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Improve face detection (#17202)

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Nicolas Mowen 2025-03-17 08:05:53 -06:00 committed by GitHub
parent 8f4e86e476
commit 95e141ed15
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1 changed files with 21 additions and 4 deletions
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25
frigate/data_processing/real_time/face.py
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@ -27,6 +27,7 @@ from .api import RealTimeProcessorApi
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
MAX_DETECTION_HEIGHT = 1080
MIN_MATCHING_FACES = 2 MIN_MATCHING_FACES = 2
@ -88,7 +89,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
os.path.join(MODEL_CACHE_DIR, "facedet/facedet.onnx"), os.path.join(MODEL_CACHE_DIR, "facedet/facedet.onnx"),
config="", config="",
input_size=(320, 320), input_size=(320, 320),
score_threshold=self.face_config.detection_threshold, score_threshold=0.5,
nms_threshold=0.3, nms_threshold=0.3,
) )
self.landmark_detector = cv2.face.createFacemarkLBF() self.landmark_detector = cv2.face.createFacemarkLBF()
@ -212,11 +213,21 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
self.face_recognizer = None self.face_recognizer = None
self.label_map = {} self.label_map = {}
def __detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]: def __detect_face(
self, input: np.ndarray, threshold: float
) -> tuple[int, int, int, int]:
"""Detect faces in input image.""" """Detect faces in input image."""
if not self.face_detector: if not self.face_detector:
return None return None
# YN face detector fails at extreme definitions
# this rescales to a size that can properly detect faces
# still retaining plenty of detail
if input.shape[0] > MAX_DETECTION_HEIGHT:
scale_factor = MAX_DETECTION_HEIGHT / input.shape[0]
new_width = int(scale_factor * input.shape[1])
input = cv2.resize(input, (new_width, MAX_DETECTION_HEIGHT))
self.face_detector.setInputSize((input.shape[1], input.shape[0])) self.face_detector.setInputSize((input.shape[1], input.shape[0]))
faces = self.face_detector.detect(input) faces = self.face_detector.detect(input)
@ -226,6 +237,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
face = None face = None
for _, potential_face in enumerate(faces[1]): for _, potential_face in enumerate(faces[1]):
if potential_face[-1] < threshold:
continue
raw_bbox = potential_face[0:4].astype(np.uint16) raw_bbox = potential_face[0:4].astype(np.uint16)
x: int = max(raw_bbox[0], 0) x: int = max(raw_bbox[0], 0)
y: int = max(raw_bbox[1], 0) y: int = max(raw_bbox[1], 0)
@ -300,7 +314,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420) rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
left, top, right, bottom = person_box left, top, right, bottom = person_box
person = rgb[top:bottom, left:right] person = rgb[top:bottom, left:right]
face_box = self.__detect_face(person) face_box = self.__detect_face(person, self.face_config.detection_threshold)
if not face_box: if not face_box:
logger.debug("Detected no faces for person object.") logger.debug("Detected no faces for person object.")
@ -406,7 +420,10 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
), ),
cv2.IMREAD_COLOR, cv2.IMREAD_COLOR,
) )
face_box = self.__detect_face(img)
# detect faces with lower confidence since we expect the face
# to be visible in uploaded images
face_box = self.__detect_face(img, 0.5)
if not face_box: if not face_box:
return { return {