mirror of
https://github.com/zebrajr/faceswap.git
synced 2025-12-06 00:20:09 +01:00
1c081aea7d
* tools.alignments - add export job * plugins.extract: Update __repr__ for ExtractorBatch dataclass * plugins.extract: Initial implementation of external import plugins * plugins.extract: Disable lm masks on ROI alignment data import * lib.align: Add `landmark_type` property to AlignedFace and return dummy data for ROI Landmarks pose estimate * plugins.extract: Add centering config item for align import and fix filename mapping for images * plugins.extract: Log warning on downstream plugins on limited alignment data * tools: Fix plugins for 4 point ROI landmarks (alignments, sort, mask) * tools.manual: Fix for 2D-4 ROI landmarks * training: Fix for 4 point ROI landmarks * lib.convert: Average color plugin. Avoid divide by zero errors * extract - external: - Default detector to 'external' when importing alignments - Handle different frame origin co-ordinates * alignments: Store video extension in alignments file * plugins.extract.external: Handle video file keys * plugins.extract.external: Output warning if missing data * locales + docs * plugins.extract.align.external: Roll the corner points to top-left for different origins * Clean up * linting fix
112 lines
5.5 KiB
Python
112 lines
5.5 KiB
Python
#!/usr/bin/env python3
|
|
""" Constants that are required across faceswap's lib.align package """
|
|
from __future__ import annotations
|
|
|
|
import typing as T
|
|
from enum import Enum
|
|
|
|
import numpy as np
|
|
|
|
CenteringType = T.Literal["face", "head", "legacy"]
|
|
|
|
EXTRACT_RATIOS: dict[CenteringType, float] = {"legacy": 0.375, "face": 0.5, "head": 0.625}
|
|
"""dict[Literal["legacy", "face", head"] float]: The amount of padding applied to each
|
|
centering type when generating aligned faces """
|
|
|
|
|
|
class LandmarkType(Enum):
|
|
""" Enumeration for the landmark types that Faceswap supports """
|
|
LM_2D_4 = 1
|
|
LM_2D_51 = 2
|
|
LM_2D_68 = 3
|
|
LM_3D_26 = 4
|
|
|
|
@classmethod
|
|
def from_shape(cls, shape: tuple[int, ...]) -> LandmarkType:
|
|
""" The landmark type for a given shape
|
|
|
|
Parameters
|
|
----------
|
|
shape: tuple[int, ...]
|
|
The shape to get the landmark type for
|
|
|
|
Returns
|
|
-------
|
|
Type[LandmarkType]
|
|
The enum for the given shape
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If the requested shape is not valid
|
|
"""
|
|
shapes: dict[tuple[int, ...], LandmarkType] = {(4, 2): cls.LM_2D_4,
|
|
(51, 2): cls.LM_2D_51,
|
|
(68, 2): cls.LM_2D_68,
|
|
(26, 3): cls.LM_3D_26}
|
|
if shape not in shapes:
|
|
raise ValueError(f"The given shape {shape} is not valid. Valid shapes: {list(shapes)}")
|
|
return shapes[shape]
|
|
|
|
|
|
_MEAN_FACE: dict[LandmarkType, np.ndarray] = {
|
|
LandmarkType.LM_2D_4: np.array(
|
|
[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]]), # Clockwise from TL
|
|
LandmarkType.LM_2D_51: np.array([
|
|
[0.010086, 0.106454], [0.085135, 0.038915], [0.191003, 0.018748], [0.300643, 0.034489],
|
|
[0.403270, 0.077391], [0.596729, 0.077391], [0.699356, 0.034489], [0.808997, 0.018748],
