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Introduce matrix only face helper

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henryruhs 2024-03-25 22:23:45 +01:00
parent b717201f9f
commit f976b42174
2 changed files with 8 additions and 4 deletions
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5
facefusion/face_analyser.py
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@ -8,7 +8,7 @@ import onnxruntime
import facefusion.globals import facefusion.globals
from facefusion import process_manager from facefusion import process_manager
from facefusion.common_helper import get_first from facefusion.common_helper import get_first
from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender, WARP_TEMPLATES from facefusion.face_helper import estimate_matrix_by_face_landmark_5, warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender
from facefusion.face_store import get_static_faces, set_static_faces from facefusion.face_store import get_static_faces, set_static_faces
from facefusion.execution import apply_execution_provider_options from facefusion.execution import apply_execution_provider_options
from facefusion.download import conditional_download from facefusion.download import conditional_download
@ -399,8 +399,7 @@ def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : Boun
def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68: def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5') face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5')
normed_warp_template = WARP_TEMPLATES.get('ffhq_512') * 512 affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (512, 512))
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2) face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
face_landmark_5 = face_landmark_5 / 512 face_landmark_5 = face_landmark_5 / 512
face_landmark_68_5 = face_landmarker.run(None, face_landmark_68_5 = face_landmarker.run(None,

7
facefusion/face_helper.py
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@ -43,9 +43,14 @@ WARP_TEMPLATES : WarpTemplateSet =\
} }
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]: def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix:
normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0] affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
return affine_matrix
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, warp_template, crop_size)
crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA) crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
return crop_vision_frame, affine_matrix return crop_vision_frame, affine_matrix