Algo Vision

Nano Model, Big Impact: Counting Cars with YOLOv8 🚗 Ever wondered how traffic flow is monitored in real-time? It's often done with sophisticated computer vision systems, and I've been experimenting with one of the latest and greatest: YOLOv8! This video shows how I'm using a small YOLOv8 model (specifically designed for efficiency) to identify and count vehicles in real-time. It's amazing to see how it picks out buses, cars. The best part? This nano model is surprisingly powerful. It's resource-efficient, fast, and easy to deploy. It's a fascinating journey exploring how we can use AI to make our roads smarter and safer! What do you think about using AI for traffic monitoring? I'd love to hear your thoughts and ideas! Grow your business with AI

C bir qator pastka tushdi. C++ bir qator yuqoriga

I've been experimenting with Ultralytics YOLOv8, OpenCV and achieving great results! 🚀 I'm particularly excited about its ability to detect small objects, which is crucial in real-world applications like autonomous driving and traffic monitoring. In this experiment, I used my YOLOv8 model to detect and estimate the distance of vehicles in a challenging scenario. 🛣 I'm still working on calibrating the distance measurements, but the results so far are promising. What you see: Accurate detection of various vehicles, including those that are small and distant. Distance estimates for each detected vehicle. Future improvements: Fine-tune the model further to enhance distance estimation accuracy. Explore different approaches for calibration and optimization. I'm looking forward to pushing the boundaries of object detection and distance estimation with YOLOv8. What are your experiences with YOLOv8?

Check out this real-time demo of YOLOv8 in action! 🤩 It's accurately detecting and tracking vehicles on the road, even calculating their speeds. 💨 Let me know what you think! 🚀

Avval matematika keyin dasturlash + ingliz tili

Pythonda backend yozish shunchalaram osonkiiii "Mazza"

@app.route('/api/register', methods=['POST'])
def register():
     # Ensure you're handling JSON data
    data = request.get_json()
    new_user = User()
    new_user.UserName = data["username"]
    new_user.Email = data["email"]
    new_user.set_password(data["password"])
    try:
        db.session.add(new_user)
        db.session.commit()
        return jsonify({"message": "Registration successful"}), 201
    except Exception as e:
        db.session.rollback()
        return jsonify({"message": "Registration failed", "error": str(e)}), 500

@app.route('/api/logout')
def logout():
    session.pop('logged_in', None)
    session.pop('user_id', None)
    flash('You have been logged out!', 'success')
    return redirect(url_for('index'))

@app.route("/api/clients", methods = ["GET", "POST"])
def clients():
    if True:
        if request.method == "GET":
            clients = Tourist.query.all()
            return jsonify([client.to_dict() for client in clients]), 200
        elif request.method == "POST":
            client = Tourist.from_dict(request.get_json())
            db.session.add(client)
            db.session.commit()
            return jsonify({"message":"Add new tourist"}) , 201

Xuddi hech narsa yozmay uzi ishlab ketadigandek

Microsoft rasmiy ravishda telegramda Copilot botini taqdim etdi. Bilaszlar Copilot backendida GPT-4 bepul ishlaydi. Foydalanamiz!!!!!!! https://www.microsoft.com/ru-ru/edge/copilot-for-social?form=MY02F9 https://t.me/CopilotOfficialBot

Just start implement top view vehicle detection for parking managment system. Lekin negadir yuqori natija bulmayapti hozircha. Model non commercial (albatta sekin ishlaydigan versiyasi) kimdir sinov qilishi uchun tashlab berishim mumkin.

from ultralytics import YOLO

model = YOLO(model="model path")
model.predict(source="image path", show=True, save = True)

Vilosiped bu hamma narsani qulda yozib chiqish. Vilosiped yozmaslikka harakat qilish kerak. Tugri bazida bu imkonsiz Masalan logikani butkul uzgacha qilishga tugri keladi. Lekin aniq bir ehtiyoj bulmasa bu ishni qilmang!!

#include <iostream>
#include <vector>
#include <ranges>
 
int main()
{
    std::vector<std::string> people { "Tom", "Bob", "Alice", "Sam", "Kate" };
    auto condition = [](const std::string& s) {return s.length()==3; };
 
    auto view = std::ranges::take_while_view{people, condition};
 
    for(const auto& person: view)
    {
        std::cout << person << std::endl;
    }
}

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