|
|
[0.914864, 0.038915], [0.989913, 0.106454], [0.500000, 0.203352], [0.500000, 0.307009],
|
|
[0.500000, 0.409805], [0.500000, 0.515625], [0.376753, 0.587326], [0.435909, 0.609345],
|
|
[0.500000, 0.628106], [0.564090, 0.609345], [0.623246, 0.587326], [0.131610, 0.216423],
|
|
[0.196995, 0.178758], [0.275698, 0.179852], [0.344479, 0.231733], [0.270791, 0.245099],
|
|
[0.192616, 0.244077], [0.655520, 0.231733], [0.724301, 0.179852], [0.803005, 0.178758],
|
|
[0.868389, 0.216423], [0.807383, 0.244077], [0.729208, 0.245099], [0.264022, 0.780233],
|
|
[0.350858, 0.745405], [0.438731, 0.727388], [0.500000, 0.742578], [0.561268, 0.727388],
|
|
[0.649141, 0.745405], [0.735977, 0.780233], [0.652032, 0.864805], [0.566594, 0.902192],
|
|
[0.500000, 0.909281], [0.433405, 0.902192], [0.347967, 0.864805], [0.300252, 0.784792],
|
|
[0.437969, 0.778746], [0.500000, 0.785343], [0.562030, 0.778746], [0.699747, 0.784792],
|
|
[0.563237, 0.824182], [0.500000, 0.831803], [0.436763, 0.824182]]),
|
|
LandmarkType.LM_3D_26: np.array([
|
|
[4.056931, -11.432347, 1.636229], # 8 chin LL
|
|
[1.833492, -12.542305, 4.061275], # 7 chin L
|
|
[0.0, -12.901019, 4.070434], # 6 chin C
|
|
[-1.833492, -12.542305, 4.061275], # 5 chin R
|
|
[-4.056931, -11.432347, 1.636229], # 4 chin RR
|
|
[6.825897, 1.275284, 4.402142], # 33 L eyebrow L
|
|
[1.330353, 1.636816, 6.903745], # 29 L eyebrow R
|
|
[-1.330353, 1.636816, 6.903745], # 34 R eyebrow L
|
|
[-6.825897, 1.275284, 4.402142], # 38 R eyebrow R
|
|
[1.930245, -5.060977, 5.914376], # 54 nose LL
|
|
[0.746313, -5.136947, 6.263227], # 53 nose L
|
|
[0.0, -5.485328, 6.76343], # 52 nose C
|
|
[-0.746313, -5.136947, 6.263227], # 51 nose R
|
|
[-1.930245, -5.060977, 5.914376], # 50 nose RR
|
|
[5.311432, 0.0, 3.987654], # 13 L eye L
|
|
[1.78993, -0.091703, 4.413414], # 17 L eye R
|
|
[-1.78993, -0.091703, 4.413414], # 25 R eye L
|
|
[-5.311432, 0.0, 3.987654], # 21 R eye R
|
|
[2.774015, -7.566103, 5.048531], # 43 mouth L
|
|
[0.509714, -7.056507, 6.566167], # 42 mouth top L
|
|
[0.0, -7.131772, 6.704956], # 41 mouth top C
|
|
[-0.509714, -7.056507, 6.566167], # 40 mouth top R
|
|
[-2.774015, -7.566103, 5.048531], # 39 mouth R
|
|
[-0.589441, -8.443925, 6.109526], # 46 mouth bottom R
|
|
[0.0, -8.601736, 6.097667], # 45 mouth bottom C
|
|
[0.589441, -8.443925, 6.109526]])} # 44 mouth bottom L
|
|
"""dict[:class:`~LandmarkType, np.ndarray]: 'Mean' landmark points for various landmark types. Used
|
|
for aligning faces """
|
|
|
|
LANDMARK_PARTS: dict[LandmarkType, dict[str, tuple[int, int, bool]]] = {
|
|
LandmarkType.LM_2D_68: {"mouth_outer": (48, 60, True),
|
|
"mouth_inner": (60, 68, True),
|
|
"right_eyebrow": (17, 22, False),
|
|
"left_eyebrow": (22, 27, False),
|
|
"right_eye": (36, 42, True),
|
|
"left_eye": (42, 48, True),
|
|
"nose": (27, 36, False),
|
|
"jaw": (0, 17, False),
|
|
"chin": (8, 11, False)},
|
|
LandmarkType.LM_2D_4: {"face": (0, 4, True)}}
|
|
"""dict[:class:`LandmarkType`, dict[str, tuple[int, int, bool]]: For each landmark type, stores
|
|
the (start index, end index, is polygon) information about each part of the face. """
|