vnyash
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
from typing import Any, Dict
|
|
import numpy
|
|
|
|
import deepfuze.globals
|
|
from deepfuze.face_store import FACE_STORE
|
|
from deepfuze.typing import FaceSet
|
|
from deepfuze 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 deepfuze.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())
|