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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# code was heavily based on https://github.com/AliaksandrSiarohin/first-order-model
import numpy as np
from scipy.spatial import ConvexHull
import paddle
def normalize_kp(kp_source,
kp_driving,
kp_driving_initial,
adapt_movement_scale=False,
use_relative_movement=False,
use_relative_jacobian=False):
if adapt_movement_scale:
source_area = ConvexHull(kp_source['value'][0].numpy()).volume
driving_area = ConvexHull(kp_driving_initial['value'][0].numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
else:
adapt_movement_scale = 1
kp_new = {k: v for k, v in kp_driving.items()}
if use_relative_movement:
kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
kp_value_diff *= adapt_movement_scale
kp_new['value'] = kp_value_diff + kp_source['value']
if use_relative_jacobian:
jacobian_diff = paddle.matmul(
kp_driving['jacobian'],
paddle.inverse(kp_driving_initial['jacobian']))
kp_new['jacobian'] = paddle.matmul(jacobian_diff,
kp_source['jacobian'])
return kp_new