Encoding face on images¶

def encode_faces(
    datasets=datasets_path, encodings=encodings_path, detection_method="cnn"
):
    """Encodes (128-d vectors) given images on datasets.
    Args:
      datasets (str):
        Directory path of datasets that contains images.
      encodings (str):
        Directory path of encoding file.
      detection_method (str):
        Face detection method. Options: `cnn` or `hog`.
    """

    known_encodings = []
    known_names = []
    image_paths = list(paths.list_images(datasets))

    for (i, image_path) in enumerate(image_paths):
        print(
            "[face_encoder:encode_faces] processing image {}/{}".format(
                i + 1, len(image_paths)
            )
        )
        name = image_path.split(os.path.sep)[-2]
        if os.path.isfile(image_path):
            image = cv2.imread(image_path)
            rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            # detect the (x, y) coordinates of the bounding boxes
            boxes = face_recognition.face_locations(rgb, model=detection_method)
            # compute the facial embedding for the face
            face_encodings = face_recognition.face_encodings(rgb, boxes)
            # loop over the encodings
            for face_encode in face_encodings:
                known_encodings.append(face_encode)
                known_names.append(name)
    # save facial encodings and names to disk
    print("[face_encoder:encode_faces] serializing encodings...")
    data = {"encodings": known_encodings, "names": known_names}
    f = open(encodings, "wb")
    f.write(pickle.dumps(data))
    f.close()