diff --git a/.github/actions/upload-dated-artifact/action.yaml b/.github/actions/upload-dated-artifact/action.yaml new file mode 100644 index 0000000..7fc3fbe --- /dev/null +++ b/.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 diff --git a/.github/workflows/create-binaries.yaml b/.github/workflows/create-binaries.yaml new file mode 100644 index 0000000..26572bc --- /dev/null +++ b/.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: + - 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(Experimental!)")] + public bool useCameraPerfectSync = false; //[Tooltip("When set to true, expression and audio data will be processed in FixedUpdate, otherwise it will be processed in Update.")] //public bool fixedUpdate = true; [Tooltip("When enabled, the avatar will blink automatically. Otherwise, blinks will be applied according to the face tracking's blink detection, allowing it to wink. Even for tracked eye blinks, the eye blink setting of the current expression is used.")] @@ -35,8 +57,19 @@ public class OpenSeeVRMDriver : MonoBehaviour { [Tooltip("This is the blink smoothing factor for camera based blink tracking, with 0 being no smoothing and 1 being a fixed blink state.")] [Range(0, 1)] public float blinkSmoothing = 0.75f; - [Tooltip("When enabled, the blink state for both eyes is linked together.")] + [Tooltip("With tracked eye blinking, the eyes will close when looking down. Enable this to compensate this by modifying thresholds.")] + public bool lookDownCompensation = false; + [Tooltip("This is an angle value that can be used to adjust the angle from which a face is considered to be looking downwards.")] + [Range(-10, 20)] + public float lookDownAdjustment = 0f; + [Tooltip("When enabled, auto blinking will be enabled while looking down.")] + public bool autoBlinkLookingDown = false; + [Tooltip("When enabled, the blink state for both eyes is permanently linked together.")] public bool linkBlinks = true; + [Tooltip("When enabled, it becomes possible to wink even with link blinks enabled.")] + public bool allowWinking = false; + [Tooltip("If allowWinking is enabled, if the eye blink values are within this distance, the blink is linked.")] + public float smartWinkThreshold = 0.85f; [Tooltip("When enabled, the avatar's eye will move according to the face tracker's gaze tracking.")] public bool gazeTracking = true; [Tooltip("This is the right eye bone. When either eye bone is not set, the VRM look direction blendshapes are used instead.")] @@ -54,8 +87,6 @@ public class OpenSeeVRMDriver : MonoBehaviour { [Tooltip("This is the sensitivity of the eyebrow tracking, with 0 meaning no eyebrow movement.")] [Range(0, 2)] public float eyebrowSensitivity = 1.0f; - [Tooltip("If enabled, the eye will be animated according to camera based gaze tracking.")] - public bool trackGaze = true; [Tooltip("This is the gaze smoothing factor, with 0 being no smoothing and 1 being a fixed gaze.")] [Range(0, 1)] public float gazeSmoothing = 0.6f; @@ -91,6 +122,12 @@ public class OpenSeeVRMDriver : MonoBehaviour { public bool hybridLipSync = false; [Tooltip("When enabled, viseme size will not depend on the OVR Lip Sync weight.")] public bool maximizeLipSync = false; + [Tooltip("When enabled, mouth blendshapes are normalized if they add up to a value above 1.")] + public bool visemeNormalization = true; + [Tooltip("When enabled and the animator of the avatar has a float parameter called JawMovement, this parameter will be set to values from 0 to 1, corresponding to a closed to open mouth. This can be used for animating jaw bones.")] + public bool animateJawBone = false; + [Tooltip("This should be set to the included mecanim animation, which sets the jaw bone to closed on the first frame and to open on the second frame.")] + public AnimationClip jawBoneAnimation; [Tooltip("This is the mouth tracking smoothing factor, with 0 being no smoothing and 1 being a fixed mouth.")] [Range(0, 1)] public float mouthSmoothing = 0.6f; @@ -105,8 +142,10 @@ public class OpenSeeVRMDriver : MonoBehaviour { public float mouthSquelch = 0.01f; [Tooltip("With this, the squelch threshold above can be disabled.")] public bool mouthUseSquelch = false; + #if WINDOWS_BUILD [Tooltip("This allows you to select the OVRLipSync provider.")] public OVRLipSync.ContextProviders provider = OVRLipSync.ContextProviders.Enhanced; + #endif [Tooltip("When enabled, audio will be read from the given audio source, otherwise it will be read from the given mic.")] public bool useCanned = false; [Tooltip("This is the audio source for canned audio.")] @@ -132,7 +171,13 @@ public class OpenSeeVRMDriver : MonoBehaviour { [Tooltip("This shows the current audio volume.")] public float audioVolume = 0f; + [HideInInspector] + public OpenSee.OpenSee.OpenSeeData openSeeData { get; private set; } = null; + + #if WINDOWS_BUILD private OVRLipSync.Frame visemeData = new OVRLipSync.Frame(); + #endif + private float fakeTimeTime = 0f; private float volume; static private bool inited = false; private int lastSmoothing = 50; @@ -148,26 +193,31 @@ public class OpenSeeVRMDriver : MonoBehaviour { private BlendShapeKey[] visemePresetMap; private Dictionary expressionMap; private OpenSeeVRMExpression currentExpression = null; - private OpenSee.OpenSee.OpenSeeData openSeeData = null; private OpenSeeVRMExpression lastExpression = null; + private bool haveFullVisemeSet = false; + private BlendShapeKey[] fullVisemePresets; + #if WINDOWS_BUILD + private Dictionary ovrMap; + #endif + private float turnLeftBoundaryAngle = -30f; private float turnRightBoundaryAngle = 20f; + private float turnDownBoundaryAngle = 205f; + private float wasLookingDown = 0f; private double lastGaze = 0f; private float lastLookUpDown = 0f; private float lastLookLeftRight = 0f; private float currentLookUpDown = 0f; private float currentLookLeftRight = 0f; - private int interpolationCount = 0; - private int interpolationState = 0; + private TimeInterpolate gazeInterpolate; private float[] lastVisemeValues; private double lastMouth = 0f; private float[] lastMouthStates; private float[] currentMouthStates; - private int mouthInterpolationCount = 0; - private int mouthInterpolationState = 0; + private TimeInterpolate mouthInterpolate; private double startedSilVisemes = -10; private double silVisemeHybridThreshold = 0.3; @@ -178,8 +228,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { private float lastBlinkRight = 0f; private float currentBlinkLeft = 0f; private float currentBlinkRight = 0f; - private int blinkInterpolationCount = 0; - private int blinkInterpolationState = 0; + private TimeInterpolate blinkInterpolate; private bool overridden = false; private OpenSeeVRMExpression toggledExpression = null; @@ -189,6 +238,15 @@ public class OpenSeeVRMDriver : MonoBehaviour { private float currentExpressionEyebrowWeight; private float currentExpressionVisemeFactor; + private OpenSeeBlendShapeProxy proxy = null; + private Animator animator; + private bool haveJawParameter; + private Transform jawBone; + private Quaternion jawRotation = Quaternion.identity; + private HumanBodyBones[] humanBodyBones; + private Transform[] humanBodyBoneTransforms; + private Quaternion[] humanBodyBoneRotations; + private VRMBlendShapeProxy lastAvatar = null; private int eyebrowIsMoving = 0; private double lastBrows = 0f; @@ -201,39 +259,340 @@ public class OpenSeeVRMDriver : MonoBehaviour { private float lastBrowUpDown = 0f; private float[] lastBrowStates; private float[] currentBrowStates; - private int browInterpolationCount = 0; - private int browInterpolationState = 0; + private TimeInterpolate browInterpolate; private BlendShapeKey[] browClips; + + private bool trackMouth = false; + private double lastPerfectSync = -1; + + private float lastAudioTime = -1f; + private class TimeInterpolate { + private float interpolateT = 0f; + private float updateT = 0f; + private double lastT = 0f; + private double currentT = 0f; + private float clamp = 1f; + private int gotData = 0; + + public TimeInterpolate() {} + public TimeInterpolate(float clamp) { + this.clamp = clamp; + } + + public void UpdateTime(double nowT) { + updateT = Time.time; + lastT = currentT; + currentT = nowT; + if (gotData < 2) + gotData++; + } + + public float Interpolate() { + interpolateT = Time.time; + if (interpolateT == updateT || gotData < 2) + return 0f; + return Mathf.Min((interpolateT - updateT) / (float)(currentT - lastT), clamp); + } + } + + // This class is used to ensure max() based instead of add() based blendshape accumulation and to allow setting animator parameters from blendshape values + private class OpenSeeBlendShapeProxy { + private HashSet browsBlendShapes = new HashSet() { "BROWS UP", "BROWS DOWN", "BROWINNERUP", "BROWDOWNLEFT", "BROWDOWNRIGHT", "BROWOUTERUPLEFT", "BROWOUTERUPRIGHT", "NOSESNEERLEFT", "NOSESNEERRIGHT" }; + private HashSet eyesBlendShapes = new HashSet() { "BLINK", "BLINK_L", "BLINK_R", "EYELOOKUPLEFT", "EYELOOKUPRIGHT", "EYELOOKDOWNLEFT", "EYELOOKDOWNRIGHT", "EYELOOKINLEFT", "EYELOOKINRIGHT", "EYELOOKOUTLEFT", "EYELOOKOUTRIGHT", "EYEBLINKLEFT", "EYEBLINKRIGHT", "EYESQUINTRIGHT", "EYESQUINTLEFT", "EYEWIDELEFT", "EYEWIDERIGHT", "NOSESNEERLEFT", "NOSESNEERRIGHT", "CHEEKSQUINTLEFT", "CHEEKSQUINTRIGHT" }; + private HashSet mouthBlendShapes = new HashSet() { "A", "I", "U", "E", "O", "SIL", "CH", "DD", "FF", "KK", "NN", "PP", "RR", "SS", "TH", "JAWOPEN", "JAWFORWARD", "JAWLEFT", "JAWRIGHT", "MOUTHFUNNEL", "MOUTHPUCKER", "MOUTHLEFT", "MOUTHRIGHT", "MOUTHROLLUPPER", "MOUTHROLLLOWER", "MOUTHSHRUGUPPER", "MOUTHSHRUGLOWER", "MOUTHCLOSE", "MOUTHSMILELEFT", "MOUTHSMILERIGHT", "MOUTHFROWNLEFT", "MOUTHFROWNRIGHT", "MOUTHDIMPLELEFT", "MOUTHDIMPLERIGHT", "MOUTHUPPERUPLEFT", "MOUTHUPPERUPRIGHT", "MOUTHLOWERDOWNLEFT", "MOUTHLOWERDOWNRIGHT", "MOUTHPRESSLEFT", "MOUTHPRESSRIGHT", "MOUTHSTRETCHLEFT", "MOUTHSTRETCHRIGHT", "TONGUEOUT", "CHEEKSQUINTLEFT", "CHEEKSQUINTRIGHT" }; + private bool clearPSBrows = false; + private bool clearPSEyes = false; + private bool clearPSMouth = false; + private Dictionary clearKeys = new Dictionary(); + + private bool perfectSync = false; + private HashSet visemeBlendShapes = new HashSet() { "A", "I", "U", "E", "O", "SIL", "CH", "DD", "FF", "KK", "NN", "PP", "RR", "SS", "TH" }; + private string[] perfectSyncNames = new string[] { "BROWINNERUP", "BROWDOWNLEFT", "BROWDOWNRIGHT", "BROWOUTERUPLEFT", "BROWOUTERUPRIGHT", "EYELOOKUPLEFT", "EYELOOKUPRIGHT", "EYELOOKDOWNLEFT", "EYELOOKDOWNRIGHT", "EYELOOKINLEFT", "EYELOOKINRIGHT", "EYELOOKOUTLEFT", "EYELOOKOUTRIGHT", "EYEBLINKLEFT", "EYEBLINKRIGHT", "EYESQUINTRIGHT", "EYESQUINTLEFT", "EYEWIDELEFT", "EYEWIDERIGHT", "CHEEKPUFF", "CHEEKSQUINTLEFT", "CHEEKSQUINTRIGHT", "NOSESNEERLEFT", "NOSESNEERRIGHT", "JAWOPEN", "JAWFORWARD", "JAWLEFT", "JAWRIGHT", "MOUTHFUNNEL", "MOUTHPUCKER", "MOUTHLEFT", "MOUTHRIGHT", "MOUTHROLLUPPER", "MOUTHROLLLOWER", "MOUTHSHRUGUPPER", "MOUTHSHRUGLOWER", "MOUTHCLOSE", "MOUTHSMILELEFT", "MOUTHSMILERIGHT", "MOUTHFROWNLEFT", "MOUTHFROWNRIGHT", "MOUTHDIMPLELEFT", "MOUTHDIMPLERIGHT", "MOUTHUPPERUPLEFT", "MOUTHUPPERUPRIGHT", "MOUTHLOWERDOWNLEFT", "MOUTHLOWERDOWNRIGHT", "MOUTHPRESSLEFT", "MOUTHPRESSRIGHT", "MOUTHSTRETCHLEFT", "MOUTHSTRETCHRIGHT", "TONGUEOUT" }; + private InterpolatedMap perfectSyncMap = new InterpolatedMap(); + private Dictionary clipMap = new Dictionary(); + + private VRMBlendShapeProxy proxy = null; + private Animator animator = null; + private RuntimeAnimatorController animatorController = null; + private Dictionary clearMap = new Dictionary(); + private Dictionary values = new Dictionary(); + private Dictionary clipNames = new Dictionary(); + private Dictionary> parameters = new Dictionary>(); + private bool interpolatedPerfectSync = false; + private float defaultWeight = 1f; + private OpenSeeVRMDriver vrmDriver = null; + + public OpenSeeBlendShapeProxy(OpenSeeVRMDriver vrmDriver) { + this.vrmDriver = vrmDriver; + } + + void InterpolatePerfectSync() { + if (!HasPerfectSync() || interpolatedPerfectSync) + return; + foreach (var name in perfectSyncNames) { + if (!perfectSyncMap.Check(name)) + continue; + float weight = perfectSyncMap.Get(name); + AccumulateValue(name, weight, 1f); + } + interpolatedPerfectSync = true; + } + + public void ClearPerfectSync() { + perfectSyncMap.Clear(); + } + + public void SetPerfectSync(string name, float weight, double nowT, float factor) { + perfectSyncMap.Store(name.ToUpper(), Mathf.Clamp(weight * factor, 0f, 1f), nowT); + } + + public void SetPerfectSync(string name, float weight, double nowT) { + SetPerfectSync(name, weight, nowT, defaultWeight); + } + + public bool HasPerfectSync() { + if (proxy == null || animator == null) + return false; + return perfectSync; + } + + public void SetWeight(float v) { + defaultWeight = v; + } + + public void DisableFaceParts(bool brows, bool eyes, bool mouth) { + clearPSBrows = brows; + clearPSEyes = eyes; + clearPSMouth = mouth; + } + + private void SetFloat(BlendShapeKey key, float weight) { + SetFloat(key, weight, defaultWeight); + } + + private void SetFloat(BlendShapeKey key, float weight, float factor) { + if (!clipNames.ContainsKey(key)) + return; + string name = clipNames[key]; + weight *= factor; + if (animator != null && parameters.ContainsKey(name)) { + var parameter = parameters[name]; + if (parameter.Item2 == AnimatorControllerParameterType.Float) + animator.SetFloat(parameter.Item1, weight); + if (parameter.Item2 == AnimatorControllerParameterType.Bool) + animator.SetBool(parameter.Item1, weight > 0.5f); + } + } + + private void CheckAnimatorController() { + if (animator == null) + return; + if (animatorController != animator.runtimeAnimatorController) { + animatorController = animator.runtimeAnimatorController; + parameters.Clear(); + foreach (var parameter in animator.parameters) { + string key = parameter.name.ToUpper(); + if (!parameters.ContainsKey(key) && (parameter.type == AnimatorControllerParameterType.Float || parameter.type == AnimatorControllerParameterType.Bool)) + parameters.Add(key, new Tuple(parameter.name, parameter.type)); + else + parameters[key] = new Tuple(parameter.name, parameter.type); + } + } + } + + public void Clear() { + CheckAnimatorController(); + interpolatedPerfectSync = false; + if (proxy != null) { + foreach (var pair in proxy.GetValues()) { + SetFloat(pair.Key, 0f); + } + foreach (var pair in values) { + SetFloat(pair.Key, 0f); + } + proxy.SetValues(clearMap); + } + values.Clear(); + } + + void NormalizeVisemes() { + float total = 0f; + foreach (var pair in values) { + string name = pair.Key.Name.ToUpper(); + if (visemeBlendShapes.Contains(name)) + total += pair.Value; + } + if (total > 1f) { + var keys = new List(values.Keys); + foreach (var key in keys) { + string name = key.Name.ToUpper(); + if (visemeBlendShapes.Contains(name)) + values[key] = values[key] / total; + } + } + } + + public void Apply() { + if (proxy == null) + return; + CheckAnimatorController(); + if (perfectSync) + InterpolatePerfectSync(); + if (vrmDriver != null && vrmDriver.visemeNormalization) + NormalizeVisemes(); + if (clearPSBrows || clearPSEyes || clearPSMouth) { + clearKeys.Clear(); + foreach (var pair in proxy.GetValues()) { + string name = pair.Key.Name.ToUpper(); + if (clearPSBrows && browsBlendShapes.Contains(name)) { + clearKeys[pair.Key] = 0f; + values[pair.Key] = 0f; + } else if (clearPSEyes && eyesBlendShapes.Contains(name)) { + clearKeys[pair.Key] = 0f; + values[pair.Key] = 0f; + } else if (clearPSMouth && mouthBlendShapes.Contains(name)) { + clearKeys[pair.Key] = 0f; + values[pair.Key] = 0f; + } + } + clearPSBrows = false; + clearPSEyes = false; + clearPSMouth = false; + } + proxy.SetValues(values); + foreach (var pair in values) { + SetFloat(pair.Key, pair.Value, 1f); + } + proxy.Apply(); + } + + public void AccumulateValue(BlendShapeKey key, float weight, float factor) { + if (!values.ContainsKey(key)) + values.Add(key, weight * factor); + else + values[key] = Mathf.Max(values[key], weight * factor); + } + + public void AccumulateValue(BlendShapeKey key, float weight) { + AccumulateValue(key, weight, defaultWeight); + } + + public void AccumulateValue(string key, float weight, float factor) { + if (!clipMap.ContainsKey(key.ToUpper())) + return; + AccumulateValue(clipMap[key.ToUpper()], weight, factor); + } + + public void AccumulateValue(string key, float weight) { + AccumulateValue(key, weight, defaultWeight); + } + + public void UpdateAvatar(VRMBlendShapeProxy vrmBlendShapeProxy, Animator animator) { + proxy = vrmBlendShapeProxy; + this.animator = animator; + + perfectSync = false; + if (proxy == null) + return; + HashSet perfectSyncSet = new HashSet(perfectSyncNames); + clipMap.Clear(); + clipNames.Clear(); + clearMap.Clear(); + perfectSyncMap.Clear(); + //perfectSyncMap.SetSmoothing(0.5f); + foreach (BlendShapeClip clip in vrmBlendShapeProxy.BlendShapeAvatar.Clips) { + if (clip.Preset == BlendShapePreset.Unknown && clip.BlendShapeName != null) { + string name = clip.BlendShapeName.ToUpper(); + clipMap.Add(name, BlendShapeKey.CreateUnknown(clip.BlendShapeName)); + clipNames.Add(BlendShapeKey.CreateUnknown(clip.BlendShapeName), name); + clearMap.Add(BlendShapeKey.CreateUnknown(clip.BlendShapeName), 0f); + if (perfectSyncSet.Contains(name)) { + perfectSyncSet.Remove(name); + } + } else if (clip.Preset != BlendShapePreset.Unknown) { + string name = clip.BlendShapeName.ToUpper(); + clipNames.Add(BlendShapeKey.CreateFromPreset(clip.Preset), name); + clearMap.Add(BlendShapeKey.CreateFromPreset(clip.Preset), 0f); + } + } + if (perfectSyncSet.Count < 1) + perfectSync = true; + } + } + + #if WINDOWS_BUILD private Dictionary catsData = null; + #endif void InitCatsData() { + #if WINDOWS_BUILD catsData = new Dictionary(); - // This is similar to what the Blender CATS plugin does, but with A, I, U, E, O - catsData.Add(OVRLipSync.Viseme.sil, new float[]{0f, 0f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.aa, new float[]{0.9998f, 0f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.CH, new float[]{0f, 0.9996f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.DD, new float[]{0.3f, 0.7f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.E, new float[]{0f, 0f, 0f, 0.9997f, 0f}); - catsData.Add(OVRLipSync.Viseme.FF, new float[]{0.2f, 0.4f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.ih, new float[]{0.5f, 0.2f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.kk, new float[]{0.7f, 0.4f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.nn, new float[]{0.2f, 0.7f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.oh, new float[]{0f, 