mirror of
https://github.com/facefusion/facefusion.git
synced 2026-04-23 18:06:08 +02:00
Henry Ruhs
c7976ec9d4
Enforce consistent space inside square brackets for all list literals across source and test files. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude <noreply@anthropic.com>
257 lines
9.9 KiB
Python
257 lines
9.9 KiB
Python
from functools import lru_cache
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from typing import List, Sequence, Tuple
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import cv2
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import numpy
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from cv2.typing import Size
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from facefusion.types import Anchors, Angle, BoundingBox, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet
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WARP_TEMPLATE_SET : WarpTemplateSet =\
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{
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'arcface_112_v1': numpy.array(
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[
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[ 0.35473214, 0.45658929 ],
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[ 0.64526786, 0.45658929 ],
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[ 0.50000000, 0.61154464 ],
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[ 0.37913393, 0.77687500 ],
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[ 0.62086607, 0.77687500 ]
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]),
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'arcface_112_v2': numpy.array(
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[
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[ 0.34191607, 0.46157411 ],
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[ 0.65653393, 0.45983393 ],
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[ 0.50022500, 0.64050536 ],
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[ 0.37097589, 0.82469196 ],
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[ 0.63151696, 0.82325089 ]
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]),
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'arcface_128': numpy.array(
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[
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[ 0.36167656, 0.40387734 ],
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[ 0.63696719, 0.40235469 ],
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[ 0.50019687, 0.56044219 ],
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[ 0.38710391, 0.72160547 ],
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[ 0.61507734, 0.72034453 ]
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]),
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'dfl_whole_face': numpy.array(
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[
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[ 0.35342266, 0.39285716 ],
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[ 0.62797622, 0.39285716 ],
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[ 0.48660713, 0.54017860 ],
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[ 0.38839287, 0.68750011 ],
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[ 0.59821427, 0.68750011 ]
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]),
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'ffhq_512': numpy.array(
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[
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[ 0.37691676, 0.46864664 ],
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[ 0.62285697, 0.46912813 ],
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[ 0.50123859, 0.61331904 ],
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[ 0.39308822, 0.72541100 ],
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[ 0.61150205, 0.72490465 ]
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]),
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'mtcnn_512': numpy.array(
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[
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[ 0.36562865, 0.46733799 ],
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[ 0.63305391, 0.46585885 ],
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[ 0.50019127, 0.61942959 ],
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[ 0.39032951, 0.77598822 ],
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[ 0.61178945, 0.77476328 ]
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]),
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'styleganex_384': numpy.array(
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[
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[ 0.42353745, 0.52289879 ],
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[ 0.57725008, 0.52319972 ],
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[ 0.50123859, 0.61331904 ],
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[ 0.43364461, 0.68337652 ],
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[ 0.57015325, 0.68306005 ]
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])
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}
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def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix:
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warp_template_norm = WARP_TEMPLATE_SET.get(warp_template) * crop_size
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affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, warp_template_norm, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
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return affine_matrix
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def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
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affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, warp_template, crop_size)
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
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return crop_vision_frame, affine_matrix
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def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
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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)
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target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ]).astype(numpy.float32)
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affine_matrix = cv2.getAffineTransform(source_points, target_points)
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if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
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interpolation_method = cv2.INTER_AREA
