def ppg_filtered(self, frames):
camera_specs = Camera_specs()
vps_940_green = list()
vps_600_red = list()
for frame in frames:
vp_600_green, vp_600_red = self.ppg_lamonaca(frame, camera_specs)
vps_940_green.append(vp_600_green)
vps_600_red.append(vp_600_red)
# First, design the Buterworth filter
vps_940_green_filtered = utils.cutoff(vps_940_green)
vps_600_red_filtered = utils.cutoff(vps_600_red)
return (vps_940_green_filtered, vps_600_red_filtered)
def add_file(self, data):
recno = self._tar.next_recno()
info = TarInfo('%08d' % recno)
self._tar.add_record(info, bytes2gzip(data))
msg = message_from_bytes(data)
text = cutoff(msg2str(msg), self.MAX_TEXT_SIZE)
self._text.add_text(recno, text)
for tag in msg2tags(msg):
self._text.add_tag(recno, tag)
return recno
def ppg_filtered_from_video_file(self, video_file):
camera_specs = Camera_specs()
vps_940_green = list()
vps_600_red = list()
frame_count = 0
cap = cv2.VideoCapture(video_file)
fps = cap.get(cv2.CAP_PROP_FPS)
timestamps = list()
while cap.isOpened():
ret, frame = cap.read()
if ret == True:
if frame_count % 50 == 0:
print("Frame:", frame_count)
timestamps.append(frame_count / fps)
frame_count = frame_count + 1
vp_600_green, vp_600_red = self.ppg_lamonaca(
frame, camera_specs)
vps_940_green.append(vp_600_green)
vps_600_red.append(vp_600_red)
else:
break
cap.release()
duration = frame_count / fps
# First, design the Buterworth filter
vps_940_green_filtered = utils.cutoff(vps_940_green)
vps_600_red_filtered = utils.cutoff(vps_600_red)
return (vps_940_green_filtered, vps_600_red_filtered, duration,
timestamps)
def NewQNewtonMethod(f, fDer, fHessian, z0, NIterate, mode, gamma0, v0,
stopCriterion, rtol, atol, dimN, verbose):
if mode == 'gd':
print(
"Use New Q-Newton method with pertubation by orthogonal matrices Full version"
)
else:
print(
"Use New Q-Newton method with pertubation by orthogonal matrices Full version for NAG with gamma ",
gamma0, "; initial direction =", v0)
print("With parameters: f=", f, "; initial point =", z0)
print("We will stop when both z_{n+1}-z_n and f(z_{n+1})-f(z_n) are small")
zn = z0
t0 = time.time()
delta = 1
print("Number of maximum iterates:", NIterate,
"; But note that the number of actual iterates may be much smaller")
for m in range(NIterate):
tmp, w = fHessian(zn)
if dimN == 1:
HessTr = tmp
if HessTr == 0:
HessTr = HessTr + delta * cutoff(L2Norm2(fDer(zn), dimN))
HessInv = 1 / HessTr
gn = fDer(zn) * HessInv
gn = NegativeOrthogonalDecomposition(HessTr, gn, dimN)
elif dimN > 1:
HessTr = tmp.transpose()
if LA.det(HessTr) == 0:
HessTr = HessTr + delta * cutoff(L2Norm2(
fDer(zn), dimN)) * np.identity(dimN, dtype=float)
HessInv = LA.inv(HessTr)
gn = np.matmul(fDer(zn), HessInv)
gn = NegativeOrthogonalDecomposition(HessTr, gn, dimN)
zn_old = zn
zn = zn - gn
if verbose:
print("Step = ", m, "; zn=", zn_old,
"; Square of Displacement = ||z_{n+1}-z_n||^2= ",
L2Norm2(gn, dimN), " ; Square of gradient ",
L2Norm2(fDer(zn), dimN))
if stopCriterion == 0:
if (L2Norm2(gn, dimN) < atol) and (
abs(f(zn) - f(zn_old)) < rtol
): # stop when meet the relative and absolute tolerances
break
elif stopCriterion == 1:
if (abs(f(zn)) < rtol
): # stop when meet the relative and absolute tolerances
break
print("Time:",
time.time() - t0, "; Step:", m, "; z_n:", zn, "; f(z_n):", f(zn),
": Error:", L2Norm2(fDer(zn), dimN))
CheckCriticalType(fHessian, zn)
return
with open('PatRec17_KWS_Data/task/train.txt', 'r') as f:
lines = f.read().splitlines()
for line in lines:
train_check.append(line)
with open('PatRec17_KWS_Data/task/valid.txt', 'r') as f:
lines = f.read().splitlines()
for line in lines:
valid_check.append(line)
train = {}
valid = {}
for filename in os.listdir("words"):
if filename.endswith(".png"):
image = utils.greyMatrix("words/" + str(filename))
image = utils.cutoff(image)
image = utils.resizeArray(image)
features = utils.featuresExtraction(image)
name = filename.replace(".png", "")
id = name.split("-")
if id[0] in train_check:
train[name] = features
elif id[0] in valid_check:
valid[name] = features
print("Train and Valid set loaded")
keyWords = utils.load_keyWords()
groundTruth = utils.load_truth()