, means, sdevs);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModel")]
-	private static extern int saveModel(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename);
+	private static extern int saveModelDll(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename);
+    protected virtual int saveModel(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename) {
+        return saveModelDll(model, filename);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModelString")]
-	private static extern string saveModelString(System.IntPtr model);
+	private static extern string saveModelStringDll(System.IntPtr model);
+    protected virtual string saveModelString(System.IntPtr model) {
+        return saveModelStringDll(model);
+    }
 
-	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)]
+	[DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroyModel")]
-	private static extern void destroyModel(System.IntPtr model);
+	private static extern void destroyModelDll(System.IntPtr model);
+    protected virtual void destroyModel(System.IntPtr model) {
+        destroyModelDll(model);
+    }
 	#endregion
 
     private bool haveModel = false;
@@ -43,13 +70,14 @@ public class SVMModel {
 
     [Serializable]
     private class SVMModelRepresentation {
-        private string modelString;
+        public string modelString;
-        private int cols;
+        public int cols;
-        private int maxClasses;
+        public int maxClasses;
-        private double[] means;
+        public double[] means;
-        private double[] sdevs;
+        public double[] sdevs;
+    }
 
-        static public System.IntPtr LoadSerialized(byte[] modelBytes, out int cols, out int maxClasses, bool compress) {
+    public System.IntPtr LoadSerialized(byte[] modelBytes, out int cols, out int maxClasses, bool compress) {
         IFormatter formatter = new BinaryFormatter();
         MemoryStream memoryStream = new MemoryStream(modelBytes);
         memoryStream.Position = 0;
@@ -67,7 +95,7 @@ public class SVMModel {
         return model;
     }
 
-        static public byte[] ToSerialized(System.IntPtr model, int cols, int maxClasses, bool compress) {
+    public byte[] ToSerialized(System.IntPtr model, int cols, int maxClasses, bool compress) {
         SVMModelRepresentation smr = new SVMModelRepresentation();
         smr.cols = cols;
         smr.maxClasses = maxClasses;
@@ -88,7 +116,6 @@ public class SVMModel {
         }
         return memoryStream.ToArray();
     }
-    }
 
     public void DestroyModel() {
         if (haveModel) {
@@ -99,7 +126,7 @@ public class SVMModel {
 
     private void LoadModel(byte[] modelBytes, bool compress) {
         DestroyModel();
-        model = SVMModelRepresentation.LoadSerialized(modelBytes, out cols, out maxClasses, compress);
+        model = LoadSerialized(modelBytes, out cols, out maxClasses, compress);
         haveModel = true;
     }
 
@@ -110,7 +137,7 @@ public class SVMModel {
     private byte[] SaveModel(bool compress) {
         if (!haveModel)
             return null;
-        return SVMModelRepresentation.ToSerialized(model, cols, maxClasses, compress);
+        return ToSerialized(model, cols, maxClasses, compress);
     }
 
     public byte[] SaveModel() {
@@ -143,7 +170,9 @@ public class SVMModel {
 
         maxClasses = max + 1;
         this.cols = cols;
+        float start = Time.realtimeSinceStartup;
         model = trainModel(features, labels, weights, rows, cols, maxClasses, probability, C);
+        Debug.Log("Training took: " + (Time.realtimeSinceStartup - start).ToString());
         haveModel = true;
 
         return true;

Unity/Plugins/ThunderSVMModel.cs

@@ -0,0 +1,71 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.IO.Compression;
+using System.Runtime.InteropServices;
+using System.Runtime.Serialization;
+using System.Runtime.Serialization.Formatters.Binary;
+using System.Text;
+using OpenSee;
+using UnityEngine;
+
+public class ThunderSVMModel : SVMModel {
+	#region DllImport
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, EntryPoint = "trainModel")]
+	private static extern System.IntPtr trainModelDll(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C);
+    protected override System.IntPtr trainModel(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C) {
+        Debug.Log("Training ThunderSVM");
+        return trainModelDll(features, labels, weights, rows, cols, classes, probability, C);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, EntryPoint = "predict")]
+	private static extern void predictDll(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows);
+    protected override void predict(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows) {
+        predictDll(model, features, predictions, probabilities, rows);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "loadModel")]
+	private static extern System.IntPtr loadModelDll([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs);
+    protected override System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs) {
+        return loadModelDll(filename, cols, classes, means, sdevs);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "loadModelString")]
+	private static extern System.IntPtr loadModelStringDll([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs);
+    protected override System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs) {
+        Debug.Log("Loading ThunderSVM");
+        return loadModelStringDll(modelString, cols, classes, means, sdevs);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, EntryPoint = "getScales")]
+	private static extern void getScalesDll(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs);
+    protected override void getScales(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs) {
+        getScalesDll(model, means, sdevs);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModel")]
+	private static extern int saveModelDll(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename);
+    protected override int saveModel(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename) {
+        return saveModelDll(model, filename);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModelString")]
+	private static extern string saveModelStringDll(System.IntPtr model);
+    protected override string saveModelString(System.IntPtr model) {
+        return saveModelStringDll(model);
+    }
+
+	[DllImport("ThunderSVMWrapper", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroyModel")]
+	private static extern void destroyModelDll(System.IntPtr model);
+    protected override void destroyModel(System.IntPtr model) {
+        destroyModelDll(model);
+    }
+	#endregion
+    
+    public ThunderSVMModel() {
+    }
+
+    public ThunderSVMModel(byte[] modelBytes) {
+        LoadModel(modelBytes);
+    }
+}

Unity/Plugins/ThunderSVMWrapper.dll.meta

@@ -0,0 +1,63 @@
+fileFormatVersion: 2
+guid: 9c58dd9d1f0cf3140bd004d536712abb
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
+  isExplicitlyReferenced: 0
+  validateReferences: 1
+  platformData:
+  - first:
+      : Any
+    second:
+      enabled: 0
+      settings:
+        Exclude Editor: 0
+        Exclude Linux64: 0
+        Exclude OSXUniversal: 0
+        Exclude Win: 1
+        Exclude Win64: 0
+  - first:
+      Any: 
+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 1
+      settings:
+        CPU: AnyCPU
+        DefaultValueInitialized: true
+        OS: AnyOS
+  - first:
+      Standalone: Linux64
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  - first:
+      Standalone: OSXUniversal
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  - first:
+      Standalone: Win
+    second:
+      enabled: 0
+      settings:
+        CPU: None
+  - first:
+      Standalone: Win64
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/Plugins/dshowcapture_x64.dll.meta

@@ -0,0 +1,27 @@
+fileFormatVersion: 2
+guid: dd0c04eb3445c3048bfaa964c6bcb16f
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
+  isExplicitlyReferenced: 0
+  validateReferences: 1
+  platformData:
+  - first:
+      Any: 
+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 0
+      settings:
+        DefaultValueInitialized: true
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/Plugins/dshowcapture_x86.dll.meta

@@ -0,0 +1,27 @@
+fileFormatVersion: 2
+guid: a504a0f747083b54c99d6e91731ccecc
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
+  isExplicitlyReferenced: 0
+  validateReferences: 1
+  platformData:
+  - first:
+      Any: 
+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 0
+      settings:
+        DefaultValueInitialized: true
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/Plugins/libminibmcapture32.dll.meta

@@ -0,0 +1,27 @@
+fileFormatVersion: 2
+guid: 51dc8f0c65df30d4daa188cdfd1dd220
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
+  isExplicitlyReferenced: 0
+  validateReferences: 1
+  platformData:
+  - first:
+      Any: 
+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 0
+      settings:
+        DefaultValueInitialized: true
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/Plugins/libminibmcapture64.dll.meta

@@ -0,0 +1,27 @@
+fileFormatVersion: 2
+guid: fcfc753ca92169e48aa69c1ecc1b0d64
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
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+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 0
+      settings:
+        DefaultValueInitialized: true
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Unity/Plugins/thundersvm.dll.meta

@@ -0,0 +1,63 @@
+fileFormatVersion: 2
+guid: e549e6cbc6a547748abc7f1b05bfa7b4
+PluginImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  iconMap: {}
+  executionOrder: {}
+  defineConstraints: []
+  isPreloaded: 0
+  isOverridable: 0
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+  validateReferences: 1
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+  - first:
+      : Any
+    second:
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+      settings:
+        Exclude Editor: 0
+        Exclude Linux64: 0
+        Exclude OSXUniversal: 0
+        Exclude Win: 1
+        Exclude Win64: 0
+  - first:
+      Any: 
+    second:
+      enabled: 1
+      settings: {}
+  - first:
+      Editor: Editor
+    second:
+      enabled: 1
+      settings:
+        CPU: AnyCPU
+        DefaultValueInitialized: true
+        OS: AnyOS
+  - first:
+      Standalone: Linux64
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  - first:
+      Standalone: OSXUniversal
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  - first:
+      Standalone: Win
+    second:
+      enabled: 0
+      settings:
+        CPU: None
+  - first:
+      Standalone: Win64
+    second:
+      enabled: 1
+      settings:
+        CPU: x86_64
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

