def main(argv):
distribution = None
per = 0.5
num = 300000
is_sample = False
is_type = False
is_distribution = False
do_create = True
try:
opts, args = getopt.getopt(argv, "hd:t:p:n:c:")
except getopt.GetoptError:
show_help_message('command')
sys.exit(2)
for opt, arg in opts:
arg = str(arg).lower()
if opt == '-h':
show_help_message('all')
return
elif opt == '-t':
if arg == "sample":
is_sample = True
is_type = True
elif arg == "full":
is_sample = False
is_type = True
else:
show_help_message('type')
return
elif opt == '-d':
if not is_type:
show_help_message('noTypeError')
return
if arg == "random":
distribution = Distribution.RANDOM
is_distribution = True
elif arg == "exponential":
distribution = Distribution.EXPONENTIAL
is_distribution = True
else:
show_help_message('distribution')
return
elif opt == '-p':
if not is_type:
show_help_message('noTypeError')
return
if not is_distribution:
show_help_message('noDistributionError')
return
per = float(arg)
if not 0.1 <= per <= 1.0:
show_help_message('percent')
return
elif opt == '-n':
if not is_type:
show_help_message('noTypeError')
return
if not is_distribution:
show_help_message('noDistributionError')
return
if is_sample:
show_help_message('snError')
return
num = int(arg)
if not 10000 <= num <= 1000000:
show_help_message('number')
return
elif opt == '-c':
if not is_distribution:
show_help_message('noDistributionError')
return
do_create = not (int(arg) == 0)
else:
print("Unknown parameters, please use -h for instructions.")
return
if not is_type:
show_help_message('noTypeError')
return
if not is_distribution:
show_help_message('noDistributionError')
return
if do_create:
create_data(distribution, num)
if is_sample:
sample_train(thresholdPool[distribution], useThresholdPool[distribution], distribution, per, filePath[distribution])
else:
train_index(thresholdPool[distribution], useThresholdPool[distribution], distribution, filePath[distribution])
def create_dataset():
creator = create_data(image_size=(128, 416),
output_csv='data/dataset.csv',
output_dir='data/images',
formula_file='data/normalized_.txt')
creator.create()