🔥 This 10-Line Python Script Can Detect Faces Like Magic – You Won’t Believe How Easy It Is!

import cv2

# Load the pre-trained Haar Cascade face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the image and convert it to grayscale
img = cv2.imread('your_image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

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

# Draw rectangles around detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

# Show the image
cv2.imshow('Detected Faces', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load the pre-trained Haar Cascade face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Start video capture from the default webcam (0)
cap = cv2.VideoCapture(0)

while True:
    # Read each frame from the webcam
    ret, frame = cap.read()
    if not ret:
        break

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

    # Draw rectangles around detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

    # Display the frame with detected faces
    cv2.imshow('Live Face Detection', frame)

    # Exit loop when 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the webcam and destroy all windows
cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np

# Load class labels MobileNet SSD was trained on
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant",
           "sheep", "sofa", "train", "tvmonitor"]

# Load the serialized pre-trained model from disk
net = cv2.dnn.readNetFromCaffe(
    'MobileNetSSD_deploy.prototxt',
    'MobileNetSSD_deploy.caffemodel'
)

# Start webcam capture
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Prepare the frame for detection
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
                                 0.007843, (300, 300), 127.5)

    net.setInput(blob)
    detections = net.forward()

    # Loop through the detections
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        # Filter out weak detections
        if confidence > 0.5:
            idx = int(detections[0, 0, i, 1])
            label = CLASSES[idx]

            # Only proceed if the detected object is an animal
            if label in ['cat', 'dog', 'bird', 'horse', 'cow', 'sheep']:
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")

                # Draw the bounding box and label
                cv2.rectangle(frame, (startX, startY), (endX, endY),
                              (0, 255, 0), 2)
                label_text = f"{label}: {round(confidence * 100, 1)}%"
                cv2.putText(frame, label_text, (startX, startY - 10),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Show the frame
    cv2.imshow("Animal Detector", frame)

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

# Clean up
cap.release()
cv2.destroyAllWindows()