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stream_hac
...
main
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@ -1,42 +0,0 @@
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'''
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'''
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@ -1,5 +1,4 @@
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import cv2
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import matplotlib.pyplot as plt
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from . import OutputProcess
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from ovtk_track import types
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@ -15,10 +14,7 @@ class Process(OutputProcess):
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super().__init__(*args)
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def setup(self):
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self.fig = plt.figure()
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self.axes = self.fig.add_subplot(projection='3d')
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self.axes.view_init(15, 0, vertical_axis='y')
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plt.show(block=False)
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pass
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def send(self):
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landmarks = self._inputs['landmarks'].get_nowait()
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@ -35,15 +31,8 @@ class Process(OutputProcess):
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landmarks.draw(image, frame, label=False, color=(130, 130, 130))
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if skeleton is not None:
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skeleton.draw(self.axes)
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skeleton.draw(image, frame)
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cv2.imshow("face", frame)
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plt.draw()
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# event loops
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plt.pause(0.0001)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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raise KeyboardInterrupt('User requested stop')
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for artist in plt.gca().lines + plt.gca().collections:
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artist.remove()
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@ -32,26 +32,6 @@ face_mesh_map = {
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],
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}
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body_map = {
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LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.LEFT: [11],
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LANDMARK_TYPES.SHOULDER | LANDMARK_TYPES.RIGHT: [12],
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LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.LEFT: [13],
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LANDMARK_TYPES.ELBOW | LANDMARK_TYPES.RIGHT: [14],
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LANDMARK_TYPES.HIP | LANDMARK_TYPES.LEFT: [23],
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LANDMARK_TYPES.HIP | LANDMARK_TYPES.RIGHT: [24],
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LANDMARK_TYPES.KNEE | LANDMARK_TYPES.LEFT: [25],
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LANDMARK_TYPES.KNEE | LANDMARK_TYPES.RIGHT: [26],
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LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.LEFT: [27],
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LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.RIGHT: [28],
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LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.LEFT: [27],
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LANDMARK_TYPES.ANKLE | LANDMARK_TYPES.RIGHT: [28],
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}
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# SEE YEAH THESE MAKE SENSE GOOGLE WHAT THE HELL
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hand_mesh_map = {LANDMARK_TYPES.HAND | LANDMARK_TYPES.WRIST: [0]}
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_finger_map = {
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@ -2,7 +2,7 @@ import mediapipe
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import numpy as np
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from ovtk_track.transform import TransformProcess
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from ovtk_track.transform.solve.mediapipe import face_mesh_map, hand_mesh_map, body_map
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from ovtk_track.transform.solve.mediapipe import face_mesh_map, hand_mesh_map
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from ovtk_track import types
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from ovtk_track.types.Landmarks import LANDMARK_TYPES
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@ -83,17 +83,6 @@ class Process(TransformProcess):
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available.append((right_hand_landmarks, mix_maps(hand_mesh_map, LANDMARK_TYPES.RIGHT)))
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if results.pose_landmarks:
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raw_landmarks = results.pose_landmarks.landmark
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body_landmarks = np.empty((33, 3), dtype=np.float32)
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for i in range(33):
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body_landmarks[i][0] = raw_landmarks[i].x
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body_landmarks[i][1] = raw_landmarks[i].y
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body_landmarks[i][2] = raw_landmarks[i].z
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available.append((body_landmarks, body_map))
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if available:
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avail_landmarks, maps = zip(*available)
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combo_map = combine_maps(zip(maps, (array.shape[0] for array in avail_landmarks)))
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@ -1,7 +1,6 @@
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import math
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import numpy as np
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from scipy.spatial.distance import cdist
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from .. import TransformProcess
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from ovtk_track.types import Quaternion, Point3d
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@ -25,97 +24,9 @@ class Process(TransformProcess):
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self.normal = np.array(normal, dtype=float)
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self.up = np.array(vec_perp(normal), dtype=float)
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# REVIEW: See calc_eye. These probably need to change based on normal / up.
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# Or maybe they dont and we just rotate the output quaternion?
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# Ugh. The code works for now, but i no understand....
