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from typing import Any, Dict
import numpy
import facefusion.globals
from facefusion.face_store import FACE_STORE
from facefusion.typing import FaceSet
from facefusion import logger
def create_statistics(static_faces : FaceSet) -> Dict[str, Any]:
face_detector_score_list = []
face_landmarker_score_list = []
statistics =\
{
'min_face_detector_score': 0,
'min_face_landmarker_score': 0,
'max_face_detector_score': 0,
'max_face_landmarker_score': 0,
'average_face_detector_score': 0,
'average_face_landmarker_score': 0,
'total_face_landmark_5_fallbacks': 0,
'total_frames_with_faces': 0,
'total_faces': 0
}
for faces in static_faces.values():
statistics['total_frames_with_faces'] = statistics.get('total_frames_with_faces') + 1
for face in faces:
statistics['total_faces'] = statistics.get('total_faces') + 1
face_detector_score_list.append(face.scores.get('detector'))
face_landmarker_score_list.append(face.scores.get('landmarker'))
if numpy.array_equal(face.landmarks.get('5'), face.landmarks.get('5/68')):
statistics['total_face_landmark_5_fallbacks'] = statistics.get('total_face_landmark_5_fallbacks') + 1
if face_detector_score_list:
statistics['min_face_detector_score'] = round(min(face_detector_score_list), 2)
statistics['max_face_detector_score'] = round(max(face_detector_score_list), 2)
statistics['average_face_detector_score'] = round(numpy.mean(face_detector_score_list), 2)
if face_landmarker_score_list:
statistics['min_face_landmarker_score'] = round(min(face_landmarker_score_list), 2)
statistics['max_face_landmarker_score'] = round(max(face_landmarker_score_list), 2)
statistics['average_face_landmarker_score'] = round(numpy.mean(face_landmarker_score_list), 2)
return statistics
def conditional_log_statistics() -> None:
if facefusion.globals.log_level == 'debug':
statistics = create_statistics(FACE_STORE.get('static_faces'))
for name, value in statistics.items():
logger.debug(str(name) + ': ' + str(value), __name__.upper())
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