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# built-in dependencies
import os
from typing import Any, List
# 3rd party dependencies
import cv2
import numpy as np
import gdown
# project dependencies
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 YuNetClient(Detector):
def __init__(self):
self.model = self.build_model()
def build_model(self) -> Any:
"""
Build a yunet detector model
Returns:
model (Any)
"""
opencv_version = cv2.__version__.split(".")
if not len(opencv_version) >= 2:
raise ValueError(
f"OpenCv's version must have major and minor values but it is {opencv_version}"
)
opencv_version_major = int(opencv_version[0])
opencv_version_minor = int(opencv_version[1])
if opencv_version_major < 4 or (opencv_version_major == 4 and opencv_version_minor < 8):
# min requirement: https://github.com/opencv/opencv_zoo/issues/172
raise ValueError(f"YuNet requires opencv-python >= 4.8 but you have {cv2.__version__}")
# pylint: disable=C0301
url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
file_name = "face_detection_yunet_2023mar.onnx"
home = folder_utils.get_deepface_home()
if os.path.isfile(home + f"/.deepface/weights/{file_name}") is False:
logger.info(f"{file_name} will be downloaded...")
output = home + f"/.deepface/weights/{file_name}"
gdown.download(url, output, quiet=False)
try:
face_detector = cv2.FaceDetectorYN_create(
home + f"/.deepface/weights/{file_name}", "", (0, 0)
)
except Exception as err:
raise ValueError(
"Exception while calling opencv.FaceDetectorYN_create module."
+ "This is an optional dependency."
+ "You can install it as pip install opencv-contrib-python."
) from err
return face_detector
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with yunet
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
# FaceDetector.detect_faces does not support score_threshold parameter.
# We can set it via environment variable.
score_threshold = float(os.environ.get("yunet_score_threshold", "0.9"))
resp = []
faces = []
height, width = img.shape[0], img.shape[1]
# resize image if it is too large (Yunet fails to detect faces on large input sometimes)
# I picked 640 as a threshold because it is the default value of max_size in Yunet.
resized = False
r = 1 # resize factor
if height > 640 or width > 640:
r = 640.0 / max(height, width)
img = cv2.resize(img, (int(width * r), int(height * r)))
height, width = img.shape[0], img.shape[1]
resized = True
self.model.setInputSize((width, height))
self.model.setScoreThreshold(score_threshold)
_, faces = self.model.detect(img)
if faces is None:
return resp
for face in faces:
# pylint: disable=W0105
"""
The detection output faces is a two-dimension array of type CV_32F,
whose rows are the detected face instances, columns are the location
of a face and 5 facial landmarks.
The format of each row is as follows:
x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt,
x_rcm, y_rcm, x_lcm, y_lcm,
where x1, y1, w, h are the top-left coordinates, width and height of
the face bounding box,
{x, y}_{re, le, nt, rcm, lcm} stands for the coordinates of right eye,
left eye, nose tip, the right corner and left corner of the mouth respectively.
"""
(x, y, w, h, x_le, y_le, x_re, y_re) = list(map(int, face[:8]))
# YuNet returns negative coordinates if it thinks part of the detected face
# is outside the frame.
x = max(x, 0)
y = max(y, 0)
if resized:
x, y, w, h = int(x / r), int(y / r), int(w / r), int(h / r)
x_re, y_re, x_le, y_le = (
int(x_re / r),
int(y_re / r),
int(x_le / r),
int(y_le / r),
)
confidence = float(face[-1])
facial_area = FacialAreaRegion(
x=x,
y=y,
w=w,
h=h,
confidence=confidence,
left_eye=(x_re, y_re),
right_eye=(x_le, y_le),
)
resp.append(facial_area)
return resp
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