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Cleanup 5 to 68 landmark convertion

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This commit is contained in:
henryruhs 2024-03-25 21:49:41 +01:00
parent ad5b598a30
commit 321920eaba
6 changed files with 15 additions and 9 deletions
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8
facefusion/face_analyser.py
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@ -321,19 +321,19 @@ def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBo
for index in keep_indices:
bounding_box = bounding_box_list[index]
face_landmark_5_68 = face_landmark_5_list[index]
face_landmark_68 = None
face_landmark_68_5 = convert_face_landmark_5_to_68(face_landmark_5_68)
face_landmark_68 = face_landmark_68_5
face_landmark_68_score = 0.0
if facefusion.globals.face_landmarker_score > 0:
face_landmark_68, face_landmark_68_score = detect_face_landmark_68(vision_frame, bounding_box)
if face_landmark_68_score > facefusion.globals.face_landmarker_score:
face_landmark_5_68 = convert_face_landmark_68_to_5(face_landmark_68)
else:
face_landmark_68 = convert_face_landmark_5_to_68(face_landmark_5_68)
landmarks : FaceLandmarkSet =\
{
'5': face_landmark_5_list[index],
'5/68': face_landmark_5_68,
'68': face_landmark_68
'68': face_landmark_68,
'68/5': face_landmark_68_5
}
scores : FaceScoreSet = \
{

2
facefusion/face_helper.py
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@ -180,7 +180,7 @@ def convert_face_landmark_5_to_68(face_landmark_5 : FaceLandmark5) -> FaceLandma
face_landmark_5 = face_landmark_5 / 512
face_landmark_68 = face_landmarker_converter.run(None,
{
face_landmarker_converter.get_inputs()[0].name.get: [ face_landmark_5 ]
face_landmarker_converter.get_inputs()[0].name: [ face_landmark_5 ]
})[0][0]
face_landmark_68 = (face_landmark_68 * 512).reshape(68, 2)
face_landmark_68 = cv2.transform(face_landmark_68.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)

2
facefusion/processors/frame/choices.py
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@ -3,7 +3,7 @@ from typing import List
from facefusion.common_helper import create_int_range
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameEnhancerModel, LipSyncerModel
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender' ]
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender' ]
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus' ]
face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256' ]
frame_enhancer_models : List[FrameEnhancerModel] = [ 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_hatgan_x4', 'span_kendata_x4' ]

7
facefusion/processors/frame/modules/face_debugger.py
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@ -74,6 +74,7 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
bounding_box = target_face.bounding_box.astype(numpy.int32)
temp_vision_frame = temp_vision_frame.copy()
has_face_landmark_5_fallback = numpy.array_equal(target_face.landmarks.get('5'), target_face.landmarks.get('5/68'))
has_face_landmark_68_fallback = numpy.array_equal(target_face.landmarks.get('68'), target_face.landmarks.get('68/5'))
if 'bounding-box' in frame_processors_globals.face_debugger_items:
cv2.rectangle(temp_vision_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), primary_color, 2)
@ -109,7 +110,11 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
if 'face-landmark-68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')):
face_landmark_68 = target_face.landmarks.get('68').astype(numpy.int32)
for index in range(face_landmark_68.shape[0]):
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, secondary_color, -1)
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, tertiary_color if has_face_landmark_68_fallback else secondary_color, -1)
if 'face-landmark-68/5' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')):
face_landmark_68 = target_face.landmarks.get('68/5').astype(numpy.int32)
for index in range(face_landmark_68.shape[0]):
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, primary_color, -1)
if bounding_box[3] - bounding_box[1] > 50 and bounding_box[2] - bounding_box[0] > 50:
top = bounding_box[1]
left = bounding_box[0] - 20

2
facefusion/processors/frame/typings.py
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@ -2,7 +2,7 @@ from typing import Literal, TypedDict
from facefusion.typing import Face, FaceSet, AudioFrame, VisionFrame
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender']
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-landmark-68/5', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender']
FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus']
FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256']
FrameEnhancerModel = Literal['lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'real_hatgan_x4', 'span_kendata_x4']

3
facefusion/typing.py
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@ -9,7 +9,8 @@ FaceLandmarkSet = TypedDict('FaceLandmarkSet',
{
'5' : FaceLandmark5, # type: ignore[valid-type]
'5/68' : FaceLandmark5, # type: ignore[valid-type]
'68' : FaceLandmark68 # type: ignore[valid-type]
'68' : FaceLandmark68, # type: ignore[valid-type]
'68/5' : FaceLandmark68 # type: ignore[valid-type]
})
Score = float
FaceScoreSet = TypedDict('FaceScoreSet',