Скачать или смотреть AIML Full Course Day 19 | Artificial Intelligence & Machine Learning Course

📌 Welcome to Day 19 of our Best Artificial Intelligence & Machine Learning Course by CodeKarma Nepal!
In today’s session, we dive into one of the most exciting applications of Computer Vision – Face Detection using OpenCV with Haar Cascade Classifiers.

🔍 What You Will Learn in This Video:

✅ Introduction to Face Detection
✅ Understanding Haar Cascade Classifier in OpenCV
✅ How to use pre-trained XML files for face detection
✅ Capturing video from Webcam using OpenCV
✅ Converting frames to Grayscale for detection
✅ Using detectMultiScale() function to detect faces
✅ Drawing bounding boxes around detected faces
✅ Breaking the loop with a keyboard event (‘q’ key)

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🖥 Code Used in This Video:

import cv2 as cv

Load pre-trained Haar Cascade Classifier for face detection
face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')

Start webcam
cap = cv.VideoCapture(0)

while True:
tr, frame = cap.read()

Convert frame to grayscale
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)

Detect faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

Draw rectangle around detected faces
for (x, y, w, h) in faces:
cv.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv.imshow("Face Detection", frame)

Exit on pressing 'q'
if cv.waitKey(1) & 0xFF == ord("q"):
break

Release resources
cap.release()
cv.destroyAllWindows()

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📝 Key Points:

Haar Cascades are machine learning object detection algorithms used to identify objects in images/video.

Pre-trained models like haarcascade_frontalface_default.xml make face detection easier.

Real-time face detection is achieved by continuously reading frames from the webcam.

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🎯 Assignment for You:

👉 Modify the code to:

1. Detect multiple faces and display the total count of faces.


2. Try different Haar Cascade files like haarcascade_eye.xml or haarcascade_smile.xml.

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