import cv2
from VisionFace import FaceDetection, FaceAnnotators

detector = FaceDetection(detector_backbone="yolo-nano")  # Fastest model
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    faces = detector.detect_faces(frame)
    annotated = FaceAnnotators.box_annotator(frame, faces)
    
    cv2.imshow('Face Detection', annotated)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

from VisionFace import FaceDetection
import glob

detector = FaceDetection(detector_backbone="yolo-medium")

# Process entire folder
image_paths = glob.glob("photos/*.jpg")
images = [cv2.imread(path) for path in image_paths]

# Detect all faces at once
all_detections = detector.detect_faces(images)

# Save cropped faces
for i, detections in enumerate(all_detections):
    for j, face in enumerate(detections):
        if face.cropped_face is not None:
            cv2.imwrite(f"faces/image_{i}_face_{j}.jpg", face.cropped_face)

from VisionFace import LandmarkDetection, FaceAnnotators

landmark_detector = LandmarkDetection(detector_backbone="mediapipe")
image = cv2.imread("portrait.jpg")

# Get 468 facial landmarks
landmarks = landmark_detector.detect_landmarks(image)

# Visualize with connections
result = FaceAnnotators.landmark_annotator(
    image, landmarks[0], connections=True
)
cv2.imwrite("landmarks.jpg", result)

from VisionFace import FaceRecognition
import os

# Initialize system
fr = FaceRecognition(db_backend="qdrant")

# Auto-enroll from employee photos folder
def enroll_employees(folder_path):
    for filename in os.listdir(folder_path):
        if filename.endswith(('.jpg', '.png')):
            name = filename.split('.')[0]  # Use filename as name
            image_path = os.path.join(folder_path, filename)
            
            fr.upsert_faces(
                images=[image_path],
                labels=[name],
                collection_name="company_employees"
            )
            print(f"Enrolled: {name}")

# Enroll all employees
enroll_employees("employee_photos/")

# Check security camera feed
def identify_person(camera_image):
    results = fr.search_faces(
        camera_image,
        collection_name="company_employees",
        score_threshold=0.8,
        top_k=1
    )
    
    if results[0]:  # If match found
        return results[0][0]['face_name']
    return "Unknown person"

Detection Models:

• yolo-nano, yolo-small, yolo-medium, yolo-large
• yoloe-small, yoloe-medium, yoloe-large (prompt-based)
• yolow-small, yolow-medium, yolow-large, yolow-xlarge (open-vocabulary)
• mediapipe, mtcnn, opencv

Embedding Models:

• FaceNet-VGG (512D) - Balanced accuracy/speed
• FaceNet-CASIA (512D) - High precision
• Dlib (128D) - Lightweight

Landmark Models:

• mediapipe - 468 points + 3D mesh
• dlib - 68 points, robust