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# 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 json
from io import BytesIO
from typing import Dict, List
import imageio
import numpy as np
def read_prompts_from_file(prompt_file: str) -> List[Dict[str, str]]:
"""Read prompts from a JSONL file where each line is a dict with 'prompt' key and optionally 'visual_input' key.
Args:
prompt_file (str): Path to JSONL file containing prompts
Returns:
List[Dict[str, str]]: List of prompt dictionaries
"""
prompts = []
with open(prompt_file, "r") as f:
for line in f:
prompt_dict = json.loads(line.strip())
prompts.append(prompt_dict)
return prompts
def save_video(video, fps, H, W, video_save_quality, video_save_path):
"""Save video frames to file.
Args:
grid (np.ndarray): Video frames array [T,H,W,C]
fps (int): Frames per second
H (int): Frame height
W (int): Frame width
video_save_quality (int): Video encoding quality (0-10)
video_save_path (str): Output video file path
"""
kwargs = {
"fps": fps,
"quality": video_save_quality,
"macro_block_size": 1,
"ffmpeg_params": ["-s", f"{W}x{H}"],
"output_params": ["-f", "mp4"],
}
imageio.mimsave(video_save_path, video, "mp4", **kwargs)
def load_from_fileobj(filepath: str, format: str = "mp4", mode: str = "rgb", **kwargs):
"""
Load video from a file-like object using imageio with specified format and color mode.
Parameters:
file (IO[bytes]): A file-like object containing video data.
format (str): Format of the video file (default 'mp4').
mode (str): Color mode of the video, 'rgb' or 'gray' (default 'rgb').
Returns:
tuple: A tuple containing an array of video frames and metadata about the video.
"""
with open(filepath, "rb") as f:
value = f.read()
with BytesIO(value) as f:
f.seek(0)
video_reader = imageio.get_reader(f, format, **kwargs)
video_frames = []
for frame in video_reader:
if mode == "gray":
import cv2 # Convert frame to grayscale if mode is gray
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = np.expand_dims(frame, axis=2) # Keep frame dimensions consistent
video_frames.append(frame)
return np.array(video_frames), video_reader.get_meta_data()
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