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from typing import List, Sequence, Tuple
import cv2
import numpy
from facefusion import inference_manager, state_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
from facefusion.face_helper import create_rotation_matrix_and_size, create_static_anchors, distance_to_bounding_box, distance_to_face_landmark_5, normalize_bounding_box, transform_bounding_box, transform_points
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import thread_semaphore
from facefusion.types import Angle, BoundingBox, Detection, DownloadScope, DownloadSet, FaceLandmark5, InferencePool, Margin, ModelSet, Score, VisionFrame
from facefusion.vision import restrict_frame, unpack_resolution
@lru_cache()
def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
return\
{
'retinaface':
{
'__metadata__':
{
'vendor': 'InsightFace',
'license': 'Non-Commercial',
'year': 2020
},
'hashes':
{
'retinaface':
{
'url': resolve_download_url('models-3.0.0', 'retinaface_10g.hash'),
'path': resolve_relative_path('../.assets/models/retinaface_10g.hash')
}
},
'sources':
{
'retinaface':
{
'url': resolve_download_url('models-3.0.0', 'retinaface_10g.onnx'),
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
}
}
},
'scrfd':
{
'__metadata__':
{
'vendor': 'InsightFace',
'license': 'Non-Commercial',
'year': 2021
},
'hashes':
{
'scrfd':
{
'url': resolve_download_url('models-3.0.0', 'scrfd_2.5g.hash'),
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.hash')
}
},
'sources':
{
'scrfd':
{
'url': resolve_download_url('models-3.0.0', 'scrfd_2.5g.onnx'),
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx')
}
}
},
'yolo_face':
{
'__metadata__':
{
'vendor': 'derronqi',
'license': 'GPL-3.0',
'year': 2022
},
'hashes':
{
'yolo_face':
{
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.hash'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.hash')
}
},
'sources':
{
'yolo_face':
{
'url': resolve_download_url('models-3.0.0', 'yoloface_8n.onnx'),
'path': resolve_relative_path('../.assets/models/yoloface_8n.onnx')
}
}
},
'yunet':
{
'__metadata__':
{
'vendor': 'OpenCV',
'license': 'MIT',
'year': 2023
},
'hashes':
{
'yunet':
{
'url': resolve_download_url('models-3.4.0', 'yunet_2023_mar.hash'),
'path': resolve_relative_path('../.assets/models/yunet_2023_mar.hash')
}
},
'sources':
{
'yunet':
{
'url': resolve_download_url('models-3.4.0', 'yunet_2023_mar.onnx'),
'path': resolve_relative_path('../.assets/models/yunet_2023_mar.onnx')
}
}
}
}
def get_inference_pool() -> InferencePool:
model_names = [ state_manager.get_item('face_detector_model') ]
_, model_source_set = collect_model_downloads()
return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
def clear_inference_pool() -> None:
model_names = [ state_manager.get_item('face_detector_model') ]
inference_manager.clear_inference_pool(__name__, model_names)
def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
model_set = create_static_model_set('full')
model_hash_set = {}
model_source_set = {}
for face_detector_model in [ 'retinaface', 'scrfd', 'yolo_face', 'yunet' ]:
if state_manager.get_item('face_detector_model') in [ 'many', face_detector_model ]:
model_hash_set[face_detector_model] = model_set.get(face_detector_model).get('hashes').get(face_detector_model)
model_source_set[face_detector_model] = model_set.get(face_detector_model).get('sources').get(face_detector_model)
return model_hash_set, model_source_set
def pre_check() -> bool:
model_hash_set, model_source_set = collect_model_downloads()
return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
def detect_faces(vision_frame : VisionFrame) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
margin_top, margin_right, margin_bottom, margin_left = prepare_margin(vision_frame)
margin_vision_frame = numpy.pad(vision_frame, ((margin_top, margin_bottom), (margin_left, margin_right), (0, 0)))
all_bounding_boxes : List[BoundingBox] = []
all_face_scores : List[Score] = []
all_face_landmarks_5 : List[FaceLandmark5] = []
if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_retinaface(margin_vision_frame, state_manager.get_item('face_detector_size'))
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_scrfd(margin_vision_frame, state_manager.get_item('face_detector_size'))
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
if state_manager.get_item('face_detector_model') in [ 'many', 'yolo_face' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_yolo_face(margin_vision_frame, state_manager.get_item('face_detector_size'))
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
