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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}")

━━━━━━━━━━━━━━━
By: @DataScienceN ✨

#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