RTSP | Technology Tutorials

AI on the Edge Lesson 37: Using RTSP and IP Cameras in OpenCV on Raspberry Pi 5

Hey guys! Welcome back to our AI on the Edge series. In our previous lessons, we’ve had a blast working with standard USB webcams, but if you are building a real-world computer vision application, an automation rig, or a security monitoring setup around your home or farm, USB cables just aren’t going to cut it. You need to pull video feeds from remote IP cameras using the Real-Time Streaming Protocol (RTSP).

Today, we are taking that exact step on the Raspberry Pi 5, connecting to an IP camera, streaming the feed smoothly into OpenCV, and—most importantly—solving the dreaded latency problem that plagues RTSP feeds.

The Big Challenge: Conquering RTSP Latency

If you’ve ever tried pulling an RTSP stream into OpenCV straight out of the box, you’ve probably noticed something frustrating: the video lags behind real-time, sometimes by several seconds or even tens of seconds.

Why does that happen? Because by default, FFmpeg and OpenCV buffer incoming frames to ensure smooth playback. But when you are doing computer vision, AI inferencing, or real-time tracking on the edge, you don’t want old history—you want right now.

To fix that, we pass the cv2.CAP_FFMPEG backend flag and immediately flush the buffer by setting the property to 0. This forces OpenCV to drop the backlog and grab the absolute newest frame available from the camera stream, keeping your Pi 5 processing live data in real-time.

Understanding the Script Structure

Let’s break down the key parts of today’s implementation:

Credentials & Resolution: We import a separate secret file to keep our camera IP addresses, usernames, and passwords safe and out of public repositories. We lock our resolution at 1280×720 to balance crisp detail with the Pi 5’s processing overhead.

Smooth FPS Calculation: Instead of a jittery raw frame-rate readout, we use an exponential moving average to give us a stable, readable performance metric on screen.

The Display Window: We configure a GUI window using OpenCV’s window flags so we can easily position and resize our output feed on the desktop.

Drop Your Questions Below

Working with network streams can sometimes be tricky depending on your specific camera’s firmware, codec settings, and network stability. If you run into any connection drops or lag spikes on your Raspberry Pi 5, drop a comment on the video!

Keep building, stay creative, and I will see you guys in Lesson 38!

Here is the code developed in the video

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================
import cv2
import time
import secret
W=1280
H=720
tStart = time.time()
fps = 0
RES = (W,H)

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)
print(secret.RTSP8)
cam = cv2.VideoCapture(secret.RTSP1, cv2.CAP_FFMPEG)
cam.set(cv2.CAP_PROP_BUFFERSIZE, 0)
cam.set(cv2.CAP_PROP_POS_FRAMES, 0)

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)
cv2.moveWindow('Camera',0,65)
cv2.resizeWindow('Camera',W,H)

while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    _, frame= cam.read()
    #frame=cv2.flip(frame,-1)
    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)
    cv2.imshow("Camera", frame)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

import secret

W=1280

H=720

tStart = time.time()

fps = 0

RES = (W,H)

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

print(secret.RTSP8)

cam = cv2.VideoCapture(secret.RTSP1, cv2.CAP_FFMPEG)

cam.set(cv2.CAP_PROP_BUFFERSIZE, 0)

cam.set(cv2.CAP_PROP_POS_FRAMES, 0)

cv2.namedWindow('Camera',cv2.WINDOW_GUI_NORMAL)

cv2.moveWindow('Camera',0,65)

cv2.resizeWindow('Camera',W,H)

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

_, frame= cam.read()

#frame=cv2.flip(frame,-1)

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.imshow("Camera", frame)

if cv2.waitKey(1)==ord('q'):

break

cv2.destroyAllWindows()

print('Program Terminated')

OK guys, you spoke, and I listened. You all are asking for a lesson on how to do object detection on a Pi 5 using YOLO and an IP Camera. Well, you are about to get what you asked for. We will make this work, or we will DIE TRYING. Never fear, once you watch the video you will both understand and be able to do it on your own. First, I am assuming you watched our previous lesson where I showed you how to do the basic install and setup of YOLO. If not, never fear, I have the commands below. NOTE: This tutorial is geared towards bookworm OS. I strongly suggest you start with a fresh bookworm SC card, as there are many dependencies, and it is most likely to work if you start exactly where I am starting . . . with a fresh OS. Thes these are the commands I shared last week to get YOLO up and working: (just open a terminal, and paste these commands one at a time)

# 1. Configure X11 (manual steps required)
sudo raspi-config
# → Go to Advanced Options → X11 → Enable X11
# → Finish and reboot when prompted

# 5. Update system and install OpenCV
sudo apt update
sudo apt full-upgrade -y
sudo apt install python3-opencv -y

# Verify OpenCV
python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"
# Expected output: something like "OpenCV version: 4.6.0" or higher

# Create and activate virtual environment for YOLO11 (Ultralytics)
python3 -m venv --system-site-packages YOLO
source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment
# Install Ultralytics YOLO11 inside it
pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model
yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation
python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:
# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you 
#just created. Open tools- options, select 'interpreter' tab, then click they Python
#executable, selecting ... and navigate from home directory, 
#to YOLO, to bin, and then select python

# 1. Configure X11 (manual steps required)

sudo raspi-config

# → Go to Advanced Options → X11 → Enable X11

# → Finish and reboot when prompted

# 5. Update system and install OpenCV

sudo apt update

sudo apt full-upgrade -y

sudo apt install python3-opencv -y

# Verify OpenCV

python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"

