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from time import sleep
from typing import Any, Dict, Tuple, List, Optional
import cv2
import gradio
import facefusion.globals
from facefusion import wording
from facefusion.capturer import get_video_frame, get_video_frame_total
from facefusion.face_analyser import get_one_face
from facefusion.face_reference import get_face_reference, set_face_reference
from facefusion.predictor import predict_frame
from facefusion.processors.frame.core import load_frame_processor_module
from facefusion.typing import Frame
from facefusion.uis import core as ui
from facefusion.uis.typing import ComponentName, Update
from facefusion.utilities import is_video, is_image
PREVIEW_IMAGE : Optional[gradio.Image] = None
PREVIEW_FRAME_SLIDER : Optional[gradio.Slider] = None
def render() -> None:
global PREVIEW_IMAGE
global PREVIEW_FRAME_SLIDER
with gradio.Box():
preview_image_args: Dict[str, Any] = {
'label': wording.get('preview_image_label')
}
preview_frame_slider_args: Dict[str, Any] = {
'label': wording.get('preview_frame_slider_label'),
'step': 1,
'visible': False
}
if is_image(facefusion.globals.target_path):
target_frame = cv2.imread(facefusion.globals.target_path)
preview_frame = extract_preview_frame(target_frame)
preview_image_args['value'] = ui.normalize_frame(preview_frame)
if is_video(facefusion.globals.target_path):
temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
preview_frame = extract_preview_frame(temp_frame)
preview_image_args['value'] = ui.normalize_frame(preview_frame)
preview_image_args['visible'] = True
preview_frame_slider_args['value'] = facefusion.globals.reference_frame_number
preview_frame_slider_args['maximum'] = get_video_frame_total(facefusion.globals.target_path)
preview_frame_slider_args['visible'] = True
PREVIEW_IMAGE = gradio.Image(**preview_image_args)
PREVIEW_FRAME_SLIDER = gradio.Slider(**preview_frame_slider_args)
ui.register_component('preview_frame_slider', PREVIEW_FRAME_SLIDER)
def listen() -> None:
PREVIEW_FRAME_SLIDER.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
update_component_names : List[ComponentName] =\
[
'source_file',
'target_file',
'face_recognition_dropdown',
'reference_face_distance_slider',
'frame_processors_checkbox_group'
]
for component_name in update_component_names:
component = ui.get_component(component_name)
if component:
component.change(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
select_component_names : List[ComponentName] =\
[
'reference_face_position_gallery',
'face_analyser_direction_dropdown',
'face_analyser_age_dropdown',
'face_analyser_gender_dropdown'
]
for component_name in select_component_names:
component = ui.get_component(component_name)
if component:
component.select(update, inputs = PREVIEW_FRAME_SLIDER, outputs = [ PREVIEW_IMAGE, PREVIEW_FRAME_SLIDER ])
def update(frame_number : int = 0) -> Tuple[Update, Update]:
sleep(0.1)
if is_image(facefusion.globals.target_path):
target_frame = cv2.imread(facefusion.globals.target_path)
preview_frame = extract_preview_frame(target_frame)
return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(value = None, maximum = None, visible = False)
if is_video(facefusion.globals.target_path):
facefusion.globals.reference_frame_number = frame_number
video_frame_total = get_video_frame_total(facefusion.globals.target_path)
temp_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
preview_frame = extract_preview_frame(temp_frame)
return gradio.update(value = ui.normalize_frame(preview_frame)), gradio.update(maximum = video_frame_total, visible = True)
return gradio.update(value = None), gradio.update(value = None, maximum = None, visible = False)
def extract_preview_frame(temp_frame : Frame) -> Frame:
if predict_frame(temp_frame):
return cv2.GaussianBlur(temp_frame, (99, 99), 0)
source_face = get_one_face(cv2.imread(facefusion.globals.source_path)) if facefusion.globals.source_path else None
temp_frame = reduce_preview_frame(temp_frame)
if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
reference_frame = get_video_frame(facefusion.globals.target_path, facefusion.globals.reference_frame_number)
reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
set_face_reference(reference_face)
reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
for frame_processor in facefusion.globals.frame_processors:
frame_processor_module = load_frame_processor_module(frame_processor)
if frame_processor_module.pre_process():
temp_frame = frame_processor_module.process_frame(
source_face,
reference_face,
temp_frame
)
return temp_frame
def reduce_preview_frame(temp_frame : Frame, max_height : int = 480) -> Frame:
height, width = temp_frame.shape[:2]
if height > max_height:
scale = max_height / height
max_width = int(width * scale)
temp_frame = cv2.resize(temp_frame, (max_width, max_height))
return temp_frame
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