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import numpy as np
import torch
from torchvision import transforms
import av
import logging
from ftplib import FTP
logging.basicConfig(filename='/mnt/data/uploads/logfile-video.log', level=logging.INFO)
def get_video_file(file_path, ftp_password):
# FTP 서버 정보
ftp_server = "121.136.96.223"
ftp_port = 21
ftp_user = "donghuna_ftp"
# folder_path = "homes/donghuna/database/Diving48_rgb/rgb/"
# FTP 연결 설정
ftp = FTP()
ftp.connect(ftp_server, ftp_port)
ftp.login(user=ftp_user, passwd=ftp_password)
ftp.set_pasv(True)
video_path = "test.mp4"
with open(video_path, 'wb') as local_file:
ftp.retrbinary(f'RETR {file_path}', local_file.write)
def read_video(file_path, num_frames=24, target_size=(224, 224)):
# logging.info(f"Reading video from: {file_path}")
container = av.open(file_path)
frames = []
for frame in container.decode(video=0):
frames.append(frame.to_ndarray(format="rgb24").astype(np.uint8))
sampled_frames = sample_frames(frames, num_frames)
processed_frames = preprocess_frames(sampled_frames, target_size)
return processed_frames
def sample_frames(frames, num_frames):
total_frames = len(frames)
if total_frames <= num_frames:
if total_frames < num_frames:
padding = [np.zeros_like(frames[0]) for _ in range(num_frames - total_frames)]
frames.extend(padding)
else:
indices = np.linspace(0, total_frames - 1, num=num_frames, dtype=int)
frames = [frames[i] for i in indices]
return np.array(frames)
def preprocess_frames(frames, target_size):
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(target_size),
transforms.ToTensor()
])
processed_frames = [transform(frame) for frame in frames]
return torch.stack(processed_frames).permute(1, 0, 2, 3).numpy() # (T, C, H, W) -> (C, T, H, W)
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