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 save_layer_img(layer_outputs, layer_name, idx, temp_folder, input_path):
filename = get_output_filename(layer_name, idx, temp_folder, input_path)
imsave(filename, deprocess_image(layer_outputs))
return relpath(filename, abspath(temp_folder))
def save_layer_img(layer_outputs, layer_name, idx, temp_folder, input_path):
filename = get_output_filename(layer_name, idx, temp_folder, input_path)
imageio.imwrite(filename, (deprocess_image(layer_outputs)*255).astype(np.uint8))
return relpath(filename, abspath(temp_folder))