dshowcapture.py

@@ -0,0 +1,341 @@
+import os
+import platform
+import sys
+import numpy as np
+from ctypes import *
+from PIL import Image
+import cv2
+import json
+
+def resolve(name):
+    f = os.path.join(os.path.dirname(__file__), name)
+    return f
+
+lib = None
+bm_lib = None
+bm_options = None
+bm_enabled = False
+
+def set_bm_enabled(v):
+    global bm_enabled
+    bm_enabled = v
+
+def set_options(str):
+    global bm_options
+    bm_options = str
+
+def create_frame_buffer(width, height, factor):
+    buffer = bytearray(width * height * 4 * factor)
+    char_array = c_char * len(buffer)
+    return char_array.from_buffer(buffer)
+
+class DShowCapture():
+    def __init__(self):
+        global lib
+        global bm_lib
+        global bm_options
+        global bm_enabled
+        if lib is None or bm_lib is None:
+            if platform.architecture()[0] == '32bit':
+                dll_path = resolve(os.path.join("dshowcapture", "dshowcapture_x86.dll"))
+                lib = cdll.LoadLibrary(dll_path)
+                if bm_enabled:
+                    dll_path = resolve(os.path.join("dshowcapture", "libminibmcapture32.dll"))
+                    bm_lib = cdll.LoadLibrary(dll_path)
+            else:
+                dll_path = resolve(os.path.join("dshowcapture", "dshowcapture_x64.dll"))
+                lib = cdll.LoadLibrary(dll_path)
+                if bm_enabled:
+                    dll_path = resolve(os.path.join("dshowcapture", "libminibmcapture64.dll"))
+                    bm_lib = cdll.LoadLibrary(dll_path)
+
+            # DirectShow
+            lib.create_capture.restype = c_void_p
+            lib.get_devices.argtypes = [c_void_p]
+            lib.get_device.argtypes = [c_void_p, c_int, c_char_p, c_int]
+            lib.capture_device.argtypes = [c_void_p, c_int, c_int, c_int, c_int]
+            lib.capture_device_by_dcap.argtypes = [c_void_p, c_int, c_int, c_int, c_int, c_longlong]
+            lib.capture_device_default.argtypes = [c_void_p, c_int]
+            lib.get_width.argtypes = [c_void_p]
+            lib.get_height.argtypes = [c_void_p]
+            lib.get_fps.argtypes = [c_void_p]
+            lib.get_flipped.argtypes = [c_void_p]
+            lib.get_colorspace.argtypes = [c_void_p]
+            lib.get_colorspace_internal.argtypes = [c_void_p]
+            lib.capturing.argtypes = [c_void_p]
+            lib.get_frame.argtypes = [c_void_p, c_int, c_char_p, c_int]
+            lib.get_size.argtypes = [c_void_p]
+            lib.stop_capture.argtypes = [c_void_p]
+            lib.get_json_length.argtypes = [c_void_p]
+            lib.get_json.argtypes = [c_void_p, c_char_p, c_int]
+            lib.destroy_capture.argtypes = [c_void_p]
+
+            # Blackmagic
+            if bm_enabled:
+                bm_lib.start_capture_single.argtypes = [c_int, c_int, c_void_p]
+                bm_lib.read_frame_bgra32_blocking.argtypes = [c_char_p, c_int]
+                bm_lib.stop_capture_single.argtypes = []
+                bm_lib.get_json_length.argtypes = []
+                bm_lib.get_json.argtypes = [c_char_p, c_int]
+                if bm_options is not None:
+                    bm_lib.set_options.argtypes = [c_char_p]
+                    bm_lib.set_options(bm_options.encode())
+        self.lib = lib
+        if bm_enabled:
+            self.bm_lib = bm_lib
+        self.cap = lib.create_capture()
+        self.name_buffer = create_string_buffer(255);
+        self.buffer = None
+        self.have_devices = False
+        self.size = None
+        self.real_size = None
+        self.info = None
+        self.type = None
+
+    def __del__(self):
+        if self.buffer is not None:
+            del self.buffer
+        del self.name_buffer
+        self.destroy_capture()
+
+    def get_devices(self):
+        self.have_devices = True
+        return self.lib.get_devices(self.cap)
+
+    def get_device(self, device_number):
+        self.lib.get_device(self.cap, device_number, self.name_buffer, 255)
+        name_str = str(self.name_buffer.value.decode('utf8', 'surrogateescape'))
+        return name_str
+
+    def get_info(self):
+        global bm_enabled
+        # DirectShow
+        json_length = self.lib.get_json_length(self.cap);
+        json_buffer = create_string_buffer(json_length)
+        self.lib.get_json(self.cap, json_buffer, json_length);
+        json_text = str(json_buffer.value.decode('utf8', 'surrogateescape'))
+        self.info = json.loads(json_text)
+        for cam in self.info:
+            cam["type"] = "DirectShow"
+            cam["index"] = cam["id"]
+        
+        # Blackmagic
+        if bm_enabled:
+            json_length = self.bm_lib.get_json_length();
+            json_buffer = create_string_buffer(json_length)
+            self.bm_lib.get_json(json_buffer, json_length);
+            json_text = str(json_buffer.value.decode('utf8', 'surrogateescape'))
+            bm_info = json.loads(json_text)
+            dshow_len = len(self.info)
+            for cam in bm_info:
+                cam["type"] = "Blackmagic"
+                cam["index"] = cam["id"] + dshow_len
+            self.info.extend(bm_info)
+
+        return self.info
+
+    def capture_device(self, cam, width, height, fps):
+        if not self.have_devices:
+            self.get_devices()
+        ret = self.lib.capture_device(self.cap, cam, width, height, fps) == 1
+        if ret:
+            self.type = "DirectShow"
+            self.width = self.get_width()
+            self.height = self.get_height()
+            self.fps = self.get_fps()
+            self.flipped = self.get_flipped()
+            self.colorspace = self.get_colorspace()
+            self.colorspace_internal = self.get_colorspace_internal()
+            self.size = self.width * self.height * 4
+            self.buffer = create_frame_buffer(self.width, self.height, 4)
+        else:
+            self.size = None
+            self.real_size = None
+        return ret;
+
+    def capture_device_by_dcap(self, cam, dcap, width, height, fps):
+        global bm_enabled
+        if not self.have_devices:
+            self.get_devices()
+        if self.info is None:
+            self.get_info()
+        fps = int(10000000 / fps)
+        ret = False
+        if self.info[cam]['type'] == "DirectShow":
+            ret = self.lib.capture_device_by_dcap(self.cap, cam, dcap, width, height, fps) == 1
+        elif bm_enabled and self.info[cam]['type'] == "Blackmagic":
+            ret = self.bm_lib.start_capture_single(self.info[cam]['id'], self.info[cam]['caps'][dcap]['bmModecode'], None) == 1
+        if ret:
+            self.type = self.info[cam]['type']
+            if self.type == "DirectShow":
+                self.width = self.get_width()
+                self.height = self.get_height()
+                self.fps = self.get_fps()
+                self.flipped = self.get_flipped()
+                self.colorspace = self.get_colorspace()
+                self.colorspace_internal = self.get_colorspace_internal()
+                self.size = self.width * self.height * 4 * 4
+                self.buffer = create_frame_buffer(self.width, self.height, 4)
+            elif bm_enabled and self.type == "Blackmagic":
+                self.width = self.info[cam]['caps'][dcap]['minCX']
+                self.height = self.info[cam]['caps'][dcap]['minCY']
+                self.fps = int(10000000 / self.info[cam]['caps'][dcap]['minInterval'])
+                self.flipped = True
+                self.colorspace = 101
+                self.colorspace_internal = self.colorspace
+                self.size = self.width * self.height * 4
+                self.buffer = create_frame_buffer(self.width, self.height, 1)
+                self.real_size = self.size
+        else:
+            self.size = None
+            self.real_size = None
+        return ret;
+
+    def capture_device_default(self, cam):
+        if not self.have_devices:
+            self.get_devices()
+        ret = self.lib.capture_device_default(self.cap, cam) == 1
+        if ret:
+            self.type = "DirectShow"
+            self.width = self.get_width()
+            self.height = self.get_height()
+            self.fps = self.get_fps()
+            self.flipped = self.get_flipped()
+            self.colorspace = self.get_colorspace()
+            self.colorspace_internal = self.get_colorspace_internal()
+            self.size = self.width * self.height * 4 * 4
+            self.buffer = create_frame_buffer(self.width, self.height, 4)
+        else:
+            self.size = None
+            self.real_size = None
+        return ret;
+
+    def get_width(self):
+        return self.lib.get_width(self.cap)
+
+    def get_height(self):
+        return self.lib.get_height(self.cap)
+
+    def get_fps(self):
+        return self.lib.get_fps(self.cap)
+
+    def get_flipped(self):
+        return self.lib.get_flipped(self.cap) != 0
+
+    def get_colorspace(self):
+        colorspace = self.lib.get_colorspace(self.cap)
+        if colorspace == 0:
+            colorspace = self.lib.get_colorspace_internal(self.cap)
+        return colorspace
+
+    def get_colorspace_internal(self):
+        return self.lib.get_colorspace_internal(self.cap)
+
+    def capturing(self):
+        if self.type == "Blackmagic":
+            return 1
+        return self.lib.capturing(self.cap) == 1
+
+    def get_frame(self, timeout):
+        global bm_enabled
+        if self.size is None:
+            return None
+        if self.type == "DirectShow":
+            self.real_size = self.lib.get_frame(self.cap, timeout, self.buffer, self.size)
+        elif bm_enabled and self.type == "Blackmagic":
+            self.bm_lib.read_frame_bgra32_blocking(self.buffer, self.size)
+        else:
+            return None
+        img = np.frombuffer(self.buffer, dtype=np.uint8)[0:self.real_size]
+        if self.colorspace in [100, 101]:
+            if self.real_size == self.height * self.width * 4:
+                img = cv2.cvtColor(img.reshape((self.height,self.width,4)), cv2.COLOR_BGRA2BGR)
+            elif self.real_size == self.height * self.width * 3:
+                img = img.reshape((self.height,self.width,3))
+            else:
+                return None
+            if not self.flipped:
+                img = cv2.flip(img, 0)
+            return img
+        elif self.colorspace == 200 and self.real_size == (3 * self.height // 2) * self.width:
+            img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_I420)
+        elif self.colorspace == 201 and self.real_size == (3 * self.height // 2) * self.width:
+            img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_NV12)
+        elif self.colorspace == 202 and self.real_size == (3 * self.height // 2) * self.width:
+            img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_YV12)
+        elif self.colorspace == 203 and self.real_size == self.height * self.width:
+            img = cv2.cvtColor(img.reshape((self.height,self.width,1)), cv2.COLOR_GRAY2BGR)
+        elif self.colorspace == 300 and self.real_size == self.height * self.width * 2:
+            img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_YVYU)
+        elif self.colorspace == 301 and self.real_size == self.height * self.width * 2:
+            img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_YUY2)
+        elif self.colorspace == 302 and self.real_size == self.height * self.width * 2:
+            img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_UYVY)
+        elif self.colorspace == 303:
+            return None
+        elif self.colorspace == 400 and self.real_size > 4:
+            img = cv2.imdecode(img, cv2.IMREAD_COLOR);
+        else:
+            return None
+        if self.flipped:
+            img = cv2.flip(img, 0)
+        return img
+
+    def stop_capture(self):
+        global bm_enabled
+        self.size = None
+        self.real_size = None
+        if self.type == "DirectShow":
+            return self.lib.stop_capture(self.cap)
+        elif bm_enabled and self.type == "Blackmagic":
+            return self.bm_lib.stop_capture_single() != 0
+        return True
+
+    def destroy_capture(self):
+        if self.cap is None:
+            return 0
+        self.stop_capture()
+        ret = self.lib.destroy_capture(self.cap)
+        self.cap = None
+        self.size = None
+        self.real_size = None
+        return ret
+
+if __name__ == "__main__":
+    cam = 0
+    width = 1280
+    height = 720
+    fps = 30
+    if len(sys.argv) > 1:
+        cam = int(sys.argv[1])
+    if len(sys.argv) > 2:
+        width = int(sys.argv[2])
+    if len(sys.argv) > 3:
+        height = int(sys.argv[3])
+    if len(sys.argv) > 4:
+        fps = int(sys.argv[4])
+
+    cap = DShowCapture()
+    devices = cap.get_devices()
+    print("Devices: ", devices)
+    for i in range(devices):
+        print(f"{i} {cap.get_device(i)}")
+
+    print(f"Capturing: {cap.capture_device(cam, width, height, fps)}")
+    width = cap.get_width()
+    height = cap.get_height()
+    flipped = cap.get_flipped()
+    print(f"Width: {width} Height: {height} FPS: {cap.get_fps()} Flipped: {flipped} Colorspace: {cap.get_colorspace()} Internal: {cap.get_colorspace_internal()}")
+    
+    while True:
+        img = cap.get_frame(1000)
+        if img is not None:
+            cv2.imshow("DShowCapture", img)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                sys.exit(0)
+        else:
+            print("Lost frame")
+
+    cap.stop_capture()
+    cap.destroy_capture()
+    del cap

facetracker.py

@@ -2,21 +2,30 @@ import copy
 import os
 import sys
 import argparse
+import traceback
+import gc
 
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 parser.add_argument("-i", "--ip", help="Set IP address for sending tracking data", default="127.0.0.1")
 parser.add_argument("-p", "--port", type=int, help="Set port for sending tracking data", default=11573)
 if os.name == 'nt':
-    parser.add_argument("-l", "--list-cameras", type=int, help="Set this to 1 to list the available cameras and quit, set this to a number higher than 1 to output only the names", default=0)
+    parser.add_argument("-l", "--list-cameras", type=int, help="Set this to 1 to list the available cameras and quit, set this to 2 or higher to output only the names", default=0)
+    parser.add_argument("-a", "--list-dcaps", type=int, help="Set this to -1 to list all cameras and their available capabilities, set this to a camera id to list that camera's capabilities", default=None)
     parser.add_argument("-W", "--width", type=int, help="Set camera and raw RGB width", default=640)
     parser.add_argument("-H", "--height", type=int, help="Set camera and raw RGB height", default=360)
-    parser.add_argument("-F", "--fps", type=int, help="Set camera frames per second", default=24)
+    parser.add_argument("-D", "--dcap", type=int, help="Set which device capability line to use or -1 to use the default camera settings (FPS still need to be set separately)", default=None)
+    parser.add_argument("-B", "--blackmagic", type=int, help="When set to 1, special support for Blackmagic devices is enabled", default=0)
 else:
     parser.add_argument("-W", "--width", type=int, help="Set raw RGB width", default=640)
     parser.add_argument("-H", "--height", type=int, help="Set raw RGB height", default=360)
+if sys.platform == 'linux':
+    parser.add_argument("--dformat", type=str, help="Set which device format to use (MJPG, YUYV, RGB3, ...)", default=None)
+parser.add_argument("-F", "--fps", type=int, help="Set camera frames per second", default=24)
 parser.add_argument("-c", "--capture", help="Set camera ID (0, 1...) or video file", default="0")
+parser.add_argument("-M", "--mirror-input", action="store_true", help="Process a mirror image of the input video")
 parser.add_argument("-m", "--max-threads", type=int, help="Set the maximum number of threads", default=1)
-parser.add_argument("-t", "--threshold", type=float, help="Set minimum confidence threshold for face detection", default=0.65)
+parser.add_argument("-t", "--threshold", type=float, help="Set minimum confidence threshold for face tracking", default=None)
+parser.add_argument("-d", "--detection-threshold", type=float, help="Set minimum confidence threshold for face detection", default=0.6)
 parser.add_argument("-v", "--visualize", type=int, help="Set this to 1 to visualize the tracking, to 2 to also show face ids, to 3 to add confidence values or to 4 to add numbers to the point display", default=0)
 parser.add_argument("-P", "--pnp-points", type=int, help="Set this to 1 to add the 3D fitting points to the visualization", default=0)
 parser.add_argument("-s", "--silent", type=int, help="Set this to 1 to prevent text output on the console", default=0)
@@ -24,31 +33,95 @@ parser.add_argument("--faces", type=int, help="Set the maximum number of faces (
 parser.add_argument("--scan-retinaface", type=int, help="When set to 1, scanning for additional faces will be performed using RetinaFace in a background thread, otherwise a simpler, faster face detection mechanism is used. When the maximum number of faces is 1, this option does nothing.", default=0)
 parser.add_argument("--scan-every", type=int, help="Set after how many frames a scan for new faces should run", default=3)
 parser.add_argument("--discard-after", type=int, help="Set the how long the tracker should keep looking for lost faces", default=10)
+parser.add_argument("--max-feature-updates", type=int, help="This is the number of seconds after which feature min/max/medium values will no longer be updated once a face has been detected.", default=900)
+parser.add_argument("--no-3d-adapt", type=int, help="When set to 1, the 3D face model will not be adapted to increase the fit", default=1)
+parser.add_argument("--try-hard", type=int, help="When set to 1, the tracker will try harder to find a face", default=0)
 parser.add_argument("--video-out", help="Set this to the filename of an AVI file to save the tracking visualization as a video", default=None)
+parser.add_argument("--video-scale", type=int, help="This is a resolution scale factor applied to the saved AVI file", default=1, choices=[1,2,3,4])
+parser.add_argument("--video-fps", type=float, help="This sets the frame rate of the output AVI file", default=24)
 parser.add_argument("--raw-rgb", type=int, help="When this is set, raw RGB frames of the size given with \"-W\" and \"-H\" are read from standard input instead of reading a video", default=0)
 parser.add_argument("--log-data", help="You can set a filename to which tracking data will be logged here", default="")
-parser.add_argument("--model", type=int, help="This can be used to select the tracking model. Higher numbers are models with better tracking quality, but slower speed. Models 1 and 0 tend to be too rigid for expression and blink detection.", default=3, choices=[0, 1, 2, 3])
+parser.add_argument("--log-output", help="You can set a filename to console output will be logged here", default="")
+parser.add_argument("--model", type=int, help="This can be used to select the tracking model. Higher numbers are models with better tracking quality, but slower speed, except for model 4, which is wink optimized. Models 1 and 0 tend to be too rigid for expression and blink detection. Model -2 is roughly equivalent to model 1, but faster. Model -3 is between models 0 and -1.", default=3, choices=[-3, -2, -1, 0, 1, 2, 3, 4])
 parser.add_argument("--model-dir", help="This can be used to specify the path to the directory containing the .onnx model files", default=None)
-parser.add_argument("--gaze-tracking", type=int, help="When set to 1, experimental blink detection and gaze tracking are enabled, which makes things slightly slower", default=1)
+parser.add_argument("--gaze-tracking", type=int, help="When set to 1, gaze tracking is enabled, which makes things slightly slower", default=1)
 parser.add_argument("--face-id-offset", type=int, help="When set, this offset is added to all face ids, which can be useful for mixing tracking data from multiple network sources", default=0)
 parser.add_argument("--repeat-video", type=int, help="When set to 1 and a video file was specified with -c, the tracker will loop the video until interrupted", default=0)
 parser.add_argument("--dump-points", type=str, help="When set to a filename, the current face 3D points are made symmetric and dumped to the given file when quitting the visualization with the \"q\" key", default="")
+parser.add_argument("--benchmark", type=int, help="When set to 1, the different tracking models are benchmarked, starting with the best and ending with the fastest and with gaze tracking disabled for models with negative IDs", default=0)
+if os.name == 'nt':
+    parser.add_argument("--use-dshowcapture", type=int, help="When set to 1, libdshowcapture will be used for video input instead of OpenCV", default=1)
+    parser.add_argument("--blackmagic-options", type=str, help="When set, this additional option string is passed to the blackmagic capture library", default=None)
+    parser.add_argument("--priority", type=int, help="When set, the process priority will be changed", default=None, choices=[0, 1, 2, 3, 4, 5])
 args = parser.parse_args()
 
