# Load file containing roto-translation from US image to global
matrix = np.load('matrices_testwire_3points_15.npz')
# Get roto-translation from US image to global reference frame
T = matrix['gTim']
# Get single points features from file
fea = readFeaturesFile('testwire15.fea') # it must contain one point feature per US image
# Indicate u, v
u = 30./278
v = 50./463
# Calculate global coordinates of P1
idxP1 = range(79,84)
P1 = singlePointFeaturesTo3DPointsMatrix(fea, u, v, idx=idxP1)
print P1
P1g = dot3(T[idxP1,:,:], P1[...,None]).squeeze()[:,0:3] # Np x 3
P1 = P1g.mean(axis=0)
print P1g.std(axis=0)
# Calculate global coordinates of P2
idxP2 = range(142,147)
P2 = singlePointFeaturesTo3DPointsMatrix(fea, u, v, idx=idxP2)
print P2
P2g = dot3(T[idxP2,:,:], P2[...,None]).squeeze()[:,0:3] # Np x 3
P2 = P2g.mean(axis=0)
print P2g.std(axis=0)
# Calculate global coordinates for P3
idxP3 = range(188,193)
# Get single points features from file
fea_dyn = readFeaturesFile('feature_julie_dyn3.fea') # it must contain one point feature per US image
# Get metadata from dynamic trial files
timing_data = np.load('us_time_vector_julie_dyn3.npz')
usTimeVector = timing_data['usTimeVector'][0]
fcOpto = 120.
# Indicate u, v (50mm as depth)
u = 30./278
v = 50./463
# Calculate GLOBAL coordinates of muscle insertion - fem - STATIC
idxP1 = [135,145,155,165,175]
P1 = singlePointFeaturesTo3DPointsMatrix(fea_pap, u, v, idx=idxP1)
P1g = dot3(T_pap[idxP1,:,:], P1[...,None]).squeeze() # Np x 4
P1 = P1g.mean(axis=0)
# Calculate GLOBAL coordinates of tendon insertion - calc - STATIC
idxP2 = [285,295,305,315,325]
P2 = singlePointFeaturesTo3DPointsMatrix(fea_pap, u, v, idx=idxP2)
P2g = dot3(T_pap[idxP2,:,:], P2[...,None]).squeeze() # Np x 4
P2 = P2g.mean(axis=0)
# Calculate GLOBAL coordinates of muscle-tendon junction - STATIC
idxP3s = [205,215,225,235,230]
P3s = singlePointFeaturesTo3DPointsMatrix(fea_pap, u, v, idx=idxP3s)
P3sg = dot3(T_pap[idxP3s,:,:], P3s[...,None]).squeeze() # Np x 4
P3s = P3sg.mean(axis=0)
# Load file containing roto-translation from US image to global
matrix = np.load('matrices_plantar_3points.npz')
# Get roto-translation from US image to global reference frame
T = matrix['gTim']
# Get single points features from file
fea = readFeaturesFile('plantar_position1.fea') # it must contain one point feature per US image
# Indicate u, v
u = 30./278
v = 50./463
# Calculate global coordinates of muscle insertion
idxP1 = [3,5,8,12,16]
P1 = singlePointFeaturesTo3DPointsMatrix(fea, u, v, idx=idxP1)
P1g = dot3(T[idxP1,:,:], P1[...,None]).squeeze()[:,0:3] # Np x 3
P1 = P1g.mean(axis=0)
# Calculate global coordinates of tendon insertion
idxP2 = [510,512,515,518,521]
P2 = singlePointFeaturesTo3DPointsMatrix(fea, u, v, idx=idxP2)
P2g = dot3(T[idxP2,:,:], P2[...,None]).squeeze()[:,0:3] # Np x 3
P2 = P2g.mean(axis=0)
# Calculate global coordinates for muscle-tendon junction
idxP3 = [146,149,154,156,159]
P3 = singlePointFeaturesTo3DPointsMatrix(fea, u, v, idx=idxP3)
P3g = dot3(T[idxP3,:,:], P3[...,None]).squeeze()[:,0:3] # Np x 3
P3 = P3g.mean(axis=0)
