def get_prediction(input_path):
with get_evaluation_context():
return safe_jsonify(
decode_predictions(
model.predict(
load_sig(
join(abspath(input_folder),
input_path[:-3] + 'npy'))), classes, top))
def get_prediction(input_path):
with get_evaluation_context():
return safe_jsonify(
decode_predictions(
model.predict(
load_img(join(abspath(input_folder), input_path),
single_input_shape,
grayscale=(input_channels == 1))), classes,
top))
def get_prediction(input_path):
with get_evaluation_context():
return safe_jsonify(
decode_predictions(
model.predict(
load_img(
join(abspath(input_folder), input_path),
single_input_shape,
grayscale=(input_channels == 1),
mean=mean,
std=std)), classes, top))
def get_prediction(input_path):
# print ("prediction", input_path)
results = [[("sa", "bot_34", 0.2)], [("sa", "bot_35", 0.6)]]
return safe_jsonify(results)