Next (#318)
* renaming and restructuring (#282) * Renaming and restructuring * Renaming and restructuring * Renaming and restructuring * Fix gender detection * Implement distance to face debugger * Implement distance to face debugger part2 * Implement distance to face debugger part3 * Mark as next * Fix reference when face_debugger comes first * Use official onnxruntime nightly * CUDA on steroids * CUDA on steroids * Add some testing * Set inswapper_128_fp16 as default * Feat/block until post check (#292) * Block until download is done * Introduce post_check() * Fix webcam * Update dependencies * Add --force-reinstall to installer * Introduce config ini (#298) * Introduce config ini * Fix output video encoder * Revert help listings back to commas, Move SSL hack to download.py * Introduce output-video-preset which defaults to veryfast * Mapping for nvenc encoders * Rework on events and non-blocking UI * Add fast bmp to temp_frame_formats * Add fast bmp to temp_frame_formats * Show total processing time on success * Show total processing time on success * Show total processing time on success * Move are_images, is_image and is_video back to filesystem * Fix some spacings * Pissing everyone of by renaming stuff * Fix seconds output * feat/video output fps (#312) * added output fps slider, removed 'keep fps' option (#311) * added output fps slider, removed 'keep fps' option * now uses passed fps instead of global fps for ffmpeg * fps values are now floats instead of ints * fix previous commit * removed default value from fps slider this is so we can implement a dynamic default value later * Fix seconds output * Some cleanup --------- Co-authored-by: Ran Shaashua <47498956+ranshaa05@users.noreply.github.com> * Allow 0.01 steps for fps * Make fps unregulated * Make fps unregulated * Remove distance from face debugger again (does not work) * Fix gender age * Fix gender age * Hotfix benchmark suite * Warp face normalize (#313) * use normalized kp templates * Update face_helper.py * My 50 cents to warp_face() --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * face-swapper-weight (#315) * Move prepare_crop_frame and normalize_crop_frame out of apply_swap * Fix UI bug with different range * feat/output video resolution (#316) * Introduce detect_video_resolution, Rename detect_fps to detect_video_fps * Add calc_video_resolution_range * Make output resolution work, does not auto-select yet * Make output resolution work, does not auto-select yet * Try to keep the origin resolution * Split code into more fragments * Add pack/unpack resolution * Move video_template_sizes to choices * Improve create_video_resolutions * Reword benchmark suite * Optimal speed for benchmark * Introduce different video memory strategies, rename max_memory to max… (#317) * Introduce different video memory strategies, rename max_memory to max_system_memory * Update readme * Fix limit_system_memory call * Apply video_memory_strategy to face debugger * Limit face swapper weight to 3.0 * Remove face swapper weight due bad render outputs * Show/dide logic for output video preset * fix uint8 conversion * Fix whitespace * Finalize layout and update preview * Fix multi renders on face debugger * Restore less restrictive rendering of preview and stream * Fix block mode for model downloads * Add testing * Cosmetic changes * Enforce valid fps and resolution via CLI * Empty config * Cosmetics on args processing * Memory workover (#319) * Cosmetics on args processing * Fix for MacOS * Rename all max_ to _limit * More fixes * Update preview * Fix whitespace --------- Co-authored-by: Ran Shaashua <47498956+ranshaa05@users.noreply.github.com> Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
GitHub
parent
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commit
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11
README.md
11
README.md
@ -45,7 +45,10 @@ execution:
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--execution-providers EXECUTION_PROVIDERS [EXECUTION_PROVIDERS ...] choose from the available execution providers (choices: cpu, ...)
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--execution-thread-count [1-128] specify the number of execution threads
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--execution-queue-count [1-32] specify the number of execution queries
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--max-memory [0-128] specify the maximum amount of ram to be used (in gb)
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memory:
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--video-memory-strategy {strict,moderate,tolerant} specify strategy to handle the video memory
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--system-memory-limit [0-128] specify the amount (gb) of system memory to be used
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face analyser:
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--face-analyser-order {left-right,right-left,top-bottom,bottom-top,small-large,large-small,best-worst,worst-best} specify the order used for the face analyser
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@ -70,15 +73,17 @@ face mask:
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frame extraction:
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--trim-frame-start TRIM_FRAME_START specify the start frame for extraction
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--trim-frame-end TRIM_FRAME_END specify the end frame for extraction
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--temp-frame-format {jpg,png} specify the image format used for frame extraction
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--temp-frame-format {jpg,png,bmp} specify the image format used for frame extraction
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--temp-frame-quality [0-100] specify the image quality used for frame extraction
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--keep-temp retain temporary frames after processing
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output creation:
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--output-image-quality [0-100] specify the quality used for the output image
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--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc} specify the encoder used for the output video
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--output-video-preset {ultrafast,superfast,veryfast,faster,fast,medium,slow,slower,veryslow} specify the preset used for the output video
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--output-video-quality [0-100] specify the quality used for the output video
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--keep-fps preserve the frames per second (fps) of the target
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--output-video-resolution OUTPUT_VIDEO_RESOLUTION specify the resolution used for the output video
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--output-video-fps OUTPUT_VIDEO_FPS specify the frames per second (fps) used for the output video
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--skip-audio omit audio from the target
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frame processors:
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66
facefusion.ini
Normal file
66
facefusion.ini
Normal file
@ -0,0 +1,66 @@
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[general]
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source_paths =
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target_path =
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output_path =
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[misc]
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skip_download =
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headless =
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log_level =
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[execution]
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execution_providers =
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execution_thread_count =
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execution_queue_count =
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[memory]
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video_memory_strategy =
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system_memory_limit =
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[face_analyser]
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face_analyser_order =
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face_analyser_age =
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face_analyser_gender =
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face_detector_model =
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face_detector_size =
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face_detector_score =
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[face_selector]
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face_selector_mode =
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reference_face_position =
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reference_face_distance =
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reference_frame_number =
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[face_mask]
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face_mask_types =
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face_mask_blur =
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face_mask_padding =
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face_mask_regions =
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[frame_extraction]
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trim_frame_start =
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trim_frame_end =
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temp_frame_format =
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temp_frame_quality =
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keep_temp =