0f, 0f, 0f, 0.9999f}); - catsData.Add(OVRLipSync.Viseme.ou, new float[]{0f, 0f, 0.9995f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.PP, new float[]{0.4f, 0f, 0f, 0f, 0.4f}); - catsData.Add(OVRLipSync.Viseme.RR, new float[]{0f, 0.5f, 0f, 0f, 0.3f}); - catsData.Add(OVRLipSync.Viseme.SS, new float[]{0f, 0.8f, 0f, 0f, 0f}); - catsData.Add(OVRLipSync.Viseme.TH, new float[]{0.4f, 0f, 0f, 0f, 0.15f}); - visemePresetMap = new BlendShapeKey[5] { + // This is similar to what the Blender CATS plugin does, but with A, I, U, E, O, JawOpen + catsData.Add(OVRLipSync.Viseme.sil, new float[]{0f, 0f, 0f, 0f, 0f, 0f}); + catsData.Add(OVRLipSync.Viseme.aa, new float[]{0.9998f, 0f, 0f, 0f, 0f, 1f}); + catsData.Add(OVRLipSync.Viseme.CH, new float[]{0f, 0.9996f, 0f, 0f, 0f, 1f}); + catsData.Add(OVRLipSync.Viseme.DD, new float[]{0.3f, 0.7f, 0f, 0f, 0f, 0.7f}); + catsData.Add(OVRLipSync.Viseme.E, new float[]{0f, 0f, 0f, 0.9997f, 0f, 0.6f}); + catsData.Add(OVRLipSync.Viseme.FF, new float[]{0.2f, 0.4f, 0f, 0f, 0f, 0.1f}); + catsData.Add(OVRLipSync.Viseme.ih, new float[]{0.5f, 0.2f, 0f, 0f, 0f, 0.5f}); + catsData.Add(OVRLipSync.Viseme.kk, new float[]{0.7f, 0.4f, 0f, 0f, 0f, 0.0f}); + catsData.Add(OVRLipSync.Viseme.nn, new float[]{0.2f, 0.7f, 0f, 0f, 0f, 0.1f}); + catsData.Add(OVRLipSync.Viseme.oh, new float[]{0f, 0f, 0f, 0f, 0.9999f, 1f}); + catsData.Add(OVRLipSync.Viseme.ou, new float[]{0f, 0f, 0.9995f, 0f, 0f, 1f}); + catsData.Add(OVRLipSync.Viseme.PP, new float[]{0.4f, 0f, 0f, 0f, 0.4f, 0f}); + catsData.Add(OVRLipSync.Viseme.RR, new float[]{0f, 0.5f, 0f, 0f, 0.3f, 0.4f}); + catsData.Add(OVRLipSync.Viseme.SS, new float[]{0f, 0.8f, 0f, 0f, 0f, 0.3f}); + catsData.Add(OVRLipSync.Viseme.TH, new float[]{0.4f, 0f, 0f, 0f, 0.15f, 0.5f}); + #endif + visemePresetMap = new BlendShapeKey[6] { BlendShapeKey.CreateFromPreset(BlendShapePreset.A), BlendShapeKey.CreateFromPreset(BlendShapePreset.I), BlendShapeKey.CreateFromPreset(BlendShapePreset.U), BlendShapeKey.CreateFromPreset(BlendShapePreset.E), - BlendShapeKey.CreateFromPreset(BlendShapePreset.O) + BlendShapeKey.CreateFromPreset(BlendShapePreset.O), + BlendShapeKey.CreateFromPreset(BlendShapePreset.Unknown) + }; + lastVisemeValues = new float[] {0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f, 0f}; + #if WINDOWS_BUILD + ovrMap = new Dictionary() { + [OVRLipSync.Viseme.sil] = 0, + [OVRLipSync.Viseme.aa] = 1, + [OVRLipSync.Viseme.CH] = 2, + [OVRLipSync.Viseme.DD] = 3, + [OVRLipSync.Viseme.E] = 4, + [OVRLipSync.Viseme.FF] = 5, + [OVRLipSync.Viseme.ih] = 6, + [OVRLipSync.Viseme.kk] = 7, + [OVRLipSync.Viseme.nn] = 8, + [OVRLipSync.Viseme.oh] = 9, + [OVRLipSync.Viseme.ou] = 10, + [OVRLipSync.Viseme.PP] = 11, + [OVRLipSync.Viseme.RR] = 12, + [OVRLipSync.Viseme.SS] = 13, + [OVRLipSync.Viseme.TH] = 14 + }; + #endif + fullVisemePresets = new BlendShapeKey[16] { + BlendShapeKey.CreateUnknown("SIL"), + BlendShapeKey.CreateFromPreset(BlendShapePreset.A), + BlendShapeKey.CreateUnknown("CH"), + BlendShapeKey.CreateUnknown("DD"), + BlendShapeKey.CreateFromPreset(BlendShapePreset.E), + BlendShapeKey.CreateUnknown("FF"), + BlendShapeKey.CreateFromPreset(BlendShapePreset.I), + BlendShapeKey.CreateUnknown("KK"), + BlendShapeKey.CreateUnknown("NN"), + BlendShapeKey.CreateFromPreset(BlendShapePreset.O), + BlendShapeKey.CreateFromPreset(BlendShapePreset.U), + BlendShapeKey.CreateUnknown("PP"), + BlendShapeKey.CreateUnknown("RR"), + BlendShapeKey.CreateUnknown("SS"), + BlendShapeKey.CreateUnknown("TH"), + BlendShapeKey.CreateFromPreset(BlendShapePreset.Unknown) }; - lastVisemeValues = new float[5] {0f, 0f, 0f, 0f, 0f}; } + #if WINDOWS_BUILD OVRLipSync.Viseme GetActiveViseme(out float bestValue) { var visemes = Enum.GetValues(typeof(OVRLipSync.Viseme)); bestValue = -1f; @@ -246,23 +605,26 @@ public class OpenSeeVRMDriver : MonoBehaviour { } return bestViseme; } - - void ApplyVisemes() { + #endif + + bool ApplyVisemes() { + #if WINDOWS_BUILD + trackMouth = false; if (vrmBlendShapeProxy == null || catsData == null) - return; + return true; float expressionFactor = 1f; if (currentExpression != null) expressionFactor = currentExpressionVisemeFactor; if (currentExpression != null && !currentExpressionEnableVisemes) { - return; + return true; } float weight; OVRLipSync.Viseme current = GetActiveViseme(out weight); - if (current == OVRLipSync.Viseme.sil && weight > 0.9999f) { + if (current == OVRLipSync.Viseme.sil) { if (wasSilViseme) { if (hybridLipSync && Time.time - startedSilVisemes > silVisemeHybridThreshold) { ApplyMouthShape(); - return; + return false; } } else { startedSilVisemes = Time.time; @@ -279,8 +641,18 @@ public class OpenSeeVRMDriver : MonoBehaviour { weight = Mathf.Clamp(weight * 1.5f, 0f, 1f); else weight = 1f; - float[] values = catsData[current]; - for (int i = 0; i < 5; i++) { + + float[] values = new float[16]; + int lastIdx = 5; + if (haveFullVisemeSet) { + lastIdx = 15; + values[ovrMap[current]] = weight; + values[lastIdx] = catsData[current][5]; + } else { + Array.Copy(catsData[current], values, 6); + } + + for (int i = 0; i <= lastIdx; i++) { lastVisemeValues[i] = values[i] * weight * (1f - visemeSmoothing) + lastVisemeValues[i] * visemeSmoothing; if (lastVisemeValues[i] < 0f) { lastVisemeValues[i] = 0f; @@ -289,8 +661,40 @@ public class OpenSeeVRMDriver : MonoBehaviour { lastVisemeValues[i] = 1f; } float result = lastVisemeValues[i] * visemeFactor * expressionFactor; - if (result > 0f) - vrmBlendShapeProxy.AccumulateValue(visemePresetMap[i], result); + if (result > 0f && i < lastIdx) { + if (haveFullVisemeSet) + proxy.AccumulateValue(fullVisemePresets[i], result, 1f); + else + proxy.AccumulateValue(visemePresetMap[i], result, 1f); + } + if (i == lastIdx) { + if (animateJawBone) + SetJaw(result * 0.99999f); + else + SetJaw(0f); + } + } + #endif + return false; + } + + void SetJaw(float v) { + if (haveJawParameter) { + animator.SetFloat("JawMovement", v); + return; + } + if (jawBoneAnimation != null && jawBone != null) { + for (int i = 0; i < humanBodyBones.Length; i++) { + if (humanBodyBoneTransforms[i] != null) + humanBodyBoneRotations[i] = humanBodyBoneTransforms[i].localRotation; + } + jawBoneAnimation.SampleAnimation(lastAvatar.gameObject, v / jawBoneAnimation.frameRate); + jawRotation = jawBone.localRotation; + for (int i = 0; i < humanBodyBones.Length; i++) { + if (humanBodyBoneTransforms[i] != null) + humanBodyBoneTransforms[i].localRotation = humanBodyBoneRotations[i]; + } + return; } } @@ -302,11 +706,44 @@ public class OpenSeeVRMDriver : MonoBehaviour { if (openSeeData == null || openSeeData.features == null) return; + if (lastMouthStates == null) + lastMouthStates = new float[]{0f, 0f, 0f, 0f, 0f, 0f}; + if (currentMouthStates == null) + currentMouthStates = new float[]{0f, 0f, 0f, 0f, 0f, 0f}; + + if (mouthInterpolate == null) + mouthInterpolate = new TimeInterpolate(); + float t = mouthInterpolate.Interpolate(); + + if (currentExpression != null && !currentExpressionEnableVisemes) + return; + + trackMouth = !fadeOnly; + + float expressionFactor = 1f; + if (currentExpression != null) + expressionFactor = currentExpressionVisemeFactor; + + for (int i = 0; i < 6; i++) { + float interpolated = Mathf.Lerp(lastMouthStates[i], currentMouthStates[i], t); + float result = interpolated * expressionFactor * visemeFactor; + if (i < 5 && result > 0f) + if (!useCameraPerfectSync || !proxy.HasPerfectSync()) + proxy.AccumulateValue(visemePresetMap[i], result); + if (i == 5) { + if (animateJawBone) + SetJaw(result * 0.99999f * blendShapeWeight); + else + SetJaw(0f); + } + } + if (lastMouth < openSeeData.time) { lastMouth = openSeeData.time; + mouthInterpolate.UpdateTime(lastMouth); float open = openSeeData.features.MouthOpen; float wide = openSeeData.features.MouthWide; - float[] mouthStates = new float[]{0f, 0f, 0f, 0f, 0f}; + float[] mouthStates = new float[]{0f, 0f, 0f, 0f, 0f, 0f}; float stabilizer = mouthStabilizer; float stabilizerWide = mouthStabilizerWide; @@ -320,6 +757,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { break; if (mouthUseSquelch && audioVolume < mouthSquelch) break; + mouthStates[5] = Mathf.Clamp(open / 0.55f - 0.1f, 0f, 1f); if (open < stabilizer && Mathf.Abs(wide) < stabilizer) break; if (wide > stabilizer && open < stabilizerWide) @@ -329,7 +767,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { mouthStates[4] = open; } else if (open >= 0f) { // A - mouthStates[0] = open * 1f; + mouthStates[0] = open; } if (wide >= 0f && open > stabilizer * 0.5f) { if (wide > 0.5f) { @@ -337,7 +775,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { mouthStates[1] = wide; } else { // E - mouthStates[3] = wide * 1f; + mouthStates[3] = wide; } } else if (wide < Mathf.Clamp(stabilizerWide * 1.5f, 0f, 0.8f) && open > stabilizer) { // U @@ -358,37 +796,11 @@ public class OpenSeeVRMDriver : MonoBehaviour { } } while (false); - if (mouthInterpolationState == 2) { - for (int i = 0; i < 5; i++) { - mouthStates[i] = Mathf.Lerp(currentMouthStates[i], mouthStates[i], 1f - mouthSmoothing); - } + for (int i = 0; i < 6; i++) { + lastMouthStates[i] = Mathf.Lerp(lastMouthStates[i], currentMouthStates[i], t); + mouthStates[i] = Mathf.Lerp(lastMouthStates[i], mouthStates[i], 1f - mouthSmoothing); } - - lastMouthStates = currentMouthStates; currentMouthStates = mouthStates; - if (mouthInterpolationState < 2) - mouthInterpolationState++; - mouthInterpolationCount = 0; - } - - if (mouthInterpolationState < 2) - return; - - float t = Mathf.Clamp((float)mouthInterpolationCount / openSeeIKTarget.averageInterpolations, 0f, 0.985f); - mouthInterpolationCount++; - - if (currentExpression != null && !currentExpressionEnableVisemes) - return; - - float expressionFactor = 1f; - if (currentExpression != null) - expressionFactor = currentExpressionVisemeFactor; - - for (int i = 0; i < 5; i++) { - float interpolated = Mathf.Lerp(lastMouthStates[i], currentMouthStates[i], t); - float result = interpolated * expressionFactor * visemeFactor; - if (result > 0f) - vrmBlendShapeProxy.AccumulateValue(visemePresetMap[i], result); } } @@ -396,10 +808,158 @@ public class OpenSeeVRMDriver : MonoBehaviour { ApplyMouthShape(false); } + void UpdatePerfectSync() { + if (openSeeData == null || lastPerfectSync >= openSeeData.time) + return; + lastPerfectSync = openSeeData.time; + if (useCameraPerfectSync && proxy.HasPerfectSync()) { + float upDownStrength = (openSeeData.features.EyebrowUpDownLeft + openSeeData.features.EyebrowUpDownRight) / 2f; + if (upDownStrength > 0f) + proxy.SetPerfectSync("BROWINNERUP", upDownStrength * 0.8f, lastPerfectSync); + else + proxy.SetPerfectSync("BROWINNERUP", 0, lastPerfectSync); + + if (openSeeData.features.EyebrowUpDownLeft < 0.2f) { + proxy.SetPerfectSync("BROWDOWNLEFT", -openSeeData.features.EyebrowUpDownLeft * 0.5f, lastPerfectSync); + if (openSeeData.features.EyeLeft < 0.1f && openSeeData.features.EyeLeft > -0.6f) { + proxy.SetPerfectSync("EYESQUINTLEFT", -openSeeData.features.EyeLeft, lastPerfectSync); + proxy.SetPerfectSync("EYEBLINKLEFT", 0, lastPerfectSync); + } else if (openSeeData.features.EyeLeft <= -0.6f) { + proxy.SetPerfectSync("EYEBLINKLEFT", -openSeeData.features.EyeLeft * 1.5f, lastPerfectSync); + proxy.SetPerfectSync("EYESQUINTLEFT", 0, lastPerfectSync); + } else { + proxy.SetPerfectSync("EYESQUINTLEFT", 0, lastPerfectSync); + proxy.SetPerfectSync("EYEBLINKLEFT", 0, lastPerfectSync); + } + } else { + proxy.SetPerfectSync("BROWDOWNLEFT", 0, lastPerfectSync); + proxy.SetPerfectSync("EYESQUINTLEFT", 0, lastPerfectSync); + if (openSeeData.features.EyeLeft <= -0.3f) + proxy.SetPerfectSync("EYEBLINKLEFT", -openSeeData.features.EyeLeft * 1.5f, lastPerfectSync); + else + proxy.SetPerfectSync("EYEBLINKLEFT", 0, lastPerfectSync); + } + + if (openSeeData.features.EyebrowUpDownRight < 0.2f) { + proxy.SetPerfectSync("BROWDOWNRIGHT", -openSeeData.features.EyebrowUpDownRight * 0.5f, lastPerfectSync); + if (openSeeData.features.EyeRight < 0.1f && openSeeData.features.EyeRight > -0.6f) { + proxy.SetPerfectSync("EYESQUINTRIGHT", -openSeeData.features.EyeRight, lastPerfectSync); + proxy.SetPerfectSync("EYEBLINKRIGHT", 0, lastPerfectSync); + } else if (openSeeData.features.EyeRight <= -0.6f) { + proxy.SetPerfectSync("EYEBLINKRIGHT", -openSeeData.features.EyeRight * 1.5f, lastPerfectSync); + proxy.SetPerfectSync("EYESQUINTRIGHT", 0, lastPerfectSync); + } else { + proxy.SetPerfectSync("EYESQUINTRIGHT", 0, lastPerfectSync); + proxy.SetPerfectSync("EYEBLINKRIGHT", 0, lastPerfectSync); + } + } else { + proxy.SetPerfectSync("BROWDOWNRIGHT", 0, lastPerfectSync); + proxy.SetPerfectSync("EYESQUINTRIGHT", 0, lastPerfectSync); + if (openSeeData.features.EyeRight <= -0.3f) + proxy.SetPerfectSync("EYEBLINKRIGHT", -openSeeData.features.EyeRight * 1.5f, lastPerfectSync); + else + proxy.SetPerfectSync("EYEBLINKRIGHT", 0, lastPerfectSync); + } + + if (openSeeData.features.EyebrowSteepnessLeft < 0.2f) + proxy.SetPerfectSync("BROWOUTERUPLEFT", -openSeeData.features.EyebrowSteepnessLeft, lastPerfectSync); + else + proxy.SetPerfectSync("BROWOUTERUPLEFT", 0, lastPerfectSync); + + if (openSeeData.features.EyebrowSteepnessRight < 0.2f) + proxy.SetPerfectSync("BROWOUTERUPRIGHT", -openSeeData.features.EyebrowSteepnessRight, lastPerfectSync); + else + proxy.SetPerfectSync("BROWOUTERUPRIGHT", 0, lastPerfectSync); + + if (openSeeData.features.EyeLeft > 0.5f) + proxy.SetPerfectSync("EYEWIDELEFT", openSeeData.features.EyeLeft * 0.7f, lastPerfectSync); + else + proxy.SetPerfectSync("EYEWIDELEFT", 0, lastPerfectSync); + + if (openSeeData.features.EyeRight > 0.5f) + proxy.SetPerfectSync("EYEWIDERIGHT", openSeeData.features.EyeRight * 0.7f, lastPerfectSync); + else + proxy.SetPerfectSync("EYEWIDERIGHT", 0, lastPerfectSync); + + if (trackMouth) { + if (openSeeData.features.MouthWide < 0.5f) + proxy.SetPerfectSync("MOUTHPUCKER", -openSeeData.features.MouthWide * 0.3f, lastPerfectSync); + else + proxy.SetPerfectSync("MOUTHPUCKER", 0, lastPerfectSync); + + if (openSeeData.features.MouthOpen > 0.0f) { + proxy.SetPerfectSync("JAWOPEN", openSeeData.features.MouthOpen, lastPerfectSync); + proxy.SetPerfectSync("MOUTHCLOSE", 0, lastPerfectSync); + } else if (openSeeData.features.MouthOpen < -0.0f) { + proxy.SetPerfectSync("MOUTHCLOSE", openSeeData.features.MouthOpen, lastPerfectSync); + proxy.SetPerfectSync("JAWOPEN", 0, lastPerfectSync); + } else { + proxy.SetPerfectSync("JAWOPEN", 0, lastPerfectSync); + proxy.SetPerfectSync("MOUTHCLOSE", 0, lastPerfectSync); + } + + if (openSeeData.features.MouthCornerInOutLeft > 0.3f) + proxy.SetPerfectSync("MOUTHLEFT", openSeeData.features.MouthCornerInOutLeft * 0.5f, lastPerfectSync); + else + proxy.SetPerfectSync("MOUTHLEFT", 0, lastPerfectSync); + + if (openSeeData.features.MouthCornerInOutRight > 0.3f) + proxy.SetPerfectSync("MOUTHRIGHT", openSeeData.features.MouthCornerInOutRight * 0.5f, lastPerfectSync); + else + proxy.SetPerfectSync("MOUTHRIGHT", 0, lastPerfectSync); + + if (openSeeData.features.MouthCornerUpDownLeft > 0.3f) + proxy.SetPerfectSync("MOUTHSMILELEFT", openSeeData.features.MouthCornerUpDownLeft * 0.5f, lastPerfectSync); + else if (openSeeData.features.MouthCornerUpDownLeft < -0.3f) + proxy.SetPerfectSync("MOUTHFROWNLEFT", -openSeeData.features.MouthCornerUpDownLeft, lastPerfectSync); + else { + proxy.SetPerfectSync("MOUTHSMILELEFT", 0, lastPerfectSync); + proxy.SetPerfectSync("MOUTHFROWNLEFT", 0, lastPerfectSync); + } + + if (openSeeData.features.MouthCornerUpDownRight > 0.3f) + proxy.SetPerfectSync("MOUTHSMILERIGHT", openSeeData.features.MouthCornerUpDownRight * 0.5f, lastPerfectSync); + else if (openSeeData.features.MouthCornerUpDownLeft < -0.3f) + proxy.SetPerfectSync("MOUTHFROWNRIGHT", -openSeeData.features.MouthCornerUpDownRight, lastPerfectSync); + else { + proxy.SetPerfectSync("MOUTHSMILERIGHT", 0, lastPerfectSync); + proxy.SetPerfectSync("MOUTHFROWNRIGHT", 0, lastPerfectSync); + } + } + } + } + void UpdateBrows() { if ((browClips == null && (faceMesh == null || faceType < 0)) || openSeeData == null) { return; } + if (browInterpolate == null) + browInterpolate = new TimeInterpolate(); + + float t = browInterpolate.Interpolate(); + float strength = eyebrowStrength; + if (currentExpression != null) + strength *= currentExpressionEyebrowWeight; + + if (!useCameraPerfectSync || !proxy.HasPerfectSync()) { + if (!skipApply && browClips == null) { + if (browUpIndex > -1 && strength > 0f) + faceMesh.SetBlendShapeWeight(browUpIndex, strength * Mathf.Lerp(lastBrowStates[0], currentBrowStates[0], t)); + if (browDownIndex > -1 && strength > 0f) + faceMesh.SetBlendShapeWeight(browDownIndex, strength * Mathf.Lerp(lastBrowStates[1], currentBrowStates[1], t)); + if (browAngryIndex > -1 && strength > 0f) + faceMesh.SetBlendShapeWeight(browAngryIndex, strength * Mathf.Lerp(lastBrowStates[2], currentBrowStates[2], t)); + if (browSorrowIndex > -1 && strength > 0f) + faceMesh.SetBlendShapeWeight(browSorrowIndex, strength * Mathf.Lerp(lastBrowStates[3], currentBrowStates[3], t)); + } + if (browClips != null && strength > 0f) { + if (lastBrowUpDown > 0f) + proxy.AccumulateValue(browClips[0], strength * Mathf.Lerp(lastBrowStates[0], currentBrowStates[0], t)); + else if (lastBrowUpDown < 0f) + proxy.AccumulateValue(browClips[1], strength * Mathf.Lerp(lastBrowStates[1], currentBrowStates[1], t)); + } + } + if (lastBrows < openSeeData.time) { lastBrows = openSeeData.time; float upDownStrength = (openSeeData.features.EyebrowUpDownLeft + openSeeData.features.EyebrowUpDownRight) / 2f; @@ -437,15 +997,12 @@ public class OpenSeeVRMDriver : MonoBehaviour { } if (eyebrowIsMoving > 0) eyebrowIsMoving--; - upDownStrength = Mathf.Lerp(lastBrowUpDown, upDownStrength, 1f - eyebrowSmoothing); + upDownStrength = Mathf.LerpUnclamped(lastBrowUpDown, upDownStrength, 1f - eyebrowSmoothing); lastBrowUpDown = upDownStrength; - if (browInterpolationState < 2) - browInterpolationState++; - browInterpolationCount = 0; - for (int i = 0; i < 4; i++) - lastBrowStates[i] = currentBrowStates[i]; + lastBrowStates[i] = Mathf.Lerp(lastBrowStates[i], currentBrowStates[i], t); + browInterpolate.UpdateTime(openSeeData.time); if (browClips != null) { currentBrowStates[0] = upDownStrength; @@ -487,35 +1044,35 @@ public class OpenSeeVRMDriver : MonoBehaviour { } } } - - float t = Mathf.Clamp((float)browInterpolationCount / openSeeIKTarget.averageInterpolations, 0f, 0.985f); - browInterpolationCount++; - - float strength = eyebrowStrength; - if (currentExpression != null) - strength *= currentExpressionEyebrowWeight; - - if (browUpIndex > -1 && strength > 0f) - faceMesh.SetBlendShapeWeight(browUpIndex, strength * Mathf.Lerp(lastBrowStates[0], currentBrowStates[0], t)); - if (browDownIndex > -1 && strength > 0f) - faceMesh.SetBlendShapeWeight(browDownIndex, strength * Mathf.Lerp(lastBrowStates[1], currentBrowStates[1], t)); - if (browAngryIndex > -1 && strength > 0f) - faceMesh.SetBlendShapeWeight(browAngryIndex, strength * Mathf.Lerp(lastBrowStates[2], currentBrowStates[2], t)); - if (browSorrowIndex > -1 && strength > 0f) - faceMesh.SetBlendShapeWeight(browSorrowIndex, strength * Mathf.Lerp(lastBrowStates[3], currentBrowStates[3], t)); - if (browClips != null && strength > 0f) { - if (lastBrowUpDown > 0f) - vrmBlendShapeProxy.AccumulateValue(browClips[0], strength * Mathf.Lerp(lastBrowStates[0], currentBrowStates[0], t)); - else if (lastBrowUpDown < 0f) - vrmBlendShapeProxy.AccumulateValue(browClips[1], strength * Mathf.Lerp(lastBrowStates[1], currentBrowStates[1], t)); - } } void FindFaceMesh() { if (lastAvatar == vrmBlendShapeProxy) return; + if (visemePresetMap == null) + InitCatsData(); lastAvatar = vrmBlendShapeProxy; + animator = lastAvatar.gameObject.GetComponent(); + + if (animator != null) { + haveJawParameter = false; + for (int i = 0; i < animator.parameterCount; i++) { + if (animator.parameters[i].name == "JawMovement") { + haveJawParameter = true; + break; + } + } + jawBone = animator.GetBoneTransform(HumanBodyBones.Jaw); + for (int i = 0; i < humanBodyBones.Length; i++) { + if (humanBodyBones[i] >= 0 && humanBodyBones[i] < HumanBodyBones.LastBone) + humanBodyBoneTransforms[i] = animator.GetBoneTransform(humanBodyBones[i]); + else + humanBodyBoneTransforms[i] = null; + } + } + proxy.UpdateAvatar(lastAvatar, animator); + faceMesh = null; faceType = -1; browUpIndex = -1; @@ -529,17 +1086,31 @@ public class OpenSeeVRMDriver : MonoBehaviour { if (vrmBlendShapeProxy == null) return; + HashSet visemeClipNames = new HashSet(); + foreach (var key in fullVisemePresets) + if (key.Preset == BlendShapePreset.Unknown && key.Name != "") + visemeClipNames.Add(key.Name.ToUpper()); + // Check if the model has set up blendshape clips. Thanks do Deat for this idea! string foundClipUp = null; string foundClipDown = null; + HashSet foundVisemeClips = new HashSet(); foreach (BlendShapeClip clip in vrmBlendShapeProxy.BlendShapeAvatar.Clips) { if (clip.Preset == BlendShapePreset.Unknown && clip.BlendShapeName != null) { - if (clip.BlendShapeName.ToUpper() == "BROWS UP") + string name = clip.BlendShapeName.ToUpper(); + if (name == "BROWS UP") foundClipUp = clip.BlendShapeName; - if (clip.BlendShapeName.ToUpper() == "BROWS DOWN") + if (name == "BROWS DOWN") foundClipDown = clip.BlendShapeName; + if (visemeClipNames.Contains(name)) + foundVisemeClips.Add(name); } } + haveFullVisemeSet = false; + if (visemeClipNames.SetEquals(foundVisemeClips)) { + Debug.Log("Detected full viseme set on avatar."); + haveFullVisemeSet = true; + } if (foundClipUp != null && foundClipDown != null) { browClips = new BlendShapeKey[2] { BlendShapeKey.CreateUnknown(foundClipUp), @@ -846,19 +1417,20 @@ public class OpenSeeVRMDriver : MonoBehaviour { toggledExpression.triggered = false; if (!overridden && !trigger) { - if (openSeeExpression.expression == null || openSeeExpression.expression == "" || !expressionMap.ContainsKey(openSeeExpression.expression)) { + if (only30Points || !openSeeExpression.enabled || openSeeExpression.expressionTime + 15f < Time.time || openSeeExpression.expression == null || openSeeExpression.expression == "" || !expressionMap.ContainsKey(openSeeExpression.expression)) { currentExpression = null; } else { currentExpression = expressionMap[openSeeExpression.expression]; currentExpression.triggered = true; } - } else if (openSeeExpression.expression != null && openSeeExpression.expression != "" && expressionMap.ContainsKey(openSeeExpression.expression)) { + } else if (!only30Points && openSeeExpression.enabled && openSeeExpression.expressionTime + 15f > Time.time && openSeeExpression.expression != null && openSeeExpression.expression != "" && expressionMap.ContainsKey(openSeeExpression.expression)) { if (expressionMap[openSeeExpression.expression].additive) expressionMap[openSeeExpression.expression].triggered = true; } if (currentExpression == null && expressionMap.ContainsKey("neutral")) { currentExpression = expressionMap["neutral"]; + currentExpression.triggered = true; } // Reset limits on expressions @@ -875,7 +1447,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { foreach (var expression in expressionMap.Values) { float weight = expression.GetWeight(baseTransitionTime); if (weight > 0f) { - vrmBlendShapeProxy.AccumulateValue(expression.blendShapeKey, weight); + proxy.AccumulateValue(expression.blendShapeKey, weight, 1f); // Accumulate limits on expressions currentExpressionEnableBlinking = currentExpressionEnableBlinking && expression.enableBlinking; @@ -884,44 +1456,8 @@ public class OpenSeeVRMDriver : MonoBehaviour { currentExpressionVisemeFactor = Mathf.Min(currentExpressionVisemeFactor, expression.visemeFactor); } } + proxy.DisableFaceParts(currentExpressionEyebrowWeight < 0.0005f, !currentExpressionEnableBlinking, !currentExpressionEnableVisemes || (currentExpressionVisemeFactor < 0.0005f)); lastExpression = currentExpression; - - /*if (currentExpression != lastExpression) { - expressionChangeTime = Time.time; - lastExpression = currentExpression; - }*/ - - /*float timeWeight = 1f; - if (currentExpression.transitionTime > 0f) - timeWeight = Mathf.Clamp((Time.time - expressionChangeTime) / (currentExpression.transitionTime * 0.001f), 0f, 1f); - if (timeWeight >= 1f) { - foreach (var expression in expressionMap.Values) { - if (expression != currentExpression) { - expression.maxWeight = 0f; - expression.lastTimeWeightOld = 0f; - vrmBlendShapeProxy.AccumulateValue(expression.blendShapeKey, 0f); - } - } - } else { - foreach (var expression in expressionMap.Values) { - if (expression != currentExpression) { - float timeWeightOld = 1f; - if (expression.transitionTime > 0f) - timeWeightOld = Mathf.Max(Mathf.Clamp((Time.time - expressionChangeTime) / (expression.transitionTime * 0.001f), 0f, 1f), timeWeight); - if (expression.lastTimeWeightOld >= timeWeightOld) - expression.lastTimeWeightOld = timeWeightOld; - expression.maxWeight = Mathf.Clamp(expression.maxWeight - (timeWeightOld - expression.lastTimeWeightOld), 0f, 1f); - expression.lastTimeWeightOld = timeWeightOld; - vrmBlendShapeProxy.AccumulateValue(expression.blendShapeKey, expression.weight * expression.maxWeight); - } - } - } - - currentExpression.maxWeight = timeWeight; - currentExpression.lastTimeWeightOld = 0f; - - if (currentExpression != null) - vrmBlendShapeProxy.AccumulateValue(currentExpression.blendShapeKey, currentExpression.weight * currentExpression.maxWeight);*/ } void GetLookParameters(ref float lookLeftRight, ref float lookUpDown, bool update, int gazePoint, int right, int left, int topRight, int topLeft, int bottomRight, int bottomLeft) { @@ -949,11 +1485,49 @@ public class OpenSeeVRMDriver : MonoBehaviour { } void UpdateGaze() { - if (openSeeData == null || vrmBlendShapeProxy == null || openSeeIKTarget == null || !(openSeeIKTarget.averageInterpolations > 0f)) + if (openSeeData == null || vrmBlendShapeProxy == null || openSeeIKTarget == null || skipApplyEyes) return; float lookUpDown = currentLookUpDown; float lookLeftRight = currentLookLeftRight; + + if (gazeInterpolate == null) + gazeInterpolate = new TimeInterpolate(); + float t = gazeInterpolate.Interpolate(); + + lookLeftRight = Mathf.Lerp(lastLookLeftRight, currentLookLeftRight, t); + lookUpDown = Mathf.Lerp(lastLookUpDown, currentLookUpDown, t); + + if (!openSeeIKTarget.mirrorMotion) + lookLeftRight = -lookLeftRight; + + if (leftEye == null && rightEye == null) { + if (gazeTracking) { + if (lookUpDown > 0f) { + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookUp), gazeFactor.x * lookUpDown); + } else { + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookDown), -gazeFactor.x * lookUpDown); + } + + if (lookLeftRight > 0f) { + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookLeft), gazeFactor.y * lookLeftRight); + } else { + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookRight), -gazeFactor.y * lookLeftRight); + } + } + } else { + if (rightEye != null) + rightEye.localRotation = Quaternion.identity; + if (leftEye != null) + leftEye.localRotation = Quaternion.identity; + if (gazeTracking) { + Quaternion rotation = Quaternion.Slerp(Quaternion.identity, Quaternion.AngleAxis(-gazeFactor.x * gazeStrength * lookUpDown, Vector3.right) * Quaternion.AngleAxis(-gazeFactor.y * gazeStrength * lookLeftRight, Vector3.up), blendShapeWeight); + if (rightEye != null) + rightEye.localRotation = rotation; + if (leftEye != null) + leftEye.localRotation = rotation; + } + } if (lastGaze < openSeeData.time) { GetLookParameters(ref lookLeftRight, ref lookUpDown, false, 66, 36, 39, 37, 38, 41, 40); @@ -963,55 +1537,12 @@ public class OpenSeeVRMDriver : MonoBehaviour { lookUpDown = Mathf.Lerp(currentLookUpDown, lookUpDown, 1f - gazeSmoothing); if (Mathf.Abs(lookLeftRight - currentLookLeftRight) + Mathf.Abs(lookUpDown - currentLookUpDown) > gazeStabilizer) { - lastLookLeftRight = currentLookLeftRight; - lastLookUpDown = currentLookUpDown; + lastLookLeftRight = Mathf.Lerp(lastLookLeftRight, currentLookLeftRight, t); + lastLookUpDown = Mathf.Lerp(lastLookUpDown, currentLookUpDown, t); currentLookLeftRight = lookLeftRight; currentLookUpDown = lookUpDown; lastGaze = openSeeData.time; - if (interpolationState < 2) - interpolationState++; - interpolationCount = 0; - } - } - - if (interpolationState < 2) - return; - - float t = Mathf.Clamp((float)interpolationCount / openSeeIKTarget.averageInterpolations, 0f, 0.985f); - lookLeftRight = Mathf.Lerp(lastLookLeftRight, currentLookLeftRight, t); - lookUpDown = Mathf.Lerp(lastLookUpDown, currentLookUpDown, t); - interpolationCount++; - - if (!openSeeIKTarget.mirrorMotion) - lookLeftRight = -lookLeftRight; - - if (leftEye == null || rightEye == null) { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookUp), 0f); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookDown), 0f); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookLeft), 0); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookRight), 0f); - if (gazeTracking) { - if (lookUpDown > 0f) { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookUp), gazeFactor.x * lookUpDown); - } else { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookDown), -gazeFactor.x * lookUpDown); - } - - if (lookLeftRight > 0f) { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookLeft), gazeFactor.y * lookLeftRight); - } else { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.LookRight), -gazeFactor.y * lookLeftRight); - } - } - } else { - //vrmLookAtHead.UpdateType = VRM.UpdateType.None; - //vrmLookAtHead.RaiseYawPitchChanged(gazeFactor.y * lookLeftRight, gazeFactor.x * lookUpDown); - rightEye.localRotation = Quaternion.identity; - leftEye.localRotation = Quaternion.identity; - if (gazeTracking) { - Quaternion rotation = Quaternion.AngleAxis(-gazeFactor.x * gazeStrength * lookUpDown, Vector3.right) * Quaternion.AngleAxis(-gazeFactor.y * gazeStrength * lookLeftRight, Vector3.up); - rightEye.localRotation = rotation; - leftEye.localRotation = rotation; + gazeInterpolate.UpdateTime(lastGaze); } } } @@ -1019,67 +1550,36 @@ public class OpenSeeVRMDriver : MonoBehaviour { void BlinkEyes() { if (vrmBlendShapeProxy == null || eyeBlinker == null) return; - if (autoBlink) { + if (autoBlink || only30Points) { if (currentExpressionEnableBlinking) { float blink = eyeBlinker.Blink(); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink), blink); + if (!useCameraPerfectSync || ! proxy.HasPerfectSync()) + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink), blink, 1); } - } else if (openSeeExpression != null && openSeeExpression.openSee != null) { + } else if (openSeeExpression != null && openSeeExpression.openSee != null && openSeeData != null) { if (!currentExpressionEnableBlinking) return; float right = 1f; float left = 1f; - if (openSeeData != null && lastBlink < openSeeData.time) { - lastBlink = openSeeData.time; - float openThreshold = eyeOpenedThreshold; - - if (openSeeData.rawEuler.y < turnLeftBoundaryAngle || openSeeData.rawEuler.y > turnRightBoundaryAngle) - openThreshold = Mathf.Lerp(openThreshold, eyeClosedThreshold, 0.4f); - - if (openSeeData.rightEyeOpen > openThreshold) - right = 0f; - else if (openSeeData.rightEyeOpen < eyeClosedThreshold) - right = 1f; - else - right = 1f - (openSeeData.rightEyeOpen - eyeClosedThreshold) / (openThreshold - eyeClosedThreshold); - - if (openSeeData.leftEyeOpen > openThreshold) - left = 0f; - else if (openSeeData.leftEyeOpen < eyeClosedThreshold) - left = 1f; - else - left = 1f - (openSeeData.leftEyeOpen - eyeClosedThreshold) / (openThreshold - eyeClosedThreshold); - - if (openSeeData.rawEuler.y < turnLeftBoundaryAngle) - left = right; - if (openSeeData.rawEuler.y > turnRightBoundaryAngle) - right = left; - - lastBlinkLeft = currentBlinkLeft; - lastBlinkRight = currentBlinkRight; - currentBlinkLeft = Mathf.Lerp(currentBlinkLeft, left, 1f - blinkSmoothing); - currentBlinkRight = Mathf.Lerp(currentBlinkRight, right, 1f - blinkSmoothing); - - if (left == 0f) - currentBlinkLeft = 0f; - if (right == 0f) - currentBlinkRight = 0f; - if (left == 1f) - currentBlinkLeft = 1f; - if (right == 1f) - currentBlinkRight = 1f; - - if (blinkInterpolationState < 2) - blinkInterpolationState++; - } - - if (blinkInterpolationState < 2) + float upDownAngle = openSeeData.rawEuler.x; + while (upDownAngle < 0f) + upDownAngle += 360f; + while (upDownAngle >= 360f) + upDownAngle -= 360f; + if (lookDownCompensation && upDownAngle > turnDownBoundaryAngle - lookDownAdjustment - wasLookingDown && autoBlinkLookingDown) { + if (!(wasLookingDown > 0f)) + eyeBlinker.Blink(); + float blink = eyeBlinker.Blink(); + if (!useCameraPerfectSync || ! proxy.HasPerfectSync()) + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink), blink); return; - - float t = Mathf.Clamp((float)blinkInterpolationCount / openSeeIKTarget.averageInterpolations, 0f, 0.985f); - blinkInterpolationCount++; + } + + if (blinkInterpolate == null) + blinkInterpolate = new TimeInterpolate(); + float t = blinkInterpolate.Interpolate(); left = Mathf.Lerp(lastBlinkLeft, currentBlinkLeft, t); right = Mathf.Lerp(lastBlinkRight, currentBlinkRight, t); @@ -1090,12 +1590,79 @@ public class OpenSeeVRMDriver : MonoBehaviour { right = tmp; } - if (linkBlinks) { - float v = Mathf.Max(left, right); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink), v); - } else { - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_R), right); - vrmBlendShapeProxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_L), left); + if (!useCameraPerfectSync || !proxy.HasPerfectSync()) { + if (linkBlinks && !allowWinking) { + float v = Mathf.Max(left, right); + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink), v); + } else { + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_R), right); + proxy.AccumulateValue(BlendShapeKey.CreateFromPreset(BlendShapePreset.Blink_L), left); + } + } + + if (openSeeData != null && lastBlink < openSeeData.time) { + lastBlink = openSeeData.time; + blinkInterpolate.UpdateTime(lastBlink); + float openThreshold = eyeOpenedThreshold; + float closedThreshold = eyeClosedThreshold; + + if (openSeeData.rawEuler.y < turnLeftBoundaryAngle || openSeeData.rawEuler.y > turnRightBoundaryAngle) + openThreshold = Mathf.Lerp(eyeOpenedThreshold, eyeClosedThreshold, 0.4f); + + if (lookDownCompensation && upDownAngle > turnDownBoundaryAngle - lookDownAdjustment - wasLookingDown) { + openThreshold = Mathf.Lerp(eyeOpenedThreshold, eyeClosedThreshold, 0.85f); + closedThreshold = Mathf.Lerp(eyeClosedThreshold, 0f, 0.6f); + if (upDownAngle > turnDownBoundaryAngle - lookDownAdjustment + 10f) { + openThreshold = 0.1f; + closedThreshold = 0f; + } + wasLookingDown = 1.5f; + } else { + wasLookingDown = 0f; + } + + if (openSeeData.rightEyeOpen > openThreshold) + right = 0f; + else if (openSeeData.rightEyeOpen < closedThreshold) + right = 1f; + else + right = 1f - (openSeeData.rightEyeOpen - closedThreshold) / (openThreshold - closedThreshold); + + if (openSeeData.leftEyeOpen > openThreshold) + left = 0f; + else if (openSeeData.leftEyeOpen < closedThreshold) + left = 1f; + else + left = 1f - (openSeeData.leftEyeOpen - closedThreshold) / (openThreshold - closedThreshold); + + if (openSeeData.rawEuler.y < turnLeftBoundaryAngle) + left = right; + if (openSeeData.rawEuler.y > turnRightBoundaryAngle) + right = left; + + if (linkBlinks) { + if (Mathf.Abs(right - left) < smartWinkThreshold || !allowWinking) { + float v = Mathf.Max(left, right); + if (v < 0.3f) + v = Mathf.Min(left, right); + left = v; + right = v; + } + } + + lastBlinkLeft = Mathf.Lerp(lastBlinkLeft, currentBlinkLeft, t); + lastBlinkRight = Mathf.Lerp(lastBlinkRight, currentBlinkRight, t); + currentBlinkLeft = Mathf.Lerp(currentBlinkLeft, left, 1f - blinkSmoothing); + currentBlinkRight = Mathf.Lerp(currentBlinkRight, right, 1f - blinkSmoothing); + + if (left < 0.00001f) + currentBlinkLeft = 0f; + if (right < 0.00001f) + currentBlinkRight = 0f; + if (left > 0.99999f) + currentBlinkLeft = 1f; + if (right > 0.99999f) + currentBlinkRight = 1f; } } } @@ -1166,7 +1733,9 @@ public class OpenSeeVRMDriver : MonoBehaviour { } public void InitializeLipSync() { + #if WINDOWS_BUILD active = false; + lastAudioTime = -1f; if (catsData == null) InitCatsData(); if (!isCanned && clip != null) @@ -1203,7 +1772,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { freq = maxFreq; lastMic = mic; - Microphone.GetDeviceCaps(lastMic, out minFreq, out maxFreq); + try { Microphone.GetDeviceCaps(lastMic, out minFreq, out maxFreq); } catch (Exception e) { Debug.LogError("Failed to get device capabilities: " + e); } if (maxFreq > 0) freq = maxFreq; @@ -1218,11 +1787,12 @@ public class OpenSeeVRMDriver : MonoBehaviour { inited = true; } - clip = Microphone.Start(lastMic, true, 1, freq); + try { clip = Microphone.Start(lastMic, true, 1, freq); } catch (Exception e) { Debug.LogError("Failed to start microphone: " + e); lipSync = false;} channels = clip.channels; partialAudio = new float[1024 * channels]; lastPos = 0; active = true; + #endif } float[] ReadMic() { @@ -1254,6 +1824,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { } void ProcessBuffer(float[] buffer) { + #if WINDOWS_BUILD if (buffer == null) return; @@ -1285,6 +1856,9 @@ public class OpenSeeVRMDriver : MonoBehaviour { } } audioVolume /= buffer.Length; + + if (audioVolume > 0f) + lastAudioTime = fakeTimeTime; int totalLen = partialPos + buffer.Length; int bufferPos = 0; @@ -1320,9 +1894,11 @@ public class OpenSeeVRMDriver : MonoBehaviour { Array.Copy(buffer, bufferPos, partialAudio, partialPos, buffer.Length - bufferPos); partialPos += (buffer.Length - bufferPos) / channels; } + #endif } void OnAudioFilterRead(float[] data, int channels) { + #if WINDOWS_BUILD if (isCanned) { if (channels > 3) { Debug.Log("Audio with more than 3 channels is not supported."); @@ -1340,9 +1916,15 @@ public class OpenSeeVRMDriver : MonoBehaviour { if (isCanned) ProcessBuffer(buffer); } + #endif } void ReadAudio() { + #if WINDOWS_BUILD + if (lastAudioTime >= 0f && Time.time > lastAudioTime + 3f && lipSync) { + Debug.Log("Audio device might have disappeared, reinitializing lip sync."); + InitializeLipSync(); + } if (clip == null) return; if (OVRLipSync.IsInitialized() != OVRLipSync.Result.Success) { @@ -1354,9 +1936,11 @@ public class OpenSeeVRMDriver : MonoBehaviour { return; if (!isCanned) ProcessBuffer(buffer); + #endif } void CreateContext() { + #if WINDOWS_BUILD lock (this) { if (context == 0) { if (OVRLipSync.CreateContext(ref context, provider) != OVRLipSync.Result.Success) { @@ -1365,10 +1949,30 @@ public class OpenSeeVRMDriver : MonoBehaviour { } } } + #endif } void Start() { InitExpressionMap(); + proxy = new OpenSeeBlendShapeProxy(this); + humanBodyBones = (HumanBodyBones[])Enum.GetValues(typeof(HumanBodyBones)); + humanBodyBoneTransforms = new Transform[humanBodyBones.Length]; + humanBodyBoneRotations = new Quaternion[humanBodyBones.Length]; + } + + public void SetProxyValues(Dictionary dict) { + foreach (var kv in dict) { + proxy.AccumulateValue(kv.Key, kv.Value, 1f); + } + } + + public void SetExternalPerfectSync(Dictionary blendshapes, float time) { + if (blendshapes == null) { + proxy.ClearPerfectSync(); + return; + } + foreach (var entry in blendshapes) + proxy.SetPerfectSync(entry.Key, entry.Value, time, 1f); } void RunUpdates() { @@ -1381,29 +1985,47 @@ public class OpenSeeVRMDriver : MonoBehaviour { InitializeLipSync(); initializeLipSync = false; } - if (vrmBlendShapeProxy != null) { - foreach (var pair in vrmBlendShapeProxy.GetValues()) { - vrmBlendShapeProxy.ImmediatelySetValue(pair.Key, 0f); - } - } + proxy.SetWeight(blendShapeWeight); + proxy.Clear(); FindFaceMesh(); - UpdateExpression(); - if (trackGaze) - UpdateGaze(); - BlinkEyes(); + if (!skipApply) + UpdateExpression(); + if (!skipApply) + BlinkEyes(); + bool doMouthTracking = true; + #if WINDOWS_BUILD ReadAudio(); if (lipSync) { - ApplyVisemes(); - } else { - ApplyMouthShape(); + if (!skipApply) + doMouthTracking = ApplyVisemes(); } - if (vrmBlendShapeProxy != null && browClips == null) - vrmBlendShapeProxy.Apply(); - UpdateBrows(); - if (vrmBlendShapeProxy != null && browClips != null) - vrmBlendShapeProxy.Apply(); + #endif + if (doMouthTracking && !skipApply) + ApplyMouthShape(); + if (!skipApply) + UpdatePerfectSync(); + if (!skipApply && vrmBlendShapeProxy != null && browClips == null) + proxy.Apply(); + if (!skipApply) + UpdateBrows(); + if (!skipApply && vrmBlendShapeProxy != null && browClips != null) + proxy.Apply(); } - + + void LateUpdate() { + if (!skipApplyJaw && jawBone != null && jawBoneAnimation != null && !haveJawParameter) { + jawBone.localRotation = jawRotation; + } + if (!skipApplyEyes && gazeTracking && !only30Points) { + UpdateGaze(); + } + // If blendshapes are received over VMC, apply expressions afterwards if they should be applied + if (skipApply && stillApplyExpressions) { + UpdateExpression(); + } + proxy.Apply(); + } + void FixedUpdate() { if (openSeeIKTarget.fixedUpdate) { RunUpdates(); @@ -1411,8 +2033,11 @@ public class OpenSeeVRMDriver : MonoBehaviour { } void Update() { + fakeTimeTime = Time.time; if (inited && lastSmoothing != smoothAmount) { + #if WINDOWS_BUILD OVRLipSync.SendSignal(context, OVRLipSync.Signals.VisemeSmoothing, smoothAmount, 0); + #endif lastSmoothing = smoothAmount; } if (!openSeeIKTarget.fixedUpdate) { @@ -1421,6 +2046,7 @@ public class OpenSeeVRMDriver : MonoBehaviour { } void DestroyContext() { + #if WINDOWS_BUILD active = false; lock (this) { if (context != 0) { @@ -1430,9 +2056,27 @@ public class OpenSeeVRMDriver : MonoBehaviour { context = 0; } } + #endif } void OnDestroy() { DestroyContext(); } + + public void SetAnimateJawBone(bool value) { + animateJawBone = value; + } + + public void SetSmartWinkThreshold(float v) { + smartWinkThreshold = v; + } + + public void SetGazeSmoothing(float v) { + gazeSmoothing = v; + } + + public void SetVisemeNormalization(bool v) { + visemeNormalization = v; + } + } \ No newline at end of file diff --git a/Examples/OpenSeeWebcamInfo.cs b/Examples/OpenSeeWebcamInfo.cs new file mode 100644 index 0000000..6663f28 --- /dev/null +++ b/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 prettyCaps = null; + + private List> 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 GetPrettyCapabilities() { + if (splitCaps != null && prettyCaps != null) + return prettyCaps; + + splitCaps = new List>(); + + 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(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(min, i)); + splitCaps.Add(new Tuple(max, i)); + } + } + + splitCaps.Sort((a, b) => CompareCaps(a.Item1, b.Item1)); + + prettyCaps = new List(); + 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 list; + } + + public List 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 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 details = JsonUtility.FromJson("{\"list\":" + jsonData + "}").list; + foreach (var cam in details) + cam.type = OpenSeeWebcamType.DirectShow; + if (includeBlackMagic) { + List bmDetails = JsonUtility.FromJson("{\"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(); + } +} +} \ No newline at end of file diff --git a/Examples/OpenSeeWebcamInfo.cs.meta b/Examples/OpenSeeWebcamInfo.cs.meta new file mode 100644 index 0000000..8fef5a0 --- /dev/null +++ b/Examples/OpenSeeWebcamInfo.cs.meta @@ -0,0 +1,11 @@ +fileFormatVersion: 2 +guid: 2e9598f1218283b418c7e72604be0335 +MonoImporter: + externalObjects: {} + serializedVersion: 2 + defaultReferences: [] + executionOrder: -50 + icon: {instanceID: 0} + userData: + assetBundleName: + assetBundleVariant: diff --git a/Images/3DDFA_V2.jpg b/Images/3DDFA_V2.jpg new file mode 100644 index 0000000..4a2f5fe Binary files /dev/null and b/Images/3DDFA_V2.jpg differ diff --git a/Images/3DDFA_V2_800.jpg b/Images/3DDFA_V2_800.jpg new file mode 100644 index 0000000..14b0816 Binary files /dev/null and b/Images/3DDFA_V2_800.jpg differ diff --git a/Images/OSF.png b/Images/OSF.png new file mode 100644 index 0000000..b8e23d8 Binary files /dev/null and b/Images/OSF.png differ diff --git a/Images/Points.png b/Images/Points.png new file mode 100644 index 0000000..0bc8a07 Binary files /dev/null and b/Images/Points.png differ diff --git a/Licenses/ThunderSVM.txt b/Licenses/ThunderSVM.txt new file mode 100644 index 0000000..8dada3e --- /dev/null +++ b/Licenses/ThunderSVM.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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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. diff --git a/README.md b/README.md index 0f0125f..4d17be5 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ +![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 [such](https://www.vseeface.icu/) an application, which is currently in a semi-public beta, with releases being posted in the `#tech-dev` channel of [@Virtual_Deat](https://twitter.com/Virtual_Deat)'s Discord server. +**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. @@ -8,15 +10,21 @@ As Pytorch 1.3 CPU inference speed on Windows is very low, the model was convert If anyone is curious, the name is a silly pun on the open seas and seeing faces. There's no deeper meaning. -[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) - -These sample videos use older versions of the tracking models. Tracking accuracy of the current models is better. - 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. +# Tracking quality + +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. @@ -30,6 +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: @@ -83,6 +93,7 @@ To save both the trained model and the captured training data, type in a filenam 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. (59fps) * Model **2**: This is a slower model with good accuracy. (50fps) @@ -90,7 +101,7 @@ Four pretrained face landmark models are included. Using the `--model` switch, i 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). +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 @@ -118,9 +129,8 @@ 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 +# Dependencies (Python 3.6 - 3.9) -* Python 3.7 * ONNX Runtime * OpenCV * Pillow @@ -130,6 +140,22 @@ 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 + +* onnxruntime +* OpenCV +* Pillow +* Numpy + +The required libraries can be installed using pip: + + pip install onnxruntime opencv-python pillow numpy + # References ## Training dataset @@ -155,6 +181,8 @@ 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, diff --git a/Source/ThunderSVMWrapper.cpp b/Source/ThunderSVMWrapper.cpp new file mode 100644 index 0000000..6743ebb --- /dev/null +++ b/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 + +using namespace std; + +extern "C" void __cdecl print_log(const char *str) { + return; +} + +struct problem { + int r; + int c; + DataSet::node2d x; + vector 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(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(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 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 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; + } +} \ No newline at end of file diff --git a/Unity/OpenSee.cs b/Unity/OpenSee.cs index ef5f00a..009972f 100644 --- a/Unity/OpenSee.cs +++ b/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); diff --git a/Unity/OpenSeeExpression.cs b/Unity/OpenSeeExpression.cs index 11c4f30..1c05057 100644 --- a/Unity/OpenSeeExpression.cs +++ b/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; @@ -71,6 +83,44 @@ public class OpenSeeExpression : MonoBehaviour public string confusionMatrixString = ""; [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 { @@ -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> 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(); @@ -309,7 +364,10 @@ public class OpenSeeExpression : MonoBehaviour } ResetInfo(); expressions = new Dictionary>(); - model = new SVMModel(); + 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 keys = new List(); List accuracyWarnings = new List(); @@ -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."); - accuracyWarnings.Add("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. (" + list.Count + ")"); samples += list.Count; keys.Add(key); } else { - Debug.Log("[Training warning] Skipping expression " + key + " due to lack of collected data."); - accuracyWarnings.Add("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. (" + 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++) { - weights[i] = weightMap[classLabels[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; diff --git a/Unity/OpenSeeIKTarget.cs b/Unity/OpenSeeIKTarget.cs index 41d85d6..4f775f8 100644 --- a/Unity/OpenSeeIKTarget.cs +++ b/Unity/OpenSeeIKTarget.cs @@ -2,17 +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.")] @@ -80,17 +87,46 @@ public class OpenSeeIKTarget : MonoBehaviour 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); - transform.localPosition = Vector3.Lerp(lastT, currentT, t); - transform.localRotation = Quaternion.Lerp(lastR, currentR, t); + float t = Mathf.Clamp((float)interpolationCount / avgInterps, 0f, 1.5f); + transform.localPosition = Vector3.SlerpUnclamped(lastT, currentT, 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) { @@ -103,23 +139,33 @@ public class OpenSeeIKTarget : MonoBehaviour void RunUpdate() { var openSeeData = openSee.GetOpenSeeData(faceId); - if (openSeeData == null || openSeeData.fit3DError > openSee.maxFit3DError) - return; - if (openSeeData.time > updated) { - updated = openSeeData.time; - trackingFrames++; - if (trackingFrames < skipFirst) + 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; + } + + convertedQuaternion = new Quaternion(-openSeeData.rawQuaternion.y, -openSeeData.rawQuaternion.x, openSeeData.rawQuaternion.z, openSeeData.rawQuaternion.w); + t = openSeeData.translation; + t.x = -t.x; + t.z = -t.z; + convertedTranslation = new Vector3(t.x, t.y, t.z); } else { - Interpolate(); - return; + convertedQuaternion = externalTarget.localRotation; + convertedTranslation = externalTarget.localPosition; + t = convertedTranslation; } - - Quaternion convertedQuaternion = new Quaternion(-openSeeData.rawQuaternion.y, -openSeeData.rawQuaternion.x, openSeeData.rawQuaternion.z, openSeeData.rawQuaternion.w); - Vector3 t = openSeeData.translation; - t.x = -t.x; - t.z = -t.z; - Vector3 convertedTranslation = new Vector3(t.x, t.y, t.z); // Check for angular outliers if (gotAccepted) { @@ -184,6 +230,9 @@ public class OpenSeeIKTarget : MonoBehaviour outlierSkipsDistance += 1; return; } + + calibrate = calibrate || (externalTarget != lastExternalTarget); + lastExternalTarget = externalTarget; if (calibrate) { dR = convertedQuaternion; @@ -210,29 +259,43 @@ public class OpenSeeIKTarget : MonoBehaviour convertedQuaternion = MirrorQuaternion(convertedQuaternion); t = MirrorTranslation(t); } - + if (interpolateState > 1) avgInterps = Mathf.Lerp(avgInterps, (float)interpolationCount, 0.15f); interpolationCount = 0; averageInterpolations = avgInterps; - - lastT = currentT; - lastR = currentR; + + lastT = transform.localPosition; + lastR = transform.localRotation; if (interpolateState > 0) { - currentT = Vector3.Lerp(currentT, (t - dT) * translationScale, 1f - smooth); - currentR = Quaternion.Lerp(transform.localRotation, convertedQuaternion * dR, 1f - smooth); + currentT = FilterPos(transform.localPosition, (t - dT) * translationScale); + currentR = FilterRot(transform.localRotation, convertedQuaternion * dR); } else { currentT = (t - dT) * translationScale; currentR = convertedQuaternion * dR; } if (interpolateState < 2) interpolateState++; - + if (interpolate) { Interpolate(); } else { - transform.localPosition = Vector3.Lerp(transform.localPosition, (t - dT) * translationScale, 1f - smooth); - transform.localRotation = Quaternion.Lerp(transform.localRotation, convertedQuaternion * dR, 1f - smooth); + transform.localPosition = FilterPos(transform.localPosition, (t - dT) * translationScale); + 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) { @@ -247,6 +310,10 @@ public class OpenSeeIKTarget : MonoBehaviour translationOffset = new Vector3(dT.x, dT.y, dT.z); } } + + public void SetPreventVerticalMotion(bool v) { + preventVerticalMotion = v; + } void FixedUpdate() { diff --git a/Unity/OpenSeeIKTarget.cs.meta b/Unity/OpenSeeIKTarget.cs.meta index 2507d88..dd50d92 100644 --- a/Unity/OpenSeeIKTarget.cs.meta +++ b/Unity/OpenSeeIKTarget.cs.meta @@ -4,7 +4,7 @@ MonoImporter: externalObjects: {} serializedVersion: 2 defaultReferences: [] - executionOrder: 0 + executionOrder: -5 icon: {instanceID: 0} userData: assetBundleName: diff --git a/Unity/OpenSeeInterpolation.cs b/Unity/OpenSeeInterpolation.cs new file mode 100644 index 0000000..91980b9 --- /dev/null +++ b/Unity/OpenSeeInterpolation.cs @@ -0,0 +1,232 @@ +using System.Collections; +using System.Collections.Generic; +using UnityEngine; + +namespace OpenSee { + public class InterpolatedMap where V : InterpolatedValue, new() { + private Dictionary map = new Dictionary(); + 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 { + 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 { + 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 { + 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 { + 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 { + 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 { + 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 { + public override float ValueLerpUnclamped(float a, float b, float t) { + return Mathf.LerpUnclamped(a, b, t); + } + public override float GetBuiltinDefault() { + return 0f; + } + } +} diff --git a/Unity/OpenSeeInterpolation.cs.meta b/Unity/OpenSeeInterpolation.cs.meta new file mode 100644 index 0000000..a5eda33 --- /dev/null +++ b/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: diff --git a/Unity/OpenSeeKinematicInterpolation.cs b/Unity/OpenSeeKinematicInterpolation.cs new file mode 100644 index 0000000..7b347a8 --- /dev/null +++ b/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(); + } +} + +} \ No newline at end of file diff --git a/Unity/OpenSeeKinematicInterpolation.cs.meta b/Unity/OpenSeeKinematicInterpolation.cs.meta new file mode 100644 index 0000000..1a31547 --- /dev/null +++ b/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: diff --git a/Unity/OpenSeeLauncher.cs b/Unity/OpenSeeLauncher.cs index 3944823..2fa8280 100644 --- a/Unity/OpenSeeLauncher.cs +++ b/Unity/OpenSeeLauncher.cs @@ -51,14 +51,18 @@ 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 commandlineOptions = new List(new string[] { "--silent", "1", "--max-threads", "4" }); + public List commandlineOptions = new List(new string[] { "--silent", "1", "--max-threads", "1" }); [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 libdshowcapture DLLs directly, which can be faster. Make sure the DLLs are in your Unity project.")] @@ -79,6 +83,30 @@ public class OpenSeeLauncher : MonoBehaviour { private System.IntPtr processStdOut = System.IntPtr.Zero; 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) @@ -195,6 +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) { @@ -227,6 +297,27 @@ public class OpenSeeLauncher : MonoBehaviour { List arguments = new List(); 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"); @@ -243,6 +334,9 @@ public class OpenSeeLauncher : MonoBehaviour { string argumentString = EscapeArguments(arguments.ToArray()); if (extraOptions != "") argumentString = argumentString + " " + extraOptions; + + if (logCommandline) + UnityEngine.Debug.Log("Starting tracker: " + argumentString); StopTracker(); diff --git a/Unity/OpenSeeShowPoints.cs b/Unity/OpenSeeShowPoints.cs index 1211044..64c2fe1 100644 --- a/Unity/OpenSeeShowPoints.cs +++ b/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; @@ -17,6 +18,7 @@ public class OpenSeeShowPoints : MonoBehaviour { public bool showLines = false; public float lineWidth = 0.01f; public Material lineMaterial; + public bool receiveShadows = false; private OpenSee.OpenSeeData openSeeData; private GameObject[] gameObjects; @@ -25,7 +27,10 @@ public class OpenSeeShowPoints : MonoBehaviour { 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, 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 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 pt30Set = new HashSet { 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) { @@ -42,6 +47,7 @@ public class OpenSeeShowPoints : MonoBehaviour { gameObjects[i].name = "Point " + (i + 1); if (material != null) gameObjects[i].GetComponent().material = material; + gameObjects[i].GetComponent().receiveShadows = receiveShadows; gameObjects[i].layer = gameObject.layer; gameObjects[i].transform.SetParent(transform); gameObjects[i].transform.localScale = new Vector3(0.025f, 0.025f, 0.025f); @@ -49,22 +55,31 @@ public class OpenSeeShowPoints : MonoBehaviour { GameObject cylinder = GameObject.CreatePrimitive(PrimitiveType.Cylinder); if (material != null) cylinder.GetComponent().material = material; + cylinder.GetComponent().