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🔥 Trending Repository: nano-vllm 📝 Description: Nano vLLM 🔗 Repository URL: https://github.com/GeeeekExplorer/nano-vllm 📖 Readme: https://github.com/GeeeekExplorer/nano-vllm#readme 📊 Statistics: 🌟 Stars: 7.4K stars 👀 Watchers: 62 🍴 Forks: 949 forks 💻 Programming Languages: Python 🏷️ Related Topics:

#nlp #deep_learning #inference #pytorch #transformer #llm

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

Discussion and Potential Improvements: Real Barcode Scanner: This application works directly with a USB barcode scanner. A scanner acts as a keyboard, so when it scans a code, it types the numbers and sends an "Enter" keystroke, which perfectly triggers our returnPressed signal. Data Integrity: We added a basic check for stock (quantity > 0). A more robust system would check if the quantity in the cart exceeds the quantity in stock before allowing the sale to complete. Features for a Real Pharmacy: A production-level system would need many more features: prescription management, patient records, batch tracking for recalls, advanced reporting (e.g., top-selling drugs, low-stock alerts), user accounts with different permission levels, and receipt printing. Database: SQLite is perfect for a single-user, standalone application. For a pharmacy with multiple terminals, a client-server database like PostgreSQL or MySQL would be necessary. This project provides a solid foundation, demonstrating how to integrate hardware (like a barcode scanner) with a database-backed desktop application to solve a real-world business problem. #ProjectComplete #SoftwareEngineering #PythonGUI #HealthTech ━━━━━━━━━━━━━━━ By: @DataScienceN ✨

def add_item_to_sale(self):
    barcode = self.barcode_input.text()
    if not barcode:
        return
    
    drug = db.find_drug_by_barcode(barcode)
    
    if not drug:
        QMessageBox.warning(self, "Not Found", "No drug found with this barcode.")
        self.barcode_input.clear()
        return
    
    drug_id = drug[0]
    
    if drug[3] <= 0: # Check quantity
         QMessageBox.warning(self, "Out of Stock", f"{drug[1]} is out of stock.")
         self.barcode_input.clear()
         return

    if drug_id in self.current_sale_items:
        # Item already in sale, increment quantity
        self.current_sale_items[drug_id]['quantity'] += 1
    else:
        # Add new item to sale
        self.current_sale_items[drug_id] = {
            'data': drug,
            'quantity': 1
        }
    
    self.update_sales_table()
    self.barcode_input.clear()

def update_sales_table(self):
    self.sales_table.setRowCount(len(self.current_sale_items))
    total_sale_amount = 0.0
    
    for row, item in enumerate(self.current_sale_items.values()):
        drug_data = item['data']
        quantity = item['quantity']
        unit_price = drug_data[4]
        total_price = quantity * unit_price
        
        self.sales_table.setItem(row, 0, QTableWidgetItem(str(drug_data[0]))) # ID
        self.sales_table.setItem(row, 1, QTableWidgetItem(drug_data[1]))      # Name
        self.sales_table.setItem(row, 2, QTableWidgetItem(str(quantity)))
        self.sales_table.setItem(row, 3, QTableWidgetItem(f"{unit_price:.2f}"))
        self.sales_table.setItem(row, 4, QTableWidgetItem(f"{total_price:.2f}"))
        
        total_sale_amount += total_price
        
    self.total_amount_label.setText(f"{total_sale_amount:.2f}")

def complete_sale(self):
    if not self.current_sale_items:
        return

    for drug_id, item in self.current_sale_items.items():
        db.update_drug_quantity(drug_id, item['quantity'])
    
    QMessageBox.information(self, "Success", f"Sale completed. Total: {self.total_amount_label.text()}")
    
    self.clear_sale()
    self.load_inventory_data() # Refresh inventory tab to show new quantities

def clear_sale(self):
    self.current_sale_items.clear()
    self.update_sales_table()

#Hashtags: #BusinessLogic #PointOfSale #PythonCode #Transaction --- #Step 5: Results and Discussion With all the code in place, you have a fully functional pharmacy management system. How to Use It: • Run the main.py script. • Go to the "Inventory Management" tab and add a few drugs with unique barcodes. • Go to the "Point of Sale" tab. The cursor will be in the barcode input field. • Type a barcode of a drug you added and press Enter. The drug will appear in the sales table. • Scan the same barcode again. The quantity for that drug in the sales table will increase to 2. • Click "Complete Sale". A success message will appear. The sales table will clear. • Switch back to the "Inventory Management" tab. You will see that the quantity of the sold drugs has decreased accordingly.