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[output_creation]
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output_image_quality =
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output_video_encoder =
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output_video_preset =
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output_video_quality =
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output_video_resolution =
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output_video_fps =
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skip_audio =
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[frame_processors]
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frame_processors =
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face_debugger_items =
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face_enhancer_model =
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face_enhancer_blend =
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face_swapper_model =
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frame_enhancer_model =
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frame_enhancer_blend =
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[uis]
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ui_layouts =
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@ -1,8 +1,9 @@
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from typing import List
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from facefusion.typing import FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, TempFrameFormat, OutputVideoEncoder
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from facefusion.common_helper import create_range
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from facefusion.typing import VideoMemoryStrategy, FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, TempFrameFormat, OutputVideoEncoder, OutputVideoPreset
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from facefusion.common_helper import create_int_range, create_float_range
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video_memory_strategies : List[VideoMemoryStrategy] = [ 'strict', 'moderate', 'tolerant' ]
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face_analyser_orders : List[FaceAnalyserOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
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face_analyser_ages : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior' ]
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face_analyser_genders : List[FaceAnalyserGender] = [ 'male', 'female' ]
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@ -11,16 +12,19 @@ face_detector_sizes : List[str] = [ '160x160', '320x320', '480x480', '512x512',
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face_selector_modes : List[FaceSelectorMode] = [ 'reference', 'one', 'many' ]
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face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
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face_mask_regions : List[FaceMaskRegion] = [ 'skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip' ]
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temp_frame_formats : List[TempFrameFormat] = [ 'jpg', 'png' ]
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temp_frame_formats : List[TempFrameFormat] = [ 'jpg', 'png', 'bmp' ]
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output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc' ]
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output_video_presets : List[OutputVideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
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execution_thread_count_range : List[float] = create_range(1, 128, 1)
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execution_queue_count_range : List[float] = create_range(1, 32, 1)
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max_memory_range : List[float] = create_range(0, 128, 1)
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face_detector_score_range : List[float] = create_range(0.0, 1.0, 0.05)
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face_mask_blur_range : List[float] = create_range(0.0, 1.0, 0.05)
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face_mask_padding_range : List[float] = create_range(0, 100, 1)
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reference_face_distance_range : List[float] = create_range(0.0, 1.5, 0.05)
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temp_frame_quality_range : List[float] = create_range(0, 100, 1)
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output_image_quality_range : List[float] = create_range(0, 100, 1)
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output_video_quality_range : List[float] = create_range(0, 100, 1)
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video_template_sizes : List[int] = [ 240, 360, 480, 540, 720, 1080, 1440, 2160 ]
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execution_thread_count_range : List[int] = create_int_range(1, 128, 1)
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execution_queue_count_range : List[int] = create_int_range(1, 32, 1)
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system_memory_limit_range : List[int] = create_int_range(0, 128, 1)
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face_detector_score_range : List[float] = create_float_range(0.0, 1.0, 0.05)
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face_mask_blur_range : List[float] = create_float_range(0.0, 1.0, 0.05)
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face_mask_padding_range : List[int] = create_int_range(0, 100, 1)
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reference_face_distance_range : List[float] = create_float_range(0.0, 1.5, 0.05)
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temp_frame_quality_range : List[int] = create_int_range(0, 100, 1)
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output_image_quality_range : List[int] = create_int_range(0, 100, 1)
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output_video_quality_range : List[int] = create_int_range(0, 100, 1)
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@ -6,5 +6,9 @@ def create_metavar(ranges : List[Any]) -> str:
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return '[' + str(ranges[0]) + '-' + str(ranges[-1]) + ']'
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def create_range(start : float, stop : float, step : float) -> List[float]:
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def create_int_range(start : int, stop : int, step : int) -> List[int]:
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return (numpy.arange(start, stop + step, step)).tolist()
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def create_float_range(start : float, stop : float, step : float) -> List[float]:
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return (numpy.around(numpy.arange(start, stop + step, step), decimals = 2)).tolist()
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80
facefusion/config.py
Normal file
80
facefusion/config.py
Normal file
@ -0,0 +1,80 @@
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from configparser import ConfigParser
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from typing import Optional, List
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from facefusion.filesystem import resolve_relative_path
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CONFIG = None
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def get_config() -> ConfigParser:
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global CONFIG
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if CONFIG is None:
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config_path = resolve_relative_path('../facefusion.ini')
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CONFIG = ConfigParser()
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CONFIG.read(config_path)
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return CONFIG
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def clear_config() -> None:
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global CONFIG
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CONFIG = None
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def get_str_value(key : str, fallback : Optional[str] = None) -> Optional[str]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return str(value or fallback)
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return None
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def get_int_value(key : str, fallback : Optional[str] = None) -> Optional[int]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return int(value or fallback)
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return None
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def get_float_value(key : str, fallback : Optional[str] = None) -> Optional[float]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return float(value or fallback)
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return None
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def get_bool_value(key : str, fallback : Optional[str] = None) -> Optional[bool]:
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section, option = key.split('.')
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value = get_config()[section].get(option, fallback)
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if value == 'True' or fallback == 'True':
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return True
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if value == 'False' or fallback == 'False':
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return False
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return None
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def get_str_list(key : str, fallback : Optional[str] = None) -> Optional[List[str]]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return [ str(value) for value in (value or fallback).split(' ') ]