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"); - lineGameObject.transform.SetParent(gameObjects[48].transform); + 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"); - lineGameObject.transform.SetParent(gameObjects[53].transform); + 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; } @@ -73,6 +88,7 @@ public class OpenSeeShowPoints : MonoBehaviour { lineRenderers[i].useWorldSpace = true; lineRenderers[i].generateLightingData = true; lineRenderers[i].material = lineMaterial; + lineRenderers[i].receiveShadows = receiveShadows; lineRenderers[i].widthMultiplier = lineWidth; lineRenderers[i].positionCount = 2; } @@ -161,6 +177,7 @@ public class OpenSeeShowPoints : MonoBehaviour { for (int i = 0; i < 66; i++) { Renderer renderer = gameObjects[i].GetComponent(); + 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; @@ -172,21 +189,29 @@ public class OpenSeeShowPoints : MonoBehaviour { } } for (int i = 0; i < 68; i++) { - if (lines[i] == -1) + 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 (i == 66) - a = 48; - if (i == 67) - a = 53; + 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].material = lineMaterial; + lineRenderers[i].receiveShadows = receiveShadows; lineRenderers[i].material.SetColor("_Color", color); lineRenderers[i].startColor = color; lineRenderers[i].endColor = color; diff --git a/Unity/Plugins/SVMModel.cs b/Unity/Plugins/SVMModel.cs index 705dd2c..fb9ca1b 100644 --- a/Unity/Plugins/SVMModel.cs +++ b/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)] - private static extern System.IntPtr trainModel(float[] features, float[] labels, float[] weights, int rows, int cols, int classes, int probability, float C); + [DllImport("SVMModel", 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 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)] - private static extern void predict(System.IntPtr model, float[] features, [Out] float[] predictions, [Out] double[] probabilities, int rows); + [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "predict")] + 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)] - private static extern System.IntPtr loadModel([MarshalAs(UnmanagedType.LPStr)]string filename, int cols, int classes, double[] means, double[] sdevs); + [DllImport("SVMModel", 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 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)] - private static extern System.IntPtr loadModelString([MarshalAs(UnmanagedType.LPStr)]string modelString, int cols, int classes, double[] means, double[] sdevs); + [DllImport("SVMModel", 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 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)] - private static extern void getScales(System.IntPtr model, [Out] double[] means, [Out] double[] sdevs); + [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "getScales")] + 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)] - private static extern int saveModel(System.IntPtr model, [MarshalAs(UnmanagedType.LPStr)]string filename); + [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModel")] + 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)] - private static extern string saveModelString(System.IntPtr model); + [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, CharSet = CharSet.Ansi, EntryPoint = "saveModelString")] + private static extern string saveModelStringDll(System.IntPtr model); + protected virtual string saveModelString(System.IntPtr model) { + return saveModelStringDll(model); + } - [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl)] - private static extern void destroyModel(System.IntPtr model); + [DllImport("SVMModel", CallingConvention = CallingConvention.Cdecl, EntryPoint = "destroyModel")] + private static extern void destroyModelDll(System.IntPtr model); + protected virtual void destroyModel(System.IntPtr model) { + destroyModelDll(model); + } #endregion private bool haveModel = false; @@ -43,51 +70,51 @@ public class SVMModel { [Serializable] private class SVMModelRepresentation { - private string modelString; - private int cols; - private int maxClasses; - private double[] means; - private double[] sdevs; + public string modelString; + public int cols; + public int maxClasses; + public double[] means; + public double[] sdevs; + } - static 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; - SVMModelRepresentation smr; - if (compress) { - using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Decompress)) { - smr = formatter.Deserialize(gzipStream) as SVMModelRepresentation; - } - } else { - smr = formatter.Deserialize(memoryStream) as SVMModelRepresentation; + 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; + SVMModelRepresentation smr; + if (compress) { + using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Decompress)) { + smr = formatter.Deserialize(gzipStream) as SVMModelRepresentation; } - cols = smr.cols; - maxClasses = smr.maxClasses; - System.IntPtr model = loadModelString(smr.modelString, smr.cols, smr.maxClasses, smr.means, smr.sdevs); - return model; + } else { + smr = formatter.Deserialize(memoryStream) as SVMModelRepresentation; } + cols = smr.cols; + maxClasses = smr.maxClasses; + System.IntPtr model = loadModelString(smr.modelString, smr.cols, smr.maxClasses, smr.means, smr.sdevs); + return model; + } - static public byte[] ToSerialized(System.IntPtr model, int cols, int maxClasses, bool compress) { - SVMModelRepresentation smr = new SVMModelRepresentation(); - smr.cols = cols; - smr.maxClasses = maxClasses; - smr.means = new double[cols]; - smr.sdevs = new double[cols]; - getScales(model, smr.means, smr.sdevs); - smr.modelString = saveModelString(model); + public byte[] ToSerialized(System.IntPtr model, int cols, int maxClasses, bool compress) { + SVMModelRepresentation smr = new SVMModelRepresentation(); + smr.cols = cols; + smr.maxClasses = maxClasses; + smr.means = new double[cols]; + smr.sdevs = new double[cols]; + getScales(model, smr.means, smr.sdevs); + smr.modelString = saveModelString(model); - IFormatter formatter = new BinaryFormatter(); - MemoryStream memoryStream = new MemoryStream(); - if (compress) { - using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Compress)) { - formatter.Serialize(gzipStream, smr); - gzipStream.Flush(); - } - } else { - formatter.Serialize(memoryStream, smr); + IFormatter formatter = new BinaryFormatter(); + MemoryStream memoryStream = new MemoryStream(); + if (compress) { + using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Compress)) { + formatter.Serialize(gzipStream, smr); + gzipStream.Flush(); } - return memoryStream.ToArray(); + } else { + formatter.Serialize(memoryStream, smr); } + return memoryStream.ToArray(); } public void DestroyModel() { @@ -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; diff --git a/Unity/Plugins/ThunderSVMModel.cs b/Unity/Plugins/ThunderSVMModel.cs new file mode 100644 index 0000000..945260d --- /dev/null +++ b/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); + } +} \ No newline at end of file diff --git a/Unity/Plugins/ThunderSVMWrapper.dll b/Unity/Plugins/ThunderSVMWrapper.dll new file mode 100644 index 0000000..ff85cdb Binary files /dev/null and b/Unity/Plugins/ThunderSVMWrapper.dll differ diff --git a/Unity/Plugins/ThunderSVMWrapper.dll.meta b/Unity/Plugins/ThunderSVMWrapper.dll.meta new file mode 100644 index 0000000..24d19ea --- /dev/null +++ b/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: diff --git a/Unity/Plugins/dshowcapture_x64.dll b/Unity/Plugins/dshowcapture_x64.dll new file mode 100644 index 0000000..88e3752 Binary files /dev/null and b/Unity/Plugins/dshowcapture_x64.dll differ diff --git a/Unity/Plugins/dshowcapture_x64.dll.meta b/Unity/Plugins/dshowcapture_x64.dll.meta new file mode 100644 index 0000000..172154a --- /dev/null +++ b/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: diff --git a/Unity/Plugins/dshowcapture_x86.dll b/Unity/Plugins/dshowcapture_x86.dll new file mode 100644 index 0000000..55222da Binary files /dev/null and b/Unity/Plugins/dshowcapture_x86.dll differ diff --git a/Unity/Plugins/dshowcapture_x86.dll.meta b/Unity/Plugins/dshowcapture_x86.dll.meta new file mode 100644 index 0000000..b5dc9a7 --- /dev/null +++ b/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: diff --git a/Unity/Plugins/libminibmcapture32.dll b/Unity/Plugins/libminibmcapture32.dll new file mode 100644 index 0000000..156b184 Binary files /dev/null and b/Unity/Plugins/libminibmcapture32.dll differ diff --git a/Unity/Plugins/libminibmcapture32.dll.meta b/Unity/Plugins/libminibmcapture32.dll.meta new file mode 100644 index 0000000..fcc39c3 --- /dev/null +++ b/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: diff --git a/Unity/Plugins/libminibmcapture64.dll b/Unity/Plugins/libminibmcapture64.dll new file mode 100644 index 0000000..a9139ba Binary files /dev/null and b/Unity/Plugins/libminibmcapture64.dll differ diff --git a/Unity/Plugins/libminibmcapture64.dll.meta b/Unity/Plugins/libminibmcapture64.dll.meta new file mode 100644 index 0000000..2be2e1b --- /dev/null +++ b/Unity/Plugins/libminibmcapture64.dll.meta @@ -0,0 +1,27 @@ +fileFormatVersion: 2 +guid: fcfc753ca92169e48aa69c1ecc1b0d64 +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: diff --git a/Unity/Plugins/thundersvm.dll b/Unity/Plugins/thundersvm.dll new file mode 100644 index 0000000..9c56754 Binary files /dev/null and b/Unity/Plugins/thundersvm.dll differ diff --git a/Unity/Plugins/thundersvm.dll.meta b/Unity/Plugins/thundersvm.dll.meta new file mode 100644 index 0000000..b01d6bb --- /dev/null +++ b/Unity/Plugins/thundersvm.dll.meta @@ -0,0 +1,63 @@ +fileFormatVersion: 2 +guid: e549e6cbc6a547748abc7f1b05bfa7b4 +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: diff --git a/dshowcapture.py b/dshowcapture.py index f91650f..515125c 100644 --- a/dshowcapture.py +++ b/dshowcapture.py @@ -5,53 +5,95 @@ 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 create_frame_buffer(width, height): - buffer = bytearray(width * height * 4) +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 - if lib is None: + 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 not self.buffer is None: + if self.buffer is not None: del self.buffer del self.name_buffer self.destroy_capture() @@ -62,20 +104,88 @@ class DShowCapture(): 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')) + 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) + 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 @@ -86,12 +196,15 @@ class DShowCapture(): 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.size = self.width * self.height * 4 - self.buffer = create_frame_buffer(self.width, self.height) + 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 @@ -110,52 +223,78 @@ class DShowCapture(): return self.lib.get_flipped(self.cap) != 0 def get_colorspace(self): - return self.lib.get_colorspace(self.cap) + 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.lib.get_frame(self.cap, timeout, self.buffer, self.size) == 0: + 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 - if self.real_size is None: - self.real_size = self.lib.get_size(self.cap) img = np.frombuffer(self.buffer, dtype=np.uint8)[0:self.real_size] if self.colorspace in [100, 101]: - img = cv2.cvtColor(img.reshape((self.height,self.width,4)), cv2.COLOR_BGRA2BGR); + 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: - img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_I420); - elif self.colorspace == 201: - img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_NV12); - elif self.colorspace == 202: - img = cv2.cvtColor(img.reshape((3 * self.height // 2,self.width,1)), cv2.COLOR_YUV2BGR_YV12); - elif self.colorspace == 203: - img = cv2.cvtColor(img.reshape((self.height,self.width,1)), cv2.COLOR_GRAY2BGR); - elif self.colorspace == 300: - img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_YVYU); - elif self.colorspace == 301: - img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_YUY2); - elif self.colorspace == 302: - img = cv2.cvtColor(img.reshape((self.height,self.width,2)), cv2.COLOR_YUV2BGR_UYVY); + 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 - return self.lib.stop_capture(self.cap) + 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 @@ -186,11 +325,11 @@ if __name__ == "__main__": 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()}") + 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 not img is None: + if img is not None: cv2.imshow("DShowCapture", img) if cv2.waitKey(1) & 0xFF == ord('q'): sys.exit(0) diff --git a/dshowcapture/dshowcapture_x64.dll b/dshowcapture/dshowcapture_x64.dll index eb8a233..e7f30fd 100644 Binary files a/dshowcapture/dshowcapture_x64.dll and b/dshowcapture/dshowcapture_x64.dll differ diff --git a/dshowcapture/dshowcapture_x86.dll b/dshowcapture/dshowcapture_x86.dll index 5cb2b62..9eb7308 100644 Binary files a/dshowcapture/dshowcapture_x86.dll and b/dshowcapture/dshowcapture_x86.dll differ diff --git a/dshowcapture/libminibmcapture32.dll b/dshowcapture/libminibmcapture32.dll new file mode 100644 index 0000000..156b184 Binary files /dev/null and b/dshowcapture/libminibmcapture32.dll differ diff --git a/dshowcapture/libminibmcapture64.dll b/dshowcapture/libminibmcapture64.dll new file mode 100644 index 0000000..a9139ba Binary files /dev/null and b/dshowcapture/libminibmcapture64.dll differ diff --git a/facetracker.py b/facetracker.py index 12a5f17..205b2fb 100644 --- a/facetracker.py +++ b/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) @@ -26,33 +35,92 @@ parser.add_argument("--scan-every", type=int, help="Set after how many frames a 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): + def __init__(self, fh, output): + self.fh = fh + 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() - camera_count = cap.get_devices() - if args.list_cameras == 1: - print("Available cameras:") - for i in range(camera_count): - camera_name = cap.get_device(i) + 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(f"{i}: {camera_name}") - else: - print(camera_name) + print("Available cameras:") + for cam in info: + type = "" + if cam['type'] == "Blackmagic": + type = "Blackmagic: " + if args.list_cameras == 1: + print(f"{cam['index']}: {type}{cam['name']}") + else: + print(f"{type}{cam['name']}") cap.destroy_capture() sys.exit(0) @@ -61,8 +129,25 @@ import time import cv2 import socket import struct -from input_reader import InputReader, VideoReader -from tracker import Tracker +import json +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 @@ -70,13 +155,19 @@ 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 +fps = args.fps +dcap = None +use_dshowcapture_flag = False if os.name == 'nt': - fps = args.fps + 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) + 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, use_dshowcapture=False) + input_reader = InputReader(args.capture, args.raw_rgb, args.width, args.height, fps) if type(input_reader.reader) == VideoReader: fps = 0 @@ -87,6 +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 @@ -96,40 +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 - break + 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() @@ -138,179 +251,197 @@ 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, max_feature_updates=args.max_feature_updates, static_model=True if args.no_3d_adapt == 1 else False) - if not args.video_out is None: - out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), 24, (width,height)) + 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 args.video_out is not None: + out = cv2.VideoWriter(args.video_out, cv2.VideoWriter_fourcc('F','F','V','1'), args.video_fps, (width * args.video_scale, height * args.video_scale)) - inference_start = time.perf_counter() - faces = tracker.predict(frame) - if len(faces) > 0: - tracking_time += (time.perf_counter() - inference_start) / len(faces) - tracking_frames += 1 - packet = bytearray() - detected = False - for face_num, f in enumerate(faces): - f = copy.copy(f) - f.id += args.face_id_offset - if f.eye_blink is None: - f.eye_blink = [1, 1] - right_state = "O" if f.eye_blink[0] > 0.30 else "-" - left_state = "O" if f.eye_blink[1] > 0.30 else "-" - if args.silent == 0: - print(f"Confidence[{f.id}]: {f.conf:.4f} / 3D fitting error: {f.pnp_error:.4f} / Eyes: {left_state}, {right_state}") - detected = True - if not f.success: - pts_3d = np.zeros((70, 3), np.float32) - packet.extend(bytearray(struct.pack("d", now))) - packet.extend(bytearray(struct.pack("i", f.id))) - packet.extend(bytearray(struct.pack("f", width))) - packet.extend(bytearray(struct.pack("f", height))) - packet.extend(bytearray(struct.pack("f", f.eye_blink[0]))) - packet.extend(bytearray(struct.pack("f", f.eye_blink[1]))) - packet.extend(bytearray(struct.pack("B", 1 if f.success else 0))) - packet.extend(bytearray(struct.pack("f", f.pnp_error))) - packet.extend(bytearray(struct.pack("f", f.quaternion[0]))) - packet.extend(bytearray(struct.pack("f", f.quaternion[1]))) - packet.extend(bytearray(struct.pack("f", f.quaternion[2]))) - packet.extend(bytearray(struct.pack("f", f.quaternion[3]))) - packet.extend(bytearray(struct.pack("f", f.euler[0]))) - packet.extend(bytearray(struct.pack("f", f.euler[1]))) - packet.extend(bytearray(struct.pack("f", f.euler[2]))) - 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: - 