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import logging
import os
from pathlib import Path
from typing import Tuple
import numpy as np
import cv2
import torch
def validate_inputs(image_path: str, mask_path: str, motion_path: str) -> None:
for p in [image_path, mask_path, motion_path]:
if not Path(p).exists():
raise FileNotFoundError(f"Required file not found: {p}")
def compute_hw_from_area(
image_height: int,
image_width: int,
max_area: int,
mod_value: int,
) -> Tuple[int, int]:
"""Compute (height, width) with same math and rounding as original."""
aspect_ratio = image_height / image_width
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
return int(height), int(width)
def load_video_to_tensor(video_path):
"""Returns a video tensor from a video file. shape [1, T, C, H, W], [0, 1] range."""
# load video
cap = cv2.VideoCapture(video_path)
frames = []
while 1:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
cap.release()
# Convert frames to tensor, shape [T, H, W, C], [0, 1] range
frames = np.array(frames)
video_tensor = torch.tensor(frames)
video_tensor = video_tensor.permute(0, 3, 1, 2).float() / 255.0
video_tensor = video_tensor.unsqueeze(0).permute(0, 2, 1, 3, 4)
return video_tensor
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