mirror of
https://github.com/facefusion/facefusion.git
synced 2026-06-10 14:33:54 +02:00
harisreedhar
312 lines
16 KiB
Python
Executable File
312 lines
16 KiB
Python
Executable File
from time import sleep
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from typing import List, Optional, Tuple
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import cv2
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import gradio
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import numpy
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from facefusion import logger, process_manager, state_manager, translator
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from facefusion.audio import create_empty_audio_frame, get_voice_frame
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from facefusion.common_helper import get_first
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from facefusion.content_analyser import analyse_frame
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from facefusion.face_analyser import get_one_face
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from facefusion.face_selector import select_faces
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from facefusion.face_store import clear_faces
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from facefusion.filesystem import filter_audio_paths, is_image, is_video
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from facefusion.processors.core import get_processors_modules
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from facefusion.types import AudioFrame, Face, Mask, VisionFrame
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from facefusion.uis import choices as uis_choices
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from facefusion.uis.core import get_ui_component, get_ui_components, register_ui_component
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from facefusion.uis.types import ComponentOptions, PreviewMode
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from facefusion.vision import detect_frame_orientation, extract_vision_mask, fit_cover_frame, merge_vision_mask, obscure_frame, read_static_image, read_static_images, read_video_frame, restrict_frame, unpack_resolution
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PREVIEW_IMAGE : Optional[gradio.Image] = None
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def render() -> None:
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global PREVIEW_IMAGE
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preview_image_options : ComponentOptions =\
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{
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'label': translator.get('uis.preview_image')
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}
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source_vision_frames = read_static_images(state_manager.get_item('source_paths'))
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source_audio_path = get_first(filter_audio_paths(state_manager.get_item('source_paths')))
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source_audio_frame = create_empty_audio_frame()
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source_voice_frame = create_empty_audio_frame()
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if source_audio_path and state_manager.get_item('output_video_fps') and state_manager.get_item('reference_frame_number'):
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temp_voice_frame = get_voice_frame(source_audio_path, state_manager.get_item('output_video_fps'), state_manager.get_item('reference_frame_number'))
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if numpy.any(temp_voice_frame):
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source_voice_frame = temp_voice_frame
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if is_image(state_manager.get_item('target_path')):
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target_vision_frame = read_static_image(state_manager.get_item('target_path'))
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reference_vision_frame = read_static_image(state_manager.get_item('target_path'))
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preview_vision_frame = process_preview_frame(reference_vision_frame, source_vision_frames, source_audio_frame, source_voice_frame, target_vision_frame, uis_choices.preview_modes[0], uis_choices.preview_resolutions[-1])
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preview_image_options['value'] = cv2.cvtColor(preview_vision_frame, cv2.COLOR_BGR2RGB)
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preview_image_options['elem_classes'] = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ]
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if is_video(state_manager.get_item('target_path')):
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temp_vision_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
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reference_vision_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
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if numpy.any(temp_vision_frame):
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preview_vision_frame = process_preview_frame(reference_vision_frame, source_vision_frames, source_audio_frame, source_voice_frame, temp_vision_frame, uis_choices.preview_modes[0], uis_choices.preview_resolutions[-1])
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preview_image_options['value'] = cv2.cvtColor(preview_vision_frame, cv2.COLOR_BGR2RGB)
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preview_image_options['elem_classes'] = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ]
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preview_image_options['visible'] = True
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PREVIEW_IMAGE = gradio.Image(**preview_image_options)
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register_ui_component('preview_image', PREVIEW_IMAGE)
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def listen() -> None:
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preview_frame_slider = get_ui_component('preview_frame_slider')
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preview_mode_dropdown = get_ui_component('preview_mode_dropdown')
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preview_resolution_dropdown = get_ui_component('preview_resolution_dropdown')
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if preview_mode_dropdown:
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preview_mode_dropdown.change(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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if preview_resolution_dropdown:
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preview_resolution_dropdown.change(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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if preview_frame_slider:
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preview_frame_slider.release(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE, show_progress = 'hidden')