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else:
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interpolation_method = cv2.INTER_LINEAR
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, flags = interpolation_method)
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return crop_vision_frame, affine_matrix
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def warp_face_by_translation(temp_vision_frame : VisionFrame, translation : Translation, scale : float, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
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affine_matrix = numpy.array([ [ scale, 0, translation[0] ], [ 0, scale, translation[1] ] ])
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size)
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return crop_vision_frame, affine_matrix
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def paste_back(temp_vision_frame : VisionFrame, crop_vision_frame : VisionFrame, crop_vision_mask : Mask, affine_matrix : Matrix) -> VisionFrame:
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paste_bounding_box, paste_matrix = calculate_paste_area(temp_vision_frame, crop_vision_frame, affine_matrix)
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x1, y1, x2, y2 = paste_bounding_box
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paste_width = x2 - x1
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paste_height = y2 - y1
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inverse_vision_mask = cv2.warpAffine(crop_vision_mask, paste_matrix, (paste_width, paste_height)).clip(0, 1)
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inverse_vision_mask = numpy.expand_dims(inverse_vision_mask, axis = -1)
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inverse_vision_frame = cv2.warpAffine(crop_vision_frame, paste_matrix, (paste_width, paste_height), borderMode = cv2.BORDER_REPLICATE)
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temp_vision_frame = temp_vision_frame.copy()
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paste_vision_frame = temp_vision_frame[y1:y2, x1:x2]
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paste_vision_frame = paste_vision_frame * (1 - inverse_vision_mask) + inverse_vision_frame * inverse_vision_mask
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temp_vision_frame[y1:y2, x1:x2] = paste_vision_frame.astype(temp_vision_frame.dtype)
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return temp_vision_frame
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def calculate_paste_area(temp_vision_frame : VisionFrame, crop_vision_frame : VisionFrame, affine_matrix : Matrix) -> Tuple[BoundingBox, Matrix]:
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temp_height, temp_width = temp_vision_frame.shape[:2]
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crop_height, crop_width = crop_vision_frame.shape[:2]
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inverse_matrix = cv2.invertAffineTransform(affine_matrix)
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crop_points = numpy.array([ [ 0, 0 ], [ crop_width, 0 ], [ crop_width, crop_height ], [ 0, crop_height ] ])
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paste_region_points = transform_points(crop_points, inverse_matrix)
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paste_region_point_min = numpy.floor(paste_region_points.min(axis = 0)).astype(int)
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paste_region_point_max = numpy.ceil(paste_region_points.max(axis = 0)).astype(int)
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x1, y1 = numpy.clip(paste_region_point_min, 0, [ temp_width, temp_height ])
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x2, y2 = numpy.clip(paste_region_point_max, 0, [ temp_width, temp_height ])
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paste_bounding_box = numpy.array([ x1, y1, x2, y2 ])
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paste_matrix = inverse_matrix.copy()
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paste_matrix[0, 2] -= x1
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paste_matrix[1, 2] -= y1
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return paste_bounding_box, paste_matrix
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@lru_cache()
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def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> Anchors:
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x, y = numpy.mgrid[:stride_width, :stride_height]
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anchors = numpy.stack((y, x), axis = -1)
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anchors = (anchors * feature_stride).reshape((-1, 2))
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anchors = numpy.stack([ anchors ] * anchor_total, axis = 1).reshape((-1, 2))
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return anchors
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def create_rotation_matrix_and_size(angle : Angle, size : Size) -> Tuple[Matrix, Size]:
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rotation_matrix = cv2.getRotationMatrix2D((size[0] / 2, size[1] / 2), angle, 1)
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rotation_size = numpy.dot(numpy.abs(rotation_matrix[:, :2]), size)
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rotation_matrix[:, -1] += (rotation_size - size) * 0.5 #type:ignore[misc]
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rotation_size = int(rotation_size[0]), int(rotation_size[1])
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return rotation_matrix, rotation_size
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def create_bounding_box(face_landmark_68 : FaceLandmark68) -> BoundingBox:
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x1, y1 = numpy.min(face_landmark_68, axis = 0)
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x2, y2 = numpy.max(face_landmark_68, axis = 0)
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bounding_box = normalize_bounding_box(numpy.array([ x1, y1, x2, y2 ]))
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return bounding_box
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def normalize_bounding_box(bounding_box : BoundingBox) -> BoundingBox:
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x1, y1, x2, y2 = bounding_box
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x1, x2 = sorted([ x1, x2 ])
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y1, y2 = sorted([ y1, y2 ])
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return numpy.array([ x1, y1, x2, y2 ])
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def transform_points(points : Points, matrix : Matrix) -> Points:
