def get_layer_outputs(layer_name, input_path):
return jsonify(
save_layer_outputs(
load_img(join(abspath(input_folder), input_path),
single_input_shape,
grayscale=input_channels == 1), model, layer_name,
temp_folder, input_path))
def get_layer_outputs(layer_name, input_path):
is_grayscale = (input_channels == 1)
input_img = load_img(join(abspath(input_folder), input_path),
single_input_shape,
grayscale=is_grayscale)
output_generator = get_outputs_generator(model, layer_name)
with get_evaluation_context():
layer_outputs = output_generator(input_img)[0]
output_files = []
if keras.backend.backend() == 'theano':
#correct for channel location difference betwen TF and Theano
layer_outputs = np.rollaxis(layer_outputs, 0, 3)
for z in range(0, layer_outputs.shape[2]):
img = layer_outputs[:, :, z]
deprocessed = deprocess_image(img)
filename = get_output_name(temp_folder, layer_name, input_path,
z)
output_files.append(relpath(filename, abspath(temp_folder)))
imsave(filename, deprocessed)
return jsonify(output_files)
def get_layer_outputs(layer_name, input_path):
return jsonify(
save_layer_outputs(
load_img(
join(abspath(input_folder), input_path),
single_input_shape,
grayscale=(input_channels == 1),
mean=mean,
std=std), model, layer_name, temp_folder, input_path))
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):
is_grayscale = (input_channels == 1)
input_img = load_img(input_path,
single_input_shape,
grayscale=is_grayscale)
with get_evaluation_context():
return jsonify(
json.loads(
get_json(decode_predictions(model.predict(input_img)))))
def get_layer_outputs(layer_name, input_path):
print(single_input_shape)
return jsonify(
save_layer_outputs(
load_img(join(abspath(input_folder),
input_path.replace("%23", "#")),
single_input_shape,
grayscale=(input_channels == 1),
mean=mean,
std=std), model, layer_name, temp_folder, input_path))
def get_layer_outputs(layer_name, input_path):
return jsonify(
save_layer_outputs(
custom_load_img(abspath(input_folder), input_path)
if custom_load_img else load_img(
join(abspath(input_folder), input_path),
single_input_shape,
grayscale=(input_channels == 1),
mean=mean,
std=std), model, layer_name, temp_folder, input_path))
def get_prediction(input_path):
is_grayscale = (input_channels == 1)
input_img = load_img(join(abspath(input_folder), input_path),
single_input_shape,
grayscale=is_grayscale)
with get_evaluation_context():
return jsonify(
json.loads(
get_json(
decode_predictions(model.predict(input_img), 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))