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return None
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def get_int_list(key : str, fallback : Optional[str] = None) -> Optional[List[int]]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return [ int(value) for value in (value or fallback).split(' ') ]
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return None
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def get_float_list(key : str, fallback : Optional[str] = None) -> Optional[List[float]]:
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section, option = key.split('.')
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value = get_config()[section].get(option)
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if value or fallback:
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return [ float(value) for value in (value or fallback).split(' ') ]
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return None
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@ -8,8 +8,9 @@ from tqdm import tqdm
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import facefusion.globals
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from facefusion import wording
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from facefusion.typing import Frame, ModelValue
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from facefusion.vision import get_video_frame, count_video_frame_total, read_image, detect_fps
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from facefusion.typing import Frame, ModelValue, Fps
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from facefusion.execution_helper import apply_execution_provider_options
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from facefusion.vision import get_video_frame, count_video_frame_total, read_image, detect_video_fps
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from facefusion.filesystem import resolve_relative_path
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from facefusion.download import conditional_download
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@ -23,8 +24,8 @@ MODELS : Dict[str, ModelValue] =\
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'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
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}
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}
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MAX_PROBABILITY = 0.80
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MAX_RATE = 5
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PROBABILITY_LIMIT = 0.80
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RATE_LIMIT = 5
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STREAM_COUNTER = 0
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@ -34,7 +35,7 @@ def get_content_analyser() -> Any:
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with THREAD_LOCK:
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if CONTENT_ANALYSER is None:
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model_path = MODELS.get('open_nsfw').get('path')
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CONTENT_ANALYSER = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
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CONTENT_ANALYSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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return CONTENT_ANALYSER
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@ -52,11 +53,11 @@ def pre_check() -> bool:
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return True
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|
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def analyse_stream(frame : Frame, fps : float) -> bool:
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def analyse_stream(frame : Frame, video_fps : Fps) -> bool:
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global STREAM_COUNTER
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STREAM_COUNTER = STREAM_COUNTER + 1
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if STREAM_COUNTER % int(fps) == 0:
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if STREAM_COUNTER % int(video_fps) == 0:
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return analyse_frame(frame)
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return False
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@ -75,7 +76,7 @@ def analyse_frame(frame : Frame) -> bool:
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{
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'input:0': frame
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})[0][0][1]
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return probability > MAX_PROBABILITY
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return probability > PROBABILITY_LIMIT
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@lru_cache(maxsize = None)
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@ -87,17 +88,17 @@ def analyse_image(image_path : str) -> bool:
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@lru_cache(maxsize = None)
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def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
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video_frame_total = count_video_frame_total(video_path)
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fps = detect_fps(video_path)
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video_fps = detect_video_fps(video_path)
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frame_range = range(start_frame or 0, end_frame or video_frame_total)
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rate = 0.0
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counter = 0
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with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
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for frame_number in frame_range:
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if frame_number % int(fps) == 0:
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if frame_number % int(video_fps) == 0:
|
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frame = get_video_frame(video_path, frame_number)
|
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if analyse_frame(frame):
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counter += 1
|
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rate = counter * int(fps) / len(frame_range) * 100
|
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rate = counter * int(video_fps) / len(frame_range) * 100
|
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progress.update()
|
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progress.set_postfix(rate = rate)
|
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return rate > MAX_RATE
|
||||
return rate > RATE_LIMIT
|
||||
|
||||
@ -3,101 +3,106 @@ import os
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||||
os.environ['OMP_NUM_THREADS'] = '1'
|
||||
|
||||
import signal
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||||
import ssl
|
||||
import sys
|
||||
import time
|
||||
import warnings
|
||||
import platform
|
||||
import shutil
|
||||
import numpy
|
||||
import onnxruntime
|
||||
from time import sleep
|
||||
from argparse import ArgumentParser, HelpFormatter
|
||||
|
||||
import facefusion.choices
|
||||
import facefusion.globals
|
||||
from facefusion.face_analyser import get_one_face, get_average_face
|
||||
from facefusion.face_store import get_reference_faces, append_reference_face
|
||||
from facefusion.vision import get_video_frame, detect_fps, read_image, read_static_images
|
||||
from facefusion import face_analyser, face_masker, content_analyser, metadata, logger, wording
|
||||
from facefusion import face_analyser, face_masker, content_analyser, config, metadata, logger, wording
|
||||
from facefusion.content_analyser import analyse_image, analyse_video
|
||||
from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
|
||||
from facefusion.common_helper import create_metavar
|
||||
from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
|
||||
from facefusion.normalizer import normalize_output_path, normalize_padding
|
||||
from facefusion.filesystem import is_image, is_video, list_module_names, get_temp_frame_paths, create_temp, move_temp, clear_temp
|
||||
from facefusion.normalizer import normalize_output_path, normalize_padding, normalize_fps
|
||||
from facefusion.memory import limit_system_memory
|
||||
from facefusion.filesystem import list_directory, get_temp_frame_paths, create_temp, move_temp, clear_temp, is_image, is_video
|
||||
from facefusion.ffmpeg import extract_frames, compress_image, merge_video, restore_audio
|
||||
from facefusion.vision import get_video_frame, read_image, read_static_images, pack_resolution, detect_video_resolution, detect_video_fps, create_video_resolutions
|
||||
|
||||
onnxruntime.set_default_logger_severity(3)
|
||||
warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
|
||||
warnings.filterwarnings('ignore', category = UserWarning, module = 'torchvision')
|
||||
|
||||
if platform.system().lower() == 'darwin':
|
||||
ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
|
||||
def cli() -> None:
|
||||
signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
|
||||
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 120), add_help = False)
|
||||
# general
|
||||
program.add_argument('-s', '--source', action = 'append', help = wording.get('source_help'), dest = 'source_paths')
|
||||
program.add_argument('-t', '--target', help = wording.get('target_help'), dest = 'target_path')
|
||||
program.add_argument('-o', '--output', help = wording.get('output_help'), dest = 'output_path')
|
||||
program.add_argument('-s', '--source', help = wording.get('source_help'), action = 'append', dest = 'source_paths', default = config.get_str_list('general.source_paths'))
|
||||
