ovtk_track/__init__.py

@@ -1,42 +0,0 @@
-'''
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ovtk_track/transform/solve/mediapipe/__init__.py

@@ -32,26 +32,6 @@ face_mesh_map = {
     ],
 }
 
-body_map = {
-    LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.LEFT: [11],
-    LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.RIGHT: [12],
-
-    LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.LEFT: [13],
-    LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.RIGHT: [14],
-
-    LANDMARK_TYPES.HIP | LANDMARK_TYPES.LEFT: [23],
-    LANDMARK_TYPES.HIP | LANDMARK_TYPES.RIGHT: [24],
-
-    LANDMARK_TYPES.KNEE | LANDMARK_TYPES.LEFT: [25],
-    LANDMARK_TYPES.KNEE | LANDMARK_TYPES.RIGHT: [26],
-
-    LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.LEFT: [27],
-    LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.RIGHT: [28],
-
-    LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.LEFT: [27],
-    LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.RIGHT: [28],
-}
-
 # SEE YEAH THESE MAKE SENSE GOOGLE WHAT THE HELL
 hand_mesh_map = {LANDMARK_TYPES.HAND | LANDMARK_TYPES.WRIST: [0]}
 _finger_map = {

ovtk_track/transform/solve/mediapipe/holistic.py

@@ -2,7 +2,7 @@ import mediapipe
 import numpy as np
 
 from ovtk_track.transform import TransformProcess
-from ovtk_track.transform.solve.mediapipe import face_mesh_map, hand_mesh_map, body_map
+from ovtk_track.transform.solve.mediapipe import face_mesh_map, hand_mesh_map
 from ovtk_track import types
 from ovtk_track.types.Landmarks import LANDMARK_TYPES
 
@@ -83,17 +83,6 @@ class Process(TransformProcess):
 
                 available.append((right_hand_landmarks, mix_maps(hand_mesh_map, LANDMARK_TYPES.RIGHT)))
 
-        if results.pose_landmarks:
-            raw_landmarks = results.pose_landmarks.landmark
-            body_landmarks = np.empty((33, 3), dtype=np.float32)
-
-            for i in range(33):
-                body_landmarks[i][0] = raw_landmarks[i].x
-                body_landmarks[i][1] = raw_landmarks[i].y
-                body_landmarks[i][2] = raw_landmarks[i].z
-
-            available.append((body_landmarks, body_map))
-
             if available:
                 avail_landmarks, maps = zip(*available)
                 combo_map = combine_maps(zip(maps, (array.shape[0] for array in avail_landmarks)))

ovtk_track/transform/solve/skel_from_3d_landmarks.py

@@ -1,7 +1,6 @@
 import math
 
 import numpy as np
-from scipy.spatial.distance import cdist
 
 from .. import TransformProcess
 from ovtk_track.types import Quaternion, Point3d
@@ -25,97 +24,9 @@ class Process(TransformProcess):
         self.normal = np.array(normal, dtype=float)
         self.up = np.array(vec_perp(normal), dtype=float)
 
-        # REVIEW: See calc_eye. These probably need to change based on normal / up.
-        #         Or maybe they dont and we just rotate the output quaternion?
-        #         Ugh. The code works for now, but i no understand....
-        self.SIN_LEFT_THETA = 2 * np.sin(np.pi / 2)
-        self.SIN_UP_THETA = np.sin(np.pi / 6)
-
     def setup(self):
         pass
 
