def getFFT(motionVal):
fft_len = 30
fft = np.empty((0, fft_len * 2), dtype=np.float64)
fourier = np.empty((0, fft_len), dtype=np.float64)
if not len(motionVal) or len(motionVal[0]) < 41: return [], []
check = [0] * len(motionVal)
for i in range(len(motionVal)):
check[i] = abs(0.98 - motionVal[i][0])
ind = check.index(min(check))
motionVal[ind] = motionVal[ind] - 0.98
vals = np.empty((0, len(motionVal[0]) - 1), dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m, 23, 3)
m = integrate(m)
m = savitzky_golay(m, 23, 3)
vals = np.vstack([vals, m])
m = np.fft.fft(m)
m = m[1:fft_len + 1]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2] = a
m[1::2] = b
if (max(m) - min(m)) != 0: m = m / (max(m) - min(m))
fft = np.append(fft, np.array([m]), axis=0)
fourier = np.append(fourier, np.array([a]), axis=0)
return fft, vals
def getOneMSR(file, both):
motionVal = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
line = file.readline().rstrip().split()
while line:
if line == ['40'] or line == ['80']:
val = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
line = line.rstrip().split()
check = (len(line) > 1)
while (len(line) > 1):
line = line[:3]
line = [float(x) for x in line]
val = np.append(val, line)
if not both: file.readline()
line = file.readline().rstrip().split()
if len(val) == 120: val = val[:60]
val = np.reshape(val, (val.shape[0], 1)) #transpose
if not len(motionVal): motionVal = val
else: motionVal = np.hstack([motionVal, val])
#printMSRMotion(motionVal)
fft_len = 15
fft = np.empty((0, fft_len * 2), dtype=np.float64)
if not len(motionVal): return [], []
diffs = np.empty((0, len(motionVal[0]) - 1), dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m, 21, 3)
m = np.diff(m)
if i not in [24, 25, 36, 37]:
m = savitzky_golay(m, 21, 3)
if max(m) - min(m) > 0: m = m / (max(m) - min(m))
diffs = np.append(diffs, np.array([m]), axis=0)
m = np.fft.fft(m)
m = m[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2] = a
m[1::2] = b
if (max(m) - min(m)) != 0:
m = m / (max(m) - min(m))
fft = np.append(fft, np.array([m]), axis=0)
file.close()
return fft, diff
def getKITFFT(file):
motionDict = {}
motionVal = np.zeros((0, 0), dtype=np.float64)
# ############## READ IN DATA ##############
for line in file:
if 'MotionFrame' in line:
while '/MotionFrame' not in line:
if 'JointPosition' in line and '</JointPosition>' in line:
line = line.lstrip('<JointPosition>|\t').rstrip(
'</JointPosition>|\n')
line = [float(x) for x in line.split()]
if len(line) == 44:
line = np.delete(line, [0, 1, 2, 26, 27, 42, 43])
val = np.array(line, dtype=np.float64)
if len(val) == 39: print(val)
line = file.readline()
if len(val) > 3:
if not len(motionVal): motionVal = np.append(motionVal, val)
else: motionVal = np.vstack([motionVal, val])
motionVal = np.transpose(motionVal)
fft_len = 31
fftArrays = np.empty((0, fft_len * 2), dtype=np.float64)
diffs = np.empty((0, motionVal.shape[1] - 1), dtype=np.float64)
#first_list, last_list = [],[]
for i in range(motionVal.shape[0]):
m = motionVal[i]
m = savitzky_golay(m, 95, 3) # 71
m = np.diff(m)
m = savitzky_golay(m, 95, 3)
diffs = np.append(diffs, np.array([m]), axis=0)
#first = np.mean(first_list)
#last = np.mean(last_list)
if sum(abs(m)) > 0:
m = np.fft.fft(m)[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a, b = np.real(m), np.imag(m)
tmp = np.empty((a.size + b.size, ), dtype=a.dtype)
tmp[0::2], tmp[1::2] = a, b
m = tmp
#m = m / (max(m) - min(m))
fftArrays = np.append(fftArrays, np.array([m]), axis=0)