-os.environ["OMP_NUM_THREADS"] = "1"
+os.environ["OMP_NUM_THREADS"] = str(args.max_threads)
 
-if os.name == 'nt' and args.list_cameras > 0:
+class OutputLog(object):
-    import escapi
+    def __init__(self, fh, output):
-    escapi.init()
+        self.fh = fh
-    camera_count = escapi.count_capture_devices()
+        self.output = output
+    def write(self, buf):
+        if self.fh is not None:
+            self.fh.write(buf)
+        self.output.write(buf)
+        self.flush()
+    def flush(self):
+        if self.fh is not None:
+            self.fh.flush()
+        self.output.flush()
+output_logfile = None
+if args.log_output != "":
+    output_logfile = open(args.log_output, "w")
+sys.stdout = OutputLog(output_logfile, sys.stdout)
+sys.stderr = OutputLog(output_logfile, sys.stderr)
+
+if os.name == 'nt':
+    import dshowcapture
+    if args.blackmagic == 1:
+        dshowcapture.set_bm_enabled(True)
+    if args.blackmagic_options is not None:
+        dshowcapture.set_options(args.blackmagic_options)
+    if args.priority is not None:
+        import psutil
+        classes = [psutil.IDLE_PRIORITY_CLASS, psutil.BELOW_NORMAL_PRIORITY_CLASS, psutil.NORMAL_PRIORITY_CLASS, psutil.ABOVE_NORMAL_PRIORITY_CLASS, psutil.HIGH_PRIORITY_CLASS, psutil.REALTIME_PRIORITY_CLASS]
+        p = psutil.Process(os.getpid())
+        p.nice(classes[args.priority])
+
+if os.name == 'nt' and (args.list_cameras > 0 or args.list_dcaps is not None):
+    cap = dshowcapture.DShowCapture()
+    info = cap.get_info()
+    unit = 10000000.;
+    if args.list_dcaps is not None:
+        formats = {0: "Any", 1: "Unknown", 100: "ARGB", 101: "XRGB", 200: "I420", 201: "NV12", 202: "YV12", 203: "Y800", 300: "YVYU", 301: "YUY2", 302: "UYVY", 303: "HDYC (Unsupported)", 400: "MJPEG", 401: "H264" }
+        for cam in info:
+            if args.list_dcaps == -1:
+                type = ""
+                if cam['type'] == "Blackmagic":
+                    type = "Blackmagic: "
+                print(f"{cam['index']}: {type}{cam['name']}")
+            if args.list_dcaps != -1 and args.list_dcaps != cam['index']:
+                continue
+            for caps in cam['caps']:
+                format = caps['format']
+                if caps['format'] in formats:
+                    format = formats[caps['format']]
+                if caps['minCX'] == caps['maxCX'] and caps['minCY'] == caps['maxCY']:
+                    print(f"    {caps['id']}: Resolution: {caps['minCX']}x{caps['minCY']} FPS: {unit/caps['maxInterval']:.3f}-{unit/caps['minInterval']:.3f} Format: {format}")
+                else:
+                    print(f"    {caps['id']}: Resolution: {caps['minCX']}x{caps['minCY']}-{caps['maxCX']}x{caps['maxCY']} FPS: {unit/caps['maxInterval']:.3f}-{unit/caps['minInterval']:.3f} Format: {format}")
+    else:
         if args.list_cameras == 1:
             print("Available cameras:")
-    for i in range(camera_count):
+        for cam in info:
-        camera_name = escapi.device_name(i).decode()
+            type = ""
+            if cam['type'] == "Blackmagic":
+                type = "Blackmagic: "
             if args.list_cameras == 1:
-            print(f"{i}: {camera_name}")
+                print(f"{cam['index']}: {type}{cam['name']}")
             else:
-            print(camera_name)
+                print(f"{type}{cam['name']}")
+    cap.destroy_capture()
     sys.exit(0)
 
 import numpy as np
@@ -56,8 +129,25 @@ import time
 import cv2
 import socket
 import struct
-from input_reader import InputReader, VideoReader, list_cameras
+import json
-from tracker import Tracker
+from input_reader import InputReader, VideoReader, DShowCaptureReader, try_int
+from tracker import Tracker, get_model_base_path
+
+if args.benchmark > 0:
+    model_base_path = get_model_base_path(args.model_dir)
+    im = cv2.imread(os.path.join(model_base_path, "benchmark.bin"), cv2.IMREAD_COLOR)
+    results = []
+    for model_type in [3, 2, 1, 0, -1, -2, -3]:
+        tracker = Tracker(224, 224, threshold=0.1, max_threads=args.max_threads, max_faces=1, discard_after=0, scan_every=0, silent=True, model_type=model_type, model_dir=args.model_dir, no_gaze=(model_type == -1), detection_threshold=0.1, use_retinaface=0, max_feature_updates=900, static_model=True if args.no_3d_adapt == 1 else False)
+        tracker.detected = 1
+        tracker.faces = [(0, 0, 224, 224)]
+        total = 0.0
+        for i in range(100):
+            start = time.perf_counter()
+            r = tracker.predict(im)
+            total += time.perf_counter() - start
+        print(1. / (total / 100.))
+    sys.exit(0)
 
 target_ip = args.ip
 target_port = args.port
@@ -65,9 +155,18 @@ target_port = args.port
 if args.faces >= 40:
     print("Transmission of tracking data over network is not supported with 40 or more faces.")
 
-fps = 0
-if os.name == 'nt':
 fps = args.fps
+dcap = None
+use_dshowcapture_flag = False
+if os.name == 'nt':
+    dcap = args.dcap
+    use_dshowcapture_flag = True if args.use_dshowcapture == 1 else False
+    input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps, use_dshowcapture=use_dshowcapture_flag, dcap=dcap)
+    if args.dcap == -1 and type(input_reader) == DShowCaptureReader:
+        fps = min(fps, input_reader.device.get_fps())
+elif sys.platform == 'linux' and args.dformat:
+    input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps, dcap=args.dformat)
+else:
     input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps)
 if type(input_reader.reader) == VideoReader:
     fps = 0
@@ -79,6 +178,7 @@ height = 0
 width = 0
 tracker = None
 sock = None
+total_tracking_time = 0.0
 tracking_time = 0.0
 tracking_frames = 0
 frame_count = 0
@@ -88,40 +188,61 @@ features = ["eye_l", "eye_r", "eyebrow_steepness_l", "eyebrow_updown_l", "eyebro
 if args.log_data != "":
     log = open(args.log_data, "w")
     log.write("Frame,Time,Width,Height,FPS,Face,FaceID,RightOpen,LeftOpen,AverageConfidence,Success3D,PnPError,RotationQuat.X,RotationQuat.Y,RotationQuat.Z,RotationQuat.W,Euler.X,Euler.Y,Euler.Z,RVec.X,RVec.Y,RVec.Z,TVec.X,TVec.Y,TVec.Z")
-    for i in range(66):
+    for i in range(68):
         log.write(f",Landmark[{i}].X,Landmark[{i}].Y,Landmark[{i}].Confidence")
-    for i in range(66):
+    for i in range(70):
         log.write(f",Point3D[{i}].X,Point3D[{i}].Y,Point3D[{i}].Z")
     for feature in features:
         log.write(f",{feature}")
     log.write("\r\n")
     log.flush()
 
+is_camera = args.capture == str(try_int(args.capture))
+
 try:
+    attempt = 0
     frame_time = time.perf_counter()
     target_duration = 0
     if fps > 0:
         target_duration = 1. / float(fps)
     repeat = args.repeat_video != 0 and type(input_reader.reader) == VideoReader
     need_reinit = 0
+    failures = 0
+    source_name = input_reader.name
     while repeat or input_reader.is_open():
         if not input_reader.is_open() or need_reinit == 1:
-            input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps)
+            input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps, use_dshowcapture=use_dshowcapture_flag, dcap=dcap)
+            if input_reader.name != source_name:
+                print(f"Failed to reinitialize camera and got {input_reader.name} instead of {source_name}.")
+                sys.exit(1)
             need_reinit = 2
-            time.sleep(0.001)
+            time.sleep(0.02)
             continue
         if not input_reader.is_ready():
-            time.sleep(0.001)
+            time.sleep(0.02)
             continue
 
         ret, frame = input_reader.read()
+        if ret and args.mirror_input:
+            frame = cv2.flip(frame, 1)
         if not ret:
             if repeat:
                 if need_reinit == 0:
                     need_reinit = 1
                 continue
+            elif is_camera:
+                attempt += 1
+                if attempt > 30:
                     break
+                else:
+                    time.sleep(0.02)
+                    if attempt == 3:
+                        need_reinit = 1
+                    continue
+            else:
+                break;
 
+        attempt = 0
         need_reinit = 0
         frame_count += 1
         now = time.time()
@@ -130,14 +251,17 @@ try:
             first = False
             height, width, channels = frame.shape
             sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-            tracker = Tracker(width, height, threshold=args.threshold, max_threads=args.max_threads, max_faces=args.faces, discard_after=args.discard_after, scan_every=args.scan_every, silent=False if args.silent == 0 else True, model_type=args.model, model_dir=args.model_dir, no_gaze=False if args.gaze_tracking != 0 else True, use_retinaface=args.scan_retinaface)
+            tracker = Tracker(width, height, threshold=args.threshold, max_threads=args.max_threads, max_faces=args.faces, discard_after=args.discard_after, scan_every=args.scan_every, silent=False if args.silent == 0 else True, model_type=args.model, model_dir=args.model_dir, no_gaze=False if args.gaze_tracking != 0 and args.model != -1 else True, detection_threshold=args.detection_threshold, use_retinaface=args.scan_retinaface, max_feature_updates=args.max_feature_updates, static_model=True if args.no_3d_adapt == 1 else False, try_hard=args.try_hard == 1)
-            if not args.video_out is None:
+            if args.video_out is not None:
-                out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), 24, (width,height))
+                out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), args.video_fps, (width * args.video_scale, height * args.video_scale))
 
+        try:
             inference_start = time.perf_counter()
             faces = tracker.predict(frame)
             if len(faces) > 0:
-            tracking_time += (time.perf_counter() - inference_start) / len(faces)
+                inference_time = (time.perf_counter() - inference_start)
+                total_tracking_time += inference_time
+                tracking_time += inference_time / len(faces)
                 tracking_frames += 1
             packet = bytearray()
             detected = False
@@ -171,43 +295,34 @@ try:
                 packet.extend(bytearray(struct.pack("f", f.translation[0])))
                 packet.extend(bytearray(struct.pack("f", f.translation[1])))
                 packet.extend(bytearray(struct.pack("f", f.translation[2])))
-            if not log is None:
+                if log is not None:
-                log.write(f"{frame_count},{now},{width},{height},{args.fps},{face_num},{f.id},{f.eye_blink[0]},{f.eye_blink[1]},{f.conf},{f.success},{f.pnp_error},{f.quaternion[0]},{f.quaternion[1]},{f.quaternion[2]},{f.quaternion[3]},{f.euler[0]},{f.euler[1]},{f.euler[2]},{f.rotation[0]},{f.rotation[1]},{f.rotation[2]},{f.translation[0]},{f.translation[1]},{f.translation[2]}")
+                    log.write(f"{frame_count},{now},{width},{height},{fps},{face_num},{f.id},{f.eye_blink[0]},{f.eye_blink[1]},{f.conf},{f.success},{f.pnp_error},{f.quaternion[0]},{f.quaternion[1]},{f.quaternion[2]},{f.quaternion[3]},{f.euler[0]},{f.euler[1]},{f.euler[2]},{f.rotation[0]},{f.rotation[1]},{f.rotation[2]},{f.translation[0]},{f.translation[1]},{f.translation[2]}")
                 for (x,y,c) in f.lms:
                     packet.extend(bytearray(struct.pack("f", c)))
                 if args.visualize > 1:
                     frame = cv2.putText(frame, str(f.id), (int(f.bbox[0]), int(f.bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,255))
                 if args.visualize > 2:
-                frame = cv2.putText(frame, f"{f.conf:.4f}", (int(f.bbox[0] + 18), int(f.bbox[1] - 6)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0))
+                    frame = cv2.putText(frame, f"{f.conf:.4f}", (int(f.bbox[0] + 18), int(f.bbox[1] - 6)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255))
                 for pt_num, (x,y,c) in enumerate(f.lms):
                     packet.extend(bytearray(struct.pack("f", y)))
                     packet.extend(bytearray(struct.pack("f", x)))
-                if not log is None:
+                    if log is not None:
                         log.write(f",{y},{x},{c}")
-                if pt_num == 66 and f.eye_blink[0] < 0.30:
+                    if pt_num == 66 and (f.eye_blink[0] < 0.30 or c < 0.20):
                         continue
-                if pt_num == 67 and f.eye_blink[1] < 0.30:
+                    if pt_num == 67 and (f.eye_blink[1] < 0.30 or c < 0.20):
                         continue
                     x = int(x + 0.5)
                     y = int(y + 0.5)
-                if args.visualize != 0 or not out is None:
+                    if args.visualize != 0 or out is not None:
                         if args.visualize > 3:
                             frame = cv2.putText(frame, str(pt_num), (int(y), int(x)), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255,255,0))
-                    color = (0, 0, 255)
+                        color = (0, 255, 0)
                         if pt_num >= 66:
                             color = (255, 255, 0)
                         if not (x < 0 or y < 0 or x >= height or y >= width):
-                        frame[int(x), int(y)] = color
+                            cv2.circle(frame, (y, x), 1, color, -1)
-                    x += 1
+                if args.pnp_points != 0 and (args.visualize != 0 or out is not None) and f.rotation is not None:
-                    if not (x < 0 or y < 0 or x >= height or y >= width):
-                        frame[int(x), int(y)] = color
-                    y += 1
-                    if not (x < 0 or y < 0 or x >= height or y >= width):
-                        frame[int(x), int(y)] = color
-                    x -= 1
-                    if not (x < 0 or y < 0 or x >= height or y >= width):
-                        frame[int(x), int(y)] = color
-            if args.pnp_points != 0 and (args.visualize != 0 or not out is None) and f.rotation is not None:
                     if args.pnp_points > 1:
                         projected = cv2.projectPoints(f.face_3d[0:66], f.rotation, f.translation, tracker.camera, tracker.dist_coeffs)
                     else:
@@ -230,7 +345,7 @@ try:
                     packet.extend(bytearray(struct.pack("f", x)))
                     packet.extend(bytearray(struct.pack("f", -y)))
                     packet.extend(bytearray(struct.pack("f", -z)))
-                if not log is None:
+                    if log is not None:
                         log.write(f",{x},{-y},{-z}")
                 if f.current_features is None:
                     f.current_features = {}
@@ -238,22 +353,27 @@ try:
                     if not feature in f.current_features:
                         f.current_features[feature] = 0
                     packet.extend(bytearray(struct.pack("f", f.current_features[feature])))
-                if not log is None:
+                    if log is not None:
                         log.write(f",{f.current_features[feature]}")
-            if not log is None:
+                if log is not None:
                     log.write("\r\n")
                     log.flush()
 
             if detected and len(faces) < 40:
                 sock.sendto(packet, (target_ip, target_port))
 
