mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 19:45:21 +00:00
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# built-in dependencies
|
|
import os
|
|
from typing import Any, List
|
|
|
|
# 3rd party dependencies
|
|
import numpy as np
|
|
|
|
# project dependencies
|
|
from deepface.models.Detector import Detector, FacialAreaRegion
|
|
|
|
|
|
class MediaPipeClient(Detector):
|
|
"""
|
|
MediaPipe from google.github.io/mediapipe/solutions/face_detection
|
|
"""
|
|
|
|
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
|
|
|
|
min_detection_confidence = float(os.environ.get("MEDIAPIPE_MIN_DETECTION_CONFIDENCE", 0.7))
|
|
model_selection = int(os.environ.get("MEDIAPIPE_MODEL_SELECTION", 0))
|
|
|
|
face_detection = mp_face_detection.FaceDetection(
|
|
min_detection_confidence=min_detection_confidence,
|
|
model_selection=model_selection
|
|
)
|
|
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=float(confidence),
|
|
)
|
|
resp.append(facial_area)
|
|
|
|
return resp
|