# In __init__, call the setup method
self.setup_pos_ui()
self.current_sale_items = {} # Dictionary to store {drug_id: {data, quantity}}

def setup_pos_ui(self):
    main_layout = QHBoxLayout()

    # Left side: Sale and Barcode input
    left_layout = QVBoxLayout()
    
    barcode_group = QGroupBox("Scan Barcode")
    barcode_layout = QVBoxLayout()
    self.barcode_input = QLineEdit()
    self.barcode_input.setPlaceholderText("Scan or type barcode and press Enter...")
    self.barcode_input.returnPressed.connect(self.add_item_to_sale)
    barcode_layout.addWidget(self.barcode_input)
    barcode_group.setLayout(barcode_layout)
    
    self.sales_table = QTableWidget()
    self.sales_table.setColumnCount(5)
    self.sales_table.setHorizontalHeaderLabels(['ID', 'Name', 'Quantity', 'Unit Price', 'Total Price'])
    
    left_layout.addWidget(barcode_group)
    left_layout.addWidget(self.sales_table)
    
    # Right side: Totals and Actions
    right_layout = QVBoxLayout()
    
    total_group = QGroupBox("Sale Summary")
    total_form = QFormLayout()
    self.total_amount_label = QLabel("0.00")
    total_form.addRow("Total Amount:", self.total_amount_label)
    total_group.setLayout(total_form)
    
    complete_sale_btn = QPushButton("Complete Sale")
    complete_sale_btn.clicked.connect(self.complete_sale)
    clear_sale_btn = QPushButton("Clear Sale")
    clear_sale_btn.clicked.connect(self.clear_sale)
    
    right_layout.addWidget(total_group)
    right_layout.addWidget(complete_sale_btn)
    right_layout.addWidget(clear_sale_btn)
    right_layout.addStretch()
    
    main_layout.addLayout(left_layout, stretch=3) # Left side takes 3/4 of space
    main_layout.addLayout(right_layout, stretch=1) # Right side takes 1/4
    
    self.pos_tab.setLayout(main_layout)

#Hashtags: #PointOfSale #BarcodeScanner #UIUX #PyQt5 --- #Step 4: Implementing the Sales Logic This is the core logic that connects the barcode input to the sales table and the database. When a barcode is entered, we find the drug, add it to the current sale, and update the UI. The "Complete Sale" button will finalize the transaction by updating the database. Add these methods to the PharmacyApp class:

import sys
from PyQt5.QtWidgets import *
from PyQt5.QtCore import QDate
import database as db

class PharmacyApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Pharmacy Management System")
        self.setGeometry(100, 100, 1200, 700)
        db.setup_database()

        self.tabs = QTabWidget()
        self.setCentralWidget(self.tabs)

        self.pos_tab = QWidget()
        self.inventory_tab = QWidget()

        self.tabs.addTab(self.pos_tab, "Point of Sale")
        self.tabs.addTab(self.inventory_tab, "Inventory Management")

        self.setup_inventory_ui()
        # self.setup_pos_ui() will be done in the next step

        self.load_inventory_data()

    def setup_inventory_ui(self):
        layout = QVBoxLayout()
        self.inventory_table = QTableWidget()
        self.inventory_table.setColumnCount(6)
        self.inventory_table.setHorizontalHeaderLabels(['ID', 'Name', 'Barcode', 'Quantity', 'Price', 'Expiry Date'])
        layout.addWidget(self.inventory_table)
        
        form = QFormLayout()
        self.drug_name = QLineEdit()
        self.drug_barcode = QLineEdit()
        self.drug_qty = QSpinBox()
        self.drug_qty.setRange(0, 9999)
        self.drug_price = QLineEdit()
        self.drug_expiry = QDateEdit(QDate.currentDate().addYears(1))
        self.drug_expiry.setDisplayFormat("yyyy-MM-dd")
        
