detector.py

import pickle
from collections import Counter
from pathlib import Path

import face_recognition

DEFAULT_ENCODINGS_PATH = Path("output/encodings.pkl")

# Create directories if they don't already exist
Path("training").mkdir(exist_ok=True)
Path("output").mkdir(exist_ok=True)
Path("validation").mkdir(exist_ok=True)


def encode_known_faces(
    model: str = "hog", encodings_location: Path = DEFAULT_ENCODINGS_PATH
) -> None:
    """
    Loads images in the training directory and builds a dictionary of their
    names and encodings.
    """
    names = []
    encodings = []
    for filepath in Path("training").glob("*/*"):
        name = filepath.parent.name
        image = face_recognition.load_image_file(filepath)

        face_locations = face_recognition.face_locations(image, model=model)
        face_encodings = face_recognition.face_encodings(image, face_locations)

        for encoding in face_encodings:
            names.append(name)
            encodings.append(encoding)

    name_encodings = {"names": names, "encodings": encodings}
    with encodings_location.open(mode="wb") as f:
        pickle.dump(name_encodings, f)


def recognize_faces(
    image_location: str,
    model: str = "hog",
    encodings_location: Path = DEFAULT_ENCODINGS_PATH,
) -> None:
    """
    Given an unknown image, get the locations and encodings of any faces and
    compares them against the known encodings to find potential matches.
    """
    with encodings_location.open(mode="rb") as f:
        loaded_encodings = pickle.load(f)

    input_image = face_recognition.load_image_file(image_location)

    input_face_locations = face_recognition.face_locations(
        input_image, model=model
    )
    input_face_encodings = face_recognition.face_encodings(
        input_image, input_face_locations
    )

    for bounding_box, unknown_encoding in zip(
        input_face_locations, input_face_encodings
    ):
        name = _recognize_face(unknown_encoding, loaded_encodings)
        if not name:
            name = "Unknown"
        print(name, bounding_box)


def _recognize_face(unknown_encoding, loaded_encodings):
    """
    Given an unknown encoding and all known encodings, find the known
    encoding with the most matches.
    """
    boolean_matches = face_recognition.compare_faces(
        loaded_encodings["encodings"], unknown_encoding
    )
    votes = Counter(
        name
        for match, name in zip(boolean_matches, loaded_encodings["names"])
        if match
    )
    if votes:
        return votes.most_common(1)[0][0]


recognize_faces("unknown.jpg")