deepface/deepface/detectors/FaceDetector.py
2023-05-23 23:00:49 +02:00

127 lines
4.0 KiB
Python

import math
from PIL import Image
import numpy as np
from deepface.commons import distance
from deepface.detectors import (
OpenCvWrapper,
SsdWrapper,
DlibWrapper,
MtcnnWrapper,
RetinaFaceWrapper,
MediapipeWrapper,
Yolov8faceWrapper,
)
def build_model(detector_backend):
global face_detector_obj # singleton design pattern
backends = {
"opencv": OpenCvWrapper.build_model,
"ssd": SsdWrapper.build_model,
"dlib": DlibWrapper.build_model,
"mtcnn": MtcnnWrapper.build_model,
"retinaface": RetinaFaceWrapper.build_model,
"mediapipe": MediapipeWrapper.build_model,
"yolov8-lite-t": Yolov8faceWrapper.build_model("yolov8-lite-t"),
"yolov8-lite-s": Yolov8faceWrapper.build_model("yolov8-lite-s"),
"yolov8n": Yolov8faceWrapper.build_model("yolov8n"),
}
if not "face_detector_obj" in globals():
face_detector_obj = {}
built_models = list(face_detector_obj.keys())
if detector_backend not in built_models:
face_detector = backends.get(detector_backend)
if face_detector:
face_detector = face_detector()
face_detector_obj[detector_backend] = face_detector
else:
raise ValueError("invalid detector_backend passed - " + detector_backend)
return face_detector_obj[detector_backend]
def detect_face(face_detector, detector_backend, img, align=True):
obj = detect_faces(face_detector, detector_backend, img, align)
if len(obj) > 0:
face, region, confidence = obj[0] # discard multiple faces
else: # len(obj) == 0
face = None
region = [0, 0, img.shape[1], img.shape[0]]
confidence = 0
return face, region, confidence
def detect_faces(face_detector, detector_backend, img, align=True):
backends = {
"opencv": OpenCvWrapper.detect_face,
"ssd": SsdWrapper.detect_face,
"dlib": DlibWrapper.detect_face,
"mtcnn": MtcnnWrapper.detect_face,
"retinaface": RetinaFaceWrapper.detect_face,
"mediapipe": MediapipeWrapper.detect_face,
"yolov8-lite-t": Yolov8faceWrapper.detect_face,
"yolov8-lite-s": Yolov8faceWrapper.detect_face,
"yolov8n": Yolov8faceWrapper.detect_face,
}
detect_face_fn = backends.get(detector_backend)
if detect_face_fn: # pylint: disable=no-else-return
obj = detect_face_fn(face_detector, img, align)
# obj stores list of (detected_face, region, confidence)
return obj
else:
raise ValueError("invalid detector_backend passed - " + detector_backend)
def alignment_procedure(img, left_eye, right_eye):
# this function aligns given face in img based on left and right eye coordinates
left_eye_x, left_eye_y = left_eye
right_eye_x, right_eye_y = right_eye
# -----------------------
# find rotation direction
if left_eye_y > right_eye_y:
point_3rd = (right_eye_x, left_eye_y)
direction = -1 # rotate same direction to clock
else:
point_3rd = (left_eye_x, right_eye_y)
direction = 1 # rotate inverse direction of clock
# -----------------------
# find length of triangle edges
a = distance.findEuclideanDistance(np.array(left_eye), np.array(point_3rd))
b = distance.findEuclideanDistance(np.array(right_eye), np.array(point_3rd))
c = distance.findEuclideanDistance(np.array(right_eye), np.array(left_eye))
# -----------------------
# apply cosine rule
if b != 0 and c != 0: # this multiplication causes division by zero in cos_a calculation
cos_a = (b * b + c * c - a * a) / (2 * b * c)
angle = np.arccos(cos_a) # angle in radian
angle = (angle * 180) / math.pi # radian to degree
# -----------------------
# rotate base image
if direction == -1:
angle = 90 - angle
img = Image.fromarray(img)
img = np.array(img.rotate(direction * angle))
# -----------------------
return img # return img anyway