        form.addRow("Name:", self.drug_name)
        form.addRow("Barcode:", self.drug_barcode)
        form.addRow("Quantity:", self.drug_qty)
        form.addRow("Price:", self.drug_price)
        form.addRow("Expiry Date:", self.drug_expiry)
        add_btn = QPushButton("Add Drug to Inventory")
        add_btn.clicked.connect(self.add_drug_to_db)
        
        layout.addLayout(form)
        layout.addWidget(add_btn)
        self.inventory_tab.setLayout(layout)

    def load_inventory_data(self):
        drugs = db.get_all_drugs()
        self.inventory_table.setRowCount(len(drugs))
        for row, drug in enumerate(drugs):
            for col, data in enumerate(drug):
                self.inventory_table.setItem(row, col, QTableWidgetItem(str(data)))

    def add_drug_to_db(self):
        name = self.drug_name.text()
        barcode = self.drug_barcode.text()
        qty = self.drug_qty.value()
        price = float(self.drug_price.text())
        expiry = self.drug_expiry.date().toString("yyyy-MM-dd")

        if not all([name, barcode, qty > 0, price > 0]):
            QMessageBox.warning(self, "Input Error", "Please fill all fields correctly.")
            return
        
        if db.add_drug(name, barcode, qty, price, expiry):
            self.load_inventory_data()
        else:
            QMessageBox.warning(self, "Database Error", "A drug with this barcode already exists.")

# Main execution block at the end of the file
if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = PharmacyApp()
    window.show()
    sys.exit(app.exec_())

# Hashtags: #PyQt5 #GUI #CRUD #Inventory

--- #Step 3: Point of Sale (POS) UI and Barcode Handling Now, let's build the user interface for the sales tab. This will include an input for the barcode, a table for the current sale items (the "cart"), and buttons to finalize or clear the sale. A physical barcode scanner typically emulates a keyboard, entering the numbers and pressing "Enter". We will simulate this with the returnPressed signal on a QLineEdit. Add these methods to the PharmacyApp class:

#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.

# --- Loitering Logic --- (This code continues inside the while loop)

    for box, track_id in zip(boxes, track_ids):
        x, y, w, h = box
        center_point = (int(x), int(y))

        # Check if the center of the person is inside the alert zone
        is_inside_zone = cv2.pointPolygonTest(ALERT_ZONE_POLYGON, center_point, False) >= 0

        if is_inside_zone:
            # If person is inside, start or check their timer
            if track_id not in loitering_timers:
                # First time this person is detected in the zone
                loitering_timers[track_id] = time.time()
            else:
                # Person is still in the zone, check duration
                elapsed_time = time.time() - loitering_timers[track_id]
                
                if elapsed_time > LOITERING_THRESHOLD_SECONDS:
                    # Loitering detected! Trigger alert.
                    alert_triggered_ids.add(track_id)
        else:
            # If person leaves the zone, reset their timer
            if track_id in loitering_timers:
                del loitering_timers[track_id]
            if track_id in alert_triggered_ids:
                alert_triggered_ids.remove(track_id) # Optional: reset alert when they leave

    # Add visual alert text for tracked individuals who triggered the alert
    for track_id in alert_triggered_ids:
        # This part requires re-iterating through boxes to find the right one to draw on.
        # A more optimized way would be to store box coordinates with the track_id.
        for box, tid in zip(results[0].boxes.xyxy.cpu(), track_ids):
            if tid == track_id:
                x1, y1, _, _ = map(int, box)
                cv2.putText(annotated_frame, "ALERT: LOITERING!", (x1, y1 - 10), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                break

    # Display the annotated frame
    cv2.imshow("Suspicious Activity Tracker", annotated_frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