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self.SIN_LEFT_THETA = 2 * np.sin(np.pi / 2)
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self.SIN_UP_THETA = np.sin(np.pi / 6)
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def setup(self):
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pass
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def calc_head(self, landmarks):
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# REVIEW: This doesnt really work quite right!! look + roll arent mixing as expected
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# Vector pointing from head center to nose
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nose = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.NOSE | LANDMARK_TYPES.TIP]).mean(axis=0)
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head_center = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.FACE | LANDMARK_TYPES.OUTLINE]).mean(axis=0)
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look_vec = (nose - head_center)
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look_vec /= np.linalg.norm(look_vec)
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# Vector pointing left to right across the face
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eye_center_l = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.LEFT]).mean(axis=0)
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eye_center_r = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.RIGHT]).mean(axis=0)
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roll_vec = (eye_center_l - eye_center_r)
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roll_vec /= np.linalg.norm(roll_vec)
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# Quat that rotates from normal to head center -> nose vec
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look = Quaternion(np.dot(look_vec, self.normal), *np.cross(look_vec, self.normal))
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look.w += look.magnitude()
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look = look.normalize()
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# Quat that represents a rotation around the roll axis (i think??)
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roll_angle = np.sum(roll_vec * self.up)
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roll = Quaternion(math.cos(roll_angle), *(self.normal * math.sin(roll_angle)))
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roll = roll.normalize()
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combo = look + roll
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combo = combo.normalize()
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return combo, head_center
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def calc_eye(self, landmarks):
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# Get poi
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corners = np.empty((2, 2, 3), dtype=np.float32)
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centers = np.empty((2, 3), dtype=np.float32)
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pupils = np.empty((2, 3), dtype=np.float32)
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cross_heights = np.empty((2), dtype=np.float32)
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for i, side in enumerate([LANDMARK_TYPES.LEFT, LANDMARK_TYPES.RIGHT]):
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# Find corners by searching for points with the largest distance from each other
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# REVIEW: These *should* will always be the same points in the map - make a landmark type selector?
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eye_outline = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.EYE | LANDMARK_TYPES.OUTLINE | side])
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hdist = cdist(eye_outline, eye_outline, metric='euclidean')
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best_pair = np.unravel_index(hdist.argmax(), hdist.shape)
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corners[i] = eye_outline[best_pair[0]], eye_outline[best_pair[1]]
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# Get height of eye (relative to a line passing through each corner)
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cross_heights[i] = np.array([
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np.linalg.norm(np.cross(corners[i][1]-corners[i][0],
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corners[i][0]-point))
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/ np.linalg.norm(corners[i][0]-corners[i][1])
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for point in eye_outline
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]).max()
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centers[i] = eye_outline.mean(axis=0)
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pupils[i] = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.IRIS | side]).mean(axis=0)
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# Calculate important distances based on POI
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eye_length = np.linalg.norm(np.diff(corners, axis=1), axis=(2, 1))
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ic_distance = np.linalg.norm(pupils - centers, axis=1)