program.add_argument('-t', '--target', help = wording.get('target_help'), dest = 'target_path', default = config.get_str_value('general.target_path'))
|
||||
program.add_argument('-o', '--output', help = wording.get('output_help'), dest = 'output_path', default = config.get_str_value('general.output_path'))
|
||||
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
|
||||
# misc
|
||||
group_misc = program.add_argument_group('misc')
|
||||
group_misc.add_argument('--skip-download', help = wording.get('skip_download_help'), action = 'store_true')
|
||||
group_misc.add_argument('--headless', help = wording.get('headless_help'), action = 'store_true')
|
||||
group_misc.add_argument('--log-level', help = wording.get('log_level_help'), default = 'info', choices = logger.get_log_levels())
|
||||
group_misc.add_argument('--skip-download', help = wording.get('skip_download_help'), action = 'store_true', default = config.get_bool_value('misc.skip_download'))
|
||||
group_misc.add_argument('--headless', help = wording.get('headless_help'), action = 'store_true', default = config.get_bool_value('misc.headless'))
|
||||
group_misc.add_argument('--log-level', help = wording.get('log_level_help'), default = config.get_str_value('misc.log_level', 'info'), choices = logger.get_log_levels())
|
||||
# execution
|
||||
execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
|
||||
group_execution = program.add_argument_group('execution')
|
||||
group_execution.add_argument('--execution-providers', help = wording.get('execution_providers_help').format(choices = ', '.join(execution_providers)), default = [ 'cpu' ], choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
|
||||
group_execution.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), type = int, default = 4, choices = facefusion.choices.execution_thread_count_range, metavar = create_metavar(facefusion.choices.execution_thread_count_range))
|
||||
group_execution.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), type = int, default = 1, choices = facefusion.choices.execution_queue_count_range, metavar = create_metavar(facefusion.choices.execution_queue_count_range))
|
||||
group_execution.add_argument('--max-memory', help = wording.get('max_memory_help'), type = int, choices = facefusion.choices.max_memory_range, metavar = create_metavar(facefusion.choices.max_memory_range))
|
||||
group_execution.add_argument('--execution-providers', help = wording.get('execution_providers_help').format(choices = ', '.join(execution_providers)), default = config.get_str_list('execution.execution_providers', 'cpu'), choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
|
||||
group_execution.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), type = int, default = config.get_int_value('execution.execution_thread_count', '4'), choices = facefusion.choices.execution_thread_count_range, metavar = create_metavar(facefusion.choices.execution_thread_count_range))
|
||||
group_execution.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), type = int, default = config.get_int_value('execution.execution_queue_count', '1'), choices = facefusion.choices.execution_queue_count_range, metavar = create_metavar(facefusion.choices.execution_queue_count_range))
|
||||
# memory
|
||||
group_memory = program.add_argument_group('memory')
|
||||
group_memory.add_argument('--video-memory-strategy', help = wording.get('video_memory_strategy_help'), default = config.get_str_value('memory.video_memory_strategy', 'strict'), choices = facefusion.choices.video_memory_strategies)
|
||||
group_memory.add_argument('--system-memory-limit', help = wording.get('system_memory_limit_help'), type = int, default = config.get_int_value('memory.system_memory_limit', '0'), choices = facefusion.choices.system_memory_limit_range, metavar = create_metavar(facefusion.choices.system_memory_limit_range))
|
||||
# face analyser
|
||||
group_face_analyser = program.add_argument_group('face analyser')
|
||||
group_face_analyser.add_argument('--face-analyser-order', help = wording.get('face_analyser_order_help'), default = 'left-right', choices = facefusion.choices.face_analyser_orders)
|
||||
group_face_analyser.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), choices = facefusion.choices.face_analyser_ages)
|
||||
group_face_analyser.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), choices = facefusion.choices.face_analyser_genders)
|
||||
group_face_analyser.add_argument('--face-detector-model', help = wording.get('face_detector_model_help'), default = 'retinaface', choices = facefusion.choices.face_detector_models)
|
||||
group_face_analyser.add_argument('--face-detector-size', help = wording.get('face_detector_size_help'), default = '640x640', choices = facefusion.choices.face_detector_sizes)
|
||||
group_face_analyser.add_argument('--face-detector-score', help = wording.get('face_detector_score_help'), type = float, default = 0.5, choices = facefusion.choices.face_detector_score_range, metavar = create_metavar(facefusion.choices.face_detector_score_range))
|
||||
group_face_analyser.add_argument('--face-analyser-order', help = wording.get('face_analyser_order_help'), default = config.get_str_value('face_analyser.face_analyser_order', 'left-right'), choices = facefusion.choices.face_analyser_orders)
|
||||
group_face_analyser.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), default = config.get_str_value('face_analyser.face_analyser_age'), choices = facefusion.choices.face_analyser_ages)
|
||||
group_face_analyser.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), default = config.get_str_value('face_analyser.face_analyser_gender'), choices = facefusion.choices.face_analyser_genders)
|
||||
group_face_analyser.add_argument('--face-detector-model', help = wording.get('face_detector_model_help'), default = config.get_str_value('face_analyser.face_detector_model', 'retinaface'), choices = facefusion.choices.face_detector_models)
|
||||
group_face_analyser.add_argument('--face-detector-size', help = wording.get('face_detector_size_help'), default = config.get_str_value('face_analyser.face_detector_size', '640x640'), choices = facefusion.choices.face_detector_sizes)
|
||||
group_face_analyser.add_argument('--face-detector-score', help = wording.get('face_detector_score_help'), type = float, default = config.get_float_value('face_analyser.face_detector_score', '0.5'), choices = facefusion.choices.face_detector_score_range, metavar = create_metavar(facefusion.choices.face_detector_score_range))
|
||||
# face selector
|
||||
group_face_selector = program.add_argument_group('face selector')
|
||||
group_face_selector.add_argument('--face-selector-mode', help = wording.get('face_selector_mode_help'), default = 'reference', choices = facefusion.choices.face_selector_modes)
|
||||
group_face_selector.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), type = int, default = 0)
|
||||
group_face_selector.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), type = float, default = 0.6, choices = facefusion.choices.reference_face_distance_range, metavar = create_metavar(facefusion.choices.reference_face_distance_range))
|
||||
group_face_selector.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), type = int, default = 0)
|
||||
group_face_selector.add_argument('--face-selector-mode', help = wording.get('face_selector_mode_help'), default = config.get_str_value('face_selector.face_selector_mode', 'reference'), choices = facefusion.choices.face_selector_modes)
|
||||
group_face_selector.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), type = int, default = config.get_int_value('face_selector.reference_face_position', '0'))
|
||||
group_face_selector.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), type = float, default = config.get_float_value('face_selector.reference_face_distance', '0.6'), choices = facefusion.choices.reference_face_distance_range, metavar = create_metavar(facefusion.choices.reference_face_distance_range))
|
||||
group_face_selector.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), type = int, default = config.get_int_value('face_selector.reference_frame_number', '0'))
|
||||
# face mask
|
||||
group_face_mask = program.add_argument_group('face mask')
|
||||
group_face_mask.add_argument('--face-mask-types', help = wording.get('face_mask_types_help').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = [ 'box' ], choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
|
||||
group_face_mask.add_argument('--face-mask-blur', help = wording.get('face_mask_blur_help'), type = float, default = 0.3, choices = facefusion.choices.face_mask_blur_range, metavar = create_metavar(facefusion.choices.face_mask_blur_range))
|
||||
group_face_mask.add_argument('--face-mask-padding', help = wording.get('face_mask_padding_help'), type = int, default = [ 0, 0, 0, 0 ], nargs = '+')
|
||||
group_face_mask.add_argument('--face-mask-regions', help = wording.get('face_mask_regions_help').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = facefusion.choices.face_mask_regions, choices = facefusion.choices.face_mask_regions, nargs = '+', metavar = 'FACE_MASK_REGIONS')
|
||||
group_face_mask.add_argument('--face-mask-types', help = wording.get('face_mask_types_help').format(choices = ', '.join(facefusion.choices.face_mask_types)), default = config.get_str_list('face_mask.face_mask_types', 'box'), choices = facefusion.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
|
||||
group_face_mask.add_argument('--face-mask-blur', help = wording.get('face_mask_blur_help'), type = float, default = config.get_float_value('face_mask.face_mask_blur', '0.3'), choices = facefusion.choices.face_mask_blur_range, metavar = create_metavar(facefusion.choices.face_mask_blur_range))
|
||||
group_face_mask.add_argument('--face-mask-padding', help = wording.get('face_mask_padding_help'), type = int, default = config.get_int_list('face_mask.face_mask_padding', '0 0 0 0'), nargs = '+')
|
||||