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preview_frame_slider.change(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE, show_progress = 'hidden', trigger_mode = 'once')
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reference_face_position_gallery = get_ui_component('reference_face_position_gallery')
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if reference_face_position_gallery:
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reference_face_position_gallery.select(clear_and_update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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for ui_component in get_ui_components(
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[
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'source_audio',
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'source_image',
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'target_image',
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'target_video'
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]):
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for method in [ 'change', 'clear' ]:
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getattr(ui_component, method)(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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for ui_component in get_ui_components(
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[
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'background_remover_fill_color_red_number',
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'background_remover_fill_color_green_number',
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'background_remover_fill_color_blue_number',
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'background_remover_fill_color_alpha_number',
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'background_remover_despill_color_red_number',
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'background_remover_despill_color_green_number',
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'background_remover_despill_color_blue_number',
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'background_remover_despill_color_alpha_number',
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'face_debugger_items_checkbox_group',
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'frame_colorizer_size_dropdown',
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'face_mask_types_checkbox_group',
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'face_mask_areas_checkbox_group',
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'face_mask_regions_checkbox_group',
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'expression_restorer_areas_checkbox_group'
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]):
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ui_component.change(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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for ui_component in get_ui_components(
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[
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'age_modifier_direction_slider',
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'deep_swapper_morph_slider',
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'expression_restorer_factor_slider',
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'face_editor_eyebrow_direction_slider',
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'face_editor_eye_gaze_horizontal_slider',
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'face_editor_eye_gaze_vertical_slider',
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'face_editor_eye_open_ratio_slider',
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'face_editor_lip_open_ratio_slider',
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'face_editor_mouth_grim_slider',
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'face_editor_mouth_pout_slider',
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'face_editor_mouth_purse_slider',
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'face_editor_mouth_smile_slider',
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'face_editor_mouth_position_horizontal_slider',
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'face_editor_mouth_position_vertical_slider',
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'face_editor_head_pitch_slider',
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'face_editor_head_yaw_slider',
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'face_editor_head_roll_slider',
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'face_enhancer_blend_slider',
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'face_enhancer_weight_slider',
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'face_swapper_weight_slider',
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'frame_colorizer_blend_slider',
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'frame_enhancer_blend_slider',
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'lip_syncer_weight_slider',
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'reference_face_distance_slider',
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'face_selector_age_range_slider',
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'face_mask_blur_slider',
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'face_mask_padding_top_slider',
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'face_mask_padding_bottom_slider',
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'face_mask_padding_left_slider',
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'face_mask_padding_right_slider',
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'output_video_fps_slider'
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]):
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ui_component.release(update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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for ui_component in get_ui_components(
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[
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'age_modifier_model_dropdown',
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'background_remover_model_dropdown',
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'deep_swapper_model_dropdown',
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'expression_restorer_model_dropdown',
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'processors_checkbox_group',
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'face_editor_model_dropdown',
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'face_enhancer_model_dropdown',
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'face_swapper_model_dropdown',
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'face_swapper_pixel_boost_dropdown',
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'frame_colorizer_model_dropdown',
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'frame_enhancer_model_dropdown',
