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from typing import List, 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, 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.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, InferencePool, ModelSet, Score, VisionFrame
from facefusion.vision import resize_frame_resolution, unpack_resolution
MODEL_SET : ModelSet =\
{
'retinaface':
{
'hashes':
{
'retinaface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.hash',
'path': resolve_relative_path('../.assets/models/retinaface_10g.hash')
}
},
'sources':
{
'retinaface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/retinaface_10g.onnx',
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
}
}
},
'scrfd':
{
'hashes':
{
'scrfd':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.hash',
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.hash')
}
},
'sources':
{
'scrfd':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/scrfd_2.5g.onnx',
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx')
}
}
},
'yoloface':
{
'hashes':
{
'yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.hash',
'path': resolve_relative_path('../.assets/models/yoloface_8n.hash')
}
},
'sources':
{
'yoloface':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/yoloface_8n.onnx',
'path': resolve_relative_path('../.assets/models/yoloface_8n.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 = {}
model_sources = {}
if state_manager.get_item('face_detector_model') in [ 'many', 'retinaface' ]:
model_hashes['retinaface'] = MODEL_SET.get('retinaface').get('hashes').get('retinaface')
model_sources['retinaface'] = MODEL_SET.get('retinaface').get('sources').get('retinaface')
if state_manager.get_item('face_detector_model') in [ 'many', 'scrfd' ]:
model_hashes['scrfd'] = MODEL_SET.get('scrfd').get('hashes').get('scrfd')
model_sources['scrfd'] = MODEL_SET.get('scrfd').get('sources').get('scrfd')
if state_manager.get_item('face_detector_model') in [ 'many', 'yoloface' ]:
model_hashes['yoloface'] = MODEL_SET.get('yoloface').get('hashes').get('yoloface')
model_sources['yoloface'] = MODEL_SET.get('yoloface').get('sources').get('yoloface')
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_faces(vision_frame : VisionFrame) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
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(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(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', 'yoloface' ]:
bounding_boxes, face_scores, face_landmarks_5 = detect_with_yoloface(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) for all_bounding_box in all_bounding_boxes ]
return all_bounding_boxes, all_face_scores, all_face_landmarks_5
def detect_rotated_faces(vision_frame : VisionFrame, angle : Angle) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
rotated_matrix, rotated_size = create_rotated_matrix_and_size(angle, vision_frame.shape[:2][::-1])
rotated_vision_frame = cv2.warpAffine(vision_frame, rotated_matrix, rotated_size)
rotated_inverse_matrix = cv2.invertAffineTransform(rotated_matrix)
bounding_boxes, face_scores, face_landmarks_5 = detect_faces(rotated_vision_frame)
bounding_boxes = [ transform_bounding_box(bounding_box, rotated_inverse_matrix) for bounding_box in bounding_boxes ]
face_landmarks_5 = [ transform_points(face_landmark_5, rotated_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]]:
face_detector = get_inference_pool().get('retinaface')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(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]
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
with thread_semaphore():
detections = face_detector.run(None,
{
'input': detect_vision_frame
})
for index, feature_stride in enumerate(feature_strides):
keep_indices = numpy.where(detections[index] >= state_manager.get_item('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_box_raw = detections[index + feature_map_channel] * feature_stride
face_landmark_5_raw = detections[index + feature_map_channel * 2] * feature_stride
for bounding_box in distance_to_bounding_box(anchors, bounding_box_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box[0] * ratio_width,
bounding_box[1] * ratio_height,
bounding_box[2] * ratio_width,
bounding_box[3] * ratio_height,
]))
for score in detections[index][keep_indices]:
face_scores.append(score[0])
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_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]]:
face_detector = get_inference_pool().get('scrfd')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(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]
feature_strides = [ 8, 16, 32 ]
feature_map_channel = 3
anchor_total = 2
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
with thread_semaphore():
detections = face_detector.run(None,
{
'input': detect_vision_frame
})
for index, feature_stride in enumerate(feature_strides):
keep_indices = numpy.where(detections[index] >= state_manager.get_item('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_box_raw = detections[index + feature_map_channel] * feature_stride
face_landmark_5_raw = detections[index + feature_map_channel * 2] * feature_stride
for bounding_box in distance_to_bounding_box(anchors, bounding_box_raw)[keep_indices]:
bounding_boxes.append(numpy.array(
[
bounding_box[0] * ratio_width,
bounding_box[1] * ratio_height,
bounding_box[2] * ratio_width,
bounding_box[3] * ratio_height,
]))
for score in detections[index][keep_indices]:
face_scores.append(score[0])
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
face_landmarks_5.append(face_landmark_5 * [ ratio_width, ratio_height ])
return bounding_boxes, face_scores, face_landmarks_5
def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[Score], List[FaceLandmark5]]:
face_detector = get_inference_pool().get('yoloface')
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
temp_vision_frame = resize_frame_resolution(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]
bounding_boxes = []
face_scores = []
face_landmarks_5 = []
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
with thread_semaphore():
detections = face_detector.run(None,
{
'input': detect_vision_frame
})
detections = numpy.squeeze(detections).T
bounding_box_raw, score_raw, face_landmark_5_raw = numpy.split(detections, [ 4, 5 ], axis = 1)
keep_indices = numpy.where(score_raw > state_manager.get_item('face_detector_score'))[0]
if numpy.any(keep_indices):
bounding_box_raw, face_landmark_5_raw, score_raw = bounding_box_raw[keep_indices], face_landmark_5_raw[keep_indices], score_raw[keep_indices]
for bounding_box in bounding_box_raw:
bounding_boxes.append(numpy.array(
[
(bounding_box[0] - bounding_box[2] / 2) * ratio_width,
(bounding_box[1] - bounding_box[3] / 2) * ratio_height,
(bounding_box[0] + bounding_box[2] / 2) * ratio_width,
(bounding_box[1] + bounding_box[3] / 2) * ratio_height,
]))
face_scores = score_raw.ravel().tolist()
face_landmark_5_raw[:, 0::3] = (face_landmark_5_raw[:, 0::3]) * ratio_width
face_landmark_5_raw[:, 1::3] = (face_landmark_5_raw[:, 1::3]) * ratio_height
for face_landmark_5 in face_landmark_5_raw:
face_landmarks_5.append(numpy.array(face_landmark_5.reshape(-1, 3)[:, :2]))
return bounding_boxes, face_scores, face_landmarks_5
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 = (detect_vision_frame - 127.5) / 128.0
detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
return detect_vision_frame
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