changes

facefusion/face_helper.py

@@ -5,7 +5,7 @@ import cv2
 import numpy
 from cv2.typing import Size
 
-from facefusion.typing import Anchors, Angle, BoundingBox, Direction, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, PointsTemplate, PointsTemplateSet, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet
+from facefusion.typing import Anchors, Angle, BoundingBox, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet
 
 WARP_TEMPLATES : WarpTemplateSet =\
 {
@@ -33,13 +33,21 @@ WARP_TEMPLATES : WarpTemplateSet =\
 		[ 0.38710391, 0.72160547 ],
 		[ 0.61507734, 0.72034453 ]
 	]).astype(numpy.float32),
-	'deepfacelive': numpy.array(
+	'dfl_head': numpy.array(
 	[
-		[ 0.22549182, 0.21599032 ],
+		[ 0.39062554, 0.59114583 ],
-		[ 0.75476142, 0.21599032 ],
+		[ 0.60481776, 0.58951825 ],
-		[ 0.49012712, 0.51562511 ],
+		[ 0.49414062, 0.70898442 ],
-		[ 0.25414925, 0.78023333 ],
+		[ 0.41796875, 0.82031255 ],
-		[ 0.72610437, 0.78023333 ]
+		[ 0.58007813, 0.82031255 ]
+	]).astype(numpy.float32),
+	'dfl_whole_face': numpy.array(
+	[
+		[ 0.36816406, 0.40429688 ],
+		[ 0.62565106, 0.40071616 ],
+		[ 0.49023438, 0.54296875 ],
+		[ 0.39843755, 0.68554699 ],
+		[ 0.59375543, 0.68554699 ]
 	]).astype(numpy.float32),
 	'ffhq_512': numpy.array(
 	[
@@ -66,28 +74,6 @@ WARP_TEMPLATES : WarpTemplateSet =\
 		[ 0.57015325, 0.68306005 ]
 	]).astype(numpy.float32)
 }
-POINTS_TEMPLATES : PointsTemplateSet =\
-{
-	'square': numpy.array(
-	[
-		[ 0, 0 ],
-		[ 1, 0 ],
-		[ 1, 1 ],
-		[ 0, 1 ]
-	]).astype(numpy.float32),
-	'triangle_orthogonal': numpy.array(
-	[
-		[ 0, 0 ],
-		[ 1, 0 ],
-		[ 0, 1 ]
-	]).astype(numpy.float32),
-	'triangle_skew': numpy.array(
-	[
-		[ 0, 0 ],
-		[ 1, 0 ],
-		[ 1, 1 ]
-	]).astype(numpy.float32)
-}
 
 
 def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix:
@@ -96,37 +82,16 @@ def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_tem
 	return affine_matrix
 
 
-def estimate_matrix_by_points(source_points : Points, polygon_template : PointsTemplate, crop_size : Size) -> Matrix:
-	target_points = POINTS_TEMPLATES.get(polygon_template) * crop_size
-	affine_matrix = cv2.getAffineTransform(source_points, target_points.astype(numpy.float32))
-	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)
 	return crop_vision_frame, affine_matrix
 
 
-def warp_face_for_deepfacelive(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, crop_size : Size, shift : Tuple[float, float], coverage : float) -> Tuple[VisionFrame, Matrix]:
-	affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'deepfacelive', (1, 1))
-	square_points = POINTS_TEMPLATES.get('square')
-	square_points = transform_points(square_points, cv2.invertAffineTransform(affine_matrix))
-	center_point = square_points.mean(axis = 0)
-	center_point += shift[0] * numpy.subtract(square_points[1], square_points[0])
-	center_point += shift[1] * numpy.subtract(square_points[3], square_points[0])
-	scale = numpy.linalg.norm(center_point - square_points[0]) * coverage
-	top_bottom_direction = calc_points_direction(square_points[0], square_points[2]) * scale
-	bottom_top_direction = calc_points_direction(square_points[3], square_points[1]) * scale
-	source_points = numpy.array([ center_point - top_bottom_direction, center_point + bottom_top_direction, center_point + top_bottom_direction ]).astype(numpy.float32)
-	affine_matrix = estimate_matrix_by_points(source_points, 'triangle_skew', crop_size)
-	crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, flags = cv2.INTER_CUBIC)
-	return crop_vision_frame, affine_matrix
-
-
 def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
 	source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32)
-	affine_matrix = estimate_matrix_by_points(source_points, 'triangle_orthogonal', crop_size)
+	target_points = numpy.array([ [ 0, 0 ], [ 1, 0 ], [ 0, 1 ] ]).astype(numpy.float32) * crop_size
+	affine_matrix = cv2.getAffineTransform(source_points, target_points)
 	if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
 		interpolation_method = cv2.INTER_AREA
 	else:
@@ -153,12 +118,6 @@ def paste_back(temp_vision_frame : VisionFrame, crop_vision_frame : VisionFrame,
 	return paste_vision_frame
 
