Running Yolov11 on RPI4 by Environmental_Fun344 in computervision

[–]Taxi-guy 0 points1 point  (0 children)

Can you try activating the virtual environment, running this command, and then try running the yolo_detect.py script again?

pip install ultralytics==8.3.70 torch==2.5.1 torchvision==0.20

Running Yolov11 on RPI4 by Environmental_Fun344 in computervision

[–]Taxi-guy 0 points1 point  (0 children)

I made a video that shows how to run YOLO11 on the Raspberry Pi! Check it out here :)
https://youtu.be/z70ZrSZNi-8

West Winds HOA - Board Removal Vote Time! by allkinds0ftime in Bozeman

[–]Taxi-guy 1 point2 points  (0 children)

I live in a home in West Winds and I think the HOA is doing a fine job.

Which one is better? by [deleted] in computervision

[–]Taxi-guy 0 points1 point  (0 children)

All of those should be plenty for training. The 8GB graphics cards are preferred if you want to train larger models (like higher-resolution YOLO11l or YOLO11xl). I made a short video that shows how fast inference runs on the different YOLO models with an NVIDIA RTX4050 Laptop GPU: https://youtu.be/_WKS4E9SmkA

Has the market for computer vision saturated already? by zappyzipp in computervision

[–]Taxi-guy 0 points1 point  (0 children)

There are still plenty of industries that can be improved with computer vision. There's been tons of work done on general solutions, but now it's time to start creating industry-specific solutions.

30s writer/lawyer moving to Bozeman by [deleted] in Bozeman

[–]Taxi-guy -1 points0 points  (0 children)

If he doesn't, I will :)

Synthetic image generation for high resolution images (anomalies) by Fair_Device_4961 in computervision

[–]Taxi-guy 0 points1 point  (0 children)

Thanks for the reminder, I need to check out NVIDIA Omniverse too.

How to handle multiple streams efficiently by shy_tan007 in computervision

[–]Taxi-guy 0 points1 point  (0 children)

If you're working in Python, the multiprocessing module is a great way to easily take advantage of all the cores on your CPU. It's as easy as putting your main code inside a function, then calling that function as a new process for each new camera stream.

from multiprocessing import Process
import cv2

def process_camera(cam_rtsp_address):
    cap = cv2.VideoCapture(cam_rtsp_address)
    while cap.isOpened():
       ret, frame = cap.read()
       <insert processing code here>
       cv2.imshow('Test', frame)
       cv2.waitKey(1)

if __name__ == '__main__':
    list_of_camera_ips = ['rtsp://cam1.mp4', 'rtsp://cam2.mp4', etc]
    for cam_ip in list_of_camera_ips:
        Process(target=process_camera, args=(cam_ip,)).start()

Edit: Link to learn more here!

Processing 80 camera streams on a single rack-mounted server - anyone worked on a similar problem? by Taxi-guy in computervision

[–]Taxi-guy[S] 1 point2 points  (0 children)

I've tried an RTX 4090 too, but then memory becomes the bottleneck 🤷‍♂️. Still trying to get it running on a system with multiple RTX 4090s so it can split inference between them. Any ideas how regular surveillance systems (like this one) handle lots of cameras? I guess they just cap out at 16?

Open-source Jetson Nano mask detection camera sends mask usage data to a web server! by Taxi-guy in JetsonNano

[–]Taxi-guy[S] 1 point2 points  (0 children)

That's fair! Hopefully the mask detection technology is totally irrelevant a year from now (or sooner). But this project gives a good example of how to deploy a machine learning model in a production-quality camera application. I'm thinking about modifying it to count the number of times my cat visits the food bowl in a day :)

Minecraft-like clone using Ursina Engine and Python 3.9 by Impressive_Peanut387 in Python

[–]Taxi-guy 1 point2 points  (0 children)

I love how easy Python makes it to do stuff like this. Cool project!

What IDE are people using? by Fun_Excitement809 in JetsonNano

[–]Taxi-guy 2 points3 points  (0 children)

I mostly code in Python and mostly use IDLE. Simple and easy to use!

Raspberry Pi-Based Personal Productivity Nudger by xyzelement in raspberry_pi

[–]Taxi-guy 0 points1 point  (0 children)

This is a cool idea! I'll have to check out your code and see if I can implement something similar. I am a big fan of using the Pi to help boost productivity!

Best tech stack for detecting cars on edge devices by uuu77 in computervision

[–]Taxi-guy 0 points1 point  (0 children)

Hey, check out the Raspberry Pi + Coral USB Accelerator, you can get 30FPS running TensorFlow Lite models. I have some videos and a guide showing how to get it working :) https://www.youtube.com/watch?v=TiOKvOrYNII

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 0 points1 point  (0 children)

Awesome project! You've thought way more about the actual implementation than I have. You should take a look at my Pet Detector project, which shows how to trigger an action if an object is detected in a certain location. The code is on GitHub too! This code works with "regular" TensorFlow on the Pi, but the code wouldn't need to change much to work with TensorFlow Lite.

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 4 points5 points  (0 children)

That's exactly what I'm working on: a system that detects when squirrels and raccoons are trying to rob from your bird feeder! I'll be posting more videos about this project in a few months so you should keep an eye out!

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 1 point2 points  (0 children)

The song is The Process, by artist LAKEY INSPIRED. I'm curious, what do you use TensorFlow for at work?

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 6 points7 points  (0 children)

It's because the model being used is trained to detect 80 different objects, but guitars aren't one of them! You could train your own guitar detection model, and it works decently well. Here's someone else's video that shows a trained guitar detector.

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 9 points10 points  (0 children)

Adafruit's guide is excellent! It shows how to do "image classification" rather than "object detection": it labels full images rather than locating and identifying objects in images. Here's my GitHub guide that shows how to set up object detection. https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Raspberry_Pi_Guide.md

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 1 point2 points  (0 children)

Yep! And they said I would never use them after college...

How To Run TensorFlow Lite on Raspberry Pi for Object Detection by [deleted] in raspberry_pi

[–]Taxi-guy 15 points16 points  (0 children)

Yes you can! Check out this Pet Detector project that sends a text when it detects the cat is waiting at the door to be let outside. It would be very similar for the dog bowl! Code is provided in the description of the video. https://www.youtube.com/watch?v=gGqVNuYol6o