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points = points.reshape(-1, 1, 2)
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points = cv2.transform(points, matrix) #type:ignore[assignment]
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points = points.reshape(-1, 2)
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return points
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def transform_bounding_box(bounding_box : BoundingBox, matrix : Matrix) -> BoundingBox:
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points = numpy.array(
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[
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[ bounding_box[0], bounding_box[1] ],
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[ bounding_box[2], bounding_box[1] ],
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[ bounding_box[2], bounding_box[3] ],
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[ bounding_box[0], bounding_box[3] ]
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])
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points = transform_points(points, matrix)
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x1, y1 = numpy.min(points, axis = 0)
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x2, y2 = numpy.max(points, axis = 0)
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return normalize_bounding_box(numpy.array([ x1, y1, x2, y2 ]))
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def distance_to_bounding_box(points : Points, distance : Distance) -> BoundingBox:
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x1 = points[:, 0] - distance[:, 0]
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y1 = points[:, 1] - distance[:, 1]
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x2 = points[:, 0] + distance[:, 2]
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y2 = points[:, 1] + distance[:, 3]
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bounding_box = numpy.column_stack([ x1, y1, x2, y2 ])
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return bounding_box
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def distance_to_face_landmark_5(points : Points, distance : Distance) -> FaceLandmark5:
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x = points[:, 0::2] + distance[:, 0::2]
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y = points[:, 1::2] + distance[:, 1::2]
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face_landmark_5 = numpy.stack((x, y), axis = -1)
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return face_landmark_5
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def scale_face_landmark_5(face_landmark_5 : FaceLandmark5, scale : Scale) -> FaceLandmark5:
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face_landmark_5_scale = face_landmark_5 - face_landmark_5[2]
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face_landmark_5_scale *= scale
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face_landmark_5_scale += face_landmark_5[2]
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return face_landmark_5_scale
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def convert_to_face_landmark_5(face_landmark_68 : FaceLandmark68) -> FaceLandmark5:
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face_landmark_5 = numpy.array(
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[
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numpy.mean(face_landmark_68[36:42], axis = 0),
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numpy.mean(face_landmark_68[42:48], axis = 0),
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face_landmark_68[30],
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face_landmark_68[48],
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face_landmark_68[54]
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])
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return face_landmark_5
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def estimate_face_angle(face_landmark_68 : FaceLandmark68) -> Angle:
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x1, y1 = face_landmark_68[0]
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x2, y2 = face_landmark_68[16]
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theta = numpy.arctan2(y2 - y1, x2 - x1)
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theta = numpy.degrees(theta) % 360
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angles = numpy.linspace(0, 360, 5)
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index = numpy.argmin(numpy.abs(angles - theta))
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face_angle = int(angles[index] % 360)
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return face_angle
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def apply_nms(bounding_boxes : List[BoundingBox], scores : List[Score], score_threshold : float, nms_threshold : float) -> Sequence[int]:
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bounding_boxes_norm = [ (x1, y1, x2 - x1, y2 - y1) for (x1, y1, x2, y2) in bounding_boxes ]
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keep_indices = cv2.dnn.NMSBoxes(bounding_boxes_norm, scores, score_threshold = score_threshold, nms_threshold = nms_threshold)
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return keep_indices
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def get_nms_threshold(face_detector_model : FaceDetectorModel, face_detector_angles : List[Angle]) -> float:
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if face_detector_model == 'many':
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return 0.1
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if len(face_detector_angles) == 2:
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return 0.3
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if len(face_detector_angles) == 3:
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return 0.2
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if len(face_detector_angles) == 4:
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return 0.1
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return 0.4
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def merge_matrix(temp_matrices : List[Matrix]) -> Matrix:
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matrix = numpy.vstack([ temp_matrices[0], [ 0, 0, 1 ] ])
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for temp_matrix in temp_matrices[1:]:
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temp_matrix = numpy.vstack([ temp_matrix, [ 0, 0, 1 ] ])
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matrix = numpy.dot(temp_matrix, matrix)
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return matrix[:2, :]
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