Create utils/util.py

utils/util.py

@@ -0,0 +1,74 @@
+# 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