group_face_mask.add_argument('--face-mask-regions', help = wording.get('face_mask_regions_help').format(choices = ', '.join(facefusion.choices.face_mask_regions)), default = config.get_str_list('face_mask.face_mask_regions', ' '.join(facefusion.choices.face_mask_regions)), choices = facefusion.choices.face_mask_regions, nargs = '+', metavar = 'FACE_MASK_REGIONS')
|
||||
# frame extraction
|
||||
group_frame_extraction = program.add_argument_group('frame extraction')
|
||||
group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), type = int)
|
||||
group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), type = int)
|
||||
group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), default = 'jpg', choices = facefusion.choices.temp_frame_formats)
|
||||
group_frame_extraction.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), type = int, default = 100, choices = facefusion.choices.temp_frame_quality_range, metavar = create_metavar(facefusion.choices.temp_frame_quality_range))
|
||||
group_frame_extraction.add_argument('--keep-temp', help = wording.get('keep_temp_help'), action = 'store_true')
|
||||
group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_start'))
|
||||
group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_end'))
|
||||
group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), default = config.get_str_value('frame_extraction.temp_frame_format', 'jpg'), choices = facefusion.choices.temp_frame_formats)
|
||||
group_frame_extraction.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), type = int, default = config.get_int_value('frame_extraction.temp_frame_quality', '100'), choices = facefusion.choices.temp_frame_quality_range, metavar = create_metavar(facefusion.choices.temp_frame_quality_range))
|
||||
group_frame_extraction.add_argument('--keep-temp', help = wording.get('keep_temp_help'), action = 'store_true', default = config.get_bool_value('frame_extraction.keep_temp'))
|
||||
# output creation
|
||||
group_output_creation = program.add_argument_group('output creation')
|
||||
group_output_creation.add_argument('--output-image-quality', help = wording.get('output_image_quality_help'), type = int, default = 80, choices = facefusion.choices.output_image_quality_range, metavar = create_metavar(facefusion.choices.output_image_quality_range))
|
||||
group_output_creation.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), default = 'libx264', choices = facefusion.choices.output_video_encoders)
|
||||
group_output_creation.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), type = int, default = 80, choices = facefusion.choices.output_video_quality_range, metavar = create_metavar(facefusion.choices.output_video_quality_range))
|
||||
group_output_creation.add_argument('--keep-fps', help = wording.get('keep_fps_help'), action = 'store_true')
|
||||
group_output_creation.add_argument('--skip-audio', help = wording.get('skip_audio_help'), action = 'store_true')
|
||||
group_output_creation.add_argument('--output-image-quality', help = wording.get('output_image_quality_help'), type = int, default = config.get_int_value('output_creation.output_image_quality', '80'), choices = facefusion.choices.output_image_quality_range, metavar = create_metavar(facefusion.choices.output_image_quality_range))
|
||||
group_output_creation.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), default = config.get_str_value('output_creation.output_video_encoder', 'libx264'), choices = facefusion.choices.output_video_encoders)
|
||||
group_output_creation.add_argument('--output-video-preset', help = wording.get('output_video_preset_help'), default = config.get_str_value('output_creation.output_video_preset', 'veryfast'), choices = facefusion.choices.output_video_presets)
|
||||
group_output_creation.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), type = int, default = config.get_int_value('output_creation.output_video_quality', '80'), choices = facefusion.choices.output_video_quality_range, metavar = create_metavar(facefusion.choices.output_video_quality_range))
|
||||
group_output_creation.add_argument('--output-video-resolution', help = wording.get('output_video_resolution_help'), default = config.get_str_value('output_creation.output_video_resolution'))
|
||||
group_output_creation.add_argument('--output-video-fps', help = wording.get('output_video_fps_help'), type = float)
|
||||
group_output_creation.add_argument('--skip-audio', help = wording.get('skip_audio_help'), action = 'store_true', default = config.get_bool_value('output_creation.skip_audio'))
|
||||
# frame processors
|
||||
available_frame_processors = list_module_names('facefusion/processors/frame/modules')
|
||||
available_frame_processors = list_directory('facefusion/processors/frame/modules')
|
||||
program = ArgumentParser(parents = [ program ], formatter_class = program.formatter_class, add_help = True)
|
||||
group_frame_processors = program.add_argument_group('frame processors')
|
||||
group_frame_processors.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(available_frame_processors)), default = [ 'face_swapper' ], nargs = '+')
|
||||
group_frame_processors.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(available_frame_processors)), default = config.get_str_list('frame_processors.frame_processors', 'face_swapper'), nargs = '+')
|
||||
for frame_processor in available_frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
frame_processor_module.register_args(group_frame_processors)
|
||||
# uis
|
||||
available_ui_layouts = list_directory('facefusion/uis/layouts')
|
||||
group_uis = program.add_argument_group('uis')
|
||||
group_uis.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(list_module_names('facefusion/uis/layouts'))), default = [ 'default' ], nargs = '+')
|
||||
group_uis.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(available_ui_layouts)), default = config.get_str_list('uis.ui_layout', 'default'), nargs = '+')
|
||||
run(program)
|
||||
|
||||
|
||||
@ -115,7 +120,9 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
facefusion.globals.execution_providers = decode_execution_providers(args.execution_providers)
|
||||
facefusion.globals.execution_thread_count = args.execution_thread_count
|
||||
facefusion.globals.execution_queue_count = args.execution_queue_count
|
||||
facefusion.globals.max_memory = args.max_memory
|
||||
# memory
|
||||
facefusion.globals.video_memory_strategy = args.video_memory_strategy
|
||||
facefusion.globals.system_memory_limit = args.system_memory_limit
|
||||
# face analyser
|
||||
facefusion.globals.face_analyser_order = args.face_analyser_order
|
||||
facefusion.globals.face_analyser_age = args.face_analyser_age
|
||||
@ -142,11 +149,20 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
# output creation
|
||||
facefusion.globals.output_image_quality = args.output_image_quality
|
||||
facefusion.globals.output_video_encoder = args.output_video_encoder
|
||||
facefusion.globals.output_video_preset = args.output_video_preset
|
||||
facefusion.globals.output_video_quality = args.output_video_quality
|
||||
facefusion.globals.keep_fps = args.keep_fps
|
||||
if is_video(args.target_path):
|
||||
target_video_resolutions = create_video_resolutions(args.target_path)
|
||||
if args.output_video_resolution in target_video_resolutions:
|
||||
facefusion.globals.output_video_resolution = args.output_video_resolution
|
||||
else:
|
||||
target_video_resolution = detect_video_resolution(args.target_path)
|
||||
facefusion.globals.output_video_resolution = pack_resolution(target_video_resolution)
|
||||
if args.output_video_fps or is_video(args.target_path):
|
||||
facefusion.globals.output_video_fps = normalize_fps(args.output_video_fps) or detect_video_fps(args.target_path)
|
||||
facefusion.globals.skip_audio = args.skip_audio
|
||||
# frame processors
|
||||
available_frame_processors = list_module_names('facefusion/processors/frame/modules')
|
||||
available_frame_processors = list_directory('facefusion/processors/frame/modules')
|
||||
facefusion.globals.frame_processors = args.frame_processors
|
||||
for frame_processor in available_frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
@ -158,7 +174,8 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
def run(program : ArgumentParser) -> None:
|
||||
apply_args(program)
|
||||
logger.init(facefusion.globals.log_level)
|
||||
limit_resources()
|
||||
if facefusion.globals.system_memory_limit > 0:
|
||||
limit_system_memory(facefusion.globals.system_memory_limit)
|
||||
if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check() or not face_masker.pre_check():
|
||||
return
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
@ -178,23 +195,7 @@ def run(program : ArgumentParser) -> None:
|
||||
def destroy() -> None:
|
||||
if facefusion.globals.target_path:
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
sys.exit()
|
||||
|
||||
|
||||
def limit_resources() -> None:
|
||||
if facefusion.globals.max_memory:
|
||||
memory = facefusion.globals.max_memory * 1024 ** 3
|
||||
if platform.system().lower() == 'darwin':
|
||||
memory = facefusion.globals.max_memory * 1024 ** 6
|
||||
if platform.system().lower() == 'windows':
|
||||
import ctypes
|
||||
|
||||
kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
|
||||
kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
|
||||
else:
|
||||
import resource
|
||||
|
||||
resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
|
||||
sys.exit(0)
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
@ -208,14 +209,19 @@ def pre_check() -> bool:
|
||||
|
||||
|
||||
def conditional_process() -> None:
|
||||
conditional_append_reference_faces()
|
||||
start_time = time.time()
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
while not frame_processor_module.post_check():
|
||||
logger.disable()
|
||||
sleep(0.5)
|
||||
logger.enable()
|
||||
if not frame_processor_module.pre_process('output'):
|
||||
return
|
||||
conditional_append_reference_faces()
|
||||
if is_image(facefusion.globals.target_path):
|
||||
process_image()
|
||||
process_image(start_time)
|
||||
if is_video(facefusion.globals.target_path):
|
||||
process_video()
|
||||
process_video(start_time)
|
||||
|
||||
|
||||
