Remove statistics

facefusion/benchmarker.py

@@ -92,7 +92,7 @@ def render() -> None:
 	headers =\
 	[
 		'target_path',
-		'benchmark_cycle_count',
+		'cycle_count',
 		'average_run',
 		'fastest_run',
 		'slowest_run',

facefusion/core.py

@@ -23,7 +23,6 @@ from facefusion.memory import limit_system_memory
 from facefusion.processors.core import get_processors_modules
 from facefusion.program import create_program
 from facefusion.program_helper import validate_args
-from facefusion.statistics import conditional_log_statistics
 from facefusion.temp_helper import clear_temp_directory, create_temp_directory, get_temp_file_path, move_temp_file, resolve_temp_frame_paths
 from facefusion.types import Args, ErrorCode
 from facefusion.vision import pack_resolution, read_image, read_static_images, read_video_frame, restrict_image_resolution, restrict_trim_frame, restrict_video_fps, restrict_video_resolution, unpack_resolution
@@ -409,7 +408,6 @@ def process_image(start_time : float) -> ErrorCode:
 	if is_image(state_manager.get_item('output_path')):
 		seconds = '{:.2f}'.format((time() - start_time) % 60)
 		logger.info(wording.get('processing_image_succeed').format(seconds = seconds), __name__)
-		conditional_log_statistics()
 	else:
 		logger.error(wording.get('processing_image_failed'), __name__)
 		process_manager.end()
@@ -500,7 +498,6 @@ def process_video(start_time : float) -> ErrorCode:
 	if is_video(state_manager.get_item('output_path')):
 		seconds = '{:.2f}'.format((time() - start_time))
 		logger.info(wording.get('processing_video_succeed').format(seconds = seconds), __name__)
-		conditional_log_statistics()
 	else:
 		logger.error(wording.get('processing_video_failed'), __name__)
 		process_manager.end()

facefusion/statistics.py

@@ -1,51 +0,0 @@
-from typing import Any, Dict
-
-import numpy
-
-from facefusion import logger, state_manager
-from facefusion.face_store import get_face_store
-from facefusion.types import FaceSet
-
-
-def create_statistics(static_faces : FaceSet) -> Dict[str, Any]:
-	face_detector_scores = []
-	face_landmarker_scores = []
-	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_scores.append(face.score_set.get('detector'))
-			face_landmarker_scores.append(face.score_set.get('landmarker'))
-			if numpy.array_equal(face.landmark_set.get('5'), face.landmark_set.get('5/68')):
-				statistics['total_face_landmark_5_fallbacks'] = statistics.get('total_face_landmark_5_fallbacks') + 1
-
-	if face_detector_scores:
-		statistics['min_face_detector_score'] = round(min(face_detector_scores), 2)
-		statistics['max_face_detector_score'] = round(max(face_detector_scores), 2)
-		statistics['average_face_detector_score'] = round(numpy.mean(face_detector_scores), 2)
-	if face_landmarker_scores:
-		statistics['min_face_landmarker_score'] = round(min(face_landmarker_scores), 2)
-		statistics['max_face_landmarker_score'] = round(max(face_landmarker_scores), 2)
-		statistics['average_face_landmarker_score'] = round(numpy.mean(face_landmarker_scores), 2)
-	return statistics
-
-
-def conditional_log_statistics() -> None:
-	if state_manager.get_item('log_level') == 'debug':
-		statistics = create_statistics(get_face_store().get('static_faces'))
-
-		for name, value in statistics.items():
-			logger.debug(str(name) + ': ' + str(value), __name__)