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.
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#1PyTorch IterableDataset RTSP Implementation
Implement a non-blocking stream generator for PyTorch:
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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