-        if not out is None:
+            if out is not None:
-            out.write(frame)
+                video_frame = frame
+                if args.video_scale != 1:
+                    video_frame = cv2.resize(frame, (width * args.video_scale, height * args.video_scale), interpolation=cv2.INTER_NEAREST)
+                out.write(video_frame)
+                if args.video_scale != 1:
+                    del video_frame
 
             if args.visualize != 0:
                 cv2.imshow('OpenSeeFace Visualization', frame)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
-                if args.dump_points != "" and not faces is None and len(faces) > 0:
+                    if args.dump_points != "" and faces is not None and len(faces) > 0:
                         np.set_printoptions(threshold=sys.maxsize, precision=15)
                         pairs = [
                             (0, 16),
@@ -299,10 +419,29 @@ try:
                         with open(args.dump_points, "w") as fh:
                             fh.write(repr(points))
                     break
+            failures = 0
+        except Exception as e:
+            if e.__class__ == KeyboardInterrupt:
+                if args.silent == 0:
+                    print("Quitting")
+                break
+            traceback.print_exc()
+            failures += 1
+            if failures > 30:
+                break
+
+        collected = False
+        del frame
 
         duration = time.perf_counter() - frame_time
         while duration < target_duration:
-            time.sleep(target_duration - duration)
+            if not collected:
+                gc.collect()
+                collected = True
+            duration = time.perf_counter() - frame_time
+            sleep_time = target_duration - duration
+            if sleep_time > 0:
+                time.sleep(sleep_time)
             duration = time.perf_counter() - frame_time
         frame_time = time.perf_counter()
 except KeyboardInterrupt:
@@ -310,10 +449,12 @@ except KeyboardInterrupt:
         print("Quitting")
 
 input_reader.close()
-if not out is None:
+if out is not None:
     out.release()
+if args.visualize != 0:
     cv2.destroyAllWindows()
 
 if args.silent == 0 and tracking_frames > 0:
-    tracking_time = 1000 * tracking_time / tracking_frames
+    average_tracking_time = 1000 * tracking_time / tracking_frames
-    print(f"Average tracking time per detected face: {tracking_time:.2f} ms")
+    print(f"Average tracking time per detected face: {average_tracking_time:.2f} ms")
+    print(f"Tracking time: {total_tracking_time:.3f} s\nFrames: {tracking_frames}")

facetracker.spec

@@ -0,0 +1,53 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+block_cipher = None
+
+
+a = Analysis(['facetracker.py'],
+             pathex=['C:\\OpenSeeFaceBuild'],
+             binaries=[('dshowcapture/dshowcapture_x86.dll', '.'), ('dshowcapture/dshowcapture_x64.dll', '.'), ('dshowcapture/libminibmcapture32.dll', '.'), ('dshowcapture/libminibmcapture64.dll', '.'), ('escapi/escapi_x86.dll', '.'), ('escapi/escapi_x64.dll', '.'), ('run.bat', '.'), ('msvcp140.dll', '.'), ('vcomp140.dll', '.'), ('concrt140.dll', '.'), ('vccorlib140.dll', '.')],
+             datas=[],
+             hiddenimports=[],
+             hookspath=[],
+             runtime_hooks=[],
+             excludes=['matplotlib', 'mpl-data', 'PyInstaller', 'pywt', 'skimage', 'scipy', 'pyinstaller'],
+             win_no_prefer_redirects=False,
+             win_private_assemblies=False,
+             cipher=block_cipher,
+             noarchive=False)
+
+remove_bin = []
+for bin in a.binaries:
+    if bin[0].startswith("opencv_video") or bin[0].startswith("PyInstaller"):
+        remove_bin.append(bin[0])
+        print(bin)
+remove_dat = []
+for bin in a.datas:
+    if bin[0].startswith("opencv_video") or bin[0].startswith("PyInstaller"):
+        remove_dat.append(bin[0])
+        print("data ", bin)
+
+a.binaries = [x for x in a.binaries if not x[0] in remove_bin]
+a.datas = [x for x in a.datas if not x[0] in remove_dat]
+
+pyz = PYZ(a.pure, a.zipped_data,
+             cipher=block_cipher)
+exe = EXE(pyz,
+          a.scripts,
+          [],
+          exclude_binaries=True,
+          name='facetracker',
+          debug=False,
+          bootloader_ignore_signals=False,
+          strip=False,
+          upx=True,
+          console=True )
+
+coll = COLLECT(exe,
+               a.binaries,
+               a.zipfiles,
+               a.datas,
+               strip=False,
+               upx=True,
+               upx_exclude=[],
+               name='facetracker')

input_reader.py

@@ -4,16 +4,21 @@ import os
 import cv2
 import numpy as np
 import escapi
-
+import dshowcapture
-def list_cameras():
+import time
-    print("Available cameras:")
+import traceback
+import gc
 
 class VideoReader():
-    def __init__(self, capture):
+    def __init__(self, capture, camera=False):
+        if os.name == 'nt' and camera:
+            self.cap = cv2.VideoCapture(capture, cv2.CAP_DSHOW)
+        else:
             self.cap = cv2.VideoCapture(capture)
         if self.cap is None:
             print("The video source cannot be opened")
             sys.exit(0)
+        self.name = str(capture)
     def is_open(self):
         return self.cap.isOpened()
     def is_ready(self):
@@ -23,32 +28,22 @@ class VideoReader():
     def close(self):
         self.cap.release()
 
-class CameraReader(VideoReader):
+class EscapiReader(VideoReader):
     def __init__(self, capture, width, height, fps):
         self.device = None
         self.width = width
         self.height = height
         self.fps = fps
-        if os.name == 'nt':
-            escapi.init()
         self.device = capture
+        escapi.count_capture_devices()
+        self.name = str(escapi.device_name(self.device).decode('utf8', 'surrogateescape'))
         self.buffer = escapi.init_camera(self.device, self.width, self.height, self.fps)
         escapi.do_capture(self.device)
-        else:
-            super(CameraReader, self).__init__(capture)
-            self.cap.set(3, width)
-            self.cap.set(4, height)
     def is_open(self):
-        if self.device is None:
-            return super(CameraReader, self).is_open()
         return True
     def is_ready(self):
-        if self.device is None:
-            return super(CameraReader, self).is_ready()
         return escapi.is_capture_done(self.device)
     def read(self):
-        if self.device is None:
-            return super(CameraReader, self).read()
         if escapi.is_capture_done(self.device):
             image = escapi.read(self.device, self.width, self.height, self.buffer)
             escapi.do_capture(self.device)
@@ -56,11 +51,100 @@ class CameraReader(VideoReader):
         else:
             return False, None
     def close(self):
-        if self.device is None:
-            super(CameraReader, self).close()
-        else:
         escapi.deinit_camera(self.device)
 
+class DShowCaptureReader(VideoReader):
+    def __init__(self, capture, width, height, fps, use_dshowcapture=True, dcap=None):
+        self.device = None
+        self.width = width
+        self.height = height
+        self.fps = fps
+        self.dcap = dcap;
+        self.device = dshowcapture.DShowCapture()
+        self.device.get_devices()
+        info = self.device.get_info()
+        self.name = info[capture]['name']
+        if info[capture]['type'] == "Blackmagic":
+            self.name = "Blackmagic: " + self.name
+            if dcap is None or dcap < 0:
+                dcap = 0
+        ret = False
+        if dcap is None:
+            ret = self.device.capture_device(capture, self.width, self.height, self.fps)
+        else:
+            if dcap < 0:
+                ret = self.device.capture_device_default(capture)
+            else:
+                ret = self.device.capture_device_by_dcap(capture, dcap, self.width, self.height, self.fps)
+        if not ret:
+            raise Exception("Failed to start capture.")
+        self.width = self.device.width
+        self.height = self.device.height
+        self.fps = self.device.fps
+        print(f"Camera: \"{self.name}\" Capability ID: {dcap} Resolution: {self.device.width}x{self.device.height} Frame rate: {self.device.fps} Colorspace: {self.device.colorspace} Internal: {self.device.colorspace_internal} Flipped: {self.device.flipped}")
+        self.timeout = 1000
+    def is_open(self):
+        return self.device.capturing()
+    def is_ready(self):
+        return self.device.capturing()
+    def read(self):
+        img = None
+        try:
+            img = self.device.get_frame(self.timeout)
+        except:
+            gc.collect()
+            img = self.device.get_frame(self.timeout)
+        if img is None:
+            return False, None
+        else:
+            return True, img
+    def close(self):
+        self.device.destroy_capture()
+
+class OpenCVReader(VideoReader):
+    def __init__(self, capture, width, height, fps):
+        self.device = None
+        self.width = width
+        self.height = height
+        self.fps = fps
+        self.name = str(capture)
+        super(OpenCVReader, self).__init__(capture, camera=True)
+        self.cap.set(3, width)
+        self.cap.set(4, height)
+        self.cap.set(38, 1)
+    def is_open(self):
+        return super(OpenCVReader, self).is_open()
+    def is_ready(self):
+        return super(OpenCVReader, self).is_ready()
+    def read(self):
+        return super(OpenCVReader, self).read()
+    def close(self):
+        super(OpenCVReader, self).close()
+
+class V4L2Reader(OpenCVReader):
+    def __init__(self, capture, width, height, fps, dformat):
+        self.cap = cv2.VideoCapture(capture, cv2.CAP_V4L2)
+        if dformat:
+            fourcc = cv2.VideoWriter.fourcc(*dformat)
+            self.cap.set(6, fourcc)
+        self.device = None
+        self.width = width
+        self.height = height
+        self.fps = fps
+        self.name = str(capture)
+        self.cap.set(3, width)
+        self.cap.set(4, height)
+        self.cap.set(38, 1)
+        self.cap.set(5, fps)
+    def is_open(self):
+        return super(V4L2Reader, self).is_open()
+    def is_ready(self):
+        return super(V4L2Reader, self).is_ready()
+    def read(self):
+        return super(V4L2Reader, self).read()
+    def close(self):
+        super(V4L2Reader, self).close()
+
 class RawReader:
     def __init__(self, width, height):
         self.width = int(width)
@@ -87,16 +171,101 @@ class RawReader:
     def close(self):
         self.open = False
 
+def try_int(s):
+    try:
+        return int(s)
+    except:
+        return None
+
+def test_reader(reader):
+    got_any = 0
+    try:
+        for i in range(30):
+            if not reader.is_ready():
+                time.sleep(0.02)
+            ret, frame = reader.read()
+            if not ret:
+                time.sleep(0.02)
+                print("No frame")
+            else:
+                print("Got frame")
+                got_any += 1
+                if got_any > 10:
+                    break
+        if reader.is_open():
+            return got_any > 0
+        print("Fail")
+        return False
+    except:
+        traceback.print_exc()
+        print("Except")
+        return False
+
 class InputReader():
-    def __init__(self, capture, raw_rgb, width, height, fps):
+    def __init__(self, capture, raw_rgb, width, height, fps, use_dshowcapture=False, dcap=None):
         self.reader = None
+        self.name = str(capture)
         try:
             if raw_rgb > 0:
                 self.reader = RawReader(width, height)
             elif os.path.exists(capture):
                 self.reader = VideoReader(capture)
-            elif capture == str(int(capture)):
+            elif capture == str(try_int(capture)):
-                self.reader = CameraReader(int(capture), width, height, fps)
+                if os.name == 'nt':
+                    # Try with DShowCapture
+                    good = True
+                    name = ""
+                    try:
+                        if use_dshowcapture:
+                            self.reader = DShowCaptureReader(int(capture), width, height, fps, dcap=dcap)
+                            name = self.reader.name
+                            good = test_reader(self.reader)
+                            self.name = name
+                        else:
+                            good = False
+                    except:
+                        print("DShowCapture exception: ")
+                        traceback.print_exc()
+                        good = False
+                    if good:
+                        return
+                    # Try with Escapi
+                    good = True
+                    try:
+                        print(f"DShowCapture failed. Falling back to escapi for device {name}.", file=sys.stderr)
+                        escapi.init()
+                        devices = escapi.count_capture_devices()
+                        found = None
+                        for i in range(devices):
+                            escapi_name = str(escapi.device_name(i).decode('utf8', 'surrogateescape'))
+                            if name == escapi_name:
+                                found = i
+                        if found is None:
+                            good = False
+                        else:
+                            print(f"Found device {name} as {i}.", file=sys.stderr)
+                            self.reader = EscapiReader(found, width, height, fps)
+                            good = test_reader(self.reader)
+                    except:
+                        print("Escapi exception: ")
+                        traceback.print_exc()
+                        good = False
+                    if good:
+                        return
+                    # Try with OpenCV
+                    print(f"Escapi failed. Falling back to OpenCV. If this fails, please change your camera settings.", file=sys.stderr)
+                    self.reader = OpenCVReader(int(capture), width, height, fps)
+                    self.name = self.reader.name
+                elif sys.platform == 'linux':
+                    try:
+                        self.reader = V4L2Reader(int(capture), width, height, fps, dcap)
+                    except:
+                        print("V4L2 exception: ")
+                        traceback.print_exc()
+                        print(f"V4L2 failed. Falling back to OpenCV. If this fails, please change your camera settings.", file=sys.stderr)
+                        self.reader = OpenCVReader(int(capture), width, height, fps)
+                else:
+                    self.reader = OpenCVReader(int(capture), width, height, fps)
         except Exception as e:
             print("Error: " + str(e))
 

make_exe.bat

@@ -1,9 +1,31 @@
-REM pyinstaller --onefile ^
+echo "Started"
-REM     --add-binary escapi/escapi_x86.dll;. ^
-REM     --add-binary escapi/escapi_x64.dll;. ^
-REM     facetracker.py
 
