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from libs import *
import configVars
from tools import tools
from data import ECGDataset
def procesar_archivo(format,number,unit,frec,file):
try:
prepare_data(format,number,unit,frec,file)
antonior92 = predict_antonior92()
CPSC = predict_CPSC_2018()
Chapman = predict_Chapman()
result = pd.DataFrame(data = [['Antonior92',antonior92],['CPSC-2018',CPSC],['Chapman',Chapman]],columns=['Red','Predicción'])
tools.ecgPlot("./datasets/pred.npy",500)
return result, "ecg.png"
except:
return pd.DataFrame(data = ["Se ha producido un error inesperado.","Compruebe que los datos de entrada sean correctos"],columns = ["ERROR."]), "error.jpg"
def predict_CPSC_2018():
config = {
"ecg_leads":[
0, 1,
6,
],
"ecg_length":5000,
"is_multilabel":True,
}
train_loaders = {
"pred":torch.utils.data.DataLoader(
ECGDataset(
df_path = f"{configVars.pathCasos}pred.csv", data_path = f"{configVars.pathCasos}",
config = config,
augment = False,
),
timeout=0
)
}
save_ckp_dir = f"{configVars.pathModel}CPSC-2018"
pred = tools.LightX3ECG(
train_loaders,
config,
save_ckp_dir,
)
return pred if len(pred) != 0 else ['El archivo introducido no satisface ninguno de los criterios de clasificación']
def predict_Chapman():
config = {
"ecg_leads":[
0, 1,
6,
],
"ecg_length":5000,
"is_multilabel":False,
}
train_loaders = {
"pred":torch.utils.data.DataLoader(
ECGDataset(
df_path = f"{configVars.pathCasos}pred.csv", data_path = f"{configVars.pathCasos}",
config = config,
augment = False,
),
timeout=0
)
}
save_ckp_dir = f"{configVars.pathModel}Chapman"
pred = tools.LightX3ECG(
train_loaders,
config,
save_ckp_dir,
)
return pred
def predict_antonior92():
f = h5py.File(f"{configVars.pathCasos}pred.hdf5", 'r')
model = load_model(f"{configVars.pathModel}/antonior92/model.hdf5", compile=False)
model.compile(loss='binary_crossentropy', optimizer=Adam())
pred = model.predict(f['tracings'], verbose=0)
optimal_thresholds = pd.read_csv(f"{configVars.pathThresholds}antonior92/optimal_thresholds_best.csv")
result = optimal_thresholds[optimal_thresholds["Threshold"]<=pred[0]]
result = result['Pred'].values.tolist()
f.close()
return result if len(result) != 0 else ['Normal']
def prepare_data(format,number,unit,frec,file):
units = {
'V':0.001,
'miliV':1,
'microV':1000,
'nanoV':1000000
}
if(format == 'XMLsierra'):
f = read_file(file.name)
df = pd.DataFrame()
for lead in f.leads:
df[lead.label]=lead.samples
data = df
elif(format == 'CSV'):
data = pd.read_csv(file.name,header = None)
data = data[:-200]
data = data.T
leads = len(data)
frec = frec if frec>0 else 1
scale = 1/(number*units[unit])
ecg_preprocessed = tools.preprocess_ecg(data, frec, leads,
scale=scale,######### modificar para que segun la unidad introducida se pueda convertir los datos
use_all_leads=True,
remove_baseline=True)
tools.generateH5(ecg_preprocessed,
"pred.hdf5",new_freq=400,new_len=4096,
scale=2,sample_rate = frec)
np.save(f"{configVars.pathCasos}pred.npy",ecg_preprocessed ) |