uniface/scripts/run_recognition.py

# Face recognition: extract embeddings or compare two faces
# Usage: python run_recognition.py --image path/to/image.jpg
#        python run_recognition.py --image1 face1.jpg --image2 face2.jpg

import argparse

import cv2
import numpy as np

from uniface.detection import SCRFD, RetinaFace
from uniface.face_utils import compute_similarity
from uniface.recognition import ArcFace, MobileFace, SphereFace


def get_recognizer(name: str):
    if name == "arcface":
        return ArcFace()
    elif name == "mobileface":
        return MobileFace()
    else:
        return SphereFace()


def run_inference(detector, recognizer, image_path: str):
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Failed to load image from '{image_path}'")
        return

    faces = detector.detect(image)
    if not faces:
        print("No faces detected.")
        return

    print(f"Detected {len(faces)} face(s). Extracting embedding for the first face...")

    landmarks = np.array(faces[0]["landmarks"])  # 5-point landmarks for alignment
    embedding = recognizer.get_embedding(image, landmarks)
    norm_embedding = recognizer.get_normalized_embedding(image, landmarks)  # L2 normalized

    print(f"  Embedding shape: {embedding.shape}")
    print(f"  L2 norm (raw): {np.linalg.norm(embedding):.4f}")
    print(f"  L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}")


def compare_faces(detector, recognizer, image1_path: str, image2_path: str, threshold: float = 0.35):
    img1 = cv2.imread(image1_path)
    img2 = cv2.imread(image2_path)

    if img1 is None or img2 is None:
        print("Error: Failed to load one or both images")
        return

    faces1 = detector.detect(img1)
    faces2 = detector.detect(img2)

    if not faces1 or not faces2:
        print("Error: No faces detected in one or both images")
        return

    landmarks1 = np.array(faces1[0]["landmarks"])
    landmarks2 = np.array(faces2[0]["landmarks"])

    embedding1 = recognizer.get_normalized_embedding(img1, landmarks1)
    embedding2 = recognizer.get_normalized_embedding(img2, landmarks2)

    # cosine similarity for normalized embeddings
    similarity = compute_similarity(embedding1, embedding2, normalized=True)
    is_match = similarity > threshold

    print(f"Similarity: {similarity:.4f}")
    print(f"Result: {'Same person' if is_match else 'Different person'} (threshold: {threshold})")


def main():
    parser = argparse.ArgumentParser(description="Face recognition and comparison")
    parser.add_argument("--image", type=str, help="Single image for embedding extraction")
    parser.add_argument("--image1", type=str, help="First image for comparison")
    parser.add_argument("--image2", type=str, help="Second image for comparison")
    parser.add_argument("--threshold", type=float, default=0.35, help="Similarity threshold")
    parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
    parser.add_argument(
        "--recognizer",
        type=str,
        default="arcface",
        choices=["arcface", "mobileface", "sphereface"],
    )
    args = parser.parse_args()

    detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
    recognizer = get_recognizer(args.recognizer)

    if args.image1 and args.image2:
        compare_faces(detector, recognizer, args.image1, args.image2, args.threshold)
    elif args.image:
        run_inference(detector, recognizer, args.image)
    else:
        print("Error: Provide --image or both --image1 and --image2")
        parser.print_help()


if __name__ == "__main__":
    main()