-pyinstaller --onedir ^
+:: A venv is used so it's easy to add the onnxruntime dll to the binary
-    --add-binary escapi/escapi_x86.dll;. ^
+
-    --add-binary escapi/escapi_x64.dll;. ^
+echo "Creating venv"
-    facetracker.py
+python -m venv venv
+
+echo "Activating venv"
+call venv\Scripts\activate.bat
+
+echo "Installing dependencies"
+pip install wheel
+
+pip install onnxruntime opencv-python==4.5.4.60 pillow numpy==1.23.0 pyinstaller
+
+echo "Running pyinstaller"
+pyinstaller facetracker.py --clean ^
+    --onedir ^
+    --add-binary dshowcapture/*.dll;. ^
+    --add-binary escapi/*.dll;. ^
+    --add-binary venv/lib/site-packages/onnxruntime/capi/*.dll;onnxruntime\capi ^
+    --add-binary msvcp140.dll;. ^
+    --add-binary vcomp140.dll;. ^
+    --add-binary concrt140.dll;. ^
+    --add-binary vccorlib140.dll;. ^
+    --add-binary run.bat;.
+
+echo "Deleting opencv dll"
+del dist\facetracker\cv2\opencv_videoio_*
+
+echo "Finished"

make_exe.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+
+set -e
+
+PYTHON_BINARY=$(which python3)
+
+if [[ -z "$PYTHON_BINARY" ]]; then
+    echo "python3 not found. Searching for python instead."
+    PYTHON_BINARY=$(which python)
+    if [[ -z "$PYTHON_BINARY" ]]; then
+        echo "Python 3 must be available on your path. Exiting."
+        exit 1
+    fi
+fi
+
+echo "Creating venv"
+"$PYTHON_BINARY" -m venv venv
+
+source venv/bin/activate
+
+echo "Installing packages"
+pip install wheel # Make sure this is installed beforehand
+
+pip install onnxruntime opencv-python==4.5.4.60 pillow numpy==1.23.0 pyinstaller
+
+echo "Creating binary"
+pyinstaller --onedir --clean facetracker.py \
+    --add-binary venv/lib/python3.*/site-packages/onnxruntime/capi/*.so:onnxruntime/capi
+
+echo "Files should be available in the dist/ folder"
+echo "Done!"
+

model.py

@@ -1,164 +1,225 @@
 # This file is not used by the tracking application and currently outdated
 import torch
 import torch.nn as nn
+import geffnet.mobilenetv3 # geffnet.mobilenetv3._gen_mobilenet_v3 needs to be patched to return the parameters instead of instantiating the network, based on commit c450c12ae6ffb1757f62dde3c2765da3c10f6def
+from geffnet.efficientnet_builder import round_channels
 
 class DSConv2d(nn.Module):
-    def __init__(self, in_planes, out_planes, kernels_per_layer=4, groups=1):
+    def __init__(self, in_planes, out_planes, kernels_per_layer=4, groups=1, old=0):
         super(DSConv2d, self).__init__()
+        if old == 2:
             self.conv = nn.Sequential(
                 nn.Conv2d(in_planes, in_planes * kernels_per_layer, kernel_size=3, padding=1, groups=in_planes),
                 nn.Conv2d(in_planes * kernels_per_layer, out_planes, kernel_size=1, groups=groups)
             )
+        elif old == 1:
+            self.conv = nn.Sequential(
+                nn.Conv2d(in_planes, in_planes * kernels_per_layer, kernel_size=3, padding=1, groups=in_planes, bias=False),
+                nn.BatchNorm2d(in_planes * kernels_per_layer),
+                nn.Conv2d(in_planes * kernels_per_layer, out_planes, kernel_size=1, groups=groups, bias=False),
+                nn.BatchNorm2d(out_planes),
+                nn.ReLU6(inplace=True)
+            )
+        else:
+            self.conv = nn.Sequential(
+                nn.Conv2d(in_planes, in_planes * kernels_per_layer, kernel_size=3, padding=1, groups=in_planes, bias=False),
+                nn.BatchNorm2d(in_planes * kernels_per_layer),
+                nn.ReLU6(inplace=True),
+                nn.Conv2d(in_planes * kernels_per_layer, out_planes, kernel_size=1, groups=groups, bias=False),
+                nn.BatchNorm2d(out_planes),
+                nn.ReLU6(inplace=True)
+            )
     def forward(self, x):
         x = self.conv(x)
         return x
 
 class UNetUp(nn.Module):
-    def __init__(self, in_channels, residual_in_channels, out_channels, size):
+    def __init__(self, in_channels, residual_in_channels, out_channels, size, old=0):
         super(UNetUp, self).__init__()
         self.up = nn.Upsample(size=size, mode='bilinear', align_corners=True)
-        self.conv = DSConv2d(in_channels + residual_in_channels, out_channels, 1, 1)
+        self.conv = DSConv2d(in_channels + residual_in_channels, out_channels, 1, 1, old=old)
     def forward(self, x1, x2):
         x1 = self.up(x1)
         x = torch.cat([x2, x1], dim=1)
         x = self.conv(x)
         return x
 
-# Copy of torchvision ShuffleNetV2 for type annotation
+# This is the gaze tracking model
-def channel_shuffle(x, groups: int):
+class OpenSeeFaceGaze(geffnet.mobilenetv3.MobileNetV3):
-    batchsize, num_channels, height, width = x.data.size()
-    channels_per_group = num_channels // groups
-
-    # reshape
-    x = x.view(batchsize, groups,
-               channels_per_group, height, width)
-
-    x = torch.transpose(x, 1, 2).contiguous()
-
-    # flatten
-    x = x.view(batchsize, -1, height, width)
-
-    return x
-
-class InvertedResidual(nn.Module):
-    def __init__(self, inp, oup, stride):
-        super(InvertedResidual, self).__init__()
-
-        if not (1 <= stride <= 3):
-            raise ValueError('illegal stride value')
-        self.stride = stride
-
-        branch_features = oup // 2
-        assert (self.stride != 1) or (inp == branch_features << 1)
-
-        self.branch1 = nn.Sequential(
-            self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
-            nn.BatchNorm2d(inp),
-            nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(branch_features),
-            nn.ReLU(inplace=True),
-        )
-
-        self.branch2 = nn.Sequential(
-            nn.Conv2d(inp if (self.stride > 1) else branch_features,
-                      branch_features, kernel_size=1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(branch_features),
-            nn.ReLU(inplace=True),
-            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
-            nn.BatchNorm2d(branch_features),
-            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(branch_features),
-            nn.ReLU(inplace=True),
-        )
-
-    @staticmethod
-    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
-        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
-
-    def forward(self, x):
-        if self.stride == 1:
-            x1, x2 = x.chunk(2, dim=1)
-            out = torch.cat((x1, self.branch2(x2)), dim=1)
-        else:
-            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
-
-        out = channel_shuffle(out, 2)
-
-        return out
-
-class ShuffleNetV2(nn.Module):
-    def __init__(self, stages_repeats, stages_out_channels, num_classes=1000):
-        super(ShuffleNetV2, self).__init__()
-
-        if len(stages_repeats) != 3:
-            raise ValueError('expected stages_repeats as list of 3 positive ints')
-        if len(stages_out_channels) != 5:
-            raise ValueError('expected stages_out_channels as list of 5 positive ints')
-        self._stage_out_channels = stages_out_channels
-
-        input_channels = 3
-        output_channels = self._stage_out_channels[0]
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
-            nn.BatchNorm2d(output_channels),
-            nn.ReLU(inplace=True),
-        )
-        input_channels = output_channels
-
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
-        for name, repeats, output_channels in zip(
-                stage_names, stages_repeats, self._stage_out_channels[1:]):
-            seq = [InvertedResidual(input_channels, output_channels, 2)]
-            for i in range(repeats - 1):
-                seq.append(InvertedResidual(output_channels, output_channels, 1))
-            setattr(self, name, nn.Sequential(*seq))
-            input_channels = output_channels
-
-        output_channels = self._stage_out_channels[-1]
-        self.conv5 = nn.Sequential(
-            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
-            nn.BatchNorm2d(output_channels),
-            nn.ReLU(inplace=True),
-        )
-
-        self.fc = nn.Linear(output_channels, num_classes)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.maxpool(x)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.conv5(x)
-        x = x.mean([2, 3])  # globalpool
-        x = self.fc(x)
-        return x
-
-# Facial landmark detection model
-class OpenSeeNetSNV2(ShuffleNetV2):
     def __init__(self):
-        super(OpenSeeNetSNV2, self).__init__([4, 8, 4], [24, 116, 232, 464, 1024])
+        kwargs = geffnet.mobilenetv3._gen_mobilenet_v3(['small'])
-        self.up1 = UNetUp(1024, 232, 256, (14,14))
+        super(OpenSeeFaceGaze, self).__init__(**kwargs)
-        self.up2 = UNetUp(256, 116, 198 * 1, (28,28))
+        self.up1 = UNetUp(576, 48, 64, (2,2), old=2)
-        self.group = DSConv2d(198 * 1, 198, kernels_per_layer=4, groups=3)
+        self.up2 = UNetUp(64, 24, 32, (4,4), old=2)
-
+        self.up3 = UNetUp(32, 16, 15, (8,8), old=2)
+        self.group = DSConv2d(15, 3, kernels_per_layer=4, groups=3, old=2)
     def _forward_impl(self, x):
-        x = self.conv1(x)
+        x = self.conv_stem(x)
-        x = self.maxpool(x)
+        x = self.bn1(x)
-        x = self.stage2(x)
+        x = self.act1(x)
-        r2 = x
+        r1 = None
-        x = self.stage3(x)
+        r2 = None
+        r3 = None
+        for i, feature in enumerate(self.blocks):
+            x = feature(x)
+            if i == 3:
                 r3 = x
-        x = self.stage4(x)
+            if i == 1:
-        x = self.conv5(x)
+                r2 = x
+            if i == 0:
+                r1 = x
+        x = self.up1(x, r3)
+        x = self.up2(x, r2)
+        x = self.up3(x, r1)
+        x = self.group(x)
+        return x
+    def forward(self, x):
+        return self._forward_impl(x)
 
+# This is the face detection model. Because the landmark model is very robust, it gets away with predicting very rough bounding boxes. It is fully convolutional and can be made to run on different resolutions. It was trained on 224x224 crops and the most reasonable results can be found in the range of 224x224 to 640x640.
+class OpenSeeFaceDetect(geffnet.mobilenetv3.MobileNetV3):
+    def __init__(self, size="large", channel_multiplier=0.1):
+        kwargs = geffnet.mobilenetv3._gen_mobilenet_v3([size], channel_multiplier=channel_multiplier)
+        super(OpenSeeFaceDetect, self).__init__(**kwargs)
+        if size == "large":
+            self.up1 = UNetUp(round_channels(960, channel_multiplier), round_channels(112, channel_multiplier), 256, (14,14), old=1)
+            self.up2 = UNetUp(256, round_channels(40, channel_multiplier), 128, (28,28), old=1)
+            self.up3 = UNetUp(128, round_channels(24, channel_multiplier), 64, (56,56), old=1)
+            self.group = DSConv2d(64, 2, kernels_per_layer=4, groups=2, old=1)
+            self.r1_i = 1
+            self.r2_i = 2
+            self.r3_i = 4
+        elif size == "small":
+            self.up1 = UNetUp(round_channels(576, channel_multiplier), round_channels(40, channel_multiplier), 256, (14,14), old=1)
+            self.up2 = UNetUp(256, round_channels(24, channel_multiplier), 128, (28,28), old=1)
+            self.up3 = UNetUp(128, round_channels(16, channel_multiplier), 64, (56,56), old=1)
+            self.group = DSConv2d(64, 2, kernels_per_layer=4, groups=2, old=1)
+            self.r1_i = 0
+            self.r2_i = 1
+            self.r3_i = 2
+        self.maxpool = nn.MaxPool2d(kernel_size=3, dilation=1, stride=1, padding=1)
+    def _forward_impl(self, x):
+        x = self.conv_stem(x)
+        x = self.bn1(x)
+        x = self.act1(x)
+        r2 = None
+        r3 = None
+        for i, feature in enumerate(self.blocks):
+            x = feature(x)
+            if i == self.r3_i:
+                r3 = x
+            if i == self.r2_i:
+                r2 = x
+            if i == self.r1_i:
+                r1 = x
+        x = self.up1(x, r3)
+        x = self.up2(x, r2)
+        x = self.up3(x, r1)
+        x = self.group(x)
+        x2 = self.maxpool(x)
+        return x, x2
+    def forward(self, x):
+        return self._forward_impl(x)
+
+def logit_arr(p, factor=16.0):
+    p = p.clamp(0.0000001, 0.9999999)
+    return torch.log(p / (1 - p)) / factor
+
+# Landmark detection model
+# Models:
+# 0: "small", 0.5
+# 1: "small", 1.0
+# 2: "large", 0.75
+# 3: "large", 1.0
+class OpenSeeFaceLandmarks(geffnet.mobilenetv3.MobileNetV3):
+    def __init__(self, size="large", channel_multiplier=1.0, inference=False):
+        kwargs = geffnet.mobilenetv3._gen_mobilenet_v3([size], channel_multiplier=channel_multiplier)
+        super(OpenSeeFaceLandmarks, self).__init__(**kwargs)
+        if size == "large":
+            self.up1 = UNetUp(round_channels(960, channel_multiplier), round_channels(112, channel_multiplier), 256, (14,14))
+            self.up2 = UNetUp(256, round_channels(40, channel_multiplier), 198 * 1, (28,28))
+            self.group = DSConv2d(198 * 1, 198, kernels_per_layer=4, groups=3)
+            self.r2_i = 2
+            self.r3_i = 4
+        elif size == "small":
+            self.up1 = UNetUp(round_channels(576, channel_multiplier), round_channels(40, channel_multiplier), 256, (14,14))
+            self.up2 = UNetUp(256, round_channels(24, channel_multiplier), 198 * 1, (28,28))
+            self.group = DSConv2d(198 * 1, 198, kernels_per_layer=4, groups=3)
+            self.r2_i = 1
+            self.r3_i = 2
+        self.inference = inference
+    def _forward_impl(self, x):
+        x = self.conv_stem(x)
+        x = self.bn1(x)
+        x = self.act1(x)
+        r2 = None
+        r3 = None
+        for i, feature in enumerate(self.blocks):
+            x = feature(x)
+            if i == self.r3_i:
+                r3 = x
+            if i == self.r2_i:
+                r2 = x
         x = self.up1(x, r3)
         x = self.up2(x, r2)
         x = self.group(x)
-        return x
 