-    def calc_head(self, landmarks):
-        # REVIEW: This doesnt really work quite right!! look + roll arent mixing as expected
-        # Vector pointing from head center to nose
-        nose = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.NOSE | LANDMARK_TYPES.TIP]).mean(axis=0)
-        head_center = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.FACE | LANDMARK_TYPES.OUTLINE]).mean(axis=0)
-        look_vec = (nose - head_center)
-        look_vec /= np.linalg.norm(look_vec)
-        # Vector pointing left to right across the face
-        eye_center_l = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.LEFT]).mean(axis=0)
-        eye_center_r = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.RIGHT]).mean(axis=0)
-        roll_vec = (eye_center_l - eye_center_r)
-        roll_vec /= np.linalg.norm(roll_vec)
-
-        # Quat that rotates from normal to head center -> nose vec
-        look = Quaternion(np.dot(look_vec, self.normal), *np.cross(look_vec, self.normal))
-        look.w += look.magnitude()
-        look = look.normalize()
-        # Quat that represents a rotation around the roll axis (i think??)
-        roll_angle = np.sum(roll_vec * self.up)
-        roll = Quaternion(math.cos(roll_angle), *(self.normal * math.sin(roll_angle)))
-        roll = roll.normalize()
-
-        combo = look + roll
-        combo = combo.normalize()
-
-        return combo, head_center
-
-    def calc_eye(self, landmarks):
-        # Get poi
-        corners = np.empty((2, 2, 3), dtype=np.float32)
-        centers = np.empty((2, 3), dtype=np.float32)
-        pupils = np.empty((2, 3), dtype=np.float32)
-        cross_heights = np.empty((2), dtype=np.float32)
-        for i, side in enumerate([LANDMARK_TYPES.LEFT, LANDMARK_TYPES.RIGHT]):
-            # Find corners by searching for points with the largest distance from each other
-            # REVIEW: These *should* will always be the same points in the map - make a landmark type selector?
-            eye_outline = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.OUTLINE | side])
-            hdist = cdist(eye_outline, eye_outline, metric='euclidean')
-            best_pair = np.unravel_index(hdist.argmax(), hdist.shape)
-            corners[i] = eye_outline[best_pair[0]], eye_outline[best_pair[1]]
-            # Get height of eye (relative to a line passing through each corner)
-            cross_heights[i] = np.array([
-                np.linalg.norm(np.cross(corners[i][1]-corners[i][0],
-                                        corners[i][0]-point))
-                / np.linalg.norm(corners[i][0]-corners[i][1])
-                for point in eye_outline
-            ]).max()
-
-            centers[i] = eye_outline.mean(axis=0)
-            pupils[i] = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.IRIS | side]).mean(axis=0)
-        # Calculate important distances based on POI
-        eye_length = np.linalg.norm(np.diff(corners, axis=1), axis=(2, 1))
-        ic_distance = np.linalg.norm(pupils - centers, axis=1)
-        zc_distance = np.linalg.norm(pupils - corners[:, 1], axis=1)
-        aspect_ratio = 1 / (cross_heights / eye_length)
-
-        # Takes above and spits out spherical coordiates of pupil (relative to camera)
-        # Black magic as far as i can comprehend
-        # Copied in large part from https://github.com/1996scarlet/OpenVtuber/blob/970229d3a5ebe14a7519352da039d00a0b87e2d9/service/TFLiteIrisLocalization.py#L101
-        s0 = (corners[1, :, 1] - corners[0, :, 1]) * pupils[:, 0]
-        s1 = (corners[1, :, 0] - corners[0, :, 0]) * pupils[:, 1]
-        s2 = corners[1, :, 0] * corners[0, :, 1]
-        s3 = corners[1, :, 1] * corners[0, :, 0]
-
-        delta_y = (s0 - s1 + s2 - s3) / eye_length / 2
-        delta_x = np.sqrt(abs(ic_distance**2 - delta_y**2))
-        delta = np.array((delta_x * self.SIN_LEFT_THETA,
-                          delta_y * self.SIN_UP_THETA))
-        delta /= eye_length
-        theta, pha = np.arcsin(delta)
-        inv_judge = zc_distance**2 - delta_y**2 < eye_length**2 / 4
-        theta[inv_judge] *= -1
-
-        # Convert spherical coordiates to quaternions
-        # Based on https://github.com/moble/quaternion/blob/8f6fc306306c45f0bf79331a22ef3998e4d187bc/src/quaternion/__init__.py#L599
-        quats = np.array([np.cos(pha/2) * np.cos(theta/2),
-                         -np.sin(pha/2) * np.sin(theta/2),
-                         np.cos(pha/2) * np.sin(theta/2),
-                         np.sin(pha/2) * np.cos(theta/2)]).T
-        quat_arr = [Quaternion(*quat) for quat in quats]
-        return quat_arr, aspect_ratio
-
     def process(self):
         landmarks = self._inputs['landmarks'].get()
         skeleton = None
@@ -123,41 +34,56 @@ class Process(TransformProcess):
             joints = {}
             if landmarks.has(LANDMARK_TYPES.FACE):
                 # Get head look / pos
-                look_quat, head_pos = self.calc_head(landmarks)
+                nose = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.NOSE | LANDMARK_TYPES.TIP]).mean(0)
-                eye_quats, eye_aspect = self.calc_eye(landmarks)
+                head_center = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.FACE | LANDMARK_TYPES.OUTLINE]).mean(0)
+                look_vec = (nose - head_center)
 