if state_manager.get_item('face_detector_model') == 'yunet':
bounding_boxes, face_scores, face_landmarks_5 = detect_with_yunet(margin_vision_frame, state_manager.get_item('face_detector_size'))
all_bounding_boxes.extend(bounding_boxes)
all_face_scores.extend(face_scores)
all_face_landmarks_5.extend(face_landmarks_5)
all_bounding_boxes = [ normalize_bounding_box(all_bounding_box) - numpy.array([ margin_left, margin_top, margin_left, margin_top ]) for all_bounding_box in all_bounding_boxes ]
all_face_landmarks_5 = [ all_face_landmark_5 - numpy.array([ margin_left, margin_top ]) for all_face_landmark_5 in all_face_landmarks_5 ]
return all_bounding_boxes, all_face_scores, all_face_landmarks_5
def prepare_margin(vision_frame : VisionFrame) -> Margin:
margin_top = int(vision_frame.shape[0] * numpy.interp(state_manager.get_item('face_detector_margin')[0], [ 0, 100 ], [ 0, 0.5 ]))
margin_right = int(vision_frame.shape[1] * numpy.interp(state_manager.get_item('face_detector_margin')[1], [ 0, 100 ], [ 0, 0.5 ]))
margin_bottom = int(vision_frame.shape[0] * numpy.interp(state_manager.get_item('face_detector_margin')[2], [ 0, 100 ], [ 0, 0.5 ]))
margin_left = int(vision_frame.shape[1] * numpy.interp(state_manager.get_item('face_detector_margin')[3], [ 0, 100 ], [ 0, 0.5 ]))
return margin_top, margin_right, margin_bottom, margin_left
def detect_faces_by_angle(vision_frame : VisionFrame, face_angle : Angle) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
rotation_matrix, rotation_size = create_rotation_matrix_and_size(face_angle, vision_frame.shape[:2][::-1])
rotation_vision_frame = cv2.warpAffine(vision_frame, rotation_matrix, rotation_size)
rotation_inverse_matrix = cv2.invertAffineTransform(rotation_matrix)
bounding_boxes, face_scores, face_landmarks_5 = detect_faces(rotation_vision_frame)
bounding_boxes = [ transform_bounding_box(bounding_box, rotation_inverse_matrix) for bounding_box in bounding_boxes ]
face_landmarks_5 = [ transform_points(face_landmark_5, rotation_inverse_matrix) for face_landmark_5 in face_landmarks_5 ]
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_retinaface(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
face_scores_raw = detection[index]
keep_indices = numpy.where(face_scores_raw >= face_detector_score)[0]
if numpy.any(keep_indices):
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bounding_boxes_raw = detection[index + feature_map_channel] * feature_stride
face_landmarks_5_raw = detection[index + feature_map_channel * 2] * feature_stride
for bounding_box_raw in distance_to_bounding_box(anchors, bounding_boxes_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box_raw[0] * ratio_width,
bounding_box_raw[1] * ratio_height,
bounding_box_raw[2] * ratio_width,
bounding_box_raw[3] * ratio_height
]))
for face_score_raw in face_scores_raw[keep_indices]:
face_scores.append(face_score_raw[0])
for face_landmark_raw_5 in distance_to_face_landmark_5(anchors, face_landmarks_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_raw_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_scrfd(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ -1, 1 ])
detection = forward_with_scrfd(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
face_scores_raw = detection[index]
keep_indices = numpy.where(face_scores_raw >= face_detector_score)[0]
if numpy.any(keep_indices):
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bounding_boxes_raw = detection[index + feature_map_channel] * feature_stride
face_landmarks_5_raw = detection[index + feature_map_channel * 2] * feature_stride
for bounding_box_raw in distance_to_bounding_box(anchors, bounding_boxes_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box_raw[0] * ratio_width,
bounding_box_raw[1] * ratio_height,
bounding_box_raw[2] * ratio_width,
bounding_box_raw[3] * ratio_height
]))
for face_score_raw in face_scores_raw[keep_indices]:
face_scores.append(face_score_raw[0])
for face_landmark_raw_5 in distance_to_face_landmark_5(anchors, face_landmarks_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_raw_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_yolo_face(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ 0, 1 ])
detection = forward_with_yolo_face(detect_vision_frame)
detection = numpy.squeeze(detection).T
bounding_boxes_raw, face_scores_raw, face_landmarks_5_raw = numpy.split(detection, [ 4, 5 ], axis = 1)
keep_indices = numpy.where(face_scores_raw > face_detector_score)[0]
if numpy.any(keep_indices):
bounding_boxes_raw, face_scores_raw, face_landmarks_5_raw = bounding_boxes_raw[keep_indices], face_scores_raw[keep_indices], face_landmarks_5_raw[keep_indices]
