Detecting Faces in Images

import cv2

def detect_faces_in_image(image_path, output_path="faces_detected.jpg"):
    """Detect all faces in an image and draw bounding boxes."""

    # Load cascade and image
    face_cascade = cv2.CascadeClassifier(
        cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
    )
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces
    # scaleFactor: how much the image size is reduced at each scale
    # minNeighbors: how many neighbours each rectangle should retain
    # minSize: minimum face size to detect
    faces = face_cascade.detectMultiScale(
        gray,
        scaleFactor=1.1,      # Try 1.05 for more sensitive detection
        minNeighbors=5,        # Higher = fewer false positives
        minSize=(30, 30),      # Minimum face size in pixels
        flags=cv2.CASCADE_SCALE_IMAGE
    )

    print(f"Detected {len(faces)} face(s) in '{image_path}'")

    result = image.copy()
    for i, (x, y, w, h) in enumerate(faces):
        # Draw rectangle around each face
        cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)

        # Add label
        label = f"Face {i+1}"
        cv2.putText(result, label, (x, y-10),
                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

        # Print details
        print(f"  Face {i+1}: position=({x},{y}), size={w}x{h}px")

    cv2.imwrite(output_path, result)
    print(f"Result saved: {output_path}")
    return faces

detect_faces_in_image("group_photo.jpg")