def predict_by_neural_network(keypoint_coord3d_v, known_finger_poses, pb_file,
threshold):
detection_graph = tf.Graph()
score_label = 'Undefined'
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(pb_file, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
with tf.Session(graph=detection_graph) as sess:
input_tensor = detection_graph.get_tensor_by_name('input:0')
output_tensor = detection_graph.get_tensor_by_name('output:0')
flat_keypoint = np.array(
[entry for sublist in keypoint_coord3d_v for entry in sublist])
flat_keypoint = np.expand_dims(flat_keypoint, axis=0)
outputs = sess.run(output_tensor,
feed_dict={input_tensor: flat_keypoint})[0]
max_index = np.argmax(outputs)
score_index = max_index if outputs[max_index] >= threshold else -1
score_label = 'Undefined' if score_index == -1 else get_position_name_with_pose_id(
score_index, known_finger_poses)
# print(outputs)
return score_label
def predict_by_svm(keypoint_coord3d_v, known_finger_poses, svc_file):
with open(svc_file, 'rb') as handle:
svc = pickle.load(handle)
flat_keypoint = np.array([entry for sublist in keypoint_coord3d_v for entry in sublist])
flat_keypoint = np.expand_dims(flat_keypoint, axis = 0)
max_index = svc.predict(flat_keypoint)[0]
score_label = get_position_name_with_pose_id(max_index, known_finger_poses)
return score_label