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feat/yoloface (#334)

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* added yolov8 to face_detector (#323)

* added yolov8 to face_detector

* added yolov8 to face_detector

* Initial cleanup and renaming

* Update README

* refactored detect_with_yoloface (#329)

* refactored detect_with_yoloface

* apply review

* Change order again

* Restore working code

* modified code (#330)

* refactored detect_with_yoloface

* apply review

* use temp_frame in detect_with_yoloface

* reorder

* modified

* reorder models

* Tiny cleanup

---------

Co-authored-by: tamoharu <133945583+tamoharu@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
2024-01-22 13:56:45 +01:00
committed by GitHub
co-authored by tamoharu
parent 122da0545b
commit 6aea186ee8
4 changed files with 104 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files
facefusion/choices.py
+1 -1
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@@ -7,7 +7,7 @@ video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 't
face_analyser_orders : List[FaceAnalyserOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
face_analyser_ages : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior' ]
face_analyser_genders : List[FaceAnalyserGender] = [ 'male', 'female' ]
face_detector_models : List[str] = [ 'retinaface', 'yunet' ]
face_detector_models : List[str] = [ 'retinaface', 'yoloface', 'yunet' ]
face_detector_sizes : List[str] = [ '160x160', '320x320', '480x480', '512x512', '640x640', '768x768', '960x960', '1024x1024' ]
face_selector_modes : List[FaceSelectorMode] = [ 'reference', 'one', 'many' ]
face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
facefusion/face_analyser.py
+54 -1
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@@ -23,6 +23,11 @@ MODELS : ModelSet =\
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/retinaface_10g.onnx',
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
},
'face_detector_yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/yoloface_8n.onnx',
'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
},
'face_detector_yunet':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/yunet_2023mar.onnx',
@@ -58,6 +63,8 @@ def get_face_analyser() -> Any:
if FACE_ANALYSER is None:
if facefusion.globals.face_detector_model == 'retinaface':
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_detector_model == 'yoloface':
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
if facefusion.globals.face_detector_model == 'yunet':
face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
@@ -88,6 +95,7 @@ def pre_check() -> bool:
model_urls =\
[
MODELS.get('face_detector_retinaface').get('url'),
MODELS.get('face_detector_yoloface').get('url'),
MODELS.get('face_detector_yunet').get('url'),
MODELS.get('face_recognizer_arcface_inswapper').get('url'),
MODELS.get('face_recognizer_arcface_simswap').get('url'),
@@ -104,10 +112,13 @@ def extract_faces(frame : Frame) -> List[Face]:
temp_frame_height, temp_frame_width, _ = temp_frame.shape
ratio_height = frame_height / temp_frame_height
ratio_width = frame_width / temp_frame_width
if facefusion.globals.face_detector_model == 'yoloface':
bbox_list, kps_list, score_list = detect_with_yoloface(temp_frame, temp_frame_height, temp_frame_width, face_detector_height, face_detector_width, ratio_height, ratio_width)
return create_faces(frame, bbox_list, kps_list, score_list)
if facefusion.globals.face_detector_model == 'retinaface':
bbox_list, kps_list, score_list = detect_with_retinaface(temp_frame, temp_frame_height, temp_frame_width, face_detector_height, face_detector_width, ratio_height, ratio_width)
return create_faces(frame, bbox_list, kps_list, score_list)
elif facefusion.globals.face_detector_model == 'yunet':
if facefusion.globals.face_detector_model == 'yunet':
bbox_list, kps_list, score_list = detect_with_yunet(temp_frame, temp_frame_height, temp_frame_width, ratio_height, ratio_width)
return create_faces(frame, bbox_list, kps_list, score_list)
return []
@@ -153,6 +164,48 @@ def detect_with_retinaface(temp_frame : Frame, temp_frame_height : int, temp_fra
return bbox_list, kps_list, score_list
def detect_with_yoloface(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, face_detector_height : int, face_detector_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
face_detector = get_face_analyser().get('face_detector')
bbox_list = []
kps_list = []
score_list = []
offset_width = (face_detector_width - temp_frame_width) / 2
offset_height = (face_detector_height - temp_frame_height) / 2
temp_frame = cv2.copyMakeBorder(temp_frame, round(offset_height - 0.1), round(offset_height + 0.1), round(offset_width - 0.1), round(offset_width + 0.1), cv2.BORDER_CONSTANT, value = (114, 114, 114))
temp_frame = temp_frame.astype(numpy.float32) / 255.0
temp_frame = temp_frame[..., ::-1].transpose(2, 0, 1)
temp_frame = numpy.expand_dims(temp_frame, axis = 0)
temp_frame = numpy.ascontiguousarray(temp_frame)
with THREAD_SEMAPHORE:
detections = face_detector.run(None,
{
face_detector.get_inputs()[0].name: temp_frame
})
detections = numpy.squeeze(detections).T
bbox_raw, score_raw, kps_raw = numpy.split(detections, [ 4, 5 ], axis = 1)
keep_indices = numpy.where(score_raw > facefusion.globals.face_detector_score)[0]
if keep_indices.any():
bbox_raw, kps_raw, score_raw = bbox_raw[keep_indices], kps_raw[keep_indices], score_raw[keep_indices]
for bbox in bbox_raw:
bbox_list.append(numpy.array(
[
(bbox[0] - bbox[2] / 2 - offset_width) * ratio_width,
(bbox[1] - bbox[3] / 2 - offset_height) * ratio_height,
(bbox[0] + bbox[2] / 2 - offset_width) * ratio_width,
(bbox[1] + bbox[3] / 2 - offset_height) * ratio_height
]))
kps_raw[:, 0::3] = (kps_raw[:, 0::3] - offset_width) * ratio_width
kps_raw[:, 1::3] = (kps_raw[:, 1::3] - offset_height) * ratio_height
for kps in kps_raw:
indexes = numpy.arange(0, len(kps), 3)
temp_kps = []
for index in indexes:
temp_kps.append([kps[index], kps[index + 1]])
kps_list.append(numpy.array(temp_kps))
score_list = score_raw.ravel().tolist()
return bbox_list, kps_list, score_list
def detect_with_yunet(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
face_detector = get_face_analyser().get('face_detector')
face_detector.setInputSize((temp_frame_width, temp_frame_height))
facefusion/typing.py
+1 -1
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@@ -42,7 +42,7 @@ FaceSelectorMode = Literal['reference', 'one', 'many']
FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
FaceAnalyserGender = Literal['male', 'female']
FaceDetectorModel = Literal['retinaface', 'yunet']
FaceDetectorModel = Literal['retinaface', 'yoloface', 'yunet']
FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap']
FaceMaskType = Literal['box', 'occlusion', 'region']
FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']