mirror of
https://github.com/facefusion/facefusion.git
synced 2026-06-02 19:01:35 +02:00
Henry Ruhs
e53cb63577
* testing for audio and video encoders, minor cleanups * fix lint * finish create_vpx_encoder, adjust unrelated order of width vs height args
459 lines
18 KiB
Python
459 lines
18 KiB
Python
from functools import lru_cache
|
|
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_width, stride_height)
|
|
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_width, stride_height)
|
|
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_width, stride_height)
|
|
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
|