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]}") - 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)) - 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: - log.write(f",{y},{x},{c}") - if pt_num == 66 and f.eye_blink[0] < 0.30: - continue - if pt_num == 67 and f.eye_blink[1] < 0.30: - continue - x = int(x + 0.5) - y = int(y + 0.5) - if args.visualize != 0 or not out is 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) - 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 - x += 1 - 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: - projected = cv2.projectPoints(f.contour, f.rotation, f.translation, tracker.camera, tracker.dist_coeffs) - for [(x,y)] in projected[0]: + try: + inference_start = time.perf_counter() + faces = tracker.predict(frame) + if len(faces) > 0: + 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 + for face_num, f in enumerate(faces): + f = copy.copy(f) + f.id += args.face_id_offset + if f.eye_blink is None: + f.eye_blink = [1, 1] + right_state = "O" if f.eye_blink[0] > 0.30 else "-" + left_state = "O" if f.eye_blink[1] > 0.30 else "-" + if args.silent == 0: + print(f"Confidence[{f.id}]: {f.conf:.4f} / 3D fitting error: {f.pnp_error:.4f} / Eyes: {left_state}, {right_state}") + detected = True + if not f.success: + pts_3d = np.zeros((70, 3), np.float32) + packet.extend(bytearray(struct.pack("d", now))) + packet.extend(bytearray(struct.pack("i", f.id))) + packet.extend(bytearray(struct.pack("f", width))) + packet.extend(bytearray(struct.pack("f", height))) + packet.extend(bytearray(struct.pack("f", f.eye_blink[0]))) + packet.extend(bytearray(struct.pack("f", f.eye_blink[1]))) + packet.extend(bytearray(struct.pack("B", 1 if f.success else 0))) + packet.extend(bytearray(struct.pack("f", f.pnp_error))) + packet.extend(bytearray(struct.pack("f", f.quaternion[0]))) + packet.extend(bytearray(struct.pack("f", f.quaternion[1]))) + packet.extend(bytearray(struct.pack("f", f.quaternion[2]))) + packet.extend(bytearray(struct.pack("f", f.quaternion[3]))) + packet.extend(bytearray(struct.pack("f", f.euler[0]))) + packet.extend(bytearray(struct.pack("f", f.euler[1]))) + packet.extend(bytearray(struct.pack("f", f.euler[2]))) + 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 log is not None: + 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, (0,0,255)) + 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 log is not None: + log.write(f",{y},{x},{c}") + 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 or c < 0.20): + continue x = int(x + 0.5) y = int(y + 0.5) - if not (x < 0 or y < 0 or x >= height or y >= width): - frame[int(x), int(y)] = (0, 255, 255) - x += 1 - if not (x < 0 or y < 0 or x >= height or y >= width): - frame[int(x), int(y)] = (0, 255, 255) - y += 1 - if not (x < 0 or y < 0 or x >= height or y >= width): - frame[int(x), int(y)] = (0, 255, 255) - x -= 1 - if not (x < 0 or y < 0 or x >= height or y >= width): - frame[int(x), int(y)] = (0, 255, 255) - for (x,y,z) in f.pts_3d: - 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: - log.write(f",{x},{-y},{-z}") - if f.current_features is None: - f.current_features = {} - for feature in features: - 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: - log.write(f",{f.current_features[feature]}") - if not log is None: - log.write("\r\n") - log.flush() + 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, 255, 0) + if pt_num >= 66: + color = (255, 255, 0) + if not (x < 0 or y < 0 or x >= height or y >= width): + cv2.circle(frame, (y, x), 1, color, -1) + if args.pnp_points != 0 and (args.visualize != 0 or out is not 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: + projected = cv2.projectPoints(f.contour, f.rotation, f.translation, tracker.camera, tracker.dist_coeffs) + for [(x,y)] in projected[0]: + x = int(x + 0.5) + y = int(y + 0.5) + if not (x < 0 or y < 0 or x >= height or y >= width): + frame[int(x), int(y)] = (0, 255, 255) + x += 1 + if not (x < 0 or y < 0 or x >= height or y >= width): + frame[int(x), int(y)] = (0, 255, 255) + y += 1 + if not (x < 0 or y < 0 or x >= height or y >= width): + frame[int(x), int(y)] = (0, 255, 255) + x -= 1 + if not (x < 0 or y < 0 or x >= height or y >= width): + frame[int(x), int(y)] = (0, 255, 255) + for (x,y,z) in f.pts_3d: + packet.extend(bytearray(struct.pack("f", x))) + packet.extend(bytearray(struct.pack("f", -y))) + packet.extend(bytearray(struct.pack("f", -z))) + if log is not None: + log.write(f",{x},{-y},{-z}") + if f.current_features is None: + f.current_features = {} + for feature in features: + if not feature in f.current_features: + f.current_features[feature] = 0 + packet.extend(bytearray(struct.pack("f", f.current_features[feature]))) + if log is not None: + log.write(f",{f.current_features[feature]}") + 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 detected and len(faces) < 40: + sock.sendto(packet, (target_ip, target_port)) - if not out is None: - out.write(frame) + if out is not None: + 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: - np.set_printoptions(threshold=sys.maxsize, precision=15) - pairs = [ - (0, 16), - (1, 15), - (2, 14), - (3, 13), - (4, 12), - (5, 11), - (6, 10), - (7, 9), - (17, 26), - (18, 25), - (19, 24), - (20, 23), - (21, 22), - (31, 35), - (32, 34), - (36, 45), - (37, 44), - (38, 43), - (39, 42), - (40, 47), - (41, 46), - (48, 52), - (49, 51), - (56, 54), - (57, 53), - (58, 62), - (59, 61), - (65, 63) - ] - points = copy.copy(faces[0].face_3d) - for a, b in pairs: - x = (points[a, 0] - points[b, 0]) / 2.0 - y = (points[a, 1] + points[b, 1]) / 2.0 - z = (points[a, 2] + points[b, 2]) / 2.0 - points[a, 0] = x - points[b, 0] = -x - points[[a, b], 1] = y - points[[a, b], 2] = z - points[[8, 27, 28, 29, 33, 50, 55, 60, 64], 0] = 0.0 - points[30, :] = 0.0 - with open(args.dump_points, "w") as fh: - fh.write(repr(points)) + if args.visualize != 0: + cv2.imshow('OpenSeeFace Visualization', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + if args.dump_points != "" and faces is not None and len(faces) > 0: + np.set_printoptions(threshold=sys.maxsize, precision=15) + pairs = [ + (0, 16), + (1, 15), + (2, 14), + (3, 13), + (4, 12), + (5, 11), + (6, 10), + (7, 9), + (17, 26), + (18, 25), + (19, 24), + (20, 23), + (21, 22), + (31, 35), + (32, 34), + (36, 45), + (37, 44), + (38, 43), + (39, 42), + (40, 47), + (41, 46), + (48, 52), + (49, 51), + (56, 54), + (57, 53), + (58, 62), + (59, 61), + (65, 63) + ] + points = copy.copy(faces[0].face_3d) + for a, b in pairs: + x = (points[a, 0] - points[b, 0]) / 2.0 + y = (points[a, 1] + points[b, 1]) / 2.0 + z = (points[a, 2] + points[b, 2]) / 2.0 + points[a, 0] = x + points[b, 0] = -x + points[[a, b], 1] = y + points[[a, b], 2] = z + points[[8, 27, 28, 29, 33, 50, 55, 60, 64], 0] = 0.0 + points[30, :] = 0.0 + 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: @@ -318,10 +449,12 @@ except KeyboardInterrupt: print("Quitting") input_reader.close() -if not out is None: +if out is not None: out.release() -cv2.destroyAllWindows() +if args.visualize != 0: + cv2.destroyAllWindows() if args.silent == 0 and tracking_frames > 0: - tracking_time = 1000 * tracking_time / tracking_frames - print(f"Average tracking time per detected face: {tracking_time:.2f} ms") + average_tracking_time = 1000 * tracking_time / tracking_frames + 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}") diff --git a/facetracker.spec b/facetracker.spec new file mode 100644 index 0000000..060d850 --- /dev/null +++ b/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') diff --git a/input_reader.py b/input_reader.py index 9a0b8f4..2344ada 100644 --- a/input_reader.py +++ b/input_reader.py @@ -7,6 +7,7 @@ import escapi import dshowcapture import time import traceback +import gc class VideoReader(): def __init__(self, capture, camera=False): @@ -17,6 +18,7 @@ class VideoReader(): 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): @@ -33,6 +35,8 @@ class EscapiReader(VideoReader): self.height = height self.fps = fps 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) def is_open(self): @@ -50,22 +54,46 @@ class EscapiReader(VideoReader): escapi.deinit_camera(self.device) class DShowCaptureReader(VideoReader): - def __init__(self, capture, width, height, fps, use_dshowcapture=True): + 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() - self.device_name = self.device.get_device(capture) - self.device.capture_device(capture, self.width, self.height, self.fps) + 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 = self.device.get_frame(self.timeout) + 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: @@ -79,9 +107,11 @@ class OpenCVReader(VideoReader): 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): @@ -91,6 +121,30 @@ class OpenCVReader(VideoReader): 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) @@ -124,7 +178,7 @@ def try_int(s): return None def test_reader(reader): - got_any = False + got_any = 0 try: for i in range(30): if not reader.is_ready(): @@ -135,9 +189,11 @@ def test_reader(reader): print("No frame") else: print("Got frame") - got_any = True + got_any += 1 + if got_any > 10: + break if reader.is_open(): - return got_any + return got_any > 0 print("Fail") return False except: @@ -146,8 +202,9 @@ def test_reader(reader): return False class InputReader(): - def __init__(self, capture, raw_rgb, width, height, fps, use_dshowcapture=False): + 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) @@ -160,12 +217,15 @@ class InputReader(): name = "" try: if use_dshowcapture: - self.reader = DShowCaptureReader(int(capture), width, height, fps) - name = self.reader.device_name + 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 @@ -177,7 +237,7 @@ class InputReader(): devices = escapi.count_capture_devices() found = None for i in range(devices): - escapi_name = str(escapi.device_name(i).decode('utf8')) + escapi_name = str(escapi.device_name(i).decode('utf8', 'surrogateescape')) if name == escapi_name: found = i if found is None: @@ -187,12 +247,23 @@ class InputReader(): 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: diff --git a/make_exe.bat b/make_exe.bat index 667c5a3..762442b 100644 --- a/make_exe.bat +++ b/make_exe.bat @@ -1,13 +1,31 @@ -REM pyinstaller --onefile ^ -REM --add-binary dshowcapture/dshowcapture_x86.dll;. ^ -REM --add-binary dshowcapture/dshowcapture_x64.dll;. ^ -REM --add-binary escapi/escapi_x86.dll;. ^ -REM --add-binary escapi/escapi_x64.dll;. ^ -REM facetracker.py +echo "Started" -pyinstaller --onedir ^ - --add-binary dshowcapture/dshowcapture_x86.dll;. ^ - --add-binary dshowcapture/dshowcapture_x64.dll;. ^ - --add-binary escapi/escapi_x86.dll;. ^ - --add-binary escapi/escapi_x64.dll;. ^ - facetracker.py \ No newline at end of file +:: A venv is used so it's easy to add the onnxruntime dll to the binary + +echo "Creating venv" +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" diff --git a/make_exe.sh b/make_exe.sh new file mode 100755 index 0000000..1963f23 --- /dev/null +++ b/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!" + diff --git a/model.py b/model.py index d982f03..8f769cd 100644 --- a/model.py +++ b/model.py @@ -1,7 +1,7 @@ # 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 +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): @@ -121,6 +121,10 @@ class OpenSeeFaceDetect(geffnet.mobilenetv3.MobileNetV3): 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 @@ -128,7 +132,7 @@ class OpenSeeFaceDetect(geffnet.mobilenetv3.MobileNetV3): # 2: "large", 0.75 # 3: "large", 1.0 class OpenSeeFaceLandmarks(geffnet.mobilenetv3.MobileNetV3): - def __init__(self, size="large", channel_multiplier=1.0): + 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": @@ -143,6 +147,7 @@ class OpenSeeFaceLandmarks(geffnet.mobilenetv3.MobileNetV3): 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) @@ -158,6 +163,62 @@ class OpenSeeFaceLandmarks(geffnet.mobilenetv3.MobileNetV3): x = self.up1(x, r3) x = self.up2(x, r2) x = self.group(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) @@ -190,7 +251,7 @@ 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: @@ -247,3 +308,7 @@ if __name__== "__main__": 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) diff --git a/models/benchmark.bin b/models/benchmark.bin new file mode 100644 index 0000000..04fb818 Binary files /dev/null and b/models/benchmark.bin differ diff --git a/models/mnv3_opt_very_fast.onnx b/models/lm_model0_opt.onnx similarity index 100% rename from models/mnv3_opt_very_fast.onnx rename to models/lm_model0_opt.onnx diff --git a/models/mnv3_opt_fast.onnx b/models/lm_model1_opt.onnx similarity index 100% rename from models/mnv3_opt_fast.onnx rename to models/lm_model1_opt.onnx diff --git a/models/mnv3_opt_medium.onnx b/models/lm_model2_opt.onnx similarity index 100% rename from models/mnv3_opt_medium.onnx rename to models/lm_model2_opt.onnx diff --git a/models/mnv3_opt_b.onnx b/models/lm_model3_opt.onnx similarity index 100% rename from models/mnv3_opt_b.onnx rename to 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["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/retinaface.py b/retinaface.py index c97c957..b1d1ae5 100644 --- a/retinaface.py +++ b/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())) diff --git a/run.bat b/run.bat index 644a13a..ea2e030 100644 --- a/run.bat +++ b/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 --discard-after 0 --scan-every 0 --no-3d-adapt 1 --max-feature-updates 900 +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 \ No newline at end of file diff --git a/similaritytransform.py b/similaritytransform.py new file mode 100644 index 0000000..4174787 --- /dev/null +++ b/similaritytransform.py @@ -0,0 +1,1370 @@ +# From scikit-image +import math +import numpy as np +import sys +import textwrap + +def get_bound_method_class(m): + """Return the class for a bound method. + + """ + return m.im_class if sys.version < '3' else m.__self__.__class__ + +def safe_as_int(val, atol=1e-3): + """ + Attempt to safely cast values to integer format. + + Parameters + ---------- + val : scalar or iterable of scalars + Number or container of numbers which are intended to be interpreted as + integers, e.g., for indexing purposes, but which may not carry integer + type. + atol : float + Absolute tolerance away from nearest integer to consider values in + ``val`` functionally integers. + + Returns + ------- + val_int : NumPy scalar or ndarray of dtype `np.int64` + Returns the input value(s) coerced to dtype `np.int64` assuming all + were within ``atol`` of the nearest integer. + + Notes + ----- + This operation calculates ``val`` modulo 1, which returns the mantissa of + all values. Then all mantissas greater than 0.5 are subtracted from one. + Finally, the absolute tolerance from zero is calculated. If it is less + than ``atol`` for all value(s) in ``val``, they are rounded and returned + in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is + returned. + + If any value(s) are outside the specified tolerance, an informative error + is raised. + + Examples + -------- + >>> safe_as_int(7.0) + 7 + + >>> safe_as_int([9, 4, 2.9999999999]) + array([9, 4, 3]) + + >>> safe_as_int(53.1) + Traceback (most recent call last): + ... + ValueError: Integer argument required but received 53.1, check inputs. + + >>> safe_as_int(53.01, atol=0.01) + 53 + + """ + mod = np.asarray(val) % 1 # Extract mantissa + + # Check for and subtract any mod values > 0.5 from 1 + if mod.ndim == 0: # Scalar input, cannot be indexed + if mod > 0.5: + mod = 1 - mod + else: # Iterable input, now ndarray + mod[mod > 0.5] = 1 - mod[mod > 0.5] # Test on each side of nearest int + + try: + np.testing.assert_allclose(mod, 0, atol=atol) + except AssertionError: + raise ValueError("Integer argument required but received " + "{0}, check inputs.".format(val)) + + return np.round(val).astype(np.int64) + +def _to_ndimage_mode(mode): + """Convert from `numpy.pad` mode name to the corresponding ndimage mode.""" + mode_translation_dict = dict(edge='nearest', symmetric='reflect', + reflect='mirror') + if mode in mode_translation_dict: + mode = mode_translation_dict[mode] + return mode + + +def _center_and_normalize_points(points): + """Center and normalize image points. + + The points are transformed in a two-step procedure that is expressed + as a transformation matrix. The matrix of the resulting points is usually + better conditioned than the matrix of the original points. + + Center the image points, such that the new coordinate system has its + origin at the centroid of the image points. + + Normalize the image points, such that the mean distance from the points + to the origin of the coordinate system is sqrt(2). + + Parameters + ---------- + points : (N, 2) array + The coordinates of the image points. + + Returns + ------- + matrix : (3, 3) array + The transformation matrix to obtain the new points. + new_points : (N, 2) array + The transformed image points. + + References + ---------- + .. [1] Hartley, Richard I. "In defense of the eight-point algorithm." + Pattern Analysis and Machine Intelligence, IEEE Transactions on 19.6 + (1997): 580-593. + + """ + + centroid = np.mean(points, axis=0) + + rms = math.sqrt(np.sum((points - centroid) ** 2) / points.shape[0]) + + norm_factor = math.sqrt(2) / rms + + matrix = np.array([[norm_factor, 0, -norm_factor * centroid[0]], + [0, norm_factor, -norm_factor * centroid[1]], + [0, 0, 1]]) + + pointsh = np.row_stack([points.T, np.ones((points.shape[0]),)]) + + new_pointsh = (matrix @ pointsh).T + + new_points = new_pointsh[:, :2] + new_points[:, 0] /= new_pointsh[:, 2] + new_points[:, 1] /= new_pointsh[:, 2] + + return matrix, new_points + + +def _umeyama(src, dst, estimate_scale): + """Estimate N-D similarity transformation with or without scaling. + + Parameters + ---------- + src : (M, N) array + Source coordinates. + dst : (M, N) array + Destination coordinates. + estimate_scale : bool + Whether to estimate scaling factor. + + Returns + ------- + T : (N + 1, N + 1) + The homogeneous similarity transformation matrix. The matrix contains + NaN values only if the problem is not well-conditioned. + + References + ---------- + .. [1] "Least-squares estimation of transformation parameters between two + point patterns", Shinji Umeyama, PAMI 1991, :DOI:`10.1109/34.88573` + + """ + + num = src.shape[0] + dim = src.shape[1] + + # Compute mean of src and dst. + src_mean = src.mean(axis=0) + dst_mean = dst.mean(axis=0) + + # Subtract mean from src and dst. + src_demean = src - src_mean + dst_demean = dst - dst_mean + + # Eq. (38). + A = dst_demean.T @ src_demean / num + + # Eq. (39). + d = np.ones((dim,), dtype=np.double) + if np.linalg.det(A) < 0: + d[dim - 1] = -1 + + T = np.eye(dim + 1, dtype=np.double) + + U, S, V = np.linalg.svd(A) + + # Eq. (40) and (43). + rank = np.linalg.matrix_rank(A) + if rank == 0: + return np.nan * T + elif rank == dim - 1: + if np.linalg.det(U) * np.linalg.det(V) > 0: + T[:dim, :dim] = U @ V + else: + s = d[dim - 1] + d[dim - 1] = -1 + T[:dim, :dim] = U @ np.diag(d) @ V + d[dim - 1] = s + else: + T[:dim, :dim] = U @ np.diag(d) @ V + + if estimate_scale: + # Eq. (41) and (42). + scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d) + else: + scale = 1.0 + + T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T) + T[:dim, :dim] *= scale + + return T + + +class GeometricTransform(object): + """Base class for geometric transformations. + + """ + def __call__(self, coords): + """Apply forward transformation. + + Parameters + ---------- + coords : (N, 2) array + Source coordinates. + + Returns + ------- + coords : (N, 2) array + Destination coordinates. + + """ + raise NotImplementedError() + + def inverse(self, coords): + """Apply inverse transformation. + + Parameters + ---------- + coords : (N, 2) array + Destination coordinates. + + Returns + ------- + coords : (N, 2) array + Source coordinates. + + """ + raise NotImplementedError() + + def residuals(self, src, dst): + """Determine residuals of transformed destination coordinates. + + For each transformed source coordinate the euclidean distance to the + respective destination coordinate is determined. