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42d9709 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
import torch
import cv2
def resize_frames_with_padding(
frames: torch.Tensor | np.ndarray, target_image_size: tuple, bgr_conversion: bool = False, pad_img: bool = True
) -> np.ndarray:
"""Process batch of frames with padding and resizing vectorized
Args:
frames: np.ndarray of shape [N, 256, 160, 3]
target_image_size: target size (height, width)
bgr_conversion: whether to convert BGR to RGB
pad_img: whether to resize images
"""
if isinstance(frames, torch.Tensor):
frames = frames.cpu().numpy()
elif not isinstance(frames, np.ndarray):
raise ValueError(f"Invalid frame type: {type(frames)}")
if bgr_conversion:
frames = cv2.cvtColor(frames, cv2.COLOR_BGR2RGB)
if pad_img:
top_padding = (frames.shape[2] - frames.shape[1]) // 2
bottom_padding = top_padding
# Add padding to all frames at once
frames = np.pad(
frames,
pad_width=((0, 0), (top_padding, bottom_padding), (0, 0), (0, 0)),
mode="constant",
constant_values=0,
)
# Resize all frames at once
if frames.shape[1:] != target_image_size:
frames = np.stack([cv2.resize(f, target_image_size) for f in frames])
return frames
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