+        if self.inference:
+            t_main = x[:, 0:66].reshape((-1, 66, 28*28))
+            t_m = t_main.argmax(dim=2)
+            indices = t_m.unsqueeze(2)
+            t_conf = t_main.gather(2, indices).squeeze(2)
+            t_off_x = x[:, 66:132].reshape((-1, 66, 28*28)).gather(2, indices).squeeze(2)
+            t_off_y = x[:, 132:198].reshape((-1, 66, 28*28)).gather(2, indices).squeeze(2)
+            t_off_x = (223. * logit_arr(t_off_x) + 0.5).floor()
+            t_off_y = (223. * logit_arr(t_off_y) + 0.5).floor()
+            t_x = 223. * (t_m / 28.).floor() / 27. + t_off_x
+            t_y = 223. * t_m.remainder(28.).float() / 27. + t_off_y
+            x = (t_conf.mean(1), torch.stack([t_x, t_y, t_conf], 2))
+
+        return x
+    def forward(self, x):
+        return self._forward_impl(x)
+
+# lm_modelT for 56x56 30 point inference
+class OpenSeeFaceLandmarks30Pt(geffnet.mobilenetv3.MobileNetV3):
+    def __init__(self, size="large", channel_multiplier=1.0, inference=False):
+        kwargs = geffnet.mobilenetv3._gen_mobilenet_v3([size], channel_multiplier=channel_multiplier)
+        super(OpenSeeFaceLandmarks30Pt, self).__init__(**kwargs)
+        self.up1 = UNetUp(960, 112, 256, (4,4))
+        self.up2 = UNetUp(256, 40, 180, (7,7))
+        self.group = DSConv2d(180, 90, kernels_per_layer=4, groups=3)
+        self.inference = inference
+    def _forward_impl(self, x):
+        x = self.conv_stem(x)
+        x = self.bn1(x)
+        x = self.act1(x)
+        r2 = None
+        r3 = None
+        for i, feature in enumerate(self.blocks):
+            x = feature(x)
+            if i == 4:
+                r3 = x
+            if i == 2:
+                r2 = x
+        x = self.up1(x, r3)
+        x = self.up2(x, r2)
+        x = self.group(x)
+
+        if self.inference:
+            t_main = x[:, 0:30].reshape((-1, 30, 7*7))
+            t_m = t_main.argmax(dim=2)
+            indices = t_m.unsqueeze(2)
+            t_conf = t_main.gather(2, indices).squeeze(2)
+            t_off_x = x[:, 30:60].reshape((-1, 30, 7*7)).gather(2, indices).squeeze(2)
+            t_off_y = x[:, 60:90].reshape((-1, 30, 7*7)).gather(2, indices).squeeze(2)
+            t_off_x = 55. * logit_arr(t_off_x, factor=8.0)
+            t_off_y = 55. * logit_arr(t_off_y, factor=8.0)
+            t_x = 55. * (t_m / 7.).floor() / 6. + t_off_x
+            t_y = 55. * t_m.remainder(7.).float() / 6. + t_off_y
+            x = (t_conf.mean(1), torch.stack([t_x, t_y, t_conf], 2))
+
+        return x
     def forward(self, x):
         return self._forward_impl(x)
 
@@ -190,18 +251,21 @@ def AdapWingLoss(pre_hm, gt_hm):
         img_dilate = cv2.morphologyEx(img_merge, cv2.MORPH_DILATE, kernel)
         img_dilate[img_dilate < 51] = 1  # 0*W+1
         img_dilate[img_dilate >= 51] = 11  # 1*W+1
-        img_dilate = np.array(img_dilate, dtype=np.int)
+        img_dilate = np.array(img_dilate, dtype=int)
         img_dilate = img_dilate.transpose(2, 0, 1)
         dilated = torch.from_numpy(img_dilate).float()
         if first:
             first_mask = dilated
         first = False
-        dilated[17:27] *= 1.2
+        # These weights varied between training runs and model sizes
-        dilated[17] *= 1.3
+        dilated[17:27][dilated[17:27] > 1] *= 1.4
-        dilated[18] *= 1.4
+        dilated[17, dilated[17] > 1] *= 1.6
-        dilated[25] *= 1.4
+        dilated[18, dilated[18] > 1] *= 1.8
-        dilated[26] *= 1.3
+        dilated[25, dilated[25] > 1] *= 1.8
-        dilated[36:47] *= 2.5
+        dilated[26, dilated[26] > 1] *= 1.6
+        dilated[36:48][dilated[36:48] > 1] *= 2.8
+        # Used for a very small model
+        #dilated[[37,38,40,41,43,44,46,47]][dilated[[37,38,40,41,43,44,46,47]] > 1] *= 20.8
         mask[i] = torch.cat([dilated, dilated, dilated], 0)
 
     diff_hm = torch.abs(gt_hm - pre_hm)
@@ -216,5 +280,35 @@ def AdapWingLoss(pre_hm, gt_hm):
 
     return first_mask.detach(), mean_loss
 
-# The fast model runs on grayscale 112x112 using the ShuffleNet V2 0.5x configuration with input_channels=1. It was trained using AWL, but without the additional weighting for certain features
+
-.
+
+# Checkpoint test
+if __name__== "__main__":
+    print("Checking gaze model")
+    m=OpenSeeFaceGaze()
+    ckpt = torch.load("gaze.pth")
+    m.load_state_dict(ckpt)
+    print("Checking detection model")
+    m=OpenSeeFaceDetect()
+    ckpt = torch.load("detection.pth")
+    m.load_state_dict(ckpt)
+    print("Checking lm_model0 model")
+    m=OpenSeeFaceLandmarks("small", 0.5)
+    ckpt = torch.load("lm_model0.pth")
+    m.load_state_dict(ckpt)
+    print("Checking lm_model1 model")
+    m=OpenSeeFaceLandmarks("small", 1.0)
+    ckpt = torch.load("lm_model1.pth")
+    m.load_state_dict(ckpt)
+    print("Checking lm_model2 model")
+    m=OpenSeeFaceLandmarks("large", 0.75)
+    ckpt = torch.load("lm_model2.pth")
+    m.load_state_dict(ckpt)
+    print("Checking lm_model3 model")
+    m=OpenSeeFaceLandmarks("large", 1.0)
+    ckpt = torch.load("lm_model3.pth")
+    m.load_state_dict(ckpt)
+    print("Checking lm_modelT model")
+    m=OpenSeeFaceLandmarks("large", 1.0)
+    ckpt = torch.load("lm_modelT.pth")
+    m.load_state_dict(ckpt)

poetry.lock

@@ -0,0 +1,224 @@
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pyproject.toml

@@ -0,0 +1,19 @@
+[tool.poetry]
+name = "openseeface"
+version = "1.20.4"
+description = ""
+authors = ["emilianavt"]
+license = "BSD-2-Clause"
+
+[tool.poetry.dependencies]
+python = ">=3.7,<3.11"
+numpy = "^1.21.3"
+opencv-python = "^4.5.4"
+Pillow = "^8.4.0"
+onnxruntime = "^1.9.0"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

retinaface.py

@@ -66,7 +66,8 @@ class RetinaFaceDetector():
         options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
         options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         options.log_severity_level = 3
-        self.session = onnxruntime.InferenceSession(model_path, sess_options=options)
+        providersList = onnxruntime.capi._pybind_state.get_available_providers()
+        self.session = onnxruntime.InferenceSession(model_path, sess_options=options, providers=providersList)
         self.res_w, self.res_h = res
         with open(json_path, "r") as prior_file:
             self.priorbox = np.array(json.loads(prior_file.read()))

run.bat

@@ -2,12 +2,15 @@
 
 facetracker -l 1
 
+echo Make sure that nothing is accessing your camera before you proceed.
 
 set /p cameraNum=Select your camera from the list above and enter the corresponding number:
-set /p width=Select the width:
+
-set /p height=Select the height:
+facetracker -a %cameraNum%
+
+set /p dcaps=Select your camera mode or -1 for default settings:
 set /p fps=Select the FPS:
 
-facetracker -c %cameraNum% -W %width% -H %height% -F %fps% -v 3 -P 1
+facetracker -c %cameraNum% -F %fps% -D %dcaps% -v 3 -P 1 --discard-after 0 --scan-every 0 --no-3d-adapt 1 --max-feature-updates 900
 
 pause

tracker.py

@@ -7,6 +7,7 @@ import time
 import queue
 import threading
 import copy
+from similaritytransform import SimilarityTransform
 from retinaface import RetinaFaceDetector
 from remedian import remedian
 
@@ -49,7 +50,7 @@ def compensate(p1, p2):
 def rotate_image(image, a, center):
     (h, w) = image.shape[:2]
     a = np.rad2deg(a)
-    M = cv2.getRotationMatrix2D((center[0], center[1]), a, 1.0)
+    M = cv2.getRotationMatrix2D((float(center[0]), float(center[1])), a, 1.0)
     rotated = cv2.warpAffine(image, M, (w, h))
     return rotated
 
@@ -110,6 +111,10 @@ def logit(p, factor=16.0):
     p = p/(1-p)
     return float(np.log(p)) / float(factor)
 
+def logit_arr(p, factor=16.0):
+    p = np.clip(p, 0.0000001, 0.9999999)
+    return np.log(p / (1 - p)) / float(factor)
+
 def matrix_to_quaternion(m):
     t = 0.0
     q = [0.0, 0.0, 0, 0.0]
@@ -134,13 +139,16 @@ def worker_thread(session, frame, input, crop_info, queue, input_name, idx, trac
     output = session.run([], {input_name: input})[0]
     conf, lms = tracker.landmarks(output[0], crop_info)
     if conf > tracker.threshold:
-        eye_state = tracker.get_eye_state(frame, lms, single=True)
+        try:
+            eye_state = tracker.get_eye_state(frame, lms)
+        except:
+            eye_state = [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
         queue.put((session, conf, (lms, eye_state), crop_info, idx))
     else:
         queue.put((session,))
 
 class Feature():
-    def __init__(self, threshold=0.15, alpha=0.2, hard_factor=0.15, decay=0.001):
+    def __init__(self, threshold=0.15, alpha=0.2, hard_factor=0.15, decay=0.001, max_feature_updates=0):
         self.median = remedian()
         self.min = None
         self.max = None
@@ -151,40 +159,58 @@ class Feature():
         self.hard_factor = hard_factor
         self.decay = decay
         self.last = 0
+        self.current_median = 0
+        self.update_count = 0
+        self.max_feature_updates = max_feature_updates
+        self.first_seen = -1
+        self.updating = True
 
-    def update(self, x):
+    def update(self, x, now=0):
-        new = self.update_state(x)
+        if self.max_feature_updates > 0:
+            if self.first_seen == -1:
+                self.first_seen = now;
+        new = self.update_state(x, now=now)
         filtered = self.last * self.alpha + new * (1 - self.alpha)
         self.last = filtered
         return filtered
 
-    def update_state(self, x):
+    def update_state(self, x, now=0):
+        updating = self.updating and (self.max_feature_updates == 0 or now - self.first_seen < self.max_feature_updates)
+        if updating:
             self.median + x
-        median = self.median.median()
+            self.current_median = self.median.median()
+        else:
+            self.updating = False
+        median = self.current_median
 
         if self.min is None:
             if x < median and (median - x) / median > self.threshold:
+                if updating:
                     self.min = x
                     self.hard_min = self.min + self.hard_factor * (median - self.min)
                 return -1
             return 0
         else:
             if x < self.min:
+                if updating:
                     self.min = x
                     self.hard_min = self.min + self.hard_factor * (median - self.min)
                 return -1
         if self.max is None:
             if x > median and (x - median) / median > self.threshold:
+                if updating:
                     self.max = x
                     self.hard_max = self.max - self.hard_factor * (self.max - median)
                 return 1
             return 0
         else:
             if x > self.max:
+                if updating:
                     self.max = x
                     self.hard_max = self.max - self.hard_factor * (self.max - median)
                 return 1
 
+        if updating:
             if self.min < self.hard_min:
                 self.min = self.hard_min * self.decay + self.min * (1 - self.decay)
             if self.max > self.hard_max:
@@ -198,21 +224,21 @@ class Feature():
         return 0
 
 class FeatureExtractor():
-    def __init__(self):
+    def __init__(self, max_feature_updates=0):
-        self.eye_l = Feature()
+        self.eye_l = Feature(max_feature_updates=max_feature_updates)
-        self.eye_r = Feature()
+        self.eye_r = Feature(max_feature_updates=max_feature_updates)
-        self.eyebrow_updown_l = Feature()
+        self.eyebrow_updown_l = Feature(max_feature_updates=max_feature_updates)
-        self.eyebrow_updown_r = Feature()
+        self.eyebrow_updown_r = Feature(max_feature_updates=max_feature_updates)
-        self.eyebrow_quirk_l = Feature(threshold=0.05)
+        self.eyebrow_quirk_l = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
-        self.eyebrow_quirk_r = Feature(threshold=0.05)
+        self.eyebrow_quirk_r = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
-        self.eyebrow_steepness_l = Feature(threshold=0.05)
+        self.eyebrow_steepness_l = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
-        self.eyebrow_steepness_r = Feature(threshold=0.05)
+        self.eyebrow_steepness_r = Feature(threshold=0.05, max_feature_updates=max_feature_updates)
-        self.mouth_corner_updown_l = Feature()
+        self.mouth_corner_updown_l = Feature(max_feature_updates=max_feature_updates)
-        self.mouth_corner_updown_r = Feature()
+        self.mouth_corner_updown_r = Feature(max_feature_updates=max_feature_updates)
-        self.mouth_corner_inout_l = Feature(threshold=0.02)
+        self.mouth_corner_inout_l = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
-        self.mouth_corner_inout_r = Feature(threshold=0.02)
+        self.mouth_corner_inout_r = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
-        self.mouth_open = Feature()
+        self.mouth_open = Feature(max_feature_updates=max_feature_updates)
-        self.mouth_wide = Feature(threshold=0.02)
+        self.mouth_wide = Feature(threshold=0.02, max_feature_updates=max_feature_updates)
 
     def align_points(self, a, b, pts):
         a = tuple(a)
@@ -229,56 +255,71 @@ class FeatureExtractor():
             aligned_pts.append(np.array(rotate(a, pt, alpha)))
         return alpha, np.array(aligned_pts)
 