for bounding_box_raw in bounding_boxes_raw:
bounding_boxes.append(numpy.array(
[
(bounding_box_raw[0] - bounding_box_raw[2] / 2) * ratio_width,
(bounding_box_raw[1] - bounding_box_raw[3] / 2) * ratio_height,
(bounding_box_raw[0] + bounding_box_raw[2] / 2) * ratio_width,
(bounding_box_raw[1] + bounding_box_raw[3] / 2) * ratio_height
]))
face_scores = face_scores_raw.ravel().tolist()
face_landmarks_5_raw[:, 0::3] = (face_landmarks_5_raw[:, 0::3]) * ratio_width
face_landmarks_5_raw[:, 1::3] = (face_landmarks_5_raw[:, 1::3]) * ratio_height
for face_landmark_raw_5 in face_landmarks_5_raw:
face_landmarks_5.append(numpy.array(face_landmark_raw_5.reshape(-1, 3)[:, :2]))
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 1
face_detector_score = state_manager.get_item('face_detector_score')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = restrict_frame(vision_frame, (face_detector_width, face_detector_height))
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
detect_vision_frame = normalize_detect_frame(detect_vision_frame, [ 0, 255 ])
detection = forward_with_yunet(detect_vision_frame)
for index, feature_stride in enumerate(feature_strides):
face_scores_raw = (detection[index] * detection[index + feature_map_channel]).reshape(-1)
keep_indices = numpy.where(face_scores_raw >= face_detector_score)[0]
if numpy.any(keep_indices):
stride_height = face_detector_height // feature_stride
stride_width = face_detector_width // feature_stride
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
bounding_boxes_center = detection[index + feature_map_channel * 2].squeeze(0)[:, :2] * feature_stride + anchors
bounding_boxes_size = numpy.exp(detection[index + feature_map_channel * 2].squeeze(0)[:, 2:4]) * feature_stride
face_landmarks_5_raw = detection[index + feature_map_channel * 3].squeeze(0)
bounding_boxes_raw = numpy.stack(
[
bounding_boxes_center[:, 0] - bounding_boxes_size[:, 0] / 2,
bounding_boxes_center[:, 1] - bounding_boxes_size[:, 1] / 2,
bounding_boxes_center[:, 0] + bounding_boxes_size[:, 0] / 2,
bounding_boxes_center[:, 1] + bounding_boxes_size[:, 1] / 2
], axis = -1)
for bounding_box_raw in bounding_boxes_raw[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box_raw[0] * ratio_width,
bounding_box_raw[1] * ratio_height,
bounding_box_raw[2] * ratio_width,
bounding_box_raw[3] * ratio_height
]))
face_scores.extend(face_scores_raw[keep_indices])
face_landmarks_5_raw = numpy.concatenate(
[
face_landmarks_5_raw[:, [ 0, 1 ]] * feature_stride + anchors,
face_landmarks_5_raw[:, [ 2, 3 ]] * feature_stride + anchors,
face_landmarks_5_raw[:, [ 4, 5 ]] * feature_stride + anchors,
face_landmarks_5_raw[:, [ 6, 7 ]] * feature_stride + anchors,
face_landmarks_5_raw[:, [ 8, 9 ]] * feature_stride + anchors
], axis = -1).reshape(-1, 5, 2)
for face_landmark_raw_5 in face_landmarks_5_raw[keep_indices]:
face_landmarks_5.append(face_landmark_raw_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
def forward_with_retinaface(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('retinaface')
with thread_semaphore():
detection = face_detector.run(None,
{
'input': detect_vision_frame
})
return detection
def forward_with_scrfd(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('scrfd')
with thread_semaphore():
detection = face_detector.run(None,
{
'input': detect_vision_frame
})
return detection
def forward_with_yolo_face(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('yolo_face')
with thread_semaphore():
detection = face_detector.run(None,
{
'input': detect_vision_frame
})
return detection
def forward_with_yunet(detect_vision_frame : VisionFrame) -> Detection:
face_detector = get_inference_pool().get('yunet')
with thread_semaphore():
detection = face_detector.run(None,
{
'input': detect_vision_frame
})
return detection
def prepare_detect_frame(temp_vision_frame : VisionFrame, face_detector_size : str) -> VisionFrame:
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
detect_vision_frame = numpy.zeros((face_detector_height, face_detector_width, 3))
detect_vision_frame[:temp_vision_frame.shape[0], :temp_vision_frame.shape[1], :] = temp_vision_frame
detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return detect_vision_frame
def normalize_detect_frame(detect_vision_frame : VisionFrame, normalize_range : Sequence[int]) -> VisionFrame:
if normalize_range == [ -1, 1 ]:
return (detect_vision_frame - 127.5) / 128.0
if normalize_range == [ 0, 1 ]:
return detect_vision_frame / 255.0
return detect_vision_frame
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