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| # Copyright (2025) Bytedance Ltd. and/or its affiliates | |
| # 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. | |
| import numpy as np | |
| def compute_scale_and_shift(prediction, target, mask, scale_only=False): | |
| if scale_only: | |
| return compute_scale(prediction, target, mask), 0 | |
| else: | |
| return compute_scale_and_shift_full(prediction, target, mask) | |
| def compute_scale(prediction, target, mask): | |
| # system matrix: A = [[a_00, a_01], [a_10, a_11]] | |
| prediction = prediction.astype(np.float32) | |
| target = target.astype(np.float32) | |
| mask = mask.astype(np.float32) | |
| a_00 = np.sum(mask * prediction * prediction) | |
| a_01 = np.sum(mask * prediction) | |
| a_11 = np.sum(mask) | |
| # right hand side: b = [b_0, b_1] | |
| b_0 = np.sum(mask * prediction * target) | |
| x_0 = b_0 / (a_00 + 1e-6) | |
| return x_0 | |
| def compute_scale_and_shift_full(prediction, target, mask): | |
| # system matrix: A = [[a_00, a_01], [a_10, a_11]] | |
| prediction = prediction.astype(np.float32) | |
| target = target.astype(np.float32) | |
| mask = mask.astype(np.float32) | |
| a_00 = np.sum(mask * prediction * prediction) | |
| a_01 = np.sum(mask * prediction) | |
| a_11 = np.sum(mask) | |
| b_0 = np.sum(mask * prediction * target) | |
| b_1 = np.sum(mask * target) | |
| x_0 = 1 | |
| x_1 = 0 | |
| det = a_00 * a_11 - a_01 * a_01 | |
| if det != 0: | |
| x_0 = (a_11 * b_0 - a_01 * b_1) / det | |
| x_1 = (-a_01 * b_0 + a_00 * b_1) / det | |
| return x_0, x_1 | |
| def get_interpolate_frames(frame_list_pre, frame_list_post): | |
| assert len(frame_list_pre) == len(frame_list_post) | |
| min_w = 0.0 | |
| max_w = 1.0 | |
| step = (max_w - min_w) / (len(frame_list_pre)-1) | |
| post_w_list = [min_w] + [i * step for i in range(1,len(frame_list_pre)-1)] + [max_w] | |
| interpolated_frames = [] | |
| for i in range(len(frame_list_pre)): | |
| interpolated_frames.append(frame_list_pre[i] * (1-post_w_list[i]) + frame_list_post[i] * post_w_list[i]) | |
| return interpolated_frames |