Azadov Nadirbek 15 praktika

#eye

import cv2

# Load Haar cascade for eye detection
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')

# Open the webcam
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the webcam
    ret, frame = cap.read()

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

    # Detect eyes with updated parameters
    eyes = eye_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(20, 20))

    # Draw yellow rectangles around detected eyes
    for (ex, ey, ew, eh) in eyes:
        cv2.rectangle(frame, (ex, ey), (ex+ew, ey+eh), (0, 255, 255), 2)  # Yellow rectangle

    # Display the frame
    cv2.imshow('Eye Detection', frame)

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

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


#face


import cv2

# Load Haar cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Open the webcam
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the webcam
    ret, frame = cap.read()

    # Convert to grayscale (Haar cascades work on grayscale images)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces with updated parameters
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles around detected faces and add labels
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)  # Red rectangle
        cv2.putText(frame, 'Human', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    # Display the frame
    cv2.imshow('Face Detection', frame)

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

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


#image

import cv2

# Load Haar cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Load an image
image = cv2.imread('/Users/texnik/Downloads/Project 2/diverse.jpg')

if image is None:
    print("Error: Could not read image file. Check the file path.")
    exit()

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

# Detect faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(30, 30))

# Count and print the number of faces
print(f"Number of faces detected: {len(faces)}")

# Draw ellipses around detected faces
for (x, y, w, h) in faces:
    center = (x + w // 2, y + h // 2)
    axes = (w // 2, h // 2)
    cv2.ellipse(image, center, axes, 0, 0, 360, (0, 0, 255), 2)
    cv2.putText(image, 'Human', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

# Display the result
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()