def conditional_append_reference_faces() -> None:
|
||||
@ -230,12 +236,14 @@ def conditional_append_reference_faces() -> None:
|
||||
append_reference_face('origin', reference_face)
|
||||
if source_face and reference_face:
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
reference_frame = frame_processor_module.get_reference_frame(source_face, reference_face, reference_frame)
|
||||
reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
|
||||
append_reference_face(frame_processor_module.__name__, reference_face)
|
||||
abstract_reference_frame = frame_processor_module.get_reference_frame(source_face, reference_face, reference_frame)
|
||||
if numpy.any(abstract_reference_frame):
|
||||
reference_frame = abstract_reference_frame
|
||||
reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
|
||||
append_reference_face(frame_processor_module.__name__, reference_face)
|
||||
|
||||
|
||||
def process_image() -> None:
|
||||
def process_image(start_time : float) -> None:
|
||||
if analyse_image(facefusion.globals.target_path):
|
||||
return
|
||||
shutil.copy2(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
@ -250,21 +258,21 @@ def process_image() -> None:
|
||||
logger.error(wording.get('compressing_image_failed'), __name__.upper())
|
||||
# validate image
|
||||
if is_image(facefusion.globals.output_path):
|
||||
logger.info(wording.get('processing_image_succeed'), __name__.upper())
|
||||
seconds = '{:.2f}'.format((time.time() - start_time) % 60)
|
||||
logger.info(wording.get('processing_image_succeed').format(seconds = seconds), __name__.upper())
|
||||
else:
|
||||
logger.error(wording.get('processing_image_failed'), __name__.upper())
|
||||
|
||||
|
||||
def process_video() -> None:
|
||||
def process_video(start_time : float) -> None:
|
||||
if analyse_video(facefusion.globals.target_path, facefusion.globals.trim_frame_start, facefusion.globals.trim_frame_end):
|
||||
return
|
||||
fps = detect_fps(facefusion.globals.target_path) if facefusion.globals.keep_fps else 25.0
|
||||
# create temp
|
||||
logger.info(wording.get('creating_temp'), __name__.upper())
|
||||
create_temp(facefusion.globals.target_path)
|
||||
# extract frames
|
||||
logger.info(wording.get('extracting_frames_fps').format(fps = fps), __name__.upper())
|
||||
extract_frames(facefusion.globals.target_path, fps)
|
||||
logger.info(wording.get('extracting_frames_fps').format(video_fps = facefusion.globals.output_video_fps), __name__.upper())
|
||||
extract_frames(facefusion.globals.target_path, facefusion.globals.output_video_resolution, facefusion.globals.output_video_fps)
|
||||
# process frame
|
||||
temp_frame_paths = get_temp_frame_paths(facefusion.globals.target_path)
|
||||
if temp_frame_paths:
|
||||
@ -276,8 +284,8 @@ def process_video() -> None:
|
||||
logger.error(wording.get('temp_frames_not_found'), __name__.upper())
|
||||
return
|
||||
# merge video
|
||||
logger.info(wording.get('merging_video_fps').format(fps = fps), __name__.upper())
|
||||
if not merge_video(facefusion.globals.target_path, fps):
|
||||
logger.info(wording.get('merging_video_fps').format(video_fps = facefusion.globals.output_video_fps), __name__.upper())
|
||||
if not merge_video(facefusion.globals.target_path, facefusion.globals.output_video_fps):
|
||||
logger.error(wording.get('merging_video_failed'), __name__.upper())
|
||||
return
|
||||
# handle audio
|
||||
@ -286,7 +294,7 @@ def process_video() -> None:
|
||||
move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
else:
|
||||
logger.info(wording.get('restoring_audio'), __name__.upper())
|
||||
if not restore_audio(facefusion.globals.target_path, facefusion.globals.output_path):
|
||||
if not restore_audio(facefusion.globals.target_path, facefusion.globals.output_path, facefusion.globals.output_video_fps):
|
||||
logger.warn(wording.get('restoring_audio_skipped'), __name__.upper())
|
||||
move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
# clear temp
|
||||
@ -294,6 +302,7 @@ def process_video() -> None:
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
# validate video
|
||||
if is_video(facefusion.globals.output_path):
|
||||
logger.info(wording.get('processing_video_succeed'), __name__.upper())
|
||||
seconds = '{:.2f}'.format((time.time() - start_time))
|
||||
logger.info(wording.get('processing_video_succeed').format(seconds = seconds), __name__.upper())
|
||||
else:
|
||||
logger.error(wording.get('processing_video_failed'), __name__.upper())
|
||||
|
||||
@ -1,5 +1,7 @@
|
||||
import os
|
||||
import subprocess
|
||||
import platform
|
||||
import ssl
|
||||
import urllib.request
|
||||
from typing import List
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
@ -10,6 +12,9 @@ import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.filesystem import is_file
|
||||
|
||||
if platform.system().lower() == 'darwin':
|
||||
ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
|
||||
def conditional_download(download_directory_path : str, urls : List[str]) -> None:
|
||||
with ThreadPoolExecutor() as executor:
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import List
|
||||
from typing import Any, List
|
||||
import onnxruntime
|
||||
|
||||
|
||||
@ -9,10 +9,25 @@ def encode_execution_providers(execution_providers : List[str]) -> List[str]:
|
||||
def decode_execution_providers(execution_providers: List[str]) -> List[str]:
|
||||
available_execution_providers = onnxruntime.get_available_providers()
|
||||
encoded_execution_providers = encode_execution_providers(available_execution_providers)
|
||||
|
||||
return [ execution_provider for execution_provider, encoded_execution_provider in zip(available_execution_providers, encoded_execution_providers) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers) ]
|
||||
|
||||
|
||||
def map_device(execution_providers : List[str]) -> str:
|
||||
def apply_execution_provider_options(execution_providers: List[str]) -> List[Any]:
|
||||
execution_providers_with_options : List[Any] = []
|
||||
|
||||
for execution_provider in execution_providers:
|
||||
if execution_provider == 'CUDAExecutionProvider':
|
||||
execution_providers_with_options.append((execution_provider,
|
||||
{
|
||||
'cudnn_conv_algo_search': 'DEFAULT'
|
||||
}))
|
||||
else:
|
||||
execution_providers_with_options.append(execution_provider)
|
||||
return execution_providers_with_options
|
||||
|
||||
|
||||
def map_torch_backend(execution_providers : List[str]) -> str:
|
||||
if 'CoreMLExecutionProvider' in execution_providers:
|
||||
return 'mps'
|
||||
if 'CUDAExecutionProvider' in execution_providers or 'ROCMExecutionProvider' in execution_providers :
|
||||
|
||||
@ -7,10 +7,11 @@ import onnxruntime
|
||||
import facefusion.globals
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.face_store import get_static_faces, set_static_faces
|
||||
from facefusion.face_helper import warp_face, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.face_helper import warp_face_by_kps, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.typing import Frame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, Bbox, Kps, Score, Embedding
|
||||
from facefusion.vision import resize_frame_dimension
|
||||
from facefusion.vision import resize_frame_resolution, unpack_resolution
|
||||
|
||||
FACE_ANALYSER = None
|
||||
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
|
||||
@ -56,16 +57,16 @@ def get_face_analyser() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FACE_ANALYSER is None:
|
||||
if facefusion.globals.face_detector_model == 'retinaface':
|
||||
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = facefusion.globals.execution_providers)
|
||||
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
if facefusion.globals.face_detector_model == 'yunet':
|
||||
face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
|
||||
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = facefusion.globals.execution_providers)
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = facefusion.globals.execution_providers)
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
if facefusion.globals.face_recognizer_model == 'arcface_simswap':
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = facefusion.globals.execution_providers)
|
||||
gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = facefusion.globals.execution_providers)
|
||||
face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
FACE_ANALYSER =\
|
||||
{
|
||||
'face_detector': face_detector,
|
||||
@ -96,10 +97,10 @@ def pre_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def extract_faces(frame: Frame) -> List[Face]:
|
||||
face_detector_width, face_detector_height = map(int, facefusion.globals.face_detector_size.split('x'))
|
||||
def extract_faces(frame : Frame) -> List[Face]:
|
||||
face_detector_width, face_detector_height = unpack_resolution(facefusion.globals.face_detector_size)
|
||||
frame_height, frame_width, _ = frame.shape
|
||||
temp_frame = resize_frame_dimension(frame, face_detector_width, face_detector_height)
|
||||
temp_frame = resize_frame_resolution(frame, face_detector_width, face_detector_height)
|
||||
temp_frame_height, temp_frame_width, _ = temp_frame.shape
|
||||
ratio_height = frame_height / temp_frame_height
|
||||
ratio_width = frame_width / temp_frame_width
|
||||
@ -135,7 +136,7 @@ def detect_with_retinaface(temp_frame : Frame, temp_frame_height : int, temp_fra
|
||||
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)
|
||||
bbox_raw = (detections[index + feature_map_channel] * feature_stride)
|
||||
bbox_raw = detections[index + feature_map_channel] * feature_stride
|
||||
kps_raw = detections[index + feature_map_channel * 2] * feature_stride
|
||||
for bbox in distance_to_bbox(anchors, bbox_raw)[keep_indices]:
|
||||
bbox_list.append(numpy.array(
|
||||
@ -188,7 +189,7 @@ def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], sc
|
||||
kps = kps_list[index]
|
||||
score = score_list[index]
|
||||
embedding, normed_embedding = calc_embedding(frame, kps)
|
||||
gender, age = detect_gender_age(frame, kps)
|
||||
gender, age = detect_gender_age(frame, bbox)
