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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.Detector import Detector, FacialAreaRegion
from deepface.commons import logger as log
logger = log.get_singletonish_logger()
class DlibClient(Detector):
def __init__(self):
self.model = self.build_model()
def build_model(self) -> dict:
"""
Build a dlib hog face detector model
Returns:
model (Any)
"""
home = folder_utils.get_deepface_home()
# 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 detector, ensure the library is installed."
"Please install using 'pip install dlib' "
) from e
# check required file exists in the home/.deepface/weights folder
if os.path.isfile(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat") != True:
file_name = "shape_predictor_5_face_landmarks.dat.bz2"
logger.info(f"{file_name} is going to be downloaded")
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)
face_detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
detector = {}
detector["face_detector"] = face_detector
detector["sp"] = sp
return detector
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with dlib
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp = []
face_detector = self.model["face_detector"]
# note that, by design, dlib's fhog face detector scores are >0 but not capped at 1
detections, scores, _ = face_detector.run(img, 1)
if len(detections) > 0:
for idx, detection in enumerate(detections):
left = detection.left()
right = detection.right()
top = detection.top()
bottom = detection.bottom()
y = int(max(0, top))
h = int(min(bottom, img.shape[0]) - y)
x = int(max(0, left))
w = int(min(right, img.shape[1]) - x)
shape = self.model["sp"](img, detection)
right_eye = (
int((shape.part(2).x + shape.part(3).x) // 2),
int((shape.part(2).y + shape.part(3).y) // 2),
)
left_eye = (
int((shape.part(0).x + shape.part(1).x) // 2),
int((shape.part(0).y + shape.part(1).y) // 2),
)
# never saw confidence higher than +3.5 github.com/davisking/dlib/issues/761
confidence = scores[idx]
facial_area = FacialAreaRegion(
x=x,
y=y,
w=w,
h=h,
left_eye=left_eye,
right_eye=right_eye,
confidence=min(max(0, confidence), 1.0),
)
resp.append(facial_area)
return resp
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