# Expected output: something like "OpenCV version: 4.6.0" or higher

# Create and activate virtual environment for YOLO11 (Ultralytics)

python3 -m venv --system-site-packages YOLO

source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment

# Install Ultralytics YOLO11 inside it

pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model

yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation

python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:

# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you

#just created. Open tools- options, select 'interpreter' tab, then click they Python

#executable, selecting ... and navigate from home directory,

#to YOLO, to bin, and then select python

Now, I will explain this code, and will help you configure it for your cameras, but you will need to open up thonny, and paste in the following code as a start. IMPORTANT, as mentioned above, you need to set interpreter in thonny to the virtual environment set up in the process above. If this is not familiar to you, go back and watch last weeks lesson (click previous at the bottom of this post). Without further adue, here is the code we will work with today:

import cv2
from ultralytics import YOLO
#import secret
import threading
import time

W=1280
H=720
#Now, as eplained in the video, edit the line below where 
#you put your wifi user name in place of user, and your 
#wifi password in place of password, and you put in your 
#IP address
RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"
#RTSP_URL = secret.RTSP_URL1
#I have put my wifi credentials in a secret file so no
#one sees my credentials. You will just use the RTSP_URL line above. 
# Load the exported NCNN model (replace with your model path)
model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")
lock = threading.Lock()
running = True
def frameGrabber(url):
    global ipFrame, running
    cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)  # Reset frame position
    while running:
        ret, frame = cap.read()
        if ret:
            with lock:
                #frame = cv2.resize(frame, (W, H))
                ipFrame = frame.copy()
    cap.release()
    print("Thread Terminated")
thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)
thread.start()
tStart=time.time()
time.sleep(2)
fps=0
cnt=0
# Set resolution for faster processing (optional, adjust based on your needs)
while True:
    deltaT=time.time()-tStart
    fps= fps*.9 + .1/deltaT
    tStart=time.time()
    with lock:
        ipFrameShow=ipFrame.copy()
    results = model(ipFrameShow, conf=0.25, verbose=False)[0]  # conf: confidence threshold; adjust as needed

    # Annotate the frame with detections (boxes, labels, scores)
    annotatedFrame = results.plot()
    annotatedFrame=cv2.resize(annotatedFrame, (W,H))
    cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)
    cv2.imshow("IP Camera", annotatedFrame)
    #cv2.moveWindow("IP Camera",100,100)
    if cv2.waitKey(1)==ord('q'):
        break
running=False
thread.join()  # Wait for thread to fully exit
time.sleep(1)
cv2.destroyAllWindows()
import gc
gc.collect()  # Force garbage collection to reclaim memory/connections
#Make Extra sure all processes are killed
import os
os._exit(0)
print("Program Terminated")

import cv2

from ultralytics import YOLO

#import secret

import threading

import time

W=1280

H=720

#Now, as eplained in the video, edit the line below where

#you put your wifi user name in place of user, and your

#wifi password in place of password, and you put in your

#IP address

RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"

#RTSP_URL = secret.RTSP_URL1

#I have put my wifi credentials in a secret file so no

#one sees my credentials. You will just use the RTSP_URL line above.

# Load the exported NCNN model (replace with your model path)

model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")

lock = threading.Lock()

running = True

def frameGrabber(url):

global ipFrame, running

cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)

cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

cap.set(cv2.CAP_PROP_POS_FRAMES, 0) # Reset frame position

while running:

ret, frame = cap.read()

if ret:

with lock:

#frame = cv2.resize(frame, (W, H))

ipFrame = frame.copy()

cap.release()

print("Thread Terminated")

thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)

thread.start()

tStart=time.time()

time.sleep(2)

fps=0

cnt=0

# Set resolution for faster processing (optional, adjust based on your needs)

while True:

deltaT=time.time()-tStart

fps= fps*.9 + .1/deltaT

tStart=time.time()

with lock:

ipFrameShow=ipFrame.copy()

results = model(ipFrameShow, conf=0.25, verbose=False)[0] # conf: confidence threshold; adjust as needed

# Annotate the frame with detections (boxes, labels, scores)

annotatedFrame = results.plot()

annotatedFrame=cv2.resize(annotatedFrame, (W,H))

cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)

cv2.imshow("IP Camera", annotatedFrame)

#cv2.moveWindow("IP Camera",100,100)

if cv2.waitKey(1)==ord('q'):

break

running=False

thread.join() # Wait for thread to fully exit

time.sleep(1)

cv2.destroyAllWindows()

import gc

gc.collect() # Force garbage collection to reclaim memory/connections

#Make Extra sure all processes are killed

import os

os._exit(0)

print("Program Terminated")

The video explains everything, please watch it!

Making The World a Better Place One High Tech Project at a Time. Enjoy!

Technology Tutorials

Tag Archives: RTSP

AI on the Edge LESSON 37: Using RTSP and IP Cameras in OpenCV on Raspberry Pi 5

AI on the Edge Lesson 37: Using RTSP and IP Cameras in OpenCV on Raspberry Pi 5

The Big Challenge: Conquering RTSP Latency

Understanding the Script Structure

Drop Your Questions Below

Object Detection Using YOLO and RTSP Camera on Raspberry Pi 5

Making The World a Better Place One High Tech Project at a Time. Enjoy!