def generate_cycle(length,*args,peep=None):
startpos = np.random.randint(0,len(args[0]))
delta = (length - startpos)
nzeroes = delta//len(args[0])+1
zpos = np.random.randint(2,delta//10,size=nzeroes)
res = [None]*len(args)
vrandom = [np.random.uniform(0.95,1.05) if np.random.random() > 0.01 else np.random.uniform(0.1,0.2) for i in range(nzeroes+1)]
for i in range(len(args)):
rarr = args[i][-startpos:]
# mode = stats.mode(args[i])[0]
mode = peep if (not peep is None) and (i==len(args)-1) else 0
for j in range(nzeroes):
aux = vrandom[j+1]*args[i] + (1-vrandom[j+1])*mode
# aux = 0.0
# if vrandom[j+1] < 0.9: aux = np.array([ vrandom[j+1]*value if value > mode/vrandom[j+1] else value for value in args[i]])
# else: aux = np.array([vrandom[j+1]*value if value > mode/vrandom[j+1] else value for value in args[i]])
rarr = np.concatenate((rarr,np.ones(zpos[j])*mode,aux))
res[i] = rarr[:length]
return res
def pmus_ingmar(fs, rr, pp, tp, tf):
"""
Sinusoidal profile
:param fs: sample frequency
:param rr: respiratory rate
:param pp: peak pressure
:param tp: peak time
:param tf: end of effort
:return: pmus profile
"""
ntp = np.floor(tp * fs)
ntf = np.floor(tf * fs)
ntN = np.floor(60.0 * fs / rr)
pmus1 = np.sin(np.pi * np.arange(0, ntp + 1, 1) / fs / 2.0 / tp)
pmus2 = np.sin(np.pi / 2.0 / (tf - tp) *
(np.arange(ntp + 1, ntf + 1, 1) / fs + tf - 2.0 * tp))
pmus3 = 0 * np.arange(ntf + 1, ntN + 1, 1) / fs
pmus = pp * np.concatenate((pmus1, pmus2, pmus3))
return pmus
def pmus_parexp(fs, rr, pp, tp, tf):
"""
Parabolic-exponential profile
:param fs: sample frequency
:param rr: respiratory rate
:param pp: peak pressure
:param tp: peak time
:param tf: end of effort
:return: pmus profile
"""
ntp = np.floor(tp * fs)
ntN = np.floor(60.0 / rr * fs)
taur = abs(tf - tp) / 4.0
pmus1 = pp * (60.0 * rr - np.arange(0, ntp + 1, 1) / fs) * (
np.arange(0, ntp + 1, 1) / fs) / (tp * (60.0 * rr - tp))
pmus2 = pp * (np.exp(-(np.arange(ntp + 1, ntN + 1, 1) / fs - tp) / taur) -
np.exp(-(60.0 * rr - tp) / taur)) / (
1.0 - np.exp(-(60.0 * rr - tp) / taur))
pmus = np.concatenate((pmus1, pmus2))
return pmus
RGB_image, extract_patches, patch_tiles, bal_aug_patches, extrac_patch2, test_FCN, pred_recostruction, \
weighted_categorical_crossentropy, mask_no_considered, tf, Adam, prediction, load_model, confusion_matrix, \
EarlyStopping, ModelCheckpoint, identity_block, ResNet50, color_map
root_path = './DATASETS/'
# Load images
img_t1 = load_tiff_image(root_path + 'images/18_08_2017_image' +
'.tif').astype(np.float32)
img_t1 = img_t1.transpose((1, 2, 0))
img_t2 = load_tiff_image(root_path + 'images/21_08_2018_image' +
'.tif').astype(np.float32)
img_t2 = img_t2.transpose((1, 2, 0))
# Concatenation of images
image_array1 = np.concatenate((img_t1, img_t2), axis=-1).astype(np.float32)
h_, w_, channels = image_array1.shape
print(image_array1.shape)
# Normalization
type_norm = 1
image_array = normalization(image_array1, type_norm)
print(np.min(image_array), np.max(image_array))
# Load reference
image_ref1 = load_tiff_image(root_path + 'images/REFERENCE_2018_EPSG4674' +
'.tif')
image_ref = image_ref1[:1700, :1440]
past_ref1 = load_tiff_image(root_path +
'images/PAST_REFERENCE_FOR_2018_EPSG4674' + '.tif')
def solve_model(header_params,params,header_features,features,debugmsg):
#Extracts each parameter
fs = params[header_params.index('Fs')]