|
||||
faces.append(Face(
|
||||
bbox = bbox,
|
||||
kps = kps,
|
||||
@ -203,7 +204,7 @@ def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], sc
|
||||
|
||||
def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]:
|
||||
face_recognizer = get_face_analyser().get('face_recognizer')
|
||||
crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_112_v2', (112, 112))
|
||||
crop_frame, matrix = warp_face_by_kps(temp_frame, kps, 'arcface_112_v2', (112, 112))
|
||||
crop_frame = crop_frame.astype(numpy.float32) / 127.5 - 1
|
||||
crop_frame = crop_frame[:, :, ::-1].transpose(2, 0, 1)
|
||||
crop_frame = numpy.expand_dims(crop_frame, axis = 0)
|
||||
@ -216,10 +217,15 @@ def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]
|
||||
return embedding, normed_embedding
|
||||
|
||||
|
||||
def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
|
||||
def detect_gender_age(frame : Frame, bbox : Bbox) -> Tuple[int, int]:
|
||||
gender_age = get_face_analyser().get('gender_age')
|
||||
crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_112_v2', (96, 96))
|
||||
crop_frame = numpy.expand_dims(crop_frame, axis = 0).transpose(0, 3, 1, 2).astype(numpy.float32)
|
||||
bbox = bbox.reshape(2, -1)
|
||||
scale = 64 / numpy.subtract(*bbox[::-1]).max()
|
||||
translation = 48 - bbox.sum(axis = 0) * 0.5 * scale
|
||||
affine_matrix = numpy.array([[ scale, 0, translation[0] ], [ 0, scale, translation[1] ]])
|
||||
crop_frame = cv2.warpAffine(frame, affine_matrix, (96, 96))
|
||||
crop_frame = crop_frame.astype(numpy.float32)[:, :, ::-1].transpose(2, 0, 1)
|
||||
crop_frame = numpy.expand_dims(crop_frame, axis = 0)
|
||||
prediction = gender_age.run(None,
|
||||
{
|
||||
gender_age.get_inputs()[0].name: crop_frame
|
||||
@ -297,10 +303,14 @@ def find_similar_faces(frame : Frame, reference_faces : FaceSet, face_distance :
|
||||
|
||||
|
||||
def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
|
||||
current_face_distance = calc_face_distance(face, reference_face)
|
||||
return current_face_distance < face_distance
|
||||
|
||||
|
||||
def calc_face_distance(face : Face, reference_face : Face) -> float:
|
||||
if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
|
||||
current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
|
||||
return current_face_distance < face_distance
|
||||
return False
|
||||
return 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
|
||||
return 0
|
||||
|
||||
|
||||
def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
|
||||
|
||||
@ -10,47 +10,59 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
|
||||
{
|
||||
'arcface_112_v1': numpy.array(
|
||||
[
|
||||
[ 39.7300, 51.1380 ],
|
||||
[ 72.2700, 51.1380 ],
|
||||
[ 56.0000, 68.4930 ],
|
||||
[ 42.4630, 87.0100 ],
|
||||
[ 69.5370, 87.0100 ]
|
||||
[ 0.35473214, 0.45658929 ],
|
||||
[ 0.64526786, 0.45658929 ],
|
||||
[ 0.50000000, 0.61154464 ],
|
||||
[ 0.37913393, 0.77687500 ],
|
||||
[ 0.62086607, 0.77687500 ]
|
||||
]),
|
||||
'arcface_112_v2': numpy.array(
|
||||
[
|
||||
[ 38.2946, 51.6963 ],
|
||||
[ 73.5318, 51.5014 ],
|
||||
[ 56.0252, 71.7366 ],
|
||||
[ 41.5493, 92.3655 ],
|
||||
[ 70.7299, 92.2041 ]
|
||||
[ 0.34191607, 0.46157411 ],
|
||||
[ 0.65653393, 0.45983393 ],
|
||||
[ 0.50022500, 0.64050536 ],
|
||||
[ 0.37097589, 0.82469196 ],
|
||||
[ 0.63151696, 0.82325089 ]
|
||||
]),
|
||||
'arcface_128_v2': numpy.array(
|
||||
[
|
||||
[ 46.2946, 51.6963 ],
|
||||
[ 81.5318, 51.5014 ],
|
||||
[ 64.0252, 71.7366 ],
|
||||
[ 49.5493, 92.3655 ],
|
||||
[ 78.7299, 92.2041 ]
|
||||
[ 0.36167656, 0.40387734 ],
|
||||
[ 0.63696719, 0.40235469 ],
|
||||
[ 0.50019687, 0.56044219 ],
|
||||
[ 0.38710391, 0.72160547 ],
|
||||
[ 0.61507734, 0.72034453 ]
|
||||
]),
|
||||
'ffhq_512': numpy.array(
|
||||
[
|
||||
[ 192.98138, 239.94708 ],
|
||||
[ 318.90277, 240.1936 ],
|
||||
[ 256.63416, 314.01935 ],
|
||||
[ 201.26117, 371.41043 ],
|
||||
[ 313.08905, 371.15118 ]
|
||||
[ 0.37691676, 0.46864664 ],
|
||||
[ 0.62285697, 0.46912813 ],
|
||||
[ 0.50123859, 0.61331904 ],
|
||||
[ 0.39308822, 0.72541100 ],
|
||||
[ 0.61150205, 0.72490465 ]
|
||||
])
|
||||
}
|
||||
|
||||
|
||||
def warp_face(temp_frame : Frame, kps : Kps, template : Template, size : Size) -> Tuple[Frame, Matrix]:
|
||||
normed_template = TEMPLATES.get(template) * size[1] / size[0]
|
||||
def warp_face_by_kps(temp_frame : Frame, kps : Kps, template : Template, crop_size : Size) -> Tuple[Frame, Matrix]:
|
||||
normed_template = TEMPLATES.get(template) * crop_size
|
||||
affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
|
||||
crop_frame = cv2.warpAffine(temp_frame, affine_matrix, (size[1], size[1]), borderMode = cv2.BORDER_REPLICATE)
|
||||
crop_frame = cv2.warpAffine(temp_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
|
||||
return crop_frame, affine_matrix
|
||||
|
||||
|
||||
def paste_back(temp_frame : Frame, crop_frame: Frame, crop_mask : Mask, affine_matrix : Matrix) -> Frame:
|
||||
def warp_face_by_bbox(temp_frame : Frame, bbox : Bbox, crop_size : Size) -> Tuple[Frame, Matrix]:
|
||||
source_kps = numpy.array([[ bbox[0], bbox[1] ], [bbox[2], bbox[1] ], [bbox[0], bbox[3] ]], dtype = numpy.float32)
|
||||
target_kps = numpy.array([[ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ]], dtype = numpy.float32)
|
||||
affine_matrix = cv2.getAffineTransform(source_kps, target_kps)
|
||||
if bbox[2] - bbox[0] > crop_size[0] or bbox[3] - bbox[1] > crop_size[1]:
|
||||
interpolation_method = cv2.INTER_AREA
|
||||
else:
|
||||
interpolation_method = cv2.INTER_LINEAR
|
||||
crop_frame = cv2.warpAffine(temp_frame, affine_matrix, crop_size, flags = interpolation_method)
|
||||
return crop_frame, affine_matrix
|
||||
|
||||
|
||||
def paste_back(temp_frame : Frame, crop_frame : Frame, crop_mask : Mask, affine_matrix : Matrix) -> Frame:
|
||||
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
|
||||
temp_frame_size = temp_frame.shape[:2][::-1]
|
||||
inverse_crop_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size).clip(0, 1)
|
||||
|
||||
@ -8,6 +8,7 @@ import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion.typing import Frame, Mask, Padding, FaceMaskRegion, ModelSet
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.download import conditional_download
|
||||
|
||||
@ -48,7 +49,7 @@ def get_face_occluder() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FACE_OCCLUDER is None:
|
||||
model_path = MODELS.get('face_occluder').get('path')
|
||||
FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
|
||||
FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
return FACE_OCCLUDER
|
||||
|
||||
|
||||
@ -58,7 +59,7 @@ def get_face_parser() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FACE_PARSER is None:
|
||||
model_path = MODELS.get('face_parser').get('path')
|
||||
FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
|
||||
FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
return FACE_PARSER
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from typing import Optional, List
|
||||
import hashlib
|
||||
import numpy
|
||||
|
||||
from facefusion.typing import Frame, Face, FaceStore, FaceSet
|
||||
|
||||
@ -27,8 +28,8 @@ def clear_static_faces() -> None:
|
||||
FACE_STORE['static_faces'] = {}
|
||||
|
||||
|
||||
def create_frame_hash(frame: Frame) -> Optional[str]:
|
||||
return hashlib.sha1(frame.tobytes()).hexdigest() if frame.any() else None
|
||||
def create_frame_hash(frame : Frame) -> Optional[str]:
|
||||
return hashlib.sha1(frame.tobytes()).hexdigest() if numpy.any(frame) else None
|
||||
|
||||
|
||||
def get_reference_faces() -> Optional[FaceSet]:
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
from typing import List
|
||||
from typing import List, Optional
|
||||
import subprocess
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import logger
|
||||
from facefusion.typing import OutputVideoPreset, Fps
|
||||
from facefusion.filesystem import get_temp_frames_pattern, get_temp_output_video_path
|
||||
from facefusion.vision import detect_fps
|
||||
|
||||
|
||||
def run_ffmpeg(args : List[str]) -> bool:
|
||||
@ -24,20 +24,20 @@ def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
|
||||
return subprocess.Popen(commands, stdin = subprocess.PIPE)
|
||||
|
||||
|
||||
def extract_frames(target_path : str, fps : float) -> bool:
|
||||
def extract_frames(target_path : str, video_resolution : str, video_fps : Fps) -> bool:
|
||||
temp_frame_compression = round(31 - (facefusion.globals.temp_frame_quality * 0.31))
|
||||
trim_frame_start = facefusion.globals.trim_frame_start
|
||||
trim_frame_end = facefusion.globals.trim_frame_end
|
||||
temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
|
||||
commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', str(temp_frame_compression), '-pix_fmt', 'rgb24' ]
|
||||
if trim_frame_start is not None and trim_frame_end is not None:
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
elif trim_frame_start is not None:
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(fps) ])
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
elif trim_frame_end is not None:
|
||||
commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
|
||||
commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
else:
|
||||
commands.extend([ '-vf', 'fps=' + str(fps) ])
|
||||
commands.extend([ '-vf', 'scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
commands.extend([ '-vsync', '0', temp_frames_pattern ])
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
@ -48,34 +48,51 @@ def compress_image(output_path : str) -> bool:
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def merge_video(target_path : str, fps : float) -> bool:
|
||||
def merge_video(target_path : str, video_fps : Fps) -> bool:
|
||||
temp_output_video_path = get_temp_output_video_path(target_path)
|
||||
temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
|
||||
commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
|
||||
commands = [ '-hwaccel', 'auto', '-r', str(video_fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
|
||||