 
-def calc_points_direction(start_point : Points, end_point : Points) -> Direction:
-	direction = end_point - start_point
-	direction /= numpy.linalg.norm(direction)
-	return direction
-
-
 @lru_cache(maxsize = None)
 def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> Anchors:
 	y, x = numpy.mgrid[:stride_height, :stride_width][::-1]

facefusion/processors/modules/deep_swapper.py

@@ -10,7 +10,7 @@ import facefusion.processors.core as processors
 from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording
 from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider
 from facefusion.face_analyser import get_many_faces, get_one_face
-from facefusion.face_helper import paste_back, warp_face_for_deepfacelive
+from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5
 from facefusion.face_masker import create_occlusion_mask, create_static_box_mask
 from facefusion.face_selector import find_similar_faces, sort_and_filter_faces
 from facefusion.face_store import get_reference_faces
@@ -44,9 +44,8 @@ def create_model_set() -> ModelSet:
 					'path': resolve_relative_path('../.assets/models/iperov/emma_watson_224.dfm')
 				}
 			},
+			'template': 'dfl_whole_face',
 			'size': (224, 224),
-			'shift': (0.0, 0.0),
-			'coverage': 2.2
 		},
 		'iperov/jackie_chan_224':
 		{
@@ -66,9 +65,8 @@ def create_model_set() -> ModelSet:
 					'path': resolve_relative_path('../.assets/models/iperov/jackie_chan_224.dfm')
 				}
 			},
+			'template': 'dfl_whole_face',
 			'size': (224, 224),
-			'shift': (0.0, 0.0),
-			'coverage': 2.2
 		},
 		'iperov/keanu_reeves_320':
 		{
@@ -88,9 +86,8 @@ def create_model_set() -> ModelSet:
 					'path': resolve_relative_path('../.assets/models/iperov/keanu_reeves_320.dfm')
 				}
 			},
+			'template': 'dfl_whole_face',
 			'size': (320, 320),
-			'shift': (0.0, 0.0),
-			'coverage': 2.2
 		},
 		'iperov/sylvester_stallone_224':
 		{
@@ -110,9 +107,8 @@ def create_model_set() -> ModelSet:
 					'path': resolve_relative_path('../.assets/models/iperov/sylvester_stallone_224.dfm')
 				}
 			},
+			'template': 'dfl_whole_face',
 			'size': (224, 224),
-			'shift': (0.0, 0.0),
-			'coverage': 2.2
 		},
 		'iperov/taylor_swift_224':
 		{
@@ -132,9 +128,8 @@ def create_model_set() -> ModelSet:
 					'path': resolve_relative_path('../.assets/models/iperov/taylor_swift_224.dfm')
 				}
 			},
+			'template': 'dfl_whole_face',
 			'size': (224, 224),
-			'shift': (0.0, 0.0),
-			'coverage': 2.2
 		}
 	}
 
@@ -200,10 +195,9 @@ def post_process() -> None:
 
 
 def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
+	model_template = get_model_options().get('template')
 	model_size = get_model_options().get('size')
-	model_shift = get_model_options().get('shift')
+	crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), model_template, model_size)
-	model_coverage = get_model_options().get('coverage')
-	crop_vision_frame, affine_matrix = warp_face_for_deepfacelive(temp_vision_frame, target_face.landmark_set.get('5/68'), model_size, model_shift, model_coverage)
 	crop_vision_frame_raw = crop_vision_frame.copy()
 	box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], state_manager.get_item('face_mask_blur'), state_manager.get_item('face_mask_padding'))
 	crop_masks =\

facefusion/typing.py

@@ -53,7 +53,6 @@ FaceStore = TypedDict('FaceStore',
 VisionFrame = NDArray[Any]
 Mask = NDArray[Any]
 Points = NDArray[Any]
-Direction = NDArray[Any]
 Distance = NDArray[Any]
 Matrix = NDArray[Any]
 Anchors = NDArray[Any]
@@ -86,10 +85,8 @@ ProcessStep = Callable[[str, int, Args], bool]
 
 Content = Dict[str, Any]
 
-WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'deepfacelive', 'ffhq_512', 'mtcnn_512', 'styleganex_384']
+WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'dfl_head', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384']
 WarpTemplateSet = Dict[WarpTemplate, NDArray[Any]]
-PointsTemplate = Literal['square', 'triangle_orthogonal', 'triangle_skew']
-PointsTemplateSet = Dict[PointsTemplate, NDArray[Any]]
 ProcessMode = Literal['output', 'preview', 'stream']
 
 ErrorCode = Literal[0, 1, 2, 3, 4]