import tensorflow as tf
from tensorflow.keras import models
from tensorflow.keras import layers

class FlowNet:
    """
    Uses the optical flow calculated by the Lucas-Kanade algorithm as input.
    It splits the x and y shifts and runs them though lstms before combining them again.
    The output are 2 tensors. One for the xyz position values and one for the 4 quaternions.
    """
    def __init__(self, lstms=1, activ=None, bn=0):
        self.lstms = lstms
        self.activ = activ
        self.bn = bn
        print('build IMU ...')

    def create_model(self):
        inputs = tf.keras.Input(shape=(120, 160, 2))
        if self.bn == 1:
            bnorm = layers.BatchNormalization()(inputs)
            r = layers.Lambda(lambda x: x[:, :, :, 0], name="lambda_layer1")(bnorm)
            g = layers.Lambda(lambda x: x[:, :, :, 1], name="lambda_layer2")(bnorm)
        else:
            r = layers.Lambda(lambda x: x[:, :, :, 0], name="lambda_layer1")(inputs)
            g = layers.Lambda(lambda x: x[:, :, :, 1], name="lambda_layer2")(inputs)
        if self.lstms == 2:
            lstm1 = layers.LSTM(units=100, return_sequences=True)(r)
            cls3_fc1 = layers.LSTM(units=50)(lstm1)
            lstm2 = layers.LSTM(units=100, return_sequences=True)(g)
            cls3_fc2 = layers.LSTM(units=50)(lstm2)
        elif self.lstms == 1:
            cls3_fc1 = layers.LSTM(units=50)(r)
            cls3_fc2 = layers.LSTM(units=50)(g)
        concat = layers.Concatenate(name='concat', axis=-1)([cls3_fc1, cls3_fc2])
        if self.activ == "relu":
            concat = layers.Activation("relu")(concat)
        elif self.activ == "softmax":
            concat = layers.Activation("softmax")(concat)
        pose_xyz = layers.Dense(3,name='pose_xyz')(concat)
        pose_wpqr = layers.Dense(4,name='pose_wpqr')(concat)
        return models.Model(inputs=inputs, outputs=[pose_xyz, pose_wpqr])


if __name__ == "__main__":
    # Calculates and displays the number of parameters in the model.
    m = FlowNet(lstms=1)
    Model = m.create_model()
    Model.summary()