def main():
"""
Entry point when using CRNN from the commandline
"""
args = parse_arguments()
if not args.train and not args.test:
print("If we are not training, and not testing, what is the point?")
crnn = None
if args.train:
crnn = CRNN(args.iteration_count, args.batch_size, args.model_path,
args.examples_path, args.max_image_width,
args.train_test_ratio, args.restore, 0)
crnn.train(args.iteration_count)
if args.test:
if crnn is None:
crnn = CRNN(args.iteration_count, args.batch_size, args.model_path,
args.examples_path, args.max_image_width, 0,
args.restore, 1)
crnn.test()
def main():
args = parse_arguments()
if not args.train and not args.test:
print("If we are not training,and not testing,what is the point?")
crnn = None
if args.train:
crnn = CRNN(
args.batch_size,
args.model_path,
args.example_path,
args.max_image_width,
args.train_test_ratio,
args.restore
)
crnn.train(args.iteration_count)
if args.test:
if crnn is None:
crnn = CRNN(
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
0,
args.restore
)
crnn.test()
def main():
"""
Entry point when using CRNN from the commandline
"""
args = parse_arguments()
if not args.train and not args.test:
print("If we are not training, and not testing, what is the point?")
crnn = None
charset = ""
if os.path.isfile(args.char_set_string):
# if charset is file read from file.
with open(args.char_set_string, "r") as f:
while True:
c = f.readline()
charset += c.strip("\n")
if not c:
charset += "\n" # Add line break to charset at the end
break
else:
charset = args.char_set_string
if args.train:
crnn = CRNN(
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
args.train_test_ratio,
args.restore,
charset,
args.use_trdg,
args.language,
args.learning_rate
)
crnn.train(args.iteration_count)
if args.test:
if crnn is None:
crnn = CRNN(
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
0,
args.restore,
charset,
args.use_trdg,
args.language,
args.learning_rate
)
crnn.test()
def main():
"""
Entry point when using CRNN from the commandline
"""
args = parse_arguments()
crnn = None
if crnn is None:
crnn = CRNN(
args.iteration_count,
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
0, #train/test ratio here train rate is 0
args.restore,
1)
predict_result = crnn.test()
f = open(args.output_path, 'w')
for str in predict_result:
str1 = str.split(':')[0]
str2 = str.split(':')[1]
str2 = str2.strip('_')
f.writelines(str1 + ':' + str2)
f.close()
def main():
"""
Entry point when using CRNN from the commandline
"""
args = parse_arguments()
if not args.train and not args.test:
print("If we are not training, and not testing, what is the point?")
crnn = None
if args.train:
crnn = CRNN(
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
args.train_test_ratio,
args.restore
)
crnn.train(args.iteration_count)
if args.test:
if crnn is None:
crnn = CRNN(
args.batch_size,
args.model_path,
args.examples_path,
args.max_image_width,
0,
args.restore
)
crnn.test()
def recognition2(examples_path, output_path):
"""
Entry point when using CRNN from the commandline
"""
crnn = None
if crnn is None:
crnn = CRNN(
10,
1,
"./save/",
examples_path,
230,
0, #train/test ratio here train rate is 0
True,
1)
predict_result = crnn.test()
f = open(output_path, 'w')
for str in predict_result:
str1 = str.split(':')[0]
str2 = str.split(':')[1]
str2 = str2.strip('_')
f.writelines(str1 + ':' + str2)
f.close()