🚀 Comprehensive Guide: How to Prepare for a Graph Neural Networks (GNN) Job Interview – 350 Most Common Interview Questions

Read: https://hackmd.io/@husseinsheikho/GNN-interview

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📝 Description: The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation, illustrating how AI agents are transforming sectors such as healthcare, finance, education, retail, and more.

🔗 Repository URL: https://github.com/ashishpatel26/500-AI-Agents-Projects

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🗓️ 07 Oct 2025
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Artificial Intelligence is rapidly evolving beyond chatbots and text generation. The next frontier is AI agents — intelligent, autonomous systems that can reason, take action and collaborate with tools and other agents. To help developers and practitioners build these next-generation systems, Google is launching the 5-Day AI Agents Intensive, a no-cost, online program running from ...

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Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...

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In today’s rapidly advancing technological landscape, artificial intelligence (AI) is not just a buzzword, it’s a transformative force across industries. From automating complex tasks to streamlining operations, AI is revolutionizing workflows. However, designing and deploying AI-driven workflows has traditionally required expert-level programming knowledge. Enter Open Agent Builder, a revolutionary tool that democratizes the creation of ...

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In an era where information is predominantly digital, the ability to extract, interpret and organize data from documents is crucial. From invoices and research papers to multilingual contracts and handwritten notes, document parsing stands at the intersection of vision and language. Traditional Optical Character Recognition (OCR) systems have made impressive strides but they often fall ...

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🗓️ 24 Oct 2025
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Artificial Intelligence (AI) is transforming industries across the globe, driving innovation through automation, data-driven insights and intelligent decision-making. Whether it’s predicting house prices, detecting diseases or building conversational chatbots, AI is at the core of modern digital solutions. The AI Project Gallery by Hema Kalyan Murapaka is an exceptional GitHub repository that curates a wide ...

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In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼 

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

more explain: https://hackmd.io/@husseinsheikho/imageprocessing

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#YOLOv8 #ComputerVision #TrafficManagement #Python #AI #SmartCity

Lesson: Detecting Traffic Congestion in Road Lanes with YOLOv8

This tutorial will guide you through building a system to monitor traffic on a highway from a video feed. We'll use YOLOv8 to detect vehicles and then define specific zones (lanes) to count the number of vehicles within them, determining if a lane is congested.

---

#Step 1: Project Setup and Dependencies

We need to install ultralytics for YOLOv8 and opencv-python for video and image processing. numpy is also essential for handling the coordinates of our detection zones.

pip install ultralytics opencv-python numpy

Create a Python script (e.g., traffic_monitor.py) and import the necessary libraries.

import cv2
import numpy as np
from ultralytics import YOLO

# Hashtags: #Setup #Python #OpenCV #YOLOv8

---

#Step 2: Model Loading and Lane Definition

We'll load a pre-trained YOLOv8 model, which is excellent at detecting common objects like cars, trucks, and buses. The most critical part of this step is defining the zones of interest (our lanes) as polygons on the video frame. You will need to adjust these coordinates to match the perspective of your specific video.

You will also need a video file, for example, traffic_video.mp4.

# Load a pre-trained YOLOv8 model (yolov8n.pt is small and fast)
model = YOLO('yolov8n.pt')

# Path to your video file
VIDEO_PATH = 'traffic_video.mp4'

# Define the polygons for two lanes.
# IMPORTANT: You MUST adjust these coordinates for your video's perspective.
# Each polygon is a numpy array of [x, y] coordinates.
LANE_1_POLYGON = np.array([[20, 400], [450, 400], [450, 250], [20, 250]], np.int32)
LANE_2_POLYGON = np.array([[500, 400], [980, 400], [980, 250], [500, 250]], np.int32)

# Define the congestion threshold. If vehicle count > this, the lane is congested.
CONGESTION_THRESHOLD = 10

# Hashtags: #Configuration #AIModel #SmartCity

---

#Step 3: Main Loop for Detection and Counting

This is the core of our program. We will loop through each frame of the video, run vehicle detection, and then check if the center of each detected vehicle falls inside our predefined lane polygons. We will keep a count for each lane.

cap = cv2.VideoCapture(VIDEO_PATH)

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

    # Run YOLOv8 inference on the frame
    results = model(frame)

    # Initialize vehicle counts for each lane for the current frame
    lane_1_count = 0
    lane_2_count = 0

    # Process detection results
    for r in results:
        for box in r.boxes:
            # Check if the detected object is a vehicle
            class_id = int(box.cls[0])
            class_name = model.names[class_id]
            
            if class_name in ['car', 'truck', 'bus', 'motorbike']:
                # Get bounding box coordinates
                x1, y1, x2, y2 = map(int, box.xyxy[0])
                
                # Calculate the center point of the bounding box
                center_x = (x1 + x2) // 2
                center_y = (y1 + y2) // 2

                # Check if the center point is inside Lane 1
                if cv2.pointPolygonTest(LANE_1_POLYGON, (center_x, center_y), False) >= 0:
                    lane_1_count += 1
                
                # Check if the center point is inside Lane 2
                elif cv2.pointPolygonTest(LANE_2_POLYGON, (center_x, center_y), False) >= 0:
                    lane_2_count += 1

# Hashtags: #RealTime #ObjectDetection #VideoProcessing

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

---

#Step 4: Visualization and Displaying Results