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Feat/improve content analyser (#861)

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* Introduce fit_frame to improve content analyser, rename resize_frame_resolution to restrict_frame

* Fix CI, Add some spaces

* Normalize according to face detector
This commit is contained in:
Henry Ruhs
2025-04-28 10:03:25 +02:00
committed by henryruhs
parent e79a99fac4
commit c70b45bd39
4 changed files with 42 additions and 19 deletions
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facefusion/face_detector.py
+16 -6
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@@ -1,5 +1,5 @@
from functools import lru_cache
from typing import List, Tuple
from typing import List, Sequence, Tuple
import cv2
import numpy
@@ -10,7 +10,7 @@ from facefusion.face_helper import create_rotated_matrix_and_size, create_static
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore
from facefusion.typing import Angle, BoundingBox, Detection, DownloadScope, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import resize_frame_resolution, unpack_resolution
from facefusion.vision import restrict_frame, unpack_resolution
@lru_cache(maxsize = None)
@@ -151,10 +151,11 @@ def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str)
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_retinaface(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
@@ -194,10 +195,11 @@ def detect_with_scrfd(vision_frame : VisionFrame, face_detector_size : str) -> T
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_scrfd(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
@@ -234,10 +236,11 @@ def detect_with_yolo_face(vision_frame : VisionFrame, face_detector_size : str)
face_landmarks_5 = []
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ 0, 1 ])
detection = forward_with_yolo_face(detect_vision_frame)
detection = numpy.squeeze(detection).T
bounding_boxes_raw, face_scores_raw, face_landmarks_5_raw = numpy.split(detection, [ 4, 5 ], axis = 1)
@@ -305,6 +308,13 @@ def prepare_detect_frame(temp_vision_frame : VisionFrame, face_detector_size : s
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
detect_vision_frame = numpy.zeros((face_detector_height, face_detector_width, 3))
detect_vision_frame[:temp_vision_frame.shape[0], :temp_vision_frame.shape[1], :] = temp_vision_frame
detect_vision_frame = (detect_vision_frame - 127.5) / 128.0
detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return detect_vision_frame
def normalize_detect_frame(detect_vision_frame : VisionFrame, normalize_range : Sequence[int]) -> VisionFrame:
if normalize_range == [ -1, 1 ]:
return (detect_vision_frame - 127.5) / 128.0
if normalize_range == [ 0, 1 ]:
return detect_vision_frame / 255.0
return detect_vision_frame