# Hashtags: #SecurityAI #AlertSystem #HomeSafety

--- #Step 5: Results and Discussion When you run the script, you will see your video with: • A yellow polygon outlining your defined security zone. • Bounding boxes around each person with a unique tracking ID. • If a person stays inside the zone for longer than LOITERING_THRESHOLD_SECONDS, a red "ALERT: LOITERING!" message will appear above their bounding box. Discussion and Ethical Considerations: Defining "Suspicious": This system does not understand "suspicious behavior." It only follows a simple rule: person + location + time. The interpretation of this as "suspicious" is a human one. False Positives: The system will trigger an alert for anyone who meets the criteria, including a homeowner, a delivery driver waiting for a signature, or a neighbor stopping to chat. The LOITERING_THRESHOLD_SECONDS must be carefully tuned to balance security with convenience. Tracking Stability: In low-light conditions, the tracker might lose a person and assign them a new ID when they reappear, resetting the timer. Using a higher-quality camera or a YOLO model fine-tuned on night-time data (e.g., from thermal or infrared cameras) would significantly improve performance. Bias and Fairness: AI models can be less accurate in identifying individuals from underrepresented groups in their training data. In a security context, this could lead to a higher rate of missed detections or false alarms for certain people, which is a serious ethical issue. This tool should be used to assist human judgment, not replace it. #ProjectComplete #AIforGood #ResponsibleAI ━━━━━━━━━━━━━━━ By: @DataScienceN ✨

#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.

🔥 Trending Repository: lingo.dev 📝 Description: ⚡ Lingo.dev - open-source, AI-powered i18n toolkit for instant localization with LLMs. Bring your own LLM or use Lingo.dev Localization Engine. Join discord: 🔗 Repository URL: https://github.com/lingodotdev/lingo.dev 🌐 Website: https://lingo.dev/go/discord 📖 Readme: https://github.com/lingodotdev/lingo.dev#readme 📊 Statistics: 🌟 Stars: 3.9K stars 👀 Watchers: 8 🍴 Forks: 599 forks 💻 Programming Languages: TypeScript - JavaScript - PHP - HTML - MDX - EJS 🏷️ Related Topics:

#react #javascript #i18n #typescript #react_native

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

🔥 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

🔥 Trending Repository: ruoyi-vue-pro 📝 Description: 🔥 官方推荐 🔥 RuoYi-Vue 全新 Pro 版本，优化重构所有功能。基于 Spring Boot + MyBatis Plus + Vue & Element 实现的后台管理系统 + 微信小程序，支持 RBAC 动态权限、数据权限、SaaS 多租户、Flowable 工作流、三方登录、支付、短信、商城、CRM、ERP、AI 大模型等功能。你的 ⭐️ Star ⭐️，是作者生发的动力！ 🔗 Repository URL: https://github.com/YunaiV/ruoyi-vue-pro 🌐 Website: https://doc.iocoder.cn/ 📖 Readme: https://github.com/YunaiV/ruoyi-vue-pro#readme 📊 Statistics: 🌟 Stars: 33.8K stars 👀 Watchers: 252 🍴 Forks: 7.3K forks 💻 Programming Languages: Java - PLpgSQL - TSQL 🏷️ Related Topics:

#mysql #redis #vue #spring_security #springboot #redisson #mybatis_plus #flowable

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

🔥 Trending Repository: copilot-cli 📝 Description: GitHub Copilot CLI brings the power of Copilot coding agent directly to your terminal. 🔗 Repository URL: https://github.com/github/copilot-cli 📖 Readme: https://github.com/github/copilot-cli#readme 📊 Statistics: 🌟 Stars: 4.4K stars 👀 Watchers: 42 🍴 Forks: 282 forks 💻 Programming Languages: Not available 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: Deep-Live-Cam 📝 Description: real time face swap and one-click video deepfake with only a single image 🔗 Repository URL: https://github.com/hacksider/Deep-Live-Cam 🌐 Website: https://deeplivecam.net/ 📖 Readme: https://github.com/hacksider/Deep-Live-Cam#readme 📊 Statistics: 🌟 Stars: 74.6K stars 👀 Watchers: 454 🍴 Forks: 10.9K forks 💻 Programming Languages: Python 🏷️ Related Topics:

#ai #realtime #artificial_intelligence #faceswap #gan #webcam #webcamera #deepfake #deep_fake #ai_face #video_deepfake #realtime_deepfake #deepfake_webcam #realtime_face_changer #fake_webcam #ai_webcam #ai_deep_fake #real_time_deepfake

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

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#government #documentation #django #opensource #mit #knowledge #wiki #reactjs #self_hosted #mit_license #collaborative #knowledge_base #yjs #realtime_collaboration #g2g #blocknotejs

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

🔥 Trending Repository: chef 📝 Description: The only AI app builder that knows backend 🔗 Repository URL: https://github.com/get-convex/chef 🌐 Website: https://chef.convex.dev 📖 Readme: https://github.com/get-convex/chef#readme 📊 Statistics: 🌟 Stars: 2.5K stars 👀 Watchers: 22 🍴 Forks: 497 forks 💻 Programming Languages: TypeScript - JavaScript - CSS 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

Person Detection Accuracy: YOLOv8 is highly effective at detecting people, but it can struggle with heavy occlusion (people hiding others) or very low-resolution images, potentially leading to an undercount. Gender Classification Reliability: The secondary gender classification model (cvlib) is a simplified model. Its accuracy depends heavily on clear, front-facing views of faces. It may fail on side profiles, poor lighting, or small faces. Ethical Considerations & Bias: Gender classification from images is an inherently problematic task. These models are trained on datasets that may contain biases and often rely on stereotypical features (e.g., hair length). The model's performance can be worse for certain ethnicities and it cannot account for non-binary gender identities. This tool should be seen as a rough estimator based on visual stereotypes, not a definitive measure of gender. Performance: For real-time video, this multi-step process (YOLO -> crop -> face detection -> gender detection) can be slow. A more advanced approach would be to fine-tune a single object detection model on a custom dataset with 'man' and 'woman' classes for much faster and more integrated performance. ━━━━━━━━━━━━━━━ By: @DataScienceN ✨

# Counters for male and female
male_count = 0
female_count = 0

# A copy of the original image for drawing results
output_img = img.copy()

# Loop through each person's bounding box
for (x1, y1, x2, y2) in person_boxes:
    # Crop the person from the image
    person_crop = img[y1:y2, x1:x2]
    
    label = "Unknown"
    
    try:
        # Apply gender detection on the person crop
        # padding is used to better detect faces at the edge of the crop
        face, confidence = cv.detect_face(person_crop, threshold=0.5)
        
        # We process only if one face is detected to avoid ambiguity
        if len(face) > 0:
            # Get the first face detected
            (startX, startY, endX, endY) = face[0]
            face_crop = np.copy(person_crop[startY:endY, startX:endX])

            # Predict gender of the detected face
            (gender_label, gender_confidence) = cv.detect_gender(face_crop)
            
            if gender_confidence > 0.6: # Confidence threshold
                label = gender_label
                if label == 'male':
                    male_count += 1
                elif label == 'female':
                    female_count += 1

    except Exception as e:
        # Sometimes cvlib can fail on small or unclear crops
        label = "Error"

    # Draw bounding box and label on the output image
    color = (0, 255, 0) if label in ["male", "female"] else (0, 0, 255)
    cv2.rectangle(output_img, (x1, y1), (x2, y2), color, 2)
    cv2.putText(output_img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

print(f"Males detected: {male_count}")
print(f"Females detected: {female_count}")

--- Step 5: Displaying Final Results Finally, we calculate the percentages and display the annotated image along with a summary of our findings. #Results #Visualization

# Calculate percentages
known_gender_count = male_count + female_count
if known_gender_count > 0:
    male_percentage = (male_count / known_gender_count) * 100
    female_percentage = (female_count / known_gender_count) * 100
else:
    male_percentage = 0
    female_percentage = 0

# Prepare the summary text
summary_text1 = f"Total People: {total_people}"
summary_text2 = f"Men: {male_count} ({male_percentage:.1f}%)"
summary_text3 = f"Women: {female_count} ({female_percentage:.1f}%)"

# Add summary text to the image
cv2.putText(output_img, summary_text1, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 3)
cv2.putText(output_img, summary_text2, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 3)
cv2.putText(output_img, summary_text3, (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 3)

