codeAugust 7, 2026•8 min read•RTSPLink Dev Team
Streaming PyTorch Lightning DataLoader for Live RTSP Video Feeds
Train and evaluate neural networks on continuous live security camera streams without storing video files on disk.
#PyTorch#PyTorch Lightning#Deep Learning#Python#DataLoader
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#1PyTorch IterableDataset RTSP Implementation
Implement a non-blocking stream generator for PyTorch:
PyTorch RTSP Dataset
import torch
from torch.utils.data import IterableDataset, DataLoader
import cv2
class RTSPIterableDataset(IterableDataset):
def __init__(self, rtsp_url):
self.rtsp_url = rtsp_url
def __iter__(self):
cap = cv2.VideoCapture(self.rtsp_url)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
tensor = torch.from_numpy(frame).permute(2, 0, 1).float() / 255.0
yield tensor
dataset = RTSPIterableDataset("rtsp://rtsplink.com/live/test?token=demo")
loader = DataLoader(dataset, batch_size=4)Ready to test your live RTSP video stream?
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