-    def update(self, pts):
+    def update(self, pts, full=True):
         features = {}
+        now = time.perf_counter()
 
         norm_distance_x = np.mean([pts[0, 0] - pts[16, 0], pts[1, 0] - pts[15, 0]])
         norm_distance_y = np.mean([pts[27, 1] - pts[28, 1], pts[28, 1] - pts[29, 1], pts[29, 1] - pts[30, 1]])
 
         a1, f_pts = self.align_points(pts[42], pts[45], pts[[43, 44, 47, 46]])
-        f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None)
+        f = abs((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y)
-        features["eye_l"] = self.eye_l.update(f)
+        features["eye_l"] = self.eye_l.update(f, now)
 
         a2, f_pts = self.align_points(pts[36], pts[39], pts[[37, 38, 41, 40]])
-        f = np.clip((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y, 0, None)
+        f = abs((np.mean([f_pts[0,1], f_pts[1,1]]) - np.mean([f_pts[2,1], f_pts[3,1]])) / norm_distance_y)
-        features["eye_r"] = self.eye_r.update(f)
+        features["eye_r"] = self.eye_r.update(f, now)
 
+        if full:
             a3, _ = self.align_points(pts[0], pts[16], [])
             a4, _ = self.align_points(pts[31], pts[35], [])
             norm_angle = np.mean(list(map(np.rad2deg, [a1, a2, a3, a4])))
 
             a, f_pts = self.align_points(pts[22], pts[26], pts[[22, 23, 24, 25, 26]])
-        features["eyebrow_steepness_l"] = self.eyebrow_steepness_l.update(-np.rad2deg(a) - norm_angle)
+            features["eyebrow_steepness_l"] = self.eyebrow_steepness_l.update(-np.rad2deg(a) - norm_angle, now)
-        f = (np.mean([pts[22, 1], pts[26, 1]]) - pts[27, 1]) / norm_distance_y
-        features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f)
             f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
-        features["eyebrow_quirk_l"] = self.eyebrow_quirk_l.update(f)
+            features["eyebrow_quirk_l"] = self.eyebrow_quirk_l.update(f, now)
 
             a, f_pts = self.align_points(pts[17], pts[21], pts[[17, 18, 19, 20, 21]])
-        features["eyebrow_steepness_r"] = self.eyebrow_steepness_r.update(np.rad2deg(a) - norm_angle)
+            features["eyebrow_steepness_r"] = self.eyebrow_steepness_r.update(np.rad2deg(a) - norm_angle, now)
-        f = (np.mean([pts[17, 1], pts[21, 1]]) - pts[27, 1]) / norm_distance_y
-        features["eyebrow_updown_r"] = self.eyebrow_updown_r.update(f)
             f = np.max(np.abs(np.array(f_pts[1:4]) - f_pts[0, 1])) / norm_distance_y
-        features["eyebrow_quirk_r"] = self.eyebrow_quirk_r.update(f)
+            features["eyebrow_quirk_r"] = self.eyebrow_quirk_r.update(f, now)
+        else:
+            features["eyebrow_steepness_l"] = 0.
+            features["eyebrow_steepness_r"] = 0.
+            features["eyebrow_quirk_l"] = 0.
+            features["eyebrow_quirk_r"] = 0.
+
+        f = (np.mean([pts[22, 1], pts[26, 1]]) - pts[27, 1]) / norm_distance_y
+        features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f, now)
+
+        f = (np.mean([pts[17, 1], pts[21, 1]]) - pts[27, 1]) / norm_distance_y
+        features["eyebrow_updown_r"] = self.eyebrow_updown_r.update(f, now)
 
         upper_mouth_line = np.mean([pts[49, 1], pts[50, 1], pts[51, 1]])
         center_line = np.mean([pts[50, 0], pts[60, 0], pts[27, 0], pts[30, 0], pts[64, 0], pts[55, 0]])
 
         f = (upper_mouth_line - pts[62, 1]) / norm_distance_y
-        features["mouth_corner_updown_l"] = self.mouth_corner_updown_l.update(f)
+        features["mouth_corner_updown_l"] = self.mouth_corner_updown_l.update(f, now)
+        if full:
             f = abs(center_line - pts[62, 0]) / norm_distance_x
-        features["mouth_corner_inout_l"] = self.mouth_corner_inout_l.update(f)
+            features["mouth_corner_inout_l"] = self.mouth_corner_inout_l.update(f, now)
+        else:
+            features["mouth_corner_inout_l"] = 0.
 
         f = (upper_mouth_line - pts[58, 1]) / norm_distance_y
-        features["mouth_corner_updown_r"] = self.mouth_corner_updown_r.update(f)
+        features["mouth_corner_updown_r"] = self.mouth_corner_updown_r.update(f, now)
+        if full:
             f = abs(center_line - pts[58, 0]) / norm_distance_x
-        features["mouth_corner_inout_r"] = self.mouth_corner_inout_r.update(f)
+            features["mouth_corner_inout_r"] = self.mouth_corner_inout_r.update(f, now)
+        else:
+            features["mouth_corner_inout_r"] = 0.
 
-        f = (np.mean([pts[59, 1], pts[60, 1], pts[61, 1]]) - np.mean([pts[65, 1], pts[64, 1], pts[63, 1]])) / norm_distance_y
+        f = abs(np.mean(pts[[59,60,61], 1], axis=0) - np.mean(pts[[63,64,65], 1], axis=0)) / norm_distance_y
-        features["mouth_open"] = self.mouth_open.update(f)
+        features["mouth_open"] = self.mouth_open.update(f, now)
 
         f = abs(pts[58, 0] - pts[62, 0]) / norm_distance_x
-        features["mouth_wide"] = self.mouth_wide.update(f)
+        features["mouth_wide"] = self.mouth_wide.update(f, now)
 
         return features
 
@@ -287,8 +328,10 @@ class FaceInfo():
         self.id = id
         self.frame_count = -1
         self.tracker = tracker
-        self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35,36,39,42,45]
+        self.contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
         self.face_3d = copy.copy(self.tracker.face_3d)
+        if self.tracker.model_type == -1:
+            self.contour_pts = [0,2,8,14,16,27,30,33]
         self.reset()
         self.alive = False
         self.coord = None
@@ -299,6 +342,9 @@ class FaceInfo():
         self.update_count_delta = 75.
         self.update_count_max = 7500.
 
+        if self.tracker.max_feature_updates > 0:
+            self.features = FeatureExtractor(self.tracker.max_feature_updates)
+
     def reset(self):
         self.alive = False
         self.conf = None
@@ -314,7 +360,8 @@ class FaceInfo():
         self.eye_blink = None
         self.bbox = None
         self.pnp_error = 0
-        self.features = FeatureExtractor()
+        if self.tracker.max_feature_updates < 1:
+            self.features = FeatureExtractor(0)
         self.current_features = {}
         self.contour = np.zeros((21,3))
         self.update_counts = np.zeros((66,2))
@@ -354,6 +401,7 @@ class FaceInfo():
         if self.conf < 0.4 or self.pnp_error > 300:
             return
 
+        if self.tracker.model_type != -1 and not self.tracker.static_model:
             max_runs = 1
             eligible = np.delete(np.arange(0, 66), [30])
             changed_any = False
@@ -425,70 +473,93 @@ class FaceInfo():
                 self.update_contour()
 
         self.pts_3d = self.normalize_pts3d(self.pts_3d)
+        if self.tracker.feature_level == 2:
             self.current_features = self.features.update(self.pts_3d[:, 0:2])
             self.eye_blink = []
             self.eye_blink.append(1 - min(max(0, -self.current_features["eye_r"]), 1))
             self.eye_blink.append(1 - min(max(0, -self.current_features["eye_l"]), 1))
+        elif self.tracker.feature_level == 1:
+            self.current_features = self.features.update(self.pts_3d[:, 0:2], False)
+            self.eye_blink = []
+            self.eye_blink.append(1 - min(max(0, -self.current_features["eye_r"]), 1))
+            self.eye_blink.append(1 - min(max(0, -self.current_features["eye_l"]), 1))
 
-
+def get_model_base_path(model_dir):
-class Tracker():
-    def __init__(self, width, height, model_type=3, threshold=0.6, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False):
-        options = onnxruntime.SessionOptions()
-        options.inter_op_num_threads = 1
-        options.intra_op_num_threads = max(max_threads,4)
-        options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
-        options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-        options.log_severity_level = 3
-        self.model_type = model_type
-        self.models = [
-            "mnv3_opt_very_fast.onnx",
-            "mnv3_opt_fast.onnx",
-            "mnv3_opt_medium.onnx",
-            "mnv3_opt_b.onnx"
-        ]
-        model = self.models[self.model_type]
     model_base_path = resolve(os.path.join("models"))
     if model_dir is None:
         if not os.path.exists(model_base_path):
             model_base_path = resolve(os.path.join("..", "models"))
     else:
         model_base_path = model_dir
+    return model_base_path
+
+class Tracker():
+    def __init__(self, width, height, model_type=3, detection_threshold=0.6, threshold=None, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, model_dir=None, no_gaze=False, use_retinaface=False, max_feature_updates=0, static_model=False, feature_level=2, try_hard=False):
+        options = onnxruntime.SessionOptions()
+        options.inter_op_num_threads = 1
+        options.intra_op_num_threads = min(max_threads,4)
+        options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
+        options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+        options.log_severity_level = 3
+        self.model_type = model_type
+        self.models = [
+            "lm_model0_opt.onnx",
+            "lm_model1_opt.onnx",
+            "lm_model2_opt.onnx",
+            "lm_model3_opt.onnx",
+            "lm_model4_opt.onnx"
+        ]
+        model = "lm_modelT_opt.onnx"
+        if model_type >= 0:
+            model = self.models[self.model_type]
+        if model_type == -2:
+            model = "lm_modelV_opt.onnx"
+        if model_type == -3:
+            model = "lm_modelU_opt.onnx"
+        model_base_path = get_model_base_path(model_dir)
+
+        if threshold is None:
+            threshold = 0.6
+            if model_type < 0:
+                threshold = 0.87
 
         self.retinaface = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=max(max_threads,4), top_k=max_faces, res=(640, 640))
         self.retinaface_scan = RetinaFaceDetector(model_path=os.path.join(model_base_path, "retinaface_640x640_opt.onnx"), json_path=os.path.join(model_base_path, "priorbox_640x640.json"), threads=2, top_k=max_faces, res=(640, 640))
         self.use_retinaface = use_retinaface
 
+        # OTR 1.9 and later requires specifying providers
+        # explicitly as an argument in InferenceSession()
+        providersList = onnxruntime.capi._pybind_state.get_available_providers()
+
         # Single face instance with multiple threads
-        self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options)
+        self.session = onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options, providers=providersList)
 
         # Multiple faces with single threads
         self.sessions = []
-        self.max_workers = min(max_threads, max_faces)
+        self.max_workers = max(min(max_threads, max_faces), 1)
         extra_threads = max_threads % self.max_workers
         for i in range(self.max_workers):
             options = onnxruntime.SessionOptions()
             options.inter_op_num_threads = 1
-            options.intra_op_num_threads = max_threads // self.max_workers
+            options.intra_op_num_threads = min(max(max_threads // self.max_workers, 4), 1)
             if options.intra_op_num_threads < 1:
                 options.intra_op_num_threads = 1
             elif i < extra_threads:
                 options.intra_op_num_threads += 1
             options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
             options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
-            self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options))
+            self.sessions.append(onnxruntime.InferenceSession(os.path.join(model_base_path, model), sess_options=options, providers=providersList))
         self.input_name = self.session.get_inputs()[0].name
 
         options = onnxruntime.SessionOptions()
         options.inter_op_num_threads = 1
-        options.intra_op_num_threads = max(max_threads,4)
+        options.intra_op_num_threads = 1
         options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
         options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         options.log_severity_level = 3
-        self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options)
+        self.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList)
-        options.intra_op_num_threads = 1
-        self.gaze_model_single = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options)
 
-        self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options)
+        self.detection = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_detection_opt.onnx"), sess_options=options, providers=providersList)
         self.faces = []
 
         # Image normalization constants
@@ -497,6 +568,13 @@ class Tracker():
         self.mean = self.mean / self.std
         self.std = self.std * 255.0
 
+        self.mean = - self.mean
+        self.std = 1.0 / self.std
+        self.mean_32 = np.tile(self.mean, [32, 32, 1])
+        self.std_32 = np.tile(self.std, [32, 32, 1])
+        self.mean_224 = np.tile(self.mean, [224, 224, 1])
+        self.std_224 = np.tile(self.std, [224, 224, 1])
+
         # PnP solving
         self.face_3d = np.array([
             [ 0.4551769692672  ,  0.300895790030204, -0.764429433974752],
@@ -580,6 +658,7 @@ class Tracker():
         self.width = width
         self.height = height
         self.threshold = threshold
+        self.detection_threshold = detection_threshold
         self.max_faces = max_faces
         self.max_threads = max_threads
         self.discard = 0
@@ -589,15 +668,44 @@ class Tracker():
         self.scan_every = scan_every
         self.bbox_growth = bbox_growth
         self.silent = silent
+        self.try_hard = try_hard
+
         self.res = 224.
+        self.mean_res = self.mean_224
+        self.std_res = self.std_224
+        if model_type < 0:
+            self.res = 56.
+            self.mean_res = np.tile(self.mean, [56, 56, 1])
+            self.std_res = np.tile(self.std, [56, 56, 1])
+        if model_type < -1:
+            self.res = 112.
+            self.mean_res = np.tile(self.mean, [112, 112, 1])
+            self.std_res = np.tile(self.std, [112, 112, 1])
         self.res_i = int(self.res)
+        self.out_res = 27.
+        if model_type < 0:
+            self.out_res = 6.
+        if model_type < -1:
+            self.out_res = 13.
+        self.out_res_i = int(self.out_res) + 1
+        self.logit_factor = 16.
+        if model_type < 0:
+            self.logit_factor = 8.
+        if model_type < -1:
+            self.logit_factor = 16.
+
         self.no_gaze = no_gaze
         self.debug_gaze = False
+        self.feature_level = feature_level
+        if model_type == -1:
+            self.feature_level = min(feature_level, 1)
+        self.max_feature_updates = max_feature_updates
+        self.static_model = static_model
         self.face_info = [FaceInfo(id, self) for id in range(max_faces)]
         self.fail_count = 0
 