rvent = params[header_params.index('Rvent')]
c = params[header_params.index('C')]
rins = params[header_params.index('Rins')]
rexp = rins # params[4]
peep = params[header_params.index('PEEP')]
sp = params[header_params.index('SP')]
trigger_type = features[header_features.index('Triggertype')]
trigger_arg = params[header_params.index('Triggerarg')]
rise_type = features[header_features.index('Risetype')]
rise_time = params[header_params.index('Risetime')]
cycle_off = params[header_params.index('Cycleoff')]
rr = params[header_params.index('RR')]
pmus_type = features[header_features.index('Pmustype')]
pp = params[header_params.index('Pp')]
tp = params[header_params.index('Tp')]
tf = params[header_params.index('Tf')]
noise = params[header_params.index('Noise')]
e2 = params[header_params.index('E2')]
model = features[header_features.index('Model')]
expected_len = int(np.floor(180.0 / np.min(RR) * np.max(Fs)) + 1)
#Assings pmus profile
pmus = pmus_profile(fs, rr, pmus_type, pp, tp, tf)
pmus = pmus + peep #adjusts PEEP
pmus = np.concatenate((np.array([0]), pmus)) #sets the first value to zero
#Unit conversion from cmH2O.s/L to cmH2O.s/mL
rins = rins / 1000.0
rexp = rexp / 1000.0
rvent = rvent / 1000.0
#Generates time, flow, volume, insex and paw waveforms
time = np.arange(0, np.floor(60.0 / rr * fs) + 1, 1) / fs
time = np.concatenate((np.array([0]), time))
flow = np.zeros(len(time))
volume = np.zeros(len(time))
insex = np.zeros(len(time))
paw = np.zeros(len(time)) + peep #adjusts PEEP
len_time = len(time)
#Peak flow detection
peak_flow = flow[0]
detect_peak_flow = False
#Support detection
detect_support = False
time_support = -1
#Expiration detection
detect_exp = False
time_exp = -1
if trigger_type == 'flow':
# units conversion from L/min to mL/s
trigger_arg = trigger_arg / 60.0 * 1000.0
for i in range(1, len(time)):
# period until the respiratory effort beginning
if (((trigger_type == 'flow' and flow[i] < trigger_arg) or
(trigger_type == 'pressure' and paw[i] > trigger_arg + peep) or
(trigger_type == 'delay' and time[i] < trigger_arg)) and
(not detect_support) and (not detect_exp)):
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, waiting'.format(volume[i], flow[i], paw[i]))
if (((trigger_type == 'flow' and flow[i] >= trigger_arg) or
(trigger_type == 'pressure' and paw[i] <= trigger_arg + peep) or
(trigger_type == 'delay' and time[i] >= trigger_arg))):
detect_support = True
time_support = time[i+1]
continue
# detection of inspiratory effort
# ventilator starts to support the patient
elif (detect_support and (not detect_exp)):
if rise_type == 'step':
paw[i] = sp + peep
elif rise_type == 'exp':
rise_type = rise_type if np.random.random() > 0.01 else 'linear'
if paw[i] < sp + peep:
paw[i] = (1.0 - np.exp(-(time[i] - time_support) / rise_time )) * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i] < sp + peep:
paw[i] = (time[i] - time_support) / rise_time * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, supporting'.format(volume[i], flow[i], paw[i]))
if flow[i] >= flow[i - 1]:
peak_flow = flow[i]
detect_peak_flow = False
elif flow[i] < flow[i - 1]:
detect_peak_flow = True
if (flow[i] <= cycle_off * peak_flow) and detect_peak_flow and i<len_time:
detect_exp = True
time_exp = i+1
try:
paw[i + 1] = paw[i]
except IndexError:
pass
elif detect_exp:
if rise_type == 'step':
paw[i] = peep
elif rise_type == 'exp':