else:
fftArrays = np.append(fftArrays,
np.array([[0] * (fft_len * 2)]),
axis=0)
return fftArrays, diffs
def getOneSMS(file):
line = file.readline().rstrip()
while line != 'MOTION':
line = file.readline().rstrip()
line = file.readline()
line = file.readline()
motionVal = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
while line:
line = line.rstrip().split()
line = [float(x) for x in line]
val = np.array(line)
if not len(motionVal): motionVal = val
else:
if len(val): motionVal = np.vstack([motionVal, val])
else: break
line = skipLines(file)
motionVal = np.transpose(motionVal)
fft_len = 31
fft = np.empty((0, fft_len * 2), dtype=np.float64)
if not len(motionVal): return [], []
diffs = np.empty((0, len(motionVal[0]) - 2), dtype=np.float64)
motionVal1 = np.empty((0, len(motionVal[0])), dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i][1:]
#m = savitzky_golay(m,13,3)
m = savitzky_golay(m, 45, 3)
m = np.diff(m)
#if i not in [27,28,29,33,34,45,46,47,51,52,61,66,67,78,79,84,85]:
#m = savitzky_golay(m,13,3)
m = savitzky_golay(m, 45, 3)
diffs = np.append(diffs, np.array([m]), axis=0)
motionVal1 = np.append(motionVal1,
motionVal[i].reshape(1, len(motionVal[i])),
axis=0)
m = np.fft.fft(m)
m = m[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2], m[1::2] = a, b
fft = np.append(fft, np.array([m]), axis=0)
file.close()
#print('len(fft)',len(fft))
#fft = np.delete(fft,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#diffs = np.delete(diffs,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#motionVal1 = np.delete(motionVal1,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#printSMSMotion(motionVal1)
return fft, diffs
def getMSRMotionFeature(file, ind):
motionVal = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
line = file.readline().rstrip().split()
while line:
if line == ['40'] or line == ['80']:
val = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
line = line.rstrip().split()
check = (len(line) > 1)
while (len(line) > 1):
line = line[:3]
line = [float(x) for x in line]
val = np.append(val, line)
if not 0: file.readline()
line = file.readline().rstrip().split()
if len(val) == 120: val = val[:60]
val = np.reshape(val, (val.shape[0], 1)) #transpose
if not len(motionVal): motionVal = val
else: motionVal = np.hstack([motionVal, val])
val = motionVal[ind]
val = savitzky_golay(val, 91, 3)
return val
def getMSRAction(file):
motionVal = np.zeros((0, 0), dtype=np.float64)
line = file.readline()
while line:
val = np.zeros((0, 0), dtype=np.float64)
for i in range(20):
line = line.rstrip().split()
line = [float(x) for x in line[:3]]
val = np.append(val, line)
line = file.readline()
if not len(motionVal): motionVal = val
else:
if len(val): motionVal = np.vstack([motionVal, val])
else: break
motionVal = np.transpose(motionVal)
#printMSRMotion(motionVal)
fft_len = 15
fft = np.empty((0, fft_len * 2), dtype=np.float64)
if not len(motionVal): return [], []
diffs = np.empty((0, len(motionVal[1]) - 1), dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m, 11, 3) # 21
m = np.diff(m)
m = savitzky_golay(m, 11, 3)
diffs = np.append(diffs, np.array([m]), axis=0)
m = np.fft.fft(m)
m = m[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2], m[1::2] = a, b
fft = np.append(fft, np.array([m]), axis=0)
file.close()
return fft, diffs
def printMocapDataMotion(motion):
w = round(np.sqrt(len(motion)),0)
h = int(len(motion)/w+1)
pt.figure()
for i in range(len(motion)):
sp = pt.subplot(w,h,i+1)
