help=
" <optional>, it defines the checkpoint for training<fine tuning> or testing",
required=False)
parser.add_argument(
"-image",
type=str,
help=
" <optional>, a filename for an image to be tested in -predict mode-",
required=False)
pargs = parser.parse_args()
if pargs.config != None:
configuration_file = pargs.config
else:
configuration_file = _params.configuration_file
configuration = conf.ConfigurationFile(configuration_file, pargs.name)
configuration.show()
# it is also possible to define the id of the device
if pargs.device is None:
device_name = "/cpu:0"
else:
device_name = "/" + pargs.device + ":0"
params = {'device': device_name, 'modelname': pargs.name}
if pargs.ckpt is not None:
params['ckpt'] = pargs.ckpt
my_cnn = cnn.CNN(configuration_file, params)
run_mode = pargs.mode
if run_mode == 'train':
my_cnn.train()
elif run_mode == 'test':
my_cnn.test()
def loadMapping(mapping_file):
map_dict = {}
with open(mapping_file) as file:
lines = [line.rstrip() for line in file]
for line in lines:
sline = line.split('\t')
map_dict[int(sline[1])] = sline[0]
return map_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="signature recognition")
parser.add_argument("-image", type=str, help="check image", required=True)
input_arg = parser.parse_args()
configuration_file = "configuration.config"
configuration = conf.ConfigurationFile(configuration_file,
"SIMPLE-SIGNATURE-CNN")
#loading cnn model
_params = {
"device": "/cpu:0",
"arch": "SIMPLE-SIGNATURE-CNN",
"processFun": imgproc.getProcessFun()
}
sign_cnn = cnn.CNN(configuration_file, _params)
filename = input_arg.image
image = io.imread(filename, as_gray=True)
sign_image = sutils.extractSignature(image)
#cv2.imshow("sign", sign_image)
#cv2.waitKey()
prediction = sign_cnn.predict(sign_image)[0]
idx_class = prediction['idx_predicted_class']