-                head_joint = Joint(Point3d(*head_pos), look_quat, attr=dict(eye_rot=eye_quats, eye_aspect=eye_aspect))
+                eye_center_l = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.LEFT]).mean(0)
+                eye_center_r = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.RIGHT]).mean(0)
+                roll_vec = (eye_center_l - eye_center_r)
+
+                look_vec /= np.linalg.norm(look_vec)
+                roll_vec /= np.linalg.norm(roll_vec)
+
+                roll_angle = np.sum(roll_vec * self.up)
+                roll = Quaternion(math.cos(roll_angle), * self.normal * math.sin(roll_angle))
+                roll = roll.normalize()
+
+                look = Quaternion(np.dot(look_vec, self.normal), *np.cross(look_vec, self.normal))
+                look.w += look.magnitude()
+                look = look.normalize()
+
+                combo = look + roll
+                combo = combo.normalize()
+
+                # Get eye data
+                marks_left = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.LEFT])
+                marks_right = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.RIGHT])
+                range = np.array([marks_left.max(axis=0) - marks_left.min(axis=0),
+                                  marks_right.max(axis=0) - marks_right.min(axis=0)])
+                delta = np.array([eye_center_l - Landmarks.to_numpy(landmarks[LANDMARK_TYPES.IRIS | LANDMARK_TYPES.CENTER | LANDMARK_TYPES.LEFT]).mean(0),
+                                  eye_center_r - Landmarks.to_numpy(landmarks[LANDMARK_TYPES.IRIS | LANDMARK_TYPES.CENTER | LANDMARK_TYPES.RIGHT]).mean(0)])
+
+                delta /= range
+                try:
+                    eye_aspect_ratio = range[::, 0] / range[::, 1]
+                except ZeroDivisionError:
+                    eye_aspect_ratio = None
+
+                head_joint = Joint(Point3d(*head_center), combo, dict(look_delta=delta, eye_aspect_ratio=eye_aspect_ratio))
 
                 joints[JOINT_TYPES.HEAD] = head_joint
 
-            if landmarks.has(LANDMARK_TYPES.SHOULDER):
-                shoulder_l = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.LEFT]).mean(axis=0)
-                shoulder_r = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.RIGHT]).mean(axis=0)
-                joints[JOINT_TYPES.SHOULDER_L] = Joint(Point3d(*shoulder_l), Quaternion.identity())
-                joints[JOINT_TYPES.SHOULDER_R] = Joint(Point3d(*shoulder_r), Quaternion.identity())
-
-            if landmarks.has(LANDMARK_TYPES.ELBOW):
-                elbow_l = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.LEFT]).mean(axis=0)
-                elbow_r = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.RIGHT]).mean(axis=0)
-                joints[JOINT_TYPES.ELBOW_L] = Joint(Point3d(*elbow_l), Quaternion.identity())
-                joints[JOINT_TYPES.ELBOW_R] = Joint(Point3d(*elbow_r), Quaternion.identity())
-
-            if landmarks.has(LANDMARK_TYPES.HIP):
-                hips = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.HIP]).mean(axis=0)
-                joints[JOINT_TYPES.HIPS] = Joint(Point3d(*hips), Quaternion.identity())
-
             # Synthizise other joints from existing data
-            if not joints.get(JOINT_TYPES.CHEST):
+            if not joints.get(JOINT_TYPES.CHEST) and joints.get(JOINT_TYPES.HEAD):
-                if landmarks.has(LANDMARK_TYPES.SHOULDER) and landmarks.has(LANDMARK_TYPES.HIP):
+                chest_center = joints[JOINT_TYPES.HEAD].pos.as_np()
-                    chest = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.SHOULDER, LANDMARK_TYPES.HIP]).mean(axis=0)
+                chest_center = np.power(chest_center, 3) / (1e3 + np.power(chest_center, 2))
-                    joints[JOINT_TYPES.CHEST] = Joint(Point3d(*chest), Quaternion.identity())
+                chest_center -= [0, 100, 0]
-                elif joints.get(JOINT_TYPES.HEAD):
-                    chest_center = joints[JOINT_TYPES.HEAD].pos.as_np()
-                    chest_center = np.power(chest_center, 3) / (1e3 + np.power(chest_center, 2))
-                    chest_center -= [0, 100, 0]
-                    chest_rot = Quaternion.identity().slerp(joints[JOINT_TYPES.HEAD].rot, 0.1)
 
-                    joints[JOINT_TYPES.CHEST] = Joint(Point3d(*chest_center), chest_rot)
+                chest_rot = Quaternion.identity().slerp(joints[JOINT_TYPES.HEAD].rot, 0.1)
+
+                chest_joint = Joint(Point3d(*chest_center), chest_rot)
+                joints[JOINT_TYPES.CHEST] = chest_joint
 
             skeleton = Skeleton(joints)
 

ovtk_track/types/Landmarks.py

@@ -17,17 +17,6 @@ class LANDMARK_TYPES(Flag):
     LIPS = auto()
     CHIN = auto()
 
-    # Body
-    SHOULDER = auto()
-    ELBOW = auto()
-    HIP = auto()
-    KNEE = auto()
-
-    # Feet tracking lmao
-    ANKLE = auto()
-    HEEL = auto()
-    TOE_INDEX = auto()
-
     # Hand
     HAND = auto()
     WRIST = auto()
@@ -82,9 +71,8 @@ class Landmarks(Type):
 
         return False
 
-    def draw(self, image, canvas, color=(255, 255, 255), label=True, filter=None):
+    def draw(self, image, canvas, color=(255, 255, 255), label=True):
-        points = self[filter] if filter else self.points
+        for i, (x, y, z) in enumerate(point.project_to_image(image) for point in self.points):
-        for i, (x, y, z) in enumerate(point.project_to_image(image) for point in points):
             if x > image.width or x < 0 or y > image.height or y < 0:
                 continue