m = savitzky_golay(motion[i],131,3)
pt.plot(m)
sp.set_title(f_names[i])
pt.show()
return None
def printMSRMotion(motion):
w = round(np.sqrt(len(motion)), 0)
h = int(len(motion) / w + 1)
pt.figure()
for i in range(len(motion)):
pt.subplot(w, h, i + 1)
m = savitzky_golay(motion[i], 51, 3)
pt.plot(m)
pt.show()
return None
def getOneBKL(file):
motionVal = np.zeros((0, 0), dtype=np.float64)
for line in file:
line = list(float(x) for x in line.split()[:3])
if not len(motionVal): motionVal = np.array(line)
else: motionVal = np.vstack([motionVal, np.array(line)])
motionVal = np.transpose(motionVal)
fft_len = 15
fft = np.empty((0, fft_len * 2), dtype=np.float64)
if not len(motionVal): return [], []
diffs = np.empty((0, len(motionVal[0]) - 1), dtype=np.float64)
motionVal1 = np.empty((0, len(motionVal[0])), dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m, 121, 3)
m = np.diff(m)
m = savitzky_golay(m, 121, 3)
if max(m) - min(m) > 0: m = m / (max(m) - min(m))
diffs = np.append(diffs, np.array([m]), axis=0)
motionVal1 = np.append(motionVal1,
motionVal[i].reshape(1, len(motionVal[i])),
axis=0)
m = np.fft.fft(m)
m = m[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2] = a
m[1::2] = b
if (max(m) - min(m)) != 0:
m = m / (max(m) - min(m))
fft = np.append(fft, np.array([m]), axis=0)
return fft, diffs
def getFFTArrays(file):
motionVal = np.zeros((0, 0), dtype=np.float64)
line = file.readline().rstrip().split()
while line:
if len(line) == 1 and line[0].isdigit():
val = np.zeros((0, 0), dtype=np.float64)
while True:
line = file.readline().rstrip().split()
tmp = list(float(x) for x in line[1:])
val = np.append(val, tmp)
if line[0] == 'ltoes':
if not len(motionVal): motionVal = val
else: motionVal = np.vstack([motionVal, val])
break
line = file.readline().rstrip().split()
motionVal = np.transpose(motionVal)
fft_len = 31
fft = np.empty((0, fft_len * 2), dtype=np.float64)
if not len(motionVal): return [], []
diffs = np.empty((0, len(motionVal[0]) - 1), dtype=np.float64)
#diffs = np.empty((0,len(motionVal[0])),dtype=np.float64)
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m, 65, 3) # originally 125
m = np.diff(m)
if i not in [24, 25, 36, 37]: # delete left and right clavicles
m = savitzky_golay(m, 65, 3)
diffs = np.append(diffs, np.array([m]), axis=0)
m = np.fft.fft(m)[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
#m = np.abs(m)
a, b = np.real(m), np.imag(m)
m = np.empty((a.size + b.size, ), dtype=a.dtype)
m[0::2], m[1::2] = a, b
fft = np.append(fft, np.array([m]), axis=0)
file.close()
return fft, diffs
def getOneMocapData(file):
line = file.readline().rstrip()
while line != 'MOTION':
line = file.readline().rstrip()
line = file.readline()
line = file.readline()
motionVal = np.zeros((0,0),dtype=np.float64)
line = file.readline()
while line:
line = line.rstrip().split()
line = [float(x) for x in line]
if rotation:
line = list([line[i*3],line[i*3+1],line[i*3+2]] for i in range(19))
else:
line = list([line[i*3+3],line[i*3+4],line[i*3+5]] for i in range(19))
line = list(itertools.chain(*line))
#print('len(line)',len(line))
val = np.array(line)
if not len(motionVal): motionVal = val
else:
if len(val): motionVal = np.vstack([motionVal,val])
else: break
line = file.readline()
line = file.readline()
line = file.readline()
line = file.readline()
motionVal = np.transpose(motionVal)
fft_len = 15
fft = np.empty((0,fft_len*2),dtype=np.float64)
if not len(motionVal): return [],[]
diffs = np.empty((0,len(motionVal[0])-1),dtype=np.float64)
