def main(argv):
global model
global processorOpts
# Reading and parsing args
ioOpts, _, _ = osutils.parseArgs(argv)
if ioOpts.modelFolder is None:
mypprint.printError("Model Folder parameter is not defined")
sys.exit(2)
if ioOpts.source == chestxrayio.GCP_SOURCE:
ioOpts.bucket = chestxrayio.getGCPStorageBucket(ioOpts)
# Loading model
mypprint.printText("Cargando modelo")
model = chestxrayio.loadModel("model.h5", ioOpts)
# Loading preprocesing images options
processorOpts = chestxrayio.loadPreprocesingOpts("parametersSummary.json", ioOpts)
# Run flask app
app.run()
def createFolder(folderPath):
if not os.path.isdir(folderPath):
try:
os.mkdir(folderPath)
except OSError as err:
mypprint.printError("Can not create directory " % folderPath)
sys.exit(2)
def getDataFrom(ioOpts, processorOpts, dataType):
if ioOpts.source == GCP_SOURCE:
return getDataFromGCP(dataType, processorOpts, ioOpts)
elif ioOpts.source == LOCAL_SOURCE:
return getDataFromLocalDisk(dataType, processorOpts, ioOpts)
else:
mypprint.printError("Source " + source + " not known. Can not retrieve data")
return (np.array([]), np.array([]))
def deleteFile(fileName):
try:
os.remove(fileName)
except OSError as e:
mypprint.printError("Can not delete " + fileName + ". " + str(e))
def parseArgs(argv):
ioOpts = IOOpts()
processorOpts = ProcessorOpts()
trainningOpts = TrainingOpts()
try:
opts, args = getopt.getopt(argv,
"hs:n:H:w:e:v:c:k:b:u:f:",
["help",
"source=",
"normalize=",
"height=",
"width=",
"epochs=",
"validationSplit=",
"channel=",
"gcpkey=",
"bucket=",
"batch_size=",
"folder="
]
)
except getopt.GetoptError as err:
sys.exit(2)
for opt, value in opts:
if opt in ("-h", "--help"):
mypprint.printText("Read README.md to know more about parameters")
sys.exit()
if opt in ("-s", "--source"):
if value.lower() in ("local", "gcp"):
ioOpts.source = value
else:
mypprint.printError("Source "+ value + " is not known")
sys.exit(2)
if opt in ("-n", "--normalize"):
if value.lower() == "no":
processorOpts.normalize = False
elif value.lower() == "yes":
processorOpts.normalize = True
else:
mypprint.printError("Normalize image`s pixels option "+ value + " is not known")
sys.exit(2)
if opt in ("-H", "--height"):
try:
processorOpts.height = int(value)
except ValueError as err:
mypprint.printError("Height argument not valid.")
sys.exit(2)
if opt in ("-w", "--width"):
try:
processorOpts.width = int(value)
except ValueError as err:
mypprint.printError("Width argument not valid.")
sys.exit(2)
if opt in ("-e", "--epochs"):
try:
trainningOpts.epochs = int(value)
except ValueError as err:
mypprint.printError("Epochs argument not valid.")
sys.exit(2)
if opt in ("-b", "--batch_size"):
try:
trainningOpts.batchSize = int(value)
except ValueError as err:
mypprint.printError("Batch Size argument not valid.")
sys.exit(2)
if opt in ("-v", "--validationSplit"):
try:
trainningOpts.validationSplit = float(value)
if trainningOpts.validationSplit > 1 and trainningOpts.validationSplit < 0:
mypprint.printError("Validations split must be a value between 0 and 1")
sys.exit(2)
except ValueError as err:
mypprint.printError("Validation split argument not valid.")
sys.exit(2)
if opt in ("-c", "--channel"):
if value.lower() == "rgb":
processorOpts.channels = 3
elif value.lower() == "gray":
processorOpts.channels = 1
else:
sys.exit(2)
if opt in ("-k", "--gcpkey"):
ioOpts.googleStorageKey = value
if opt in ("-u", "--bucket"):
ioOpts.bucketName = value
if opt in ("-f", "--folder"):
if value[-1] == os.sep:
value = value[:-1]
ioOpts.modelFolder = value
return ioOpts, processorOpts, trainningOpts