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zc_distance = np.linalg.norm(pupils - corners[:, 1], axis=1)
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aspect_ratio = 1 / (cross_heights / eye_length)
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# Takes above and spits out spherical coordiates of pupil (relative to camera)
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# Black magic as far as i can comprehend
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||||
# Copied in large part from https://github.com/1996scarlet/OpenVtuber/blob/970229d3a5ebe14a7519352da039d00a0b87e2d9/service/TFLiteIrisLocalization.py#L101
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||||
s0 = (corners[1, :, 1] - corners[0, :, 1]) * pupils[:, 0]
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s1 = (corners[1, :, 0] - corners[0, :, 0]) * pupils[:, 1]
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s2 = corners[1, :, 0] * corners[0, :, 1]
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s3 = corners[1, :, 1] * corners[0, :, 0]
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|
||||
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
|
||||
|
|
@ -123,42 +34,46 @@ class Process(TransformProcess):
|
|||
joints = {}
|
||||
if landmarks.has(LANDMARK_TYPES.FACE):
|
||||
# Get head look / pos
|
||||
look_quat, head_pos = self.calc_head(landmarks)
|
||||
eye_quats, eye_aspect = self.calc_eye(landmarks)
|
||||
nose = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.NOSE | LANDMARK_TYPES.TIP]).mean(0)
|
||||
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 landmarks.has(LANDMARK_TYPES.SHOULDER) and landmarks.has(LANDMARK_TYPES.HIP):
|
||||
chest = Landmarks.to_numpy(landmarks[LANDMARK_TYPES.SHOULDER, LANDMARK_TYPES.HIP]).mean(axis=0)
|
||||
joints[JOINT_TYPES.CHEST] = Joint(Point3d(*chest), Quaternion.identity())
|
||||
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)
|
||||
|
||||
skeleton = Skeleton(joints)
|
||||
|
||||
self._outputs['skel'].send(skeleton)
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
points = self[filter] if filter else self.points
|
||||
for i, (x, y, z) in enumerate(point.project_to_image(image) for point in points):
|
||||
def draw(self, image, canvas, color=(255, 255, 255), label=True):
|
||||
for i, (x, y, z) in enumerate(point.project_to_image(image) for point in self.points):
|
||||
if x > image.width or x < 0 or y > image.height or y < 0:
|
||||
continue
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from dataclasses import dataclass
|
||||
|
||||
import numpy as np
|
||||
from scipy.spatial.transform import Rotation, Slerp
|
||||
from scipy.spatial.transform import Rotation
|
||||
from .Type import Type
|
||||
|
||||
|
||||
|
|
@ -12,10 +12,6 @@ class Quaternion(Type):
|
|||
y: float
|
||||
z: float
|
||||
|
||||
@classmethod
|
||||
def identity(cls):
|
||||
return cls(1, 0, 0, 0)
|
||||
|
||||
def __mul__(self, q):
|
||||
if isinstance(q, self.__class__):
|
||||
product = self.as_np() * q.as_np()
|
||||
|
|
@ -49,16 +45,6 @@ class Quaternion(Type):
|
|||
def conjugate(self):
|
||||
return self.__class__(self.w, -self.x, -self.y, -self.z)
|
||||
|
||||
def slerp(self, other, t):
|
||||
r = Rotation.from_quat([
|
||||
[self.x, self.y, self.z, self.w],
|
||||
[other.x, other.y, other.z, other.w],
|
||||
])
|
||||
|
||||
slerp = Slerp([0, 1], r)
|
||||
x, y, z, w = slerp([t]).as_quat()[0]
|
||||
return self.__class__(w, x, y, z)
|
||||
|
||||
def draw(self, canvas, origin):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
|
|
|||
|
|
@ -2,6 +2,8 @@ from dataclasses import dataclass, field
|
|||
from enum import Enum
|
||||
import typing
|
||||
|
||||
import cv2
|
||||
|
||||
from .Type import Type
|
||||
from .Point3d import Point3d
|
||||
from .Quaternion import Quaternion
|
||||
|
|
@ -10,26 +12,20 @@ from .Quaternion import Quaternion
|
|||
class JOINT_TYPES(Enum):
|
||||
HEAD = 'head'
|
||||
CHEST = 'chest'
|
||||
HIPS = 'hips'
|
||||
|
||||
SHOULDER_L = 'shoulder_l'
|
||||
ELBOW_L = 'elbow_l'
|
||||
WRIST_L = 'wrist_l'
|
||||
HIP_L = 'hip_l'
|
||||
KNEE_L = 'knee_l'
|
||||
FOOT_L = 'foot_l'
|
||||
WRIST_L = 'wrist_l'
|
||||
|
||||
SHOULDER_R = 'shoulder_r'
|
||||
ELBOW_R = 'elbow_r'
|
||||
WRIST_R = 'wrist_r'
|
||||
HIP_R = 'hip_r'
|
||||
KNEE_R = 'knee_r'
|
||||
FOOT_R = 'foot_r'
|
||||
WRIST_R = 'wrist_r'
|
||||
|
||||
|
||||
default_colors = [
|
||||
[255, 0, 0], [0, 255, 0], [0, 0, 255],
|
||||
[255, 255, 0], [255, 0, 255], [0, 255, 255],
|
||||
[128, 255, 0], [255, 0, 128], [0, 255, 128],
|
||||
]
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -52,10 +48,14 @@ class Skeleton(Type):
|
|||
# TODO: More intelegent merge
|
||||
return Skeleton(self.joints + other.joints)
|
||||
|
||||
def draw(self, axes, colors=default_colors):
|
||||
xs, ys, zs = zip(*(joint.pos.as_np() for joint in self.joints.values()))
|
||||
color = [[v / 255 for v in color] for color in colors[:len(xs)]]
|
||||
axes.scatter(xs, ys, zs, c=color)
|
||||
def draw(self, image, canvas, color=(255, 255, 255)):
|
||||
for i, joint in enumerate(self.joints.values()):
|
||||
x, y, z = joint.pos.project_to_image(image)
|
||||
|
||||
if x > image.width or x < 0 or y > image.height or y < 0:
|
||||
continue
|
||||
|
||||
cv2.circle(canvas, (x, y), 1, color, -1, cv2.LINE_AA)
|
||||
|
||||
def serialize(self):
|
||||
return {type.value: joint for type, joint in self.joints.items()}
|
||||
|
|
|
|||
Loading…
Reference in New Issue