def _rotmat(self, vector, points):
"""
Rotates a 3xn array of 3D coordinates from the +z normal to an
arbitrary new normal vector.
"""
vector = vg.normalize(vector)
axis = vg.perpendicular(vg.basis.z, vector)
angle = vg.angle(vg.basis.z, vector, units='rad')
a = np.hstack((axis, (angle, )))
R = matrix_from_axis_angle(a)
r = Rot.from_matrix(R)
rotmat = r.apply(points)
return rotmat
def angle_calculation(df):
positions = np.array(df['Positions'])
angles = []
mean_angle = []
for fil in positions:
todo = []
for n in range(0, len(fil) - 2):
v0 = np.array(fil[n]) - np.array(fil[n + 1])
v1 = np.array(fil[n + 1]) - np.array(fil[n + 2])
# angle = np.math.atan2(np.linalg.det([v0,v1]),np.dot(v0,v1))
# angle_deg = np.degree(angle_deg)
angle = vg.angle(v0, v1)
todo.append(angle)
angles.append(todo)
avg = np.median(todo)
mean_angle.append(avg)
return angles, mean_angle
def angle_velocity(x1, y1, z1, x2, y2, z2, _time):
import vg
# if type(_head_forward2) is not dict: return None
vector1 = np.array([x1, y1, z1])
vector2 = np.array([x2, y2, z2])
return vg.angle(vector1, vector2) / _time
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
subj_bvecs[subj_id] = bvecs
subj_bvals[subj_id] = bvals
max_angle = 0
max_id_1 = None
max_id_2 = None
max_t = 0
angles = []
for t in range(288):
for k1, v1 in subj_bvecs.items():
for k2, v2 in subj_bvecs.items():
if k1 == k2:
continue
angle = vg.angle(v1[t], v2[t])
angles.append(angle)
if angle > max_angle:
max_angle = angle
max_id_1 = k1
max_id_2 = k2
max_t = t
print(max_angle, max_id_1, max_id_2, max_t)
angles = sorted(angles)
all_match = True
for k1, v1 in subj_bvals.items():
bvals1 = [round(int(x)/100)*100 for x in v1]
def main():
for i in TEST_IMAGE_LIST:
image = cv2.imread(i)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # conversion to rgb
# create pose estimator
image_size = image.shape
pose_estimator = PoseEstimator(image_size, SESSION_PATH,
PROB_MODEL_PATH)
# load model
pose_estimator.initialise()
try:
# estimation
print('START')
pose_2d, visibility, pose_3d = pose_estimator.estimate(image)
print('=================================')
print(str(i))
print('pose 3d:', pose_3d)
print('pose_2d:', pose_2d)
angle_target_2d = pose_2d[0].copy()
angle_target_3d = pose_3d[0].copy()
#投影頭部的點到 x-z 平面
angle_2d = calculate_angle_2d(
[angle_target_3d[0][8], angle_target_3d[2][8]],
[angle_target_3d[0][10], angle_target_3d[2][10]])
print(angle_2d)
#3D頭部
standard_v = np.array([0, 0, -1])
v_t = np.array([
angle_target_3d[0][8] - angle_target_3d[0][10],
angle_target_3d[1][8] - angle_target_3d[1][10],
angle_target_3d[2][8] - angle_target_3d[2][10]
])
print('====頭的角度===', vg.angle(standard_v, v_t))
#3D肩膀
vector = np.array([abs(angle_target_3d[0][6] - angle_target_3d[0][3]), \
abs(angle_target_3d[1][6] - angle_target_3d[1][3]),\
abs(angle_target_3d[2][6] - angle_target_3d[2][3])])\
if vector[0] > vector[1] and vector[0] > vector[2]:
standard_v = np.array([1, 0, 0])
elif vector[1] > vector[0] and vector[1] > vector[2]:
standard_v = np.array([0, -1, 0])
else:
print('判斷肩膀正面側面出現問題！預設正面')
standard_v = np.array([1, 0, 0])
v_t = np.array([
angle_target_3d[0][11] - angle_target_3d[0][14],
angle_target_3d[1][11] - angle_target_3d[1][14],
angle_target_3d[2][11] - angle_target_3d[2][14]
])
print('====肩膀的角度===', vg.angle(standard_v, v_t))
#3D脊椎(脊椎中間連線頸椎中間與脊椎中間連線屁股中間)
# standard_v = np.array([angle_target_3d[0][7] - angle_target_3d[0][8],
# angle_target_3d[1][7] - angle_target_3d[1][8],
# angle_target_3d[2][7] - angle_target_3d[2][8]])
# v_t = np.array([angle_target_3d[0][7] - angle_target_3d[0][0],
# angle_target_3d[1][7] - angle_target_3d[1][0],
# angle_target_3d[2][7] - angle_target_3d[2][0]])
# print('====脊椎的角度===', vg.angle(standard_v, v_t))
#3D脊椎(雙腳中間連線脊椎終點與脊椎中間連線頸椎中點)
# foot_center_x = (angle_target_3d[0][3] + angle_target_3d[0][6])/2
# foot_center_y = (angle_target_3d[1][3] + angle_target_3d[1][6])/2
# foot_center_z = (angle_target_3d[2][3] + angle_target_3d[2][6])/2
# standard_v = np.array([foot_center_x - angle_target_3d[0][0],
# foot_center_y - angle_target_3d[1][0],
# foot_center_z - angle_target_3d[2][0]])
# v_t = np.array([angle_target_3d[0][8] - angle_target_3d[0][0],
# angle_target_3d[1][8] - angle_target_3d[1][0],
# angle_target_3d[2][8] - angle_target_3d[2][0]])
# print('====脊椎的角度===', vg.angle(standard_v, v_t))
#馬術的3D脊椎(雙腳中間連線脊椎終點與脊椎中間連線頸椎中點)
standard_v = np.array([0, 0, 1])
v_t = np.array([
angle_target_3d[0][8] - angle_target_3d[0][0],
angle_target_3d[1][8] - angle_target_3d[1][0],
angle_target_3d[2][8] - angle_target_3d[2][0]
])
print('====脊椎的角度===', vg.angle(standard_v, v_t))
#3D左膝蓋
standard_v = np.array([
angle_target_3d[0][5] - angle_target_3d[0][4],
angle_target_3d[1][5] - angle_target_3d[1][4],
angle_target_3d[2][5] - angle_target_3d[2][4]
])
v_t = np.array([
angle_target_3d[0][5] - angle_target_3d[0][6],
angle_target_3d[1][5] - angle_target_3d[1][6],
angle_target_3d[2][5] - angle_target_3d[2][6]
])
print('====左膝蓋的角度===', vg.angle(standard_v, v_t))
#3D右膝蓋
standard_v = np.array([
angle_target_3d[0][2] - angle_target_3d[0][1],
angle_target_3d[1][2] - angle_target_3d[1][1],
angle_target_3d[2][2] - angle_target_3d[2][1]
])
v_t = np.array([
angle_target_3d[0][2] - angle_target_3d[0][3],
angle_target_3d[1][2] - angle_target_3d[1][3],
angle_target_3d[2][2] - angle_target_3d[2][3]
])
print('====右膝蓋的角度===', vg.angle(standard_v, v_t))
#投影法
# Show 2D and 3D poses
display_results(image, pose_2d, visibility, pose_3d)
except ValueError:
print(
'No visible people in the image. Change CENTER_TR in packages/lifting/utils/config.py ...'
)
# close model
pose_estimator.close()