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + residuals : (N, ) array + Residual for coordinate. + + """ + return np.sqrt(np.sum((self(src) - dst)**2, axis=1)) + + def __add__(self, other): + """Combine this transformation with another. + + """ + raise NotImplementedError() + + +class FundamentalMatrixTransform(GeometricTransform): + """Fundamental matrix transformation. + + The fundamental matrix relates corresponding points between a pair of + uncalibrated images. The matrix transforms homogeneous image points in one + image to epipolar lines in the other image. + + The fundamental matrix is only defined for a pair of moving images. In the + case of pure rotation or planar scenes, the homography describes the + geometric relation between two images (`ProjectiveTransform`). If the + intrinsic calibration of the images is known, the essential matrix describes + the metric relation between the two images (`EssentialMatrixTransform`). + + References + ---------- + .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in + computer vision. Cambridge university press, 2003. + + Parameters + ---------- + matrix : (3, 3) array, optional + Fundamental matrix. + + Attributes + ---------- + params : (3, 3) array + Fundamental matrix. + + """ + + def __init__(self, matrix=None): + if matrix is None: + # default to an identity transform + matrix = np.eye(3) + if matrix.shape != (3, 3): + raise ValueError("Invalid shape of transformation matrix") + self.params = matrix + + def __call__(self, coords): + """Apply forward transformation. + + Parameters + ---------- + coords : (N, 2) array + Source coordinates. + + Returns + ------- + coords : (N, 3) array + Epipolar lines in the destination image. + + """ + coords_homogeneous = np.column_stack([coords, np.ones(coords.shape[0])]) + return coords_homogeneous @ self.params.T + + def inverse(self, coords): + """Apply inverse transformation. + + Parameters + ---------- + coords : (N, 2) array + Destination coordinates. + + Returns + ------- + coords : (N, 3) array + Epipolar lines in the source image. + + """ + coords_homogeneous = np.column_stack([coords, np.ones(coords.shape[0])]) + return coords_homogeneous @ self.params + + def _setup_constraint_matrix(self, src, dst): + """Setup and solve the homogeneous epipolar constraint matrix:: + + dst' * F * src = 0. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + F_normalized : (3, 3) array + The normalized solution to the homogeneous system. If the system + is not well-conditioned, this matrix contains NaNs. + src_matrix : (3, 3) array + The transformation matrix to obtain the normalized source + coordinates. + dst_matrix : (3, 3) array + The transformation matrix to obtain the normalized destination + coordinates. + + """ + if src.shape != dst.shape: + raise ValueError('src and dst shapes must be identical.') + if src.shape[0] < 8: + raise ValueError('src.shape[0] must be equal or larger than 8.') + + # Center and normalize image points for better numerical stability. + try: + src_matrix, src = _center_and_normalize_points(src) + dst_matrix, dst = _center_and_normalize_points(dst) + except ZeroDivisionError: + self.params = np.full((3, 3), np.nan) + return 3 * [np.full((3, 3), np.nan)] + + # Setup homogeneous linear equation as dst' * F * src = 0. + A = np.ones((src.shape[0], 9)) + A[:, :2] = src + A[:, :3] *= dst[:, 0, np.newaxis] + A[:, 3:5] = src + A[:, 3:6] *= dst[:, 1, np.newaxis] + A[:, 6:8] = src + + # Solve for the nullspace of the constraint matrix. + _, _, V = np.linalg.svd(A) + F_normalized = V[-1, :].reshape(3, 3) + + return F_normalized, src_matrix, dst_matrix + + def estimate(self, src, dst): + """Estimate fundamental matrix using 8-point algorithm. + + The 8-point algorithm requires at least 8 corresponding point pairs for + a well-conditioned solution, otherwise the over-determined solution is + estimated. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + + F_normalized, src_matrix, dst_matrix = \ + self._setup_constraint_matrix(src, dst) + + # Enforcing the internal constraint that two singular values must be + # non-zero and one must be zero. + U, S, V = np.linalg.svd(F_normalized) + S[2] = 0 + F = U @ np.diag(S) @ V + + self.params = dst_matrix.T @ F @ src_matrix + + return True + + def residuals(self, src, dst): + """Compute the Sampson distance. + + The Sampson distance is the first approximation to the geometric error. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + residuals : (N, ) array + Sampson distance. + + """ + src_homogeneous = np.column_stack([src, np.ones(src.shape[0])]) + dst_homogeneous = np.column_stack([dst, np.ones(dst.shape[0])]) + + F_src = self.params @ src_homogeneous.T + Ft_dst = self.params.T @ dst_homogeneous.T + + dst_F_src = np.sum(dst_homogeneous * F_src.T, axis=1) + + return np.abs(dst_F_src) / np.sqrt(F_src[0] ** 2 + F_src[1] ** 2 + + Ft_dst[0] ** 2 + Ft_dst[1] ** 2) + + +class EssentialMatrixTransform(FundamentalMatrixTransform): + """Essential matrix transformation. + + The essential matrix relates corresponding points between a pair of + calibrated images. The matrix transforms normalized, homogeneous image + points in one image to epipolar lines in the other image. + + The essential matrix is only defined for a pair of moving images capturing a + non-planar scene. In the case of pure rotation or planar scenes, the + homography describes the geometric relation between two images + (`ProjectiveTransform`). If the intrinsic calibration of the images is + unknown, the fundamental matrix describes the projective relation between + the two images (`FundamentalMatrixTransform`). + + References + ---------- + .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in + computer vision. Cambridge university press, 2003. + + Parameters + ---------- + rotation : (3, 3) array, optional + Rotation matrix of the relative camera motion. + translation : (3, 1) array, optional + Translation vector of the relative camera motion. The vector must + have unit length. + matrix : (3, 3) array, optional + Essential matrix. + + Attributes + ---------- + params : (3, 3) array + Essential matrix. + + """ + + def __init__(self, rotation=None, translation=None, matrix=None): + if rotation is not None: + if translation is None: + raise ValueError("Both rotation and translation required") + if rotation.shape != (3, 3): + raise ValueError("Invalid shape of rotation matrix") + if abs(np.linalg.det(rotation) - 1) > 1e-6: + raise ValueError("Rotation matrix must have unit determinant") + if translation.size != 3: + raise ValueError("Invalid shape of translation vector") + if abs(np.linalg.norm(translation) - 1) > 1e-6: + raise ValueError("Translation vector must have unit length") + # Matrix representation of the cross product for t. + t_x = np.array([0, -translation[2], translation[1], + translation[2], 0, -translation[0], + -translation[1], translation[0], 0]).reshape(3, 3) + self.params = t_x @ rotation + elif matrix is not None: + if matrix.shape != (3, 3): + raise ValueError("Invalid shape of transformation matrix") + self.params = matrix + else: + # default to an identity transform + self.params = np.eye(3) + + def estimate(self, src, dst): + """Estimate essential matrix using 8-point algorithm. + + The 8-point algorithm requires at least 8 corresponding point pairs for + a well-conditioned solution, otherwise the over-determined solution is + estimated. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + + E_normalized, src_matrix, dst_matrix = \ + self._setup_constraint_matrix(src, dst) + + # Enforcing the internal constraint that two singular values must be + # equal and one must be zero. + U, S, V = np.linalg.svd(E_normalized) + S[0] = (S[0] + S[1]) / 2.0 + S[1] = S[0] + S[2] = 0 + E = U @ np.diag(S) @ V + + self.params = dst_matrix.T @ E @ src_matrix + + return True + + +class ProjectiveTransform(GeometricTransform): + r"""Projective transformation. + + Apply a projective transformation (homography) on coordinates. + + For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its + target position is calculated by multiplying with the given matrix, + :math:`H`, to give :math:`H \mathbf{x}`:: + + [[a0 a1 a2] + [b0 b1 b2] + [c0 c1 1 ]]. + + E.g., to rotate by theta degrees clockwise, the matrix should be:: + + [[cos(theta) -sin(theta) 0] + [sin(theta) cos(theta) 0] + [0 0 1]] + + or, to translate x by 10 and y by 20:: + + [[1 0 10] + [0 1 20] + [0 0 1 ]]. + + Parameters + ---------- + matrix : (3, 3) array, optional + Homogeneous transformation matrix. + + Attributes + ---------- + params : (3, 3) array + Homogeneous transformation matrix. + + """ + + _coeffs = range(8) + + def __init__(self, matrix=None): + if matrix is None: + # default to an identity transform + matrix = np.eye(3) + if matrix.shape != (3, 3): + raise ValueError("invalid shape of transformation matrix") + self.params = matrix + + @property + def _inv_matrix(self): + return np.linalg.inv(self.params) + + def _apply_mat(self, coords, matrix): + coords = np.array(coords, copy=False, ndmin=2) + + x, y = np.transpose(coords) + src = np.vstack((x, y, np.ones_like(x))) + dst = src.T @ matrix.T + + # below, we will divide by the last dimension of the homogeneous + # coordinate matrix. In order to avoid division by zero, + # we replace exact zeros in this column with a very small number. + dst[dst[:, 2] == 0, 2] = np.finfo(float).eps + # rescale to homogeneous coordinates + dst[:, :2] /= dst[:, 2:3] + + return dst[:, :2] + + def __call__(self, coords): + """Apply forward transformation. + + Parameters + ---------- + coords : (N, 2) array + Source coordinates. + + Returns + ------- + coords : (N, 2) array + Destination coordinates. + + """ + return self._apply_mat(coords, self.params) + + def inverse(self, coords): + """Apply inverse transformation. + + Parameters + ---------- + coords : (N, 2) array + Destination coordinates. + + Returns + ------- + coords : (N, 2) array + Source coordinates. + + """ + return self._apply_mat(coords, self._inv_matrix) + + def estimate(self, src, dst): + """Estimate the transformation from a set of corresponding points. + + You can determine the over-, well- and under-determined parameters + with the total least-squares method. + + Number of source and destination coordinates must match. + + The transformation is defined as:: + + X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1) + Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1) + + These equations can be transformed to the following form:: + + 0 = a0*x + a1*y + a2 - c0*x*X - c1*y*X - X + 0 = b0*x + b1*y + b2 - c0*x*Y - c1*y*Y - Y + + which exist for each set of corresponding points, so we have a set of + N * 2 equations. The coefficients appear linearly so we can write + A x = 0, where:: + + A = [[x y 1 0 0 0 -x*X -y*X -X] + [0 0 0 x y 1 -x*Y -y*Y -Y] + ... + ... + ] + x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3] + + In case of total least-squares the solution of this homogeneous system + of equations is the right singular vector of A which corresponds to the + smallest singular value normed by the coefficient c3. + + In case of the affine transformation the coefficients c0 and c1 are 0. + Thus the system of equations is:: + + A = [[x y 1 0 0 0 -X] + [0 0 0 x y 1 -Y] + ... + ... + ] + x.T = [a0 a1 a2 b0 b1 b2 c3] + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + + try: + src_matrix, src = _center_and_normalize_points(src) + dst_matrix, dst = _center_and_normalize_points(dst) + except ZeroDivisionError: + self.params = np.nan * np.empty((3, 3)) + return False + + xs = src[:, 0] + ys = src[:, 1] + xd = dst[:, 0] + yd = dst[:, 1] + rows = src.shape[0] + + # params: a0, a1, a2, b0, b1, b2, c0, c1 + A = np.zeros((rows * 2, 9)) + A[:rows, 0] = xs + A[:rows, 1] = ys + A[:rows, 2] = 1 + A[:rows, 6] = - xd * xs + A[:rows, 7] = - xd * ys + A[rows:, 3] = xs + A[rows:, 4] = ys + A[rows:, 5] = 1 + A[rows:, 6] = - yd * xs + A[rows:, 7] = - yd * ys + A[:rows, 8] = xd + A[rows:, 8] = yd + + # Select relevant columns, depending on params + A = A[:, list(self._coeffs) + [8]] + + _, _, V = np.linalg.svd(A) + # if the last element of the vector corresponding to the smallest + # singular value is close to zero, this implies a degenerate case + # because it is a rank-defective transform, which would map points + # to a line rather than a plane. + if np.isclose(V[-1, -1], 0): + return False + + H = np.zeros((3, 3)) + # solution is right singular vector that corresponds to smallest + # singular value + H.flat[list(self._coeffs) + [8]] = - V[-1, :-1] / V[-1, -1] + H[2, 2] = 1 + + # De-center and de-normalize + H = np.linalg.inv(dst_matrix) @ H @ src_matrix + + self.params = H + + return True + + def __add__(self, other): + """Combine this transformation with another. + + """ + if isinstance(other, ProjectiveTransform): + # combination of the same types result in a transformation of this + # type again, otherwise use general projective transformation + if type(self) == type(other): + tform = self.__class__ + else: + tform = ProjectiveTransform + return tform(other.params @ self.params) + elif (hasattr(other, '__name__') + and other.__name__ == 'inverse' + and hasattr(get_bound_method_class(other), '_inv_matrix')): + return ProjectiveTransform(other.__self__._inv_matrix @ self.params) + else: + raise TypeError("Cannot combine transformations of differing " + "types.") + + def __nice__(self): + """common 'paramstr' used by __str__ and __repr__""" + npstring = np.array2string(self.params, separator=', ') + paramstr = 'matrix=\n' + textwrap.indent(npstring, ' ') + return paramstr + + def __repr__(self): + """Add standard repr formatting around a __nice__ string""" + paramstr = self.__nice__() + classname = self.__class__.__name__ + classstr = classname + return '<{}({}) at {}>'.format(classstr, paramstr, hex(id(self))) + + def __str__(self): + """Add standard str formatting around a __nice__ string""" + paramstr = self.__nice__() + classname = self.__class__.__name__ + classstr = classname + return '<{}({})>'.format(classstr, paramstr) + + +class AffineTransform(ProjectiveTransform): + """2D affine transformation. + + Has the following form:: + + X = a0*x + a1*y + a2 = + = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2 + + Y = b0*x + b1*y + b2 = + = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2 + + where ``sx`` and ``sy`` are scale factors in the x and y directions, + and the homogeneous transformation matrix is:: + + [[a0 a1 a2] + [b0 b1 b2] + [0 0 1]] + + Parameters + ---------- + matrix : (3, 3) array, optional + Homogeneous transformation matrix. + scale : {s as float or (sx, sy) as array, list or tuple}, optional + Scale factor(s). If a single value, it will be assigned to both + sx and sy. + + .. versionadded:: 0.17 + Added support for supplying a single scalar value. + rotation : float, optional + Rotation angle in counter-clockwise direction as radians. + shear : float, optional + Shear angle in counter-clockwise direction as radians. + translation : (tx, ty) as array, list or tuple, optional + Translation parameters. + + Attributes + ---------- + params : (3, 3) array + Homogeneous transformation matrix. + + """ + + _coeffs = range(6) + + def __init__(self, matrix=None, scale=None, rotation=None, shear=None, + translation=None): + params = any(param is not None + for param in (scale, rotation, shear, translation)) + + if params and matrix is not None: + raise ValueError("You cannot specify the transformation matrix and" + " the implicit parameters at the same time.") + elif matrix is not None: + if matrix.shape != (3, 3): + raise ValueError("Invalid shape of transformation matrix.") + self.params = matrix + elif params: + if scale is None: + scale = (1, 1) + if rotation is None: + rotation = 0 + if shear is None: + shear = 0 + if translation is None: + translation = (0, 0) + + if np.isscalar(scale): + sx = sy = scale + else: + sx, sy = scale + + self.params = np.array([ + [sx * math.cos(rotation), -sy * math.sin(rotation + shear), 0], + [sx * math.sin(rotation), sy * math.cos(rotation + shear), 0], + [ 0, 0, 1] + ]) + self.params[0:2, 2] = translation + else: + # default to an identity transform + self.params = np.eye(3) + + @property + def scale(self): + sx = math.sqrt(self.params[0, 0] ** 2 + self.params[1, 0] ** 2) + sy = math.sqrt(self.params[0, 1] ** 2 + self.params[1, 1] ** 2) + return sx, sy + + @property + def rotation(self): + return math.atan2(self.params[1, 0], self.params[0, 0]) + + @property + def shear(self): + beta = math.atan2(- self.params[0, 1], self.params[1, 1]) + return beta - self.rotation + + @property + def translation(self): + return self.params[0:2, 2] + + + + + +class EuclideanTransform(ProjectiveTransform): + """2D Euclidean transformation. + + Has the following form:: + + X = a0 * x - b0 * y + a1 = + = x * cos(rotation) - y * sin(rotation) + a1 + + Y = b0 * x + a0 * y + b1 = + = x * sin(rotation) + y * cos(rotation) + b1 + + where the homogeneous transformation matrix is:: + + [[a0 b0 a1] + [b0 a0 b1] + [0 0 1]] + + The Euclidean transformation is a rigid transformation with rotation and + translation parameters. The similarity transformation extends the Euclidean + transformation with a single scaling factor. + + Parameters + ---------- + matrix : (3, 3) array, optional + Homogeneous transformation matrix. + rotation : float, optional + Rotation angle in counter-clockwise direction as radians. + translation : (tx, ty) as array, list or tuple, optional + x, y translation parameters. + + Attributes + ---------- + params : (3, 3) array + Homogeneous transformation matrix. + + """ + + def __init__(self, matrix=None, rotation=None, translation=None): + params = any(param is not None + for param in (rotation, translation)) + + if params and matrix is not None: + raise ValueError("You cannot specify the transformation matrix and" + " the implicit parameters at the same time.") + elif matrix is not None: + if matrix.shape != (3, 3): + raise ValueError("Invalid shape of transformation matrix.") + self.params = matrix + elif params: + if rotation is None: + rotation = 0 + if translation is None: + translation = (0, 0) + + self.params = np.array([ + [math.cos(rotation), - math.sin(rotation), 0], + [math.sin(rotation), math.cos(rotation), 0], + [ 0, 0, 1] + ]) + self.params[0:2, 2] = translation + else: + # default to an identity transform + self.params = np.eye(3) + + def estimate(self, src, dst): + """Estimate the transformation from a set of corresponding points. + + You can determine the over-, well- and under-determined parameters + with the total least-squares method. + + Number of source and destination coordinates must match. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + + self.params = _umeyama(src, dst, False) + + return True + + @property + def rotation(self): + return math.atan2(self.params[1, 0], self.params[1, 1]) + + @property + def translation(self): + return self.params[0:2, 2] + + +class SimilarityTransform(EuclideanTransform): + """2D similarity transformation. + + Has the following form:: + + X = a0 * x - b0 * y + a1 = + = s * x * cos(rotation) - s * y * sin(rotation) + a1 + + Y = b0 * x + a0 * y + b1 = + = s * x * sin(rotation) + s * y * cos(rotation) + b1 + + where ``s`` is a scale factor and the homogeneous transformation matrix is:: + + [[a0 b0 a1] + [b0 a0 b1] + [0 0 1]] + + The similarity transformation extends the Euclidean transformation with a + single scaling factor in addition to the rotation and translation + parameters. + + Parameters + ---------- + matrix : (3, 3) array, optional + Homogeneous transformation matrix. + scale : float, optional + Scale factor. + rotation : float, optional + Rotation angle in counter-clockwise direction as radians. + translation : (tx, ty) as array, list or tuple, optional + x, y translation parameters. + + Attributes + ---------- + params : (3, 3) array + Homogeneous transformation matrix. + + """ + + def __init__(self, matrix=None, scale=None, rotation=None, + translation=None): + params = any(param is not None + for param in (scale, rotation, translation)) + + if params and matrix is not None: + raise ValueError("You cannot specify the transformation matrix and" + " the implicit parameters at the same time.") + elif matrix is not None: + if matrix.shape != (3, 3): + raise ValueError("Invalid shape of transformation matrix.") + self.params = matrix + elif params: + if scale is None: + scale = 1 + if rotation is None: + rotation = 0 + if translation is None: + translation = (0, 0) + + self.params = np.array([ + [math.cos(rotation), - math.sin(rotation), 0], + [math.sin(rotation), math.cos(rotation), 0], + [ 0, 0, 1] + ]) + self.params[0:2, 0:2] *= scale + self.params[0:2, 2] = translation + else: + # default to an identity transform + self.params = np.eye(3) + + def estimate(self, src, dst): + """Estimate the transformation from a set of corresponding points. + + You can determine the over-, well- and under-determined parameters + with the total least-squares method. + + Number of source and destination coordinates must match. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + + self.params = _umeyama(src, dst, True) + + return True + + @property + def scale(self): + # det = scale**(# of dimensions), therefore scale = det**(1/2) + return np.sqrt(np.linalg.det(self.params)) + + +class PolynomialTransform(GeometricTransform): + """2D polynomial transformation. + + Has the following form:: + + X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) + Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) + + Parameters + ---------- + params : (2, N) array, optional + Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So, + a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`. + + Attributes + ---------- + params : (2, N) array + Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So, + a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`. + + """ + + def __init__(self, params=None): + if params is None: + # default to transformation which preserves original coordinates + params = np.array([[0, 1, 0], [0, 0, 1]]) + if params.shape[0] != 2: + raise ValueError("invalid shape of transformation parameters") + self.params = params + + def estimate(self, src, dst, order=2): + """Estimate the transformation from a set of corresponding points. + + You can determine the over-, well- and under-determined parameters + with the total least-squares method. + + Number of source and destination coordinates must match. + + The transformation is defined as:: + + X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) + Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) + + These equations can be transformed to the following form:: + + 0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) - X + 0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) - Y + + which exist for each set of corresponding points, so we have a set of + N * 2 equations. The coefficients appear linearly so we can write + A x = 0, where:: + + A = [[1 x y x**2 x*y y**2 ... 0 ... 0 -X] + [0 ... 0 1 x y x**2 x*y y**2 -Y] + ... + ... + ] + x.T = [a00 a10 a11 a20 a21 a22 ... ann + b00 b10 b11 b20 b21 b22 ... bnn c3] + + In case of total least-squares the solution of this homogeneous system + of equations is the right singular vector of A which corresponds to the + smallest singular value normed by the coefficient c3. + + Parameters + ---------- + src : (N, 2) array + Source coordinates. + dst : (N, 2) array + Destination coordinates. + order : int, optional + Polynomial order (number of coefficients is order + 1). + + Returns + ------- + success : bool + True, if model estimation succeeds. + + """ + xs = src[:, 0] + ys = src[:, 1] + xd = dst[:, 0] + yd = dst[:, 1] + rows = src.shape[0] + + # number of unknown polynomial coefficients + order = safe_as_int(order) + u = (order + 1) * (order + 2) + + A = np.zeros((rows * 2, u + 1)) + pidx = 0 + for j in range(order + 1): + for i in range(j + 1): + A[:rows, pidx] = xs ** (j - i) * ys ** i + A[rows:, pidx + u // 2] = xs ** (j - i) * ys ** i + pidx += 1 + + A[:rows, -1] = xd + A[rows:, -1] = yd + + _, _, V = np.linalg.svd(A) + + # solution is right singular vector that corresponds to smallest + # singular value + params = - V[-1, :-1] / V[-1, -1] + + self.params = params.reshape((2, u // 2)) + + return True + + def __call__(self, coords): + """Apply forward transformation. + + Parameters + ---------- + coords : (N, 2) array + source coordinates + + Returns + ------- + coords : (N, 2) array + Transformed coordinates. + + """ + x = coords[:, 0] + y = coords[:, 1] + u = len(self.params.ravel()) + # number of coefficients -> u = (order + 1) * (order + 2) + order = int((- 3 + math.sqrt(9 - 4 * (2 - u))) / 2) + dst = np.zeros(coords.shape) + + pidx = 0 + for j in range(order + 1): + for i in range(j + 1): + dst[:, 0] += self.params[0, pidx] * x ** (j - i) * y ** i + dst[:, 1] += self.params[1, pidx] * x ** (j - i) * y ** i + pidx += 1 + + return dst + + def inverse(self, coords): + raise Exception( + 'There is no explicit way to do the inverse polynomial ' + 'transformation. Instead, estimate the inverse transformation ' + 'parameters by exchanging source and destination coordinates,' + 'then apply the forward transformation.') + + +TRANSFORMS = { + 'euclidean': EuclideanTransform, + 'similarity': SimilarityTransform, + 'affine': AffineTransform, + 'projective': ProjectiveTransform, + 'fundamental': FundamentalMatrixTransform, + 'essential': EssentialMatrixTransform, + 'polynomial': PolynomialTransform, +} + + +def estimate_transform(ttype, src, dst, **kwargs): + """Estimate 2D geometric transformation parameters. + + You can determine the over-, well- and under-determined parameters + with the total least-squares method. + + Number of source and destination coordinates must match. + + Parameters + ---------- + ttype : {'euclidean', similarity', 'affine', 'piecewise-affine', \ + 'projective', 'polynomial'} + Type of transform. + kwargs : array or int + Function parameters (src, dst, n, angle):: + + NAME / TTYPE FUNCTION PARAMETERS + 'euclidean' `src, `dst` + 'similarity' `src, `dst` + 'affine' `src, `dst` + 'piecewise-affine' `src, `dst` + 'projective' `src, `dst` + 'polynomial' `src, `dst`, `order` (polynomial order, + default order is 2) + + Also see examples below. + + Returns + ------- + tform : :class:`GeometricTransform` + Transform object containing the transformation parameters and providing + access to forward and inverse transformation functions. + + Examples + -------- + >>> import numpy as np + >>> from skimage import transform + + >>> # estimate transformation parameters + >>> src = np.array([0, 0, 10, 10]).reshape((2, 2)) + >>> dst = np.array([12, 14, 1, -20]).reshape((2, 2)) + + >>> tform = transform.estimate_transform('similarity', src, dst) + + >>> np.allclose(tform.inverse(tform(src)), src) + True + + >>> # warp image using the estimated transformation + >>> from skimage import data + >>> image = data.camera() + + >>> warp(image, inverse_map=tform.inverse) # doctest: +SKIP + + >>> # create transformation with explicit parameters + >>> tform2 = transform.SimilarityTransform(scale=1.1, rotation=1, + ... translation=(10, 20)) + + >>> # unite transformations, applied in order from left to right + >>> tform3 = tform + tform2 + >>> np.allclose(tform3(src), tform2(tform(src))) + True + + """ + ttype = ttype.lower() + if ttype not in TRANSFORMS: + raise ValueError('the transformation type \'%s\' is not' + 'implemented' % ttype) + + tform = TRANSFORMS[ttype]() + tform.estimate(src, dst, **kwargs) + + return tform + + +def matrix_transform(coords, matrix): + """Apply 2D matrix transform. + + Parameters + ---------- + coords : (N, 2) array + x, y coordinates to transform + matrix : (3, 3) array + Homogeneous transformation matrix. + + Returns + ------- + coords : (N, 2) array + Transformed coordinates. + + """ + return ProjectiveTransform(matrix)(coords) diff --git a/tracker.py b/tracker.py index 91f0967..26f8305 100644 --- a/tracker.py +++ b/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] @@ -134,7 +139,10 @@ 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,)) @@ -247,7 +255,7 @@ 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() @@ -255,45 +263,59 @@ class FeatureExtractor(): 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, 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, now) - 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]))) + 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, now) + 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, 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, now) + 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, now) + else: + features["eyebrow_steepness_l"] = 0. + features["eyebrow_steepness_r"] = 0. + features["eyebrow_quirk_l"] = 0. + features["eyebrow_quirk_r"] = 0. - 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, 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, now) - 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, 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, 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) - 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, 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, now) - f = abs(center_line - pts[62, 0]) / norm_distance_x - features["mouth_corner_inout_l"] = self.mouth_corner_inout_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, 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, now) - f = abs(center_line - pts[58, 0]) / norm_distance_x - features["mouth_corner_inout_r"] = self.mouth_corner_inout_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, 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, now) f = abs(pts[58, 0] - pts[62, 0]) / norm_distance_x @@ -306,8 +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 @@ -377,7 +401,7 @@ class FaceInfo(): if self.conf < 0.4 or self.pnp_error > 300: return - if not self.tracker.static_model: + 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 @@ -449,70 +473,93 @@ class FaceInfo(): self.update_contour() self.pts_3d = self.normalize_pts3d(self.pts_3d) - 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)) + 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): + 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, 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, max_feature_updates=0, static_model=False): + 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 = max(max_threads,4) + 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 = [ - "mnv3_opt_very_fast.onnx", - "mnv3_opt_fast.onnx", - "mnv3_opt_medium.onnx", - "mnv3_opt_b.onnx" + "lm_model0_opt.onnx", + "lm_model1_opt.onnx", + "lm_model2_opt.onnx", + "lm_model3_opt.onnx", + "lm_model4_opt.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 + 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) - 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.gaze_model = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList) - 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 @@ -521,6 +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], @@ -604,6 +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 @@ -613,17 +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}) @@ -639,7 +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) @@ -651,31 +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): - m = int(tensor[i].argmax()) - x = m // 28 - y = m % 28 - conf = float(tensor[i][x,y]) - avg_conf = avg_conf + conf - off_x = res * ((1. * logit(tensor[66 + i][x, y])) - 0.0) - off_y = res * ((1. * logit(tensor[66 * 2 + i][x, y])) - 0.0) - off_x = math.floor(off_x + 0.5) - off_y = math.floor(off_y + 0.5) - lm_x = crop_y1 + scale_y * (res * (float(x) / 27.) + off_x) - lm_y = crop_x1 + scale_x * (res * (float(y) / 27.) + off_y) - lms.append((lm_x,lm_y,conf)) - avg_conf = avg_conf / 66. + c0, c1, c2 = 66, 132, 198 + if self.model_type == -1: + c0, c1, c2 = 30, 60, 90 + t_main = tensor[0:c0].reshape((c0,self.out_res_i * self.out_res_i)) + t_m = t_main.argmax(1) + indices = np.expand_dims(t_m, 1) + t_conf = np.take_along_axis(t_main, indices, 1).reshape((c0,)) + t_off_x = np.take_along_axis(tensor[c0:c1].reshape((c0,self.out_res_i * self.out_res_i)), indices, 1).reshape((c0,)) + t_off_y = np.take_along_axis(tensor[c1:c2].reshape((c0,self.out_res_i * self.out_res_i)), indices, 1).reshape((c0,)) + t_off_x = res * logit_arr(t_off_x, self.logit_factor) + t_off_y = res * logit_arr(t_off_y, self.logit_factor) + t_x = crop_y1 + scale_y * (res * np.floor(t_m / self.out_res_i) / self.out_res + t_off_x) + 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 = 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) @@ -691,54 +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) - face_info.translation = np.transpose(face_info.translation) + # OpenCV returns (3,1); flatten so [0]/[1]/[2] are scalars for UDP packing + # 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 - for i, pt in enumerate(face_info.face_3d): - if i == 68: + t_reference = face_info.face_3d.dot(rmat.transpose()) + t_reference = t_reference + face_info.translation + 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: - d1 = np.linalg.norm(lms[i,0:2] - lms[36,0:2]) - d2 = np.linalg.norm(lms[i,0:2] - lms[39,0:2]) + if i == 0: + d1 = np.linalg.norm(lms[66,0:2] - lms[36,0:2]) + 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: - d1 = np.linalg.norm(lms[i,0:2] - lms[42,0:2]) - d2 = np.linalg.norm(lms[i,0:2] - lms[45,0:2]) + if i == 1: + d1 = np.linalg.norm(lms[67,0:2] - lms[42,0:2]) + 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 - reference = rmat.dot(pt) - reference = reference + face_info.translation - reference = self.camera.dot(reference) - depth = reference[2] - if i < 17 or i == 30: - 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[:] + if i < 2: + reference = rmat.dot(pt) + reference = reference + face_info.translation + reference = self.camera.dot(reference) + depth = float(reference[2]) + pt_3d = np.array([lms[66+i][0] * depth, lms[66+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[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: @@ -761,9 +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, (224, 224), interpolation=cv2.INTER_LINEAR) / 255.0 - #im = (im - mean) / std + im = cv2.resize(im, (self.res_i, self.res_i), interpolation=cv2.INTER_LINEAR) * self.std_res + self.mean_res im = np.expand_dims(im, 0) im = np.transpose(im, (0,3,1,2)) return im @@ -804,7 +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 @@ -821,11 +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) @@ -841,10 +942,7 @@ 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}) + results = self.gaze_model.run([], {self.input_name: both_eyes}) open = [0, 0] open[0] = 1#results[1][0].argmax() open[1] = 1#results[1][1].argmax() @@ -888,9 +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: @@ -945,11 +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: @@ -1003,7 +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: - eye_state = self.get_eye_state(frame, lms) + 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 @@ -1065,8 +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)) - x2, y2 = tuple(lms.max(0)) + x1, y1 = tuple(lms[0:66].min(0)) + x2, y2 = tuple(lms[0:66].max(0)) bbox = (y1, x1, y2 - y1, x2 - x1) face_info.bbox = bbox detected.append(bbox) @@ -1092,10 +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)