Spaces:
Build error
Build error
File size: 8,197 Bytes
a1da63c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
from typing import Tuple
import cv2
import numpy
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.face_helper import create_rotated_matrix_and_size, estimate_matrix_by_face_landmark_5, transform_points, warp_face_by_translation
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Score, VisionFrame
MODEL_SET : ModelSet =\
{
'2dfan4':
{
'hashes':
{
'2dfan4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.hash',
'path': resolve_relative_path('../.assets/models/2dfan4.hash')
}
},
'sources':
{
'2dfan4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.onnx',
'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
}
}
},
'peppa_wutz':
{
'hashes':
{
'peppa_wutz':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.hash',
'path': resolve_relative_path('../.assets/models/peppa_wutz.hash')
}
},
'sources':
{
'peppa_wutz':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.onnx',
'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx')
}
}
},
'face_landmarker_68_5':
{
'hashes':
{
'face_landmarker_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/face_landmarker_68_5.hash',
'path': resolve_relative_path('../.assets/models/face_landmarker_68_5.hash')
}
},
'sources':
{
'face_landmarker_68_5':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/face_landmarker_68_5.onnx',
'path': resolve_relative_path('../.assets/models/face_landmarker_68_5.onnx')
}
}
}
}
def get_inference_pool() -> InferencePool:
_, model_sources = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_hashes =\
{
'face_landmarker_68_5': MODEL_SET.get('face_landmarker_68_5').get('hashes').get('face_landmarker_68_5')
}
model_sources =\
{
'face_landmarker_68_5': MODEL_SET.get('face_landmarker_68_5').get('sources').get('face_landmarker_68_5')
}
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
model_hashes['2dfan4'] = MODEL_SET.get('2dfan4').get('hashes').get('2dfan4')
model_sources['2dfan4'] = MODEL_SET.get('2dfan4').get('sources').get('2dfan4')
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
model_hashes['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('hashes').get('peppa_wutz')
model_sources['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('sources').get('peppa_wutz')
return model_hashes, model_sources
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes, model_sources = collect_model_downloads()
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
face_landmark_2dfan4 = None
face_landmark_peppa_wutz = None
face_landmark_score_2dfan4 = 0.0
face_landmark_score_peppa_wutz = 0.0
if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
face_landmark_2dfan4, face_landmark_score_2dfan4 = detect_with_2dfan4(vision_frame, bounding_box, face_angle)
if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
face_landmark_peppa_wutz, face_landmark_score_peppa_wutz = detect_with_peppa_wutz(vision_frame, bounding_box, face_angle)
if face_landmark_score_2dfan4 > face_landmark_score_peppa_wutz:
return face_landmark_2dfan4, face_landmark_score_2dfan4
return face_landmark_peppa_wutz, face_landmark_score_peppa_wutz
def detect_with_2dfan4(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
face_landmarker = get_inference_pool().get('2dfan4')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (256 - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, (256, 256))
crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (256, 256))
crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size)
crop_vision_frame = conditional_optimize_contrast(crop_vision_frame)
crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
with conditional_thread_semaphore():
face_landmark_68, face_heatmap = face_landmarker.run(None,
{
'input': [ crop_vision_frame ]
})
face_landmark_68 = face_landmark_68[:, :, :2][0] / 64 * 256
face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix))
face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
face_landmark_score_68 = numpy.amax(face_heatmap, axis = (2, 3))
face_landmark_score_68 = numpy.mean(face_landmark_score_68)
return face_landmark_68, face_landmark_score_68
def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
face_landmarker = get_inference_pool().get('peppa_wutz')
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
translation = (256 - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, (256, 256))
crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (256, 256))
crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size)
crop_vision_frame = conditional_optimize_contrast(crop_vision_frame)
crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0)
with conditional_thread_semaphore():
prediction = face_landmarker.run(None,
{
'input': crop_vision_frame
})[0]
face_landmark_68 = prediction.reshape(-1, 3)[:, :2] / 64 * 256
face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix))
face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
face_landmark_score_68 = prediction.reshape(-1, 3)[:, 2].mean()
return face_landmark_68, face_landmark_score_68
def conditional_optimize_contrast(crop_vision_frame : VisionFrame) -> VisionFrame:
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_RGB2Lab)
if numpy.mean(crop_vision_frame[:, :, 0]) < 30: # type:ignore[arg-type]
crop_vision_frame[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_vision_frame[:, :, 0])
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_Lab2RGB)
return crop_vision_frame
def estimate_face_landmark_68_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
face_landmarker = get_inference_pool().get('face_landmarker_68_5')
affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (1, 1))
face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
with conditional_thread_semaphore():
face_landmark_68_5 = face_landmarker.run(None,
{
'input': [ face_landmark_5 ]
})[0][0]
face_landmark_68_5 = cv2.transform(face_landmark_68_5.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)
return face_landmark_68_5
|