     def detect_faces(self, frame):
-        im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR)[:,:,::-1] / self.std - self.mean
+        im = cv2.resize(frame, (224, 224), interpolation=cv2.INTER_LINEAR)[:,:,::-1] * self.std_224 + self.mean_224
         im = np.expand_dims(im, 0)
         im = np.transpose(im, (0,3,1,2))
         outputs, maxpool = self.detection.run([], {'input': im})
@@ -613,7 +721,7 @@ class Tracker():
             x *= 4
             y *= 4
             r *= 1.0
-            if c < self.threshold:
+            if c < self.detection_threshold:
                 break
             results.append((x - r, y - r, 2 * r, 2 * r * 1.0))
         results = np.array(results).astype(np.float32)
@@ -625,31 +733,41 @@ class Tracker():
     def landmarks(self, tensor, crop_info):
         crop_x1, crop_y1, scale_x, scale_y, _ = crop_info
         avg_conf = 0
-        lms = []
         res = self.res - 1
-        for i in range(0, 66):
+        c0, c1, c2 = 66, 132, 198
-            m = int(tensor[i].argmax())
+        if self.model_type == -1:
-            x = m // 28
+            c0, c1, c2 = 30, 60, 90
-            y = m % 28
+        t_main = tensor[0:c0].reshape((c0,self.out_res_i * self.out_res_i))
-            conf = float(tensor[i][x,y])
+        t_m = t_main.argmax(1)
-            avg_conf = avg_conf + conf
+        indices = np.expand_dims(t_m, 1)
-            off_x = res * ((1. * logit(tensor[66 + i][x, y])) - 0.0)
+        t_conf = np.take_along_axis(t_main, indices, 1).reshape((c0,))
-            off_y = res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0)
+        t_off_x = np.take_along_axis(tensor[c0:c1].reshape((c0,self.out_res_i * self.out_res_i)), indices, 1).reshape((c0,))
-            off_x = math.floor(off_x + 0.5)
+        t_off_y = np.take_along_axis(tensor[c1:c2].reshape((c0,self.out_res_i * self.out_res_i)), indices, 1).reshape((c0,))
-            off_y = math.floor(off_y + 0.5)
+        t_off_x = res * logit_arr(t_off_x, self.logit_factor)
-            lm_x = crop_y1 + scale_y * (res * (float(x) / 27.) + off_x)
+        t_off_y = res * logit_arr(t_off_y, self.logit_factor)
-            lm_y = crop_x1 + scale_x * (res * (float(y) / 27.) + off_y)
+        t_x = crop_y1 + scale_y * (res * np.floor(t_m / self.out_res_i) / self.out_res + t_off_x)
-            lms.append((lm_x,lm_y,conf))
+        t_y = crop_x1 + scale_x * (res * np.floor(np.mod(t_m, self.out_res_i)) / self.out_res + t_off_y)
-        avg_conf = avg_conf / 66.
+        avg_conf = np.average(t_conf)
+        lms = np.stack([t_x, t_y, t_conf], 1)
+        lms[np.isnan(lms).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
+        if self.model_type == -1:
+            lms = lms[[0,0,1,1,1,2,2,2,3,3,3,4,4,4,5,5,6,7,7,8,8,9,10,10,11,11,12,21,21,21,22,23,23,23,23,23,13,14,14,15,16,16,17,18,18,19,20,20,24,25,25,25,26,26,27,27,27,24,24,28,28,28,26,29,29,29]]
+            #lms[[1,3,4,6,7,9,10,12,13,15,18,20,23,25,38,40,44,46]] += lms[[2,2,5,5,8,8,11,11,14,16,19,21,24,26,39,39,45,45]]
+            #lms[[3,4,6,7,9,10,12,13]] += lms[[5,5,8,8,11,11,14,14]]
+            #lms[[1,15,18,20,23,25,38,40,44,46]] /= 2.0
+            #lms[[3,4,6,7,9,10,12,13]] /= 3.0
+            part_avg = np.mean(np.partition(lms[:,2],3)[0:3])
+            if part_avg < 0.65:
+                avg_conf = part_avg
         return (avg_conf, np.array(lms))
 
     def estimate_depth(self, face_info):
-        lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float)), 0)
+        lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float32)), 0)
 
         image_pts = np.array(lms)[face_info.contour_pts, 0:2]
 
         success = False
-        if not face_info.rotation is None:
+        if face_info.rotation is not None:
             success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, self.camera, self.dist_coeffs, useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
         else:
             rvec = np.array([0, 0, 0], np.float32)
@@ -665,54 +783,65 @@ class Tracker():
             face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
             return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms
         else:
-            face_info.rotation = np.transpose(face_info.rotation)
+            # OpenCV returns (3,1); flatten so [0]/[1]/[2] are scalars for UDP packing
-            face_info.translation = np.transpose(face_info.translation)
+            # and 1D .dot() math does not broadcast to (1,3).
+            face_info.rotation = np.asarray(face_info.rotation, dtype=np.float32).reshape(3)
+            face_info.translation = np.asarray(face_info.translation, dtype=np.float32).reshape(3)
 
         rmat, _ = cv2.Rodrigues(rotation)
         inverse_rotation = np.linalg.inv(rmat)
-        pnp_error = 0.0
+        t_reference = face_info.face_3d.dot(rmat.transpose())
-        for i, pt in enumerate(face_info.face_3d):
+        t_reference = t_reference + face_info.translation
-            if i == 68:
+        t_reference = t_reference.dot(self.camera.transpose())
+        t_depth = t_reference[:, 2]
+        t_depth[t_depth == 0] = 0.000001
+        t_depth_e = np.expand_dims(t_depth[:],1)
+        t_reference = t_reference[:] / t_depth_e
+        pts_3d[0:66] = np.stack([lms[0:66,0], lms[0:66,1], np.ones((66,))], 1) * t_depth_e[0:66]
+        pts_3d[0:66] = (pts_3d[0:66].dot(self.inverse_camera.transpose()) - face_info.translation).dot(inverse_rotation.transpose())
+        pnp_error = np.power(lms[0:17,0:2] - t_reference[0:17,0:2], 2).sum()
+        pnp_error += np.power(lms[30,0:2] - t_reference[30,0:2], 2).sum()
+        if np.isnan(pnp_error):
+            pnp_error = 9999999.
+        for i, pt in enumerate(face_info.face_3d[66:70]):
+            if i == 2:
                 # Right eyeball
                 # Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied
                 eye_center = (pts_3d[36] + pts_3d[39]) / 2.0
                 d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39])
                 depth = 0.385 * d_corner
                 pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
-                pts_3d[i] = pt_3d
+                pts_3d[68] = pt_3d
                 continue
-            if i == 69:
+            if i == 3:
                 # Left eyeball
                 eye_center = (pts_3d[42] + pts_3d[45]) / 2.0
                 d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45])
                 depth = 0.385 * d_corner
                 pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
-                pts_3d[i] = pt_3d
+                pts_3d[69] = pt_3d
                 continue
-            if i == 66:
+            if i == 0:
-                d1 = np.linalg.norm(lms[i,0:2] - lms[36,0:2])
+                d1 = np.linalg.norm(lms[66,0:2] - lms[36,0:2])
-                d2 = np.linalg.norm(lms[i,0:2] - lms[39,0:2])
+                d2 = np.linalg.norm(lms[66,0:2] - lms[39,0:2])
                 d = d1 + d2
                 pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d
-            if i == 67:
+            if i == 1:
-                d1 = np.linalg.norm(lms[i,0:2] - lms[42,0:2])
+                d1 = np.linalg.norm(lms[67,0:2] - lms[42,0:2])
-                d2 = np.linalg.norm(lms[i,0:2] - lms[45,0:2])
+                d2 = np.linalg.norm(lms[67,0:2] - lms[45,0:2])
                 d = d1 + d2
                 pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
+            if i < 2:
                 reference = rmat.dot(pt)
                 reference = reference + face_info.translation
                 reference = self.camera.dot(reference)
-            depth = reference[2]
+                depth = float(reference[2])
-            if i < 17 or i == 30:
+                pt_3d = np.array([lms[66+i][0] * depth, lms[66+i][1] * depth, depth], np.float32)
-                reference = reference / depth
-                e1 = lms[i][0] - reference[0]
-                e2 = lms[i][1] - reference[1]
-                pnp_error += e1*e1 + e2*e2
-            pt_3d = np.array([lms[i][0] * depth, lms[i][1] * depth, depth], np.float32)
                 pt_3d = self.inverse_camera.dot(pt_3d)
                 pt_3d = pt_3d - face_info.translation
                 pt_3d = inverse_rotation.dot(pt_3d)
-            pts_3d[i,:] = pt_3d[:]
+                pts_3d[66+i,:] = pt_3d[:]
+        pts_3d[np.isnan(pts_3d).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
 
         pnp_error = np.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
         if pnp_error > 300:
@@ -735,9 +864,7 @@ class Tracker():
     def preprocess(self, im, crop):
         x1, y1, x2, y2 = crop
         im = np.float32(im[y1:y2, x1:x2,::-1]) # Crop and BGR to RGB
-        im = cv2.resize(im, (self.res_i, self.res_i), interpolation=cv2.INTER_LINEAR) / self.std - self.mean
+        im = cv2.resize(im, (self.res_i, self.res_i), interpolation=cv2.INTER_LINEAR) * self.std_res + self.mean_res
-        #im = cv2.resize(im, (224, 224), interpolation=cv2.INTER_LINEAR) / 255.0
-        #im = (im - mean) / std
         im = np.expand_dims(im, 0)
         im = np.transpose(im, (0,3,1,2))
         return im
@@ -778,7 +905,7 @@ class Tracker():
                 full_frame[0:32, 0:32] = im
             else:
                 full_frame[0:32, 32:64] = im
-        im = im.astype(np.float32)[:,:,::-1] / self.std - self.mean
+        im = im.astype(np.float32)[:,:,::-1] * self.std_32 + self.mean_32
         im = np.expand_dims(im, 0)
         im = np.transpose(im, (0,3,2,1))
         return im, x1, y1, scale, reference, a
@@ -795,11 +922,11 @@ class Tracker():
         x1, y1 = clamp_to_im(center - radius, h, w)
         x2, y2 = clamp_to_im(center + radius + 1, h, w)
         offset = np.array((x1, y1))
-        lms = (lms[:, 0:2] - offset).astype(np.int)
+        lms = (lms[:, 0:2] - offset).astype(int)
         frame = frame[y1:y2, x1:x2]
         return frame, lms, offset
 
-    def get_eye_state(self, frame, lms, single=False):
+    def get_eye_state(self, frame, lms):
         if self.no_gaze:
             return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
         lms = np.array(lms)
@@ -815,9 +942,6 @@ class Tracker():
             return [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
         both_eyes = np.concatenate((right_eye, left_eye))
         results = None
-        if single:
-            results = self.gaze_model_single.run([], {self.input_name: both_eyes})
-        else:
         results = self.gaze_model.run([], {self.input_name: both_eyes})
         open = [0, 0]
         open[0] = 1#results[1][0].argmax()
@@ -862,9 +986,15 @@ class Tracker():
             eye_y = eye_y + offset[1]
             eye_state.append([open[i], eye_y, eye_x, conf])
 
+        eye_state = np.array(eye_state)
+        eye_state[np.isnan(eye_state).any(axis=1)] = np.array([1.,0.,0.,0.], dtype=np.float32)
         return eye_state
 
     def assign_face_info(self, results):
+        if self.max_faces == 1 and len(results) == 1:
+            conf, (lms, eye_state), conf_adjust = results[0]
+            self.face_info[0].update((conf - conf_adjust, (lms, eye_state)), np.array(lms)[:, 0:2].mean(0), self.frame_count)
+            return
         result_coords = []
         adjusted_results = []
         for conf, (lms, eye_state), conf_adjust in results:
@@ -919,11 +1049,13 @@ class Tracker():
         self.wait_count += 1
         if self.detected == 0:
             start_fd = time.perf_counter()
-            if self.use_retinaface > 0:
+            if self.use_retinaface > 0 or self.try_hard:
                 retinaface_detections = self.retinaface.detect_retina(frame)
                 new_faces.extend(retinaface_detections)
-            else:
+            if self.use_retinaface == 0 or self.try_hard:
                 new_faces.extend(self.detect_faces(frame))
+            if self.try_hard:
+                new_faces.extend([(0, 0, self.width, self.height)])
             duration_fd = 1000 * (time.perf_counter() - start_fd)
             self.wait_count = 0
         elif self.detected < self.max_faces:
@@ -977,7 +1109,10 @@ class Tracker():
             output = self.session.run([], {self.input_name: crops[0]})[0]
             conf, lms = self.landmarks(output[0], crop_info[0])
             if conf > self.threshold:
+                try:
                     eye_state = self.get_eye_state(frame, lms)
+                except:
+                    eye_state = [(1.0, 0.0, 0.0, 0.0), (1.0, 0.0, 0.0, 0.0)]
                 outputs[crop_info[0]] = (conf, (lms, eye_state), 0)
         else:
             started = 0
@@ -1039,8 +1174,8 @@ class Tracker():
                 face_info.success, face_info.quaternion, face_info.euler, face_info.pnp_error, face_info.pts_3d, face_info.lms = self.estimate_depth(face_info)
                 face_info.adjust_3d()
                 lms = face_info.lms[:, 0:2]
-                x1, y1 = tuple(lms.min(0))
+                x1, y1 = tuple(lms[0:66].min(0))
-                x2, y2 = tuple(lms.max(0))
+                x2, y2 = tuple(lms[0:66].max(0))
                 bbox = (y1, x1, y2 - y1, x2 - x1)
                 face_info.bbox = bbox
                 detected.append(bbox)
@@ -1066,10 +1201,12 @@ class Tracker():
                         h += 2 * h * self.bbox_growth
                         faces.append((x,y,w,h))
                     self.faces = faces
+        self.faces = [x for x in self.faces if not np.isnan(np.array(x)).any()]
+        self.detected = len(self.faces)
 
         duration = (time.perf_counter() - start) * 1000
         if not self.silent:
-            print(f"Took {duration:.2f}ms (detect: {duration_fd:.2f}ms, crop: {duration_pp:.2f}, track: {duration_model:.2f}ms, 3D points: {duration_pnp:.2f}ms)")
+            print(f"Took {duration:.2f}ms (detect: {duration_fd:.2f}ms, crop: {duration_pp:.2f}ms, track: {duration_model:.2f}ms, 3D points: {duration_pnp:.2f}ms)")
 
         results = sorted(results, key=lambda x: x.id)