if paw[i - 1] > peep:
paw[i] = sp * (np.exp(-(time[i] - time[time_exp-1]) / rise_time )) + peep
if paw[i - 1] <= peep:
paw[i] = peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i - 1] > peep:
paw[i] = sp * (1 - (time[i] - time[time_exp-1]) / rise_time) + peep
if paw[i - 1] <= peep:
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rexp + rvent)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rexp + rvent)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, exhaling'.format(volume[i], flow[i], paw[i]))
#Generates InsEx trace
if time_exp > -1:
insex = np.concatenate((np.ones(time_exp), np.zeros(len(time) - time_exp)))
#Drops the first element
flow = flow[1:] / 1000.0 * 60.0 # converts back to L/min
volume = volume[1:]
paw = paw[1:]
pmus = pmus[1:] - peep #reajust peep again
insex = insex[1:]
flow,volume,pmus,insex,paw = generate_cycle(expected_len,flow,volume,pmus,insex,paw,peep=peep)
# paw = generate_cycle(expected_len,paw,peep=peep)[0]
flow,volume,paw,pmus,insex = generate_noise(noise,flow,volume,paw,pmus,insex)
# plt.plot(flow)
# plt.plot(volume)
# plt.plot(paw)
# plt.plot(pmus)
# plt.show()
return flow, volume, paw, pmus, insex, rins,rexp, c
parser.add_argument("--dataset",
help="dataset path", type=str, default='dataset')
args = parser.parse_args()
root_path = args.dataset
img_t1_path = 'clipped_raster_004_66_2018.tif'
img_t2_path = 'clipped_raster_004_66_2019.tif'
# Load images
img_t1 = load_tiff_image(os.path.join(root_path,img_t1_path)).astype(np.float32)
img_t1 = img_t1.transpose((1,2,0))
img_t2 = load_tiff_image(os.path.join(root_path,img_t2_path)).astype(np.float32)
img_t2 = img_t2.transpose((1,2,0))
# Concatenation of images
image_array1 = np.concatenate((img_t1, img_t2), axis = -1).astype(np.float32)
image_array1 = image_array1[:6100,:6600]
h_, w_, channels = image_array1.shape
print(f"Input image shape: {image_array1.shape}")
# Normalization
type_norm = 1
image_array = normalization(image_array1, type_norm)
print(np.min(image_array), np.max(image_array))
# Load Mask area
img_mask_ref_path = 'mask_ref.tif'
img_mask_ref = load_tiff_image(os.path.join(root_path, img_mask_ref_path))
img_mask_ref = img_mask_ref[:6100,:6600]
print(f"Mask area reference shape: {img_mask_ref.shape}")
num_examples = flow.shape[0]
num_samples = flow.shape[1]
(min_flow, max_flow, flow) = normalize_data(flow)
(min_volume, max_volume, volume) = normalize_data(volume)
(min_paw, max_paw, paw) = normalize_data(paw)
(min_resistance, max_resistance, resistances) = normalize_data(resistances)
(min_capacitance, max_capacitance, capacitances) = normalize_data(capacitances)
print("normalized data")
input_data = np.zeros((num_examples, num_samples, 3))
input_data[:, :, 0] = flow
input_data[:, :, 1] = volume
input_data[:, :, 2] = paw
output_data = np.concatenate((resistances, capacitances), axis=1)
indices = np.arange(num_examples)
print("input created")
input_train, input_test, output_train, output_test, indices_train, indices_test = \
train_test_split(input_data, output_data, indices, test_size=0.3, shuffle=False)
input_validation, input_test, output_validation, output_test, indices_validation, indices_test = \
train_test_split(input_test, output_test, indices_test, test_size=0.5, shuffle=False)
np.save('./data/input_test.npy', input_test)
np.save('./data/output_test.npy', output_test)
print("before CNN")