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'lip_syncer_model_dropdown',
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'face_selector_mode_dropdown',
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'face_selector_order_dropdown',
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'face_selector_gender_dropdown',
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'face_selector_race_dropdown',
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'face_detector_model_dropdown',
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'face_detector_size_dropdown',
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'face_detector_angles_checkbox_group',
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'face_landmarker_model_dropdown',
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'face_occluder_model_dropdown',
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'face_parser_model_dropdown',
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'voice_extractor_model_dropdown'
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]):
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ui_component.change(clear_and_update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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for ui_component in get_ui_components(
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[
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'face_detector_margin_slider',
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'face_detector_score_slider',
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'face_landmarker_score_slider'
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]):
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ui_component.release(clear_and_update_preview_image, inputs = [ preview_mode_dropdown, preview_resolution_dropdown, preview_frame_slider ], outputs = PREVIEW_IMAGE)
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def update_preview_image(preview_mode : PreviewMode, preview_resolution : str, frame_number : int = 0) -> gradio.Image:
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while process_manager.is_checking():
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sleep(0.5)
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source_vision_frames = read_static_images(state_manager.get_item('source_paths'))
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source_audio_path = get_first(filter_audio_paths(state_manager.get_item('source_paths')))
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source_audio_frame = create_empty_audio_frame()
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source_voice_frame = create_empty_audio_frame()
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if source_audio_path and state_manager.get_item('output_video_fps') and state_manager.get_item('reference_frame_number'):
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reference_audio_frame_number = state_manager.get_item('reference_frame_number')
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if state_manager.get_item('trim_frame_start'):
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reference_audio_frame_number -= state_manager.get_item('trim_frame_start')
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temp_voice_frame = get_voice_frame(source_audio_path, state_manager.get_item('output_video_fps'), reference_audio_frame_number)
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if numpy.any(temp_voice_frame):
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source_voice_frame = temp_voice_frame
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if is_image(state_manager.get_item('target_path')):
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reference_vision_frame = read_static_image(state_manager.get_item('target_path'))
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target_vision_frame = read_static_image(state_manager.get_item('target_path'), 'rgba')
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target_vision_mask = extract_vision_mask(target_vision_frame)
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target_vision_frame = merge_vision_mask(target_vision_frame, target_vision_mask)
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preview_vision_frame = process_preview_frame(reference_vision_frame, source_vision_frames, source_audio_frame, source_voice_frame, target_vision_frame, preview_mode, preview_resolution)
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preview_vision_frame = cv2.cvtColor(preview_vision_frame, cv2.COLOR_BGRA2RGBA)
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return gradio.Image(value = preview_vision_frame, elem_classes = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ])
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if is_video(state_manager.get_item('target_path')):
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reference_vision_frame = read_video_frame(state_manager.get_item('target_path'), state_manager.get_item('reference_frame_number'))
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temp_vision_frame = read_video_frame(state_manager.get_item('target_path'), frame_number)
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if numpy.any(temp_vision_frame):
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temp_vision_mask = extract_vision_mask(temp_vision_frame)
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temp_vision_frame = merge_vision_mask(temp_vision_frame, temp_vision_mask)
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preview_vision_frame = process_preview_frame(reference_vision_frame, source_vision_frames, source_audio_frame, source_voice_frame, temp_vision_frame, preview_mode, preview_resolution)
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preview_vision_frame = cv2.cvtColor(preview_vision_frame, cv2.COLOR_BGRA2RGBA)
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return gradio.Image(value = preview_vision_frame, elem_classes = [ 'image-preview', 'is-' + detect_frame_orientation(preview_vision_frame) ])
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return gradio.Image(value = None, elem_classes = None)
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def clear_and_update_preview_image(preview_mode : PreviewMode, preview_resolution : str, frame_number : int = 0) -> gradio.Image:
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clear_faces()
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return update_preview_image(preview_mode, preview_resolution, frame_number)
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def process_preview_frame(reference_vision_frame : VisionFrame, source_vision_frames : List[VisionFrame], source_audio_frame : AudioFrame, source_voice_frame : AudioFrame, target_vision_frame : VisionFrame, preview_mode : PreviewMode, preview_resolution : str) -> VisionFrame:
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target_vision_frame = restrict_frame(target_vision_frame, unpack_resolution(preview_resolution))
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temp_vision_frame = target_vision_frame.copy()