if facefusion.globals.output_video_encoder in [ 'libx264', 'libx265' ]:
|
||||
output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
|
||||
commands.extend([ '-crf', str(output_video_compression) ])
|
||||
commands.extend([ '-crf', str(output_video_compression), '-preset', facefusion.globals.output_video_preset ])
|
||||
if facefusion.globals.output_video_encoder in [ 'libvpx-vp9' ]:
|
||||
output_video_compression = round(63 - (facefusion.globals.output_video_quality * 0.63))
|
||||
commands.extend([ '-crf', str(output_video_compression) ])
|
||||
if facefusion.globals.output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
|
||||
output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
|
||||
commands.extend([ '-cq', str(output_video_compression) ])
|
||||
commands.extend([ '-cq', str(output_video_compression), '-preset', map_nvenc_preset(facefusion.globals.output_video_preset) ])
|
||||
commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def restore_audio(target_path : str, output_path : str) -> bool:
|
||||
fps = detect_fps(target_path)
|
||||
def restore_audio(target_path : str, output_path : str, video_fps : Fps) -> bool:
|
||||
trim_frame_start = facefusion.globals.trim_frame_start
|
||||
trim_frame_end = facefusion.globals.trim_frame_end
|
||||
temp_output_video_path = get_temp_output_video_path(target_path)
|
||||
commands = [ '-hwaccel', 'auto', '-i', temp_output_video_path ]
|
||||
if trim_frame_start is not None:
|
||||
start_time = trim_frame_start / fps
|
||||
start_time = trim_frame_start / video_fps
|
||||
commands.extend([ '-ss', str(start_time) ])
|
||||
if trim_frame_end is not None:
|
||||
end_time = trim_frame_end / fps
|
||||
end_time = trim_frame_end / video_fps
|
||||
commands.extend([ '-to', str(end_time) ])
|
||||
commands.extend([ '-i', target_path, '-c', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ])
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def map_nvenc_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
|
||||
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
|
||||
return 'p1'
|
||||
if output_video_preset == 'faster':
|
||||
return 'p2'
|
||||
if output_video_preset == 'fast':
|
||||
return 'p3'
|
||||
if output_video_preset == 'medium':
|
||||
return 'p4'
|
||||
if output_video_preset == 'slow':
|
||||
return 'p5'
|
||||
if output_video_preset == 'slower':
|
||||
return 'p6'
|
||||
if output_video_preset == 'veryslow':
|
||||
return 'p7'
|
||||
return None
|
||||
|
||||
@ -84,8 +84,8 @@ def resolve_relative_path(path : str) -> str:
|
||||
return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
|
||||
|
||||
|
||||
def list_module_names(path : str) -> Optional[List[str]]:
|
||||
if os.path.exists(path):
|
||||
files = os.listdir(path)
|
||||
def list_directory(directory_path : str) -> Optional[List[str]]:
|
||||
if is_directory(directory_path):
|
||||
files = os.listdir(directory_path)
|
||||
return [ Path(file).stem for file in files if not Path(file).stem.startswith(('.', '__')) ]
|
||||
return None
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from typing import List, Optional
|
||||
|
||||
from facefusion.typing import LogLevel, FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, OutputVideoEncoder, FaceDetectorModel, FaceRecognizerModel, TempFrameFormat, Padding
|
||||
from facefusion.typing import LogLevel, VideoMemoryStrategy, FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, OutputVideoEncoder, OutputVideoPreset, FaceDetectorModel, FaceRecognizerModel, TempFrameFormat, Padding
|
||||
|
||||
# general
|
||||
source_paths : Optional[List[str]] = None
|
||||
@ -14,7 +14,9 @@ log_level : Optional[LogLevel] = None
|
||||
execution_providers : List[str] = []
|
||||
execution_thread_count : Optional[int] = None
|
||||
execution_queue_count : Optional[int] = None
|
||||
max_memory : Optional[int] = None
|
||||
# memory
|
||||
video_memory_strategy : Optional[VideoMemoryStrategy] = None
|
||||
system_memory_limit : Optional[int] = None
|
||||
# face analyser
|
||||
face_analyser_order : Optional[FaceAnalyserOrder] = None
|
||||
face_analyser_age : Optional[FaceAnalyserAge] = None
|
||||
@ -42,8 +44,10 @@ keep_temp : Optional[bool] = None
|
||||
# output creation
|
||||
output_image_quality : Optional[int] = None
|
||||
output_video_encoder : Optional[OutputVideoEncoder] = None
|
||||
output_video_preset : Optional[OutputVideoPreset] = None
|
||||
output_video_quality : Optional[int] = None
|
||||
keep_fps : Optional[bool] = None
|
||||
output_video_resolution : Optional[str] = None
|
||||
output_video_fps : Optional[float] = None
|
||||
skip_audio : Optional[bool] = None
|
||||
# frame processors
|
||||
frame_processors : List[str] = []
|
||||
|
||||
@ -25,7 +25,7 @@ if platform.system().lower() == 'linux' or platform.system().lower() == 'windows
|
||||
TORCH['cuda'] = 'cu118'
|
||||
TORCH['cuda-nightly'] = 'cu121'
|
||||
ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.16.3')
|
||||
ONNXRUNTIMES['cuda-nightly'] = ('ort-nightly-gpu', '1.17.0.dev20231205004')
|
||||
ONNXRUNTIMES['cuda-nightly'] = ('onnxruntime-gpu', '1.17.0')
|
||||
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.16.0')
|
||||
if platform.system().lower() == 'linux':
|
||||
TORCH['rocm'] = 'rocm5.6'
|
||||
@ -72,9 +72,9 @@ def run(program : ArgumentParser) -> None:
|
||||
|
||||
subprocess.call([ 'pip', 'uninstall', 'torch', '-y', '-q' ])
|
||||
if torch_wheel == 'default':
|
||||
subprocess.call([ 'pip', 'install', '-r', 'requirements.txt' ])
|
||||
subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--force-reinstall' ])
|
||||
else:
|
||||
subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--extra-index-url', 'https://download.pytorch.org/whl/' + torch_wheel ])
|
||||
subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--extra-index-url', 'https://download.pytorch.org/whl/' + torch_wheel, '--force-reinstall' ])
|
||||
if onnxruntime == 'rocm':
|
||||
if python_id in [ 'cp39', 'cp310', 'cp311' ]:
|
||||
wheel_name = 'onnxruntime_training-' + onnxruntime_version + '+rocm56-' + python_id + '-' + python_id + '-manylinux_2_17_x86_64.manylinux2014_x86_64.whl'
|
||||
@ -82,11 +82,11 @@ def run(program : ArgumentParser) -> None:
|
||||
wheel_url = 'https://download.onnxruntime.ai/' + wheel_name
|
||||
subprocess.call([ 'curl', '--silent', '--location', '--continue-at', '-', '--output', wheel_path, wheel_url ])
|
||||
subprocess.call([ 'pip', 'uninstall', wheel_path, '-y', '-q' ])
|
||||
subprocess.call([ 'pip', 'install', wheel_path ])
|
||||
subprocess.call([ 'pip', 'install', wheel_path, '--force-reinstall' ])
|
||||
os.remove(wheel_path)
|
||||
else:
|
||||
subprocess.call([ 'pip', 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
|
||||
if onnxruntime == 'cuda-nightly':
|
||||
subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version, '--extra-index-url', 'https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple' ])
|
||||
subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version, '--extra-index-url', 'https://pkgs.dev.azure.com/onnxruntime/onnxruntime/_packaging/onnxruntime-cuda-12/pypi/simple', '--force-reinstall' ])
|
||||
else:
|
||||
subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version ])
|
||||
subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version, '--force-reinstall' ])
|
||||
|
||||
@ -29,6 +29,14 @@ def error(message : str, scope : str) -> None:
|
||||
get_package_logger().error('[' + scope + '] ' + message)
|
||||
|
||||
|
||||
def enable() -> None:
|
||||
get_package_logger().disabled = False
|
||||
|
||||
|
||||
def disable() -> None:
|
||||
get_package_logger().disabled = True
|
||||
|
||||
|
||||
def get_log_levels() -> Dict[LogLevel, int]:
|
||||
return\
|
||||
{
|
||||
|
||||
21
facefusion/memory.py
Normal file
21
facefusion/memory.py
Normal file
@ -0,0 +1,21 @@
|
||||
import platform
|
||||
|
||||
if platform.system().lower() == 'windows':
|
||||
import ctypes
|
||||
else:
|
||||
import resource
|
||||
|
||||
|
||||
def limit_system_memory(system_memory_limit : int = 1) -> bool:
|
||||
if platform.system().lower() == 'darwin':
|
||||
system_memory_limit = system_memory_limit * (1024 ** 6)
|
||||
else:
|
||||
system_memory_limit = system_memory_limit * (1024 ** 3)
|
||||
try:
|
||||
if platform.system().lower() == 'windows':
|
||||
ctypes.windll.kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(system_memory_limit), ctypes.c_size_t(system_memory_limit)) # type: ignore[attr-defined]
|
||||
else:
|
||||
resource.setrlimit(resource.RLIMIT_DATA, (system_memory_limit, system_memory_limit))
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
@ -2,7 +2,7 @@ METADATA =\
|
||||
{
|
||||
'name': 'FaceFusion',
|
||||
'description': 'Next generation face swapper and enhancer',
|
||||
'version': '2.1.3',
|
||||
'version': '2.2.0',
|
||||
'license': 'MIT',
|
||||
'author': 'Henry Ruhs',
|
||||
'url': 'https://facefusion.io'
|
||||
|
||||
@ -2,7 +2,7 @@ from typing import List, Optional
|
||||
import os
|
||||
|
||||
from facefusion.filesystem import is_file, is_directory
|
||||
from facefusion.typing import Padding
|
||||
from facefusion.typing import Padding, Fps
|
||||
|
||||
|
||||
def normalize_output_path(source_paths : List[str], target_path : str, output_path : str) -> Optional[str]:
|
||||
@ -32,3 +32,13 @@ def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:
|
||||
if padding and len(padding) == 4:
|
||||
return tuple(padding) # type: ignore[return-value]
|
||||
return None
|
||||
|
||||
|
||||
def normalize_fps(fps : Optional[float]) -> Optional[Fps]:
|
||||
if fps is not None:
|
||||
if fps < 1.0:
|
||||
return 1.0
|
||||
if fps > 60.0:
|
||||
return 60.0
|
||||
return fps
|
||||
return None
|
||||
|
||||
@ -1,13 +1,13 @@
|
||||
from typing import List
|
||||
import numpy
|
||||
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.frame.typings import FaceSwapperModel, FaceEnhancerModel, FrameEnhancerModel, FaceDebuggerItem
|
||||
|
||||
face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial' ]
|
||||
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer' ]
|
||||
frame_enhancer