File size: 2,855 Bytes
677c57e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | from typing import Any, List
import numpy as np
from deepface.models.Detector import Detector, FacialAreaRegion
# Link - https://google.github.io/mediapipe/solutions/face_detection
class MediaPipeClient(Detector):
def __init__(self):
self.model = self.build_model()
def build_model(self) -> Any:
"""
Build a mediapipe face detector model
Returns:
model (Any)
"""
# this is not a must dependency. do not import it in the global level.
try:
import mediapipe as mp
except ModuleNotFoundError as e:
raise ImportError(
"MediaPipe is an optional detector, ensure the library is installed."
"Please install using 'pip install mediapipe' "
) from e
mp_face_detection = mp.solutions.face_detection
face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.7)
return face_detection
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
"""
Detect and align face with mediapipe
Args:
img (np.ndarray): pre-loaded image as numpy array
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
"""
resp = []
img_width = img.shape[1]
img_height = img.shape[0]
results = self.model.process(img)
# If no face has been detected, return an empty list
if results.detections is None:
return resp
# Extract the bounding box, the landmarks and the confidence score
for current_detection in results.detections:
(confidence,) = current_detection.score
bounding_box = current_detection.location_data.relative_bounding_box
landmarks = current_detection.location_data.relative_keypoints
x = int(bounding_box.xmin * img_width)
w = int(bounding_box.width * img_width)
y = int(bounding_box.ymin * img_height)
h = int(bounding_box.height * img_height)
right_eye = (int(landmarks[0].x * img_width), int(landmarks[0].y * img_height))
left_eye = (int(landmarks[1].x * img_width), int(landmarks[1].y * img_height))
# nose = (int(landmarks[2].x * img_width), int(landmarks[2].y * img_height))
# mouth = (int(landmarks[3].x * img_width), int(landmarks[3].y * img_height))
# right_ear = (int(landmarks[4].x * img_width), int(landmarks[4].y * img_height))
# left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
facial_area = FacialAreaRegion(
x=x, y=y, w=w, h=h, left_eye=left_eye, right_eye=right_eye, confidence=confidence
)
resp.append(facial_area)
return resp
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