mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 19:45:21 +00:00
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import cv2
|
|
from deepface.detectors import FaceDetector
|
|
|
|
# Link -> https://github.com/timesler/facenet-pytorch
|
|
# Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
|
|
|
|
def build_model():
|
|
# Optional dependency
|
|
from facenet_pytorch import MTCNN as fast_mtcnn
|
|
|
|
|
|
face_detector = fast_mtcnn(image_size=160,
|
|
thresholds=[0.6, 0.7, 0.7], # MTCNN thresholds
|
|
post_process=True,
|
|
device='cpu'
|
|
)
|
|
return face_detector
|
|
|
|
def xyxy_to_xywh(xyxy):
|
|
"""
|
|
Convert xyxy format to xywh format.
|
|
"""
|
|
x, y = xyxy[0], xyxy[1]
|
|
w = xyxy[2] - x + 1
|
|
h = xyxy[3] - y + 1
|
|
return [x, y, w, h]
|
|
|
|
def detect_face(face_detector, img, align=True):
|
|
|
|
resp = []
|
|
|
|
detected_face = None
|
|
img_region = [0, 0, img.shape[1], img.shape[0]]
|
|
|
|
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB but OpenCV read BGR
|
|
detections = face_detector.detect(img_rgb, landmarks=True) # returns boundingbox, prob, landmark
|
|
if len(detections[0]) > 0:
|
|
|
|
for detection in zip(*detections):
|
|
x, y, w, h = xyxy_to_xywh(detection[0])
|
|
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
|
|
img_region = [x, y, w, h]
|
|
confidence = detection[1]
|
|
|
|
if align:
|
|
left_eye = detection[2][0]
|
|
right_eye = detection[2][1]
|
|
detected_face = FaceDetector.alignment_procedure(detected_face, left_eye, right_eye)
|
|
|
|
resp.append((detected_face, img_region, confidence))
|
|
|
|
return resp
|