# Save or display the final image
cv2.imwrite('crowd_analysis_result.jpg', output_img)
print("\n--- Analysis Complete ---")
print(summary_text1)
print(summary_text2)
print(summary_text3)
print("Result image saved as 'crowd_analysis_result.jpg'")

--- Step 6: Discussion of Results and Limitations #Discussion #Ethics #AI

#YOLOv8 #ComputerVision #ObjectDetection #Python #AI Audience Analysis with YOLOv8: Counting People & Estimating Gender Ratios This lesson demonstrates how to use the YOLOv8 model to perform a computer vision task: analyzing an image of a crowd to count the total number of people and estimate the ratio of men to women. --- Step 1: Setup and Installation First, we need to install the necessary libraries. ultralytics for the YOLOv8 model, opencv-python for image manipulation, and cvlib for a simple, pre-trained gender classification model. #Setup #Installation

# Open your terminal or command prompt and run:
pip install ultralytics opencv-python cvlib tensorflow

--- Step 2: Loading Models and Image We will load two models: the official YOLOv8 model pre-trained for object detection, and we'll use cvlib for gender detection. We also need to load the image we want to analyze. Make sure you have an image named crowd.jpg in the same directory. #DataLoading #Model

import cv2
from ultralytics import YOLO
import cvlib as cv
import numpy as np

# Load the YOLOv8 model (pre-trained on COCO dataset)
model = YOLO('yolov8n.pt')

# Load the image
image_path = 'crowd.jpg' # Make sure this image exists
img = cv2.imread(image_path)

# Check if the image was loaded correctly
if img is None:
    print(f"Error: Could not load image from {image_path}")
else:
    print("Image and YOLOv8 model loaded successfully.")

--- Step 3: Person Detection with YOLOv8 Now, we'll run the YOLOv8 model on our image to detect all objects and then filter those results to keep only the ones identified as a 'person'. #PersonDetection #Inference

# Run inference on the image
results = model(img)

# A list to store the bounding boxes of detected people
person_boxes = []

# Process the results
for result in results:
    boxes = result.boxes
    for box in boxes:
        # Get class id and check if it's a person (class 0 in COCO)
        if model.names[int(box.cls)] == 'person':
            # Get bounding box coordinates
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            person_boxes.append((x1, y1, x2, y2))

# Print the total number of people found
total_people = len(person_boxes)
print(f"Total people detected: {total_people}")

--- Step 4: Gender Classification For each detected person, we will crop their bounding box from the image. Then, we'll use cvlib to detect a face within that crop and predict the gender. This is a multi-step pipeline. #GenderClassification #CV

Clean Code Tip: For reusable setup and teardown logic, you can create your own context managers. Instead of writing a full class with __enter__ and __exit__, use the @contextmanager decorator from the contextlib module for a more concise and elegant solution. This is a pro-level technique for robust resource management. 🚀 Example:

import contextlib

# The verbose, class-based way to create a context manager
class DatabaseConnection:
    def __init__(self, db_name):
        self._db_name = db_name
        self._conn = None
        print(f"Initializing connection to {self._db_name}...")

    def __enter__(self):
        print("-> Entering context: Opening connection.")
        self._conn = f"CONNECTION_TO_{self._db_name}" # Simulate connection
        return self._conn

    def __exit__(self, exc_type, exc_val, exc_tb):
        print("<- Exiting context: Closing connection.")
        self._conn = None # Simulate closing

print("--- Class-Based Way ---")
with DatabaseConnection("users.db") as conn:
    print(f"   Performing operations with {conn}")


# The clean, Pythonic way using a generator and @contextmanager
@contextlib.contextmanager
def managed_database(db_name):
    print(f"Initializing connection to {db_name}...")
    conn = f"CONNECTION_TO_{db_name}"
    try:
        print("-> Entering context: Yielding connection.")
        yield conn # The code inside the 'with' block runs here
    finally:
        # This code is guaranteed to run, just like __exit__
        print("<- Exiting context: Closing connection in 'finally'.")
        conn = None

print("\n--- @contextmanager Way ---")
with managed_database("products.db") as conn:
    print(f"   Performing operations with {conn}")

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