🔥 Trending Repository: xiaomusic

📝 Description: 使用小爱音箱播放音乐，音乐使用 yt-dlp 下载。

🔗 Repository URL: https://github.com/hanxi/xiaomusic

🌐 Website: http://xdocs.hanxi.cc/

📖 Readme: https://github.com/hanxi/xiaomusic#readme

📊 Statistics:
🌟 Stars: 6.4K stars
👀 Watchers: 22
🍴 Forks: 637 forks

💻 Programming Languages: Python

🏷️ Related Topics:

#python #music #docker #vue #docker_compose #xiaomi #pdm #xiaoai #xiaoai_speaker #xiaomusic

==================================
🧠 By: https://t.me/DataScienceM

#YOLOv8 #ComputerVision #HomeSecurity #ObjectTracking #AI #Python

Lesson: Tracking Suspicious Individuals Near a Home at Night with YOLOv8

This tutorial demonstrates how to build an advanced security system using YOLOv8's object tracking capabilities. The system will detect people in a night-time video feed, track their movements, and trigger an alert if a person loiters for too long within a predefined "alert zone" (e.g., a driveway or porch).

---

#Step 1: Project Setup and Dependencies

We will use ultralytics for YOLOv8 and its built-in tracker, opencv-python for video processing, and numpy for defining our security zone.

pip install ultralytics opencv-python numpy

Create a Python script (e.g., security_tracker.py) and import the necessary libraries. We'll also use defaultdict to easily manage timers for each tracked person.

import cv2
import numpy as np
from ultralytics import YOLO
from collections import defaultdict
import time

# Hashtags: #Setup #Python #OpenCV #YOLOv8

---

#Step 2: Model Loading and Zone Configuration

We will load a standard YOLOv8 model capable of detecting 'person'. The key is to define a polygon representing the area we want to monitor. We will also set a time threshold to define "loitering". You will need a video file of your target area, for example, night_security_footage.mp4.

# Load the YOLOv8 model
model = YOLO('yolov8n.pt')

# Path to your night-time video file
VIDEO_PATH = 'night_security_footage.mp4'

# Define the polygon for the alert zone.
# IMPORTANT: You MUST adjust these [x, y] coordinates to fit your video's perspective.
# This example defines a rectangular area for a driveway.
ALERT_ZONE_POLYGON = np.array([
    [100, 500], [800, 500], [850, 250], [50, 250]
], np.int32)

# Time in seconds a person can be in the zone before an alert is triggered
LOITERING_THRESHOLD_SECONDS = 5.0

# Dictionaries to store tracking data
# Stores the time when a tracked object first enters the zone
loitering_timers = {} 
# Stores the IDs of individuals who have triggered an alert
alert_triggered_ids = set()

# Hashtags: #Configuration #AIModel #SecurityZone

---

#Step 3: Main Loop for Tracking and Zone Monitoring

This is the core of the system. We will read the video frame by frame and use YOLOv8's track() function. This function not only detects objects but also assigns a unique ID to each one, allowing us to follow them across frames.

cap = cv2.VideoCapture(VIDEO_PATH)

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    # Run YOLOv8 tracking on the frame, persisting tracks between frames
    results = model.track(frame, persist=True)

    # Get the bounding boxes and track IDs
    boxes = results[0].boxes.xywh.cpu()
    track_ids = results[0].boxes.id.int().cpu().tolist()

    # Visualize the results on the frame
    annotated_frame = results[0].plot()

    # Draw the alert zone polygon on the frame
    cv2.polylines(annotated_frame, [ALERT_ZONE_POLYGON], isClosed=True, color=(0, 255, 255), thickness=2)

# Hashtags: #RealTime #ObjectTracking #VideoProcessing

(Note: The code below should be placed inside the while loop of Step 3)

---

#Step 4: Implementing Loitering Logic and Alerts

Inside the main loop, we'll iterate through each tracked person. We check if their position is inside our alert zone. If it is, we start or update a timer. If the timer exceeds our threshold, we trigger an alert for that person's ID.

#PyQt5 #SQLite #DesktopApp #Pharmacy #Barcode #Python

Lesson: Building a Pharmacy Management System with PyQt5 and Barcode Scanning

This tutorial will guide you through creating a complete desktop application for managing a pharmacy. The system will use a SQLite database for inventory, and a Point of Sale (POS) interface that uses barcode scanning to add drugs to a sale and automatically deducts stock upon completion.