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temp_vision_mask = extract_vision_mask(temp_vision_frame)
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if analyse_frame(target_vision_frame[:, :, :3]):
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if preview_mode == 'frame-by-frame':
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temp_vision_frame = obscure_frame(temp_vision_frame[:, :, :3])
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return numpy.hstack((temp_vision_frame, temp_vision_frame))
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if preview_mode == 'face-by-face':
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target_crop_vision_frame, output_crop_vision_frame = create_face_by_face(reference_vision_frame, source_vision_frames, target_vision_frame[:, :, :3], temp_vision_frame[:, :, :3])
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target_crop_vision_frame = obscure_frame(target_crop_vision_frame)
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output_crop_vision_frame = obscure_frame(output_crop_vision_frame)
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return numpy.hstack((target_crop_vision_frame, output_crop_vision_frame))
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temp_vision_frame = obscure_frame(temp_vision_frame)
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return temp_vision_frame
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for processor_module in get_processors_modules(state_manager.get_item('processors')):
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logger.disable()
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if processor_module.pre_process('preview'):
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logger.enable()
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temp_vision_frame, temp_vision_mask = processor_module.process_frame(
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{
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'reference_vision_frame': reference_vision_frame,
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'source_audio_frame': source_audio_frame,
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'source_voice_frame': source_voice_frame,
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'source_vision_frames': source_vision_frames,
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'target_vision_frame': target_vision_frame[:, :, :3],
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'temp_vision_frame': temp_vision_frame[:, :, :3],
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'temp_vision_mask': temp_vision_mask
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})
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logger.enable()
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temp_vision_frame = prepare_output_frame(target_vision_frame, temp_vision_frame, temp_vision_mask)
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if preview_mode == 'frame-by-frame':
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return numpy.hstack((target_vision_frame, temp_vision_frame))
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if preview_mode == 'face-by-face':
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target_crop_vision_frame, output_crop_vision_frame = create_face_by_face(reference_vision_frame, source_vision_frames, target_vision_frame, temp_vision_frame)
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return numpy.hstack((target_crop_vision_frame, output_crop_vision_frame))
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return temp_vision_frame
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def create_face_by_face(reference_vision_frame : VisionFrame, source_vision_frames : List[VisionFrame], target_vision_frame : VisionFrame, temp_vision_frame : VisionFrame) -> Tuple[VisionFrame, VisionFrame]:
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target_faces = select_faces(reference_vision_frame[:, :, :3], source_vision_frames, target_vision_frame[:, :, :3])
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target_face = get_one_face(target_faces)
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if target_face:
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target_crop_vision_frame = extract_crop_frame(target_vision_frame, target_face)
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output_crop_vision_frame = extract_crop_frame(temp_vision_frame, target_face)
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if numpy.any(target_crop_vision_frame) and numpy.any(output_crop_vision_frame):
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target_crop_dimension = min(target_crop_vision_frame.shape[:2])
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target_crop_vision_frame = fit_cover_frame(target_crop_vision_frame, (target_crop_dimension, target_crop_dimension))
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output_crop_vision_frame = fit_cover_frame(output_crop_vision_frame, (target_crop_dimension, target_crop_dimension))
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return target_crop_vision_frame, output_crop_vision_frame
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empty_vision_frame = numpy.zeros((512, 512, 4), dtype = numpy.uint8)
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return empty_vision_frame, empty_vision_frame
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def extract_crop_frame(vision_frame : VisionFrame, face : Face) -> Optional[VisionFrame]:
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start_x, start_y, end_x, end_y = map(int, face.bounding_box)
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padding_x = int((end_x - start_x) * 0.25)
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padding_y = int((end_y - start_y) * 0.25)
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start_x = max(0, start_x - padding_x)
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start_y = max(0, start_y - padding_y)
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end_x = max(0, end_x + padding_x)
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end_y = max(0, end_y + padding_y)
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crop_vision_frame = vision_frame[start_y:end_y, start_x:end_x]
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return crop_vision_frame
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def prepare_output_frame(target_vision_frame : VisionFrame, temp_vision_frame : VisionFrame, temp_vision_mask : Mask) -> VisionFrame:
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temp_vision_mask = temp_vision_mask.clip(state_manager.get_item('background_remover_fill_color')[-1], 255)
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temp_vision_frame = merge_vision_mask(temp_vision_frame, temp_vision_mask)
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temp_vision_frame = cv2.resize(temp_vision_frame, target_vision_frame.shape[1::-1])
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return temp_vision_frame
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