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from typing import List
import os
import bz2
import gdown
import numpy as np
from deepface.commons import folder_utils
from deepface.models.FacialRecognition import FacialRecognition
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
# pylint: disable=too-few-public-methods
class DlibClient(FacialRecognition):
"""
Dlib model class
"""
def __init__(self):
self.model = DlibResNet()
self.model_name = "Dlib"
self.input_shape = (150, 150)
self.output_shape = 128
def forward(self, img: np.ndarray) -> List[float]:
"""
Find embeddings with Dlib model.
This model necessitates the override of the forward method
because it is not a keras model.
Args:
img (np.ndarray): pre-loaded image in BGR
Returns
embeddings (list): multi-dimensional vector
"""
# return self.model.predict(img)[0].tolist()
# extract_faces returns 4 dimensional images
if len(img.shape) == 4:
img = img[0]
# bgr to rgb
img = img[:, :, ::-1] # bgr to rgb
# img is in scale of [0, 1] but expected [0, 255]
if img.max() <= 1:
img = img * 255
img = img.astype(np.uint8)
img_representation = self.model.model.compute_face_descriptor(img)
img_representation = np.array(img_representation)
img_representation = np.expand_dims(img_representation, axis=0)
return img_representation[0].tolist()
class DlibResNet:
def __init__(self):
## this is not a must dependency. do not import it in the global level.
try:
import dlib
except ModuleNotFoundError as e:
raise ImportError(
"Dlib is an optional dependency, ensure the library is installed."
"Please install using 'pip install dlib' "
) from e
home = folder_utils.get_deepface_home()
weight_file = home + "/.deepface/weights/dlib_face_recognition_resnet_model_v1.dat"
# download pre-trained model if it does not exist
if os.path.isfile(weight_file) != True:
logger.info("dlib_face_recognition_resnet_model_v1.dat is going to be downloaded")
file_name = "dlib_face_recognition_resnet_model_v1.dat.bz2"
url = f"http://dlib.net/files/{file_name}"
output = f"{home}/.deepface/weights/{file_name}"
gdown.download(url, output, quiet=False)
zipfile = bz2.BZ2File(output)
data = zipfile.read()
newfilepath = output[:-4] # discard .bz2 extension
with open(newfilepath, "wb") as f:
f.write(data)
self.model = dlib.face_recognition_model_v1(weight_file)
# return None # classes must return None
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