motionVal1 = np.empty((0,len(motionVal[0])),dtype=np.float64)
#print('len(motionVal)',len(motionVal))
for i in range(len(motionVal)):
m = motionVal[i]
m = savitzky_golay(m,13,3)
m = np.diff(m)
m = savitzky_golay(m,13,3)
if max(m)-min(m) > 0: m = m / (max(m)-min(m))
diffs = np.append(diffs,np.array([m]),axis=0)
motionVal1 = np.append(motionVal1,motionVal[i].reshape(1,len(motionVal[i])),axis=0)
m = np.fft.fft(m)
m = m[:fft_len]
# interlacing the real coefficients and the imaginary coefficients
a = np.real(m)
b = np.imag(m)
m = np.empty((a.size + b.size,), dtype = a.dtype)
m[0::2] = a
m[1::2] = b
#m = a
#if (max(m)-min(m)) != 0:
# m = m / (max(m) - min(m))
fft = np.append(fft, np.array([m]),axis=0)
file.close()
#print('len(fft)',len(fft))
#fft = np.delete(fft,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#diffs = np.delete(diffs,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#motionVal1 = np.delete(motionVal1,[31,32,33,44,45,46,50,59,61,73,74,75],axis=0)
#printMocapDataMotion(motionVal1)
return fft, diffs
def get_motion_representation(features, diffs, op): #3rep
print('Features are:\n', features)
n = len(features) # number of body parts
tot_rep = []
if op:
names = ['Wave two hands', 'Wave one hand','Punching',\
'Jumping jacks','Throwing','Bending','Sitdown Standup']
else:
names = ['Walking', 'Running', 'Jumping']
for motion_type in diffs: # for each motion type
rep_list = []
for motion in motion_type: # for each motion sample
body_part_acce_list = []
body_part_nf_list = []
body_part_sig_f = []
body_part_fft_list = []
body_part_pca_list = []
body_part_ind_list = []
# get most important frequency of each body part (# of oscillations)
for i in range(n): # for each body part/joint
# get one feature to represent a body part
feat, ind, _ = choose_joint(i, features, motion,
diffs.index(motion_type))
body_part_ind_list.append(ind)
body_part = get_PCA(feat, motion)
body_part_pca = get_PCA2(feat, motion)
body_part = savitzky_golay(body_part, 21, 3)
body_part_acce_list.append(np.diff(body_part))
body_part_nf_list.append(body_part)
f = get_significant_frequency(body_part)
axes_num = len(feat)
fft_list = []
for j in range(axes_num):
ft = np.fft.fft(motion[feat[j]])[1:31]
fft_list.append(np.hstack([np.real(ft), np.imag(ft)]))
body_part_fft_list.append(fft_list)
body_part_pca_list.append(body_part_pca)
body_part_sig_f.append(f)
body_part_sig_f = np.array(body_part_sig_f)
# features' relative min magnitudes for each body part/joint
body_part_mag2 = list(
min(list(np.std(motion[x]) for x in feat)) for i in range(n))
# body parts freqs' relative magnitude
body_part_freq = list(
np.abs(np.fft.fft(v))[1:31] for v in body_part_nf_list)
body_part_f_mag = list(max(x) for x in body_part_freq)
body_part_f_mag = np.array(body_part_f_mag) / max(body_part_f_mag)
body_part_rgs = list(
np.std(body_part_pca[0])
for body_part_pca in body_part_pca_list)
body_part_arm_dc = get_arm_relations(features, motion)
e = np.e
if max(body_part_mag2) != 0:
body_part_mag2 = np.array(body_part_mag2) / max(body_part_mag2)
body_part_direc = get_directional_correlations(body_part_fft_list)
rep = np.hstack(
[
body_part_direc, body_part_sig_f, body_part_mag2,
body_part_f_mag
]
) #, body_part_acce_tmp]) # body_part_f_mag, body_part_mag2, body_part_sig_f
if np.isnan(rep).any():
print(rep)
inds = np.where(np.isnan(rep))[0]
rep[inds] = 0
print('rep', ' '.join(str(round(x, 2)) for x in rep))
rep_list.append(rep)
tot_rep.append(rep_list)
return tot_rep