---

#Step 1: Database Setup (database.py)

First, we create a dedicated file to handle all SQLite database operations. This keeps our data logic separate from our UI logic. Create a file named database.py.

import sqlite3

DB_NAME = 'pharmacy.db'

def connect():
    return sqlite3.connect(DB_NAME)

def setup_database():
    conn = connect()
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS drugs (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            barcode TEXT NOT NULL UNIQUE,
            quantity INTEGER NOT NULL,
            price REAL NOT NULL,
            expiry_date TEXT NOT NULL
        )
    ''')
    conn.commit()
    conn.close()

def add_drug(name, barcode, quantity, price, expiry_date):
    conn = connect()
    cursor = conn.cursor()
    try:
        cursor.execute("INSERT INTO drugs (name, barcode, quantity, price, expiry_date) VALUES (?, ?, ?, ?, ?)",
                       (name, barcode, quantity, price, expiry_date))
        conn.commit()
    except sqlite3.IntegrityError:
        return False # Barcode already exists
    finally:
        conn.close()
    return True

def get_all_drugs():
    conn = connect()
    cursor = conn.cursor()
    cursor.execute("SELECT id, name, barcode, quantity, price, expiry_date FROM drugs ORDER BY name")
    drugs = cursor.fetchall()
    conn.close()
    return drugs

def find_drug_by_barcode(barcode):
    conn = connect()
    cursor = conn.cursor()
    cursor.execute("SELECT id, name, barcode, quantity, price, expiry_date FROM drugs WHERE barcode = ?", (barcode,))
    drug = cursor.fetchone()
    conn.close()
    return drug

def update_drug_quantity(drug_id, sold_quantity):
    conn = connect()
    cursor = conn.cursor()
    cursor.execute("UPDATE drugs SET quantity = quantity - ? WHERE id = ?", (sold_quantity, drug_id))
    conn.commit()
    conn.close()

# Hashtags: #SQLite #DatabaseDesign #DataPersistence #Python

---

#Step 2: Main Application and Inventory Management UI

Create the main application file, main.py. We will set up the main window with tabs for "Point of Sale" and "Inventory Management". We will fully implement the inventory tab first, allowing users to view and add drugs to the database.

🔥 Trending Repository: pytorch

📝 Description: Tensors and Dynamic neural networks in Python with strong GPU acceleration

🔗 Repository URL: https://github.com/pytorch/pytorch

🌐 Website: https://pytorch.org

📖 Readme: https://github.com/pytorch/pytorch#readme

📊 Statistics:
🌟 Stars: 94.5K stars
👀 Watchers: 1.8k
🍴 Forks: 25.8K forks

💻 Programming Languages: Python - C++ - Cuda - C - Objective-C++ - CMake

🏷️ Related Topics:

#python #machine_learning #deep_learning #neural_network #gpu #numpy #autograd #tensor

==================================
🧠 By: https://t.me/DataScienceM

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

element = WebDriverWait(driver, 10).until(
    EC.presence_of_element_located((By.ID, "myDynamicElement"))
)

• Get the page source after JavaScript has executed.

dynamic_html = driver.page_source

• Close the browser window.

driver.quit()

VII. Common Tasks & Best Practices

• Handle pagination by finding the "Next" link.

next_page_url = soup.find('a', text='Next')['href']

• Save data to a CSV file.

import csv
with open('data.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['Title', 'Link'])
    # writer.writerow([title, url]) in a loop

• Save data to CSV using pandas.

import pandas as pd
df = pd.DataFrame(data, columns=['Title', 'Link'])
df.to_csv('data.csv', index=False)

• Use a proxy with requests.

proxies = {'http': 'http://10.10.1.10:3128', 'https': 'http://10.10.1.10:1080'}
requests.get('http://example.com', proxies=proxies)

• Pause between requests to be polite.

import time
time.sleep(2) # Pause for 2 seconds

• Handle JSON data from an API.

json_response = requests.get('https://api.example.com/data').json()

• Download a file (like an image).

img_url = 'http://example.com/image.jpg'
img_data = requests.get(img_url).content
with open('image.jpg', 'wb') as handler:
    handler.write(img_data)

• Parse a sitemap.xml to find all URLs.

# Get the sitemap.xml file and parse it like any other XML/HTML to extract <loc> tags.

VIII. Advanced Frameworks (Scrapy)

• Create a Scrapy spider (conceptual command).

scrapy genspider example example.com

• Define a parse method to process the response.

# In your spider class:
def parse(self, response):
    # parsing logic here
    pass

• Extract data using Scrapy's CSS selectors.

titles = response.css('h1::text').getall()

• Extract data using Scrapy's XPath selectors.

links = response.xpath('//a/@href').getall()

• Yield a dictionary of scraped data.

yield {'title': response.css('title::text').get()}

• Follow a link to parse the next page.

next_page = response.css('li.next a::attr(href)').get()
if next_page is not None:
    yield response.follow(next_page, callback=self.parse)

• Run a spider from the command line.

scrapy crawl example -o output.json

• Pass arguments to a spider.

scrapy crawl example -a category=books

• Create a Scrapy Item for structured data.

import scrapy
class ProductItem(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()

• Use an Item Loader to populate Items.

from scrapy.loader import ItemLoader
loader = ItemLoader(item=ProductItem(), response=response)
loader.add_css('name', 'h1.product-name::text')

#Python #WebScraping #BeautifulSoup #Selenium #Requests

━━━━━━━━━━━━━━━
By: @DataScienceN ✨

🔥 Trending Repository: localstack

📝 Description: 💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline

🔗 Repository URL: https://github.com/localstack/localstack

🌐 Website: https://localstack.cloud

📖 Readme: https://github.com/localstack/localstack#readme

📊 Statistics:
🌟 Stars: 61.1K stars
👀 Watchers: 514
🍴 Forks: 4.3K forks

💻 Programming Languages: Python - Shell - Makefile - ANTLR - JavaScript - Java

🏷️ Related Topics:

#python #testing #aws #cloud #continuous_integration #developer_tools #localstack

==================================
🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: TrendRadar

📝 Description: 🎯 告别信息过载，AI 助你看懂新闻资讯热点，简单的舆情监控分析 - 多平台热点聚合+基于 MCP 的AI分析工具。监控35个平台（抖音、知乎、B站、华尔街见闻、财联社等），智能筛选+自动推送+AI对话分析（用自然语言深度挖掘新闻：趋势追踪、情感分析、相似检索等13种工具）。支持企业微信/飞书/钉钉/Telegram/邮件/ntfy推送，30秒网页部署，1分钟手机通知，无需编程。支持Docker部署⭐ 让算法为你服务，用AI理解热点

🔗 Repository URL: https://github.com/sansan0/TrendRadar

🌐 Website: https://github.com/sansan0

📖 Readme: https://github.com/sansan0/TrendRadar#readme

📊 Statistics:
🌟 Stars: 6K stars
👀 Watchers: 21
🍴 Forks: 4.5K forks

💻 Programming Languages: Python - HTML - Batchfile - Shell - Dockerfile

🏷️ Related Topics:

#python #docker #mail #news #telegram_bot #mcp #data_analysis #trending_topics #wechat_robot #dingtalk_robot #ntfy #hot_news #feishu_robot #mcp_server

==================================
🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: LEANN

📝 Description: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

🔗 Repository URL: https://github.com/yichuan-w/LEANN

📖 Readme: https://github.com/yichuan-w/LEANN#readme

📊 Statistics:
🌟 Stars: 3.9K stars
👀 Watchers: 34
🍴 Forks: 403 forks

💻 Programming Languages: Python

🏷️ Related Topics:

#python #privacy #ai #offline_first #localstorage #vectors #faiss #rag #vector_search #vector_database #llm #langchain #llama_index #retrieval_augmented_generation #ollama #gpt_oss

==================================
🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: PythonRobotics

📝 Description: Python sample codes and textbook for robotics algorithms.

🔗 Repository URL: https://github.com/AtsushiSakai/PythonRobotics

🌐 Website: https://atsushisakai.github.io/PythonRobotics/

📖 Readme: https://github.com/AtsushiSakai/PythonRobotics#readme

📊 Statistics:
🌟 Stars: 26.3K stars
👀 Watchers: 509
🍴 Forks: 7K forks

💻 Programming Languages: Python

🏷️ Related Topics:

#python #algorithm #control #robot #localization #robotics #mapping #animation #path_planning #slam #autonomous_driving #autonomous_vehicles #ekf #hacktoberfest #cvxpy #autonomous_navigation

==================================
🧠 By: https://t.me/DataScienceM

• Error Handling: Always wrap dispatch logic in try-except blocks to gracefully handle network issues, authentication failures, or incorrect receiver addresses.
• Security: Never hardcode credentials directly in scripts. Use environment variables (os.environ.get()) or a secure configuration management system. Ensure starttls() is called for encrypted communication.
• Rate Limits: SMTP servers impose limits on the number of messages one can send per hour or day. Implement pauses (time.sleep()) between dispatches to respect these limits and avoid being flagged as a spammer.
• Opt-Outs: For promotional dispatches, ensure compliance with regulations (like GDPR, CAN-SPAM) by including clear unsubscribe options.

Concluding Thoughts

Automating electronic message dispatch empowers users to scale their communication efforts with remarkable efficiency. By leveraging Python's native capabilities, anyone can construct a powerful, flexible system for broadcasting anything from routine updates to extensive promotional campaigns. The journey into programmatic dispatch unveils a world of streamlined operations and enhanced communicative reach.

#python #automation #email #smtplib #emailautomation #programming #scripting #communication #developer #efficiency

━━━━━━━━━━━━━━━
By: @DataScienceN ✨