def build_model():
face_detector = RetinaFace.build_model()
return face_detector
def initialize_detector(detector_backend):
global face_detector
home = str(Path.home())
#eye detector is common for opencv and ssd
if detector_backend == 'opencv' or detector_backend == 'ssd':
opencv_path = get_opencv_path()
eye_detector_path = opencv_path + "haarcascade_eye.xml"
if os.path.isfile(eye_detector_path) != True:
raise ValueError(
"Confirm that opencv is installed on your environment! Expected path ",
eye_detector_path, " violated.")
global eye_detector
eye_detector = cv2.CascadeClassifier(eye_detector_path)
#------------------------------
#face detectors
if detector_backend == 'opencv':
opencv_path = get_opencv_path()
face_detector_path = opencv_path + "haarcascade_frontalface_default.xml"
if os.path.isfile(face_detector_path) != True:
raise ValueError(
"Confirm that opencv is installed on your environment! Expected path ",
face_detector_path, " violated.")
face_detector = cv2.CascadeClassifier(face_detector_path)
elif detector_backend == 'ssd':
#check required ssd model exists in the home/.deepface/weights folder
#model structure
if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True:
print("deploy.prototxt will be downloaded...")
url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
output = home + '/.deepface/weights/deploy.prototxt'
gdown.download(url, output, quiet=False)
#pre-trained weights
if os.path.isfile(
home +
'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
) != True:
print(
"res10_300x300_ssd_iter_140000.caffemodel will be downloaded..."
)
url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
output = home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
gdown.download(url, output, quiet=False)
face_detector = cv2.dnn.readNetFromCaffe(
home + "/.deepface/weights/deploy.prototxt", home +
"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
elif detector_backend == 'dlib':
import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0
global sp
face_detector = dlib.get_frontal_face_detector()
#check required file exists in the home/.deepface/weights folder
if os.path.isfile(
home +
'/.deepface/weights/shape_predictor_5_face_landmarks.dat'
) != True:
print(
"shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded"
)
url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
output = home + '/.deepface/weights/' + url.split("/")[-1]
gdown.download(url, output, quiet=False)
zipfile = bz2.BZ2File(output)
data = zipfile.read()
newfilepath = output[:-4] #discard .bz2 extension
open(newfilepath, 'wb').write(data)
sp = dlib.shape_predictor(
home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
elif detector_backend == 'mtcnn':
face_detector = MTCNN()
elif detector_backend == 'retinaface':
from retinaface import RetinaFace
face_detector = RetinaFace.build_model()
def build_model():
from retinaface import RetinaFace
face_detector = RetinaFace.build_model()
return face_detector
def buildRetinaModel():
retina_model = RetinaFace.build_model()
return retina_model
