class RetinaFaceDetector:
def __init__(self, min_face_size=0):
"""Face detector based on RetinaFace.
Args:
min_face_size (int): minimum size of a face to be considered a match.
Compared against min(width, height) of the face bounding box.
"""
self.min_size = min_face_size
self.model = RetinaFace(quality="normal")
def detect(self, img: np.array):
assert len(img.shape) == 3 and img.shape[2] == 3
bbs = self.model.predict(img, threshold=0.95)
return [{
"box": [b["x1"], b["y1"], b["x2"], b["y2"]],
"keypoints": {
"left_eye": (int(b["left_eye"][0]), int(b["left_eye"][1])),
"right_eye": (int(b["right_eye"][0]), int(b["right_eye"][1])),
"nose": (int(b["nose"][0]), int(b["nose"][1])),
"mouth_left": (int(b["left_lip"][0]), int(b["left_lip"][1])),
"mouth_right": (int(b["right_lip"][0]), int(b["right_lip"][1])),
}
} for b in bbs if min(b["x2"] - b["x1"], b["y2"] - b["y1"]) >= self.min_size]
# pip3 install opencv-python
import cv2
from retinaface import RetinaFace
# init with normal accuracy option
detector = RetinaFace(quality="normal")
# same with cv2.imread,cv2.cvtColor
rgb_image = detector.read("data/hian.jpg")
faces = detector.predict(rgb_image)
# faces is list of face dictionary
# each face dictionary contains x1 y1 x2 y2 left_eye right_eye nose left_lip right_lip
# faces=[{"x1":20,"y1":32, ... }, ...]
result_img = detector.draw(rgb_image, faces)
# save ([...,::-1] : rgb -> bgr )
cv2.imwrite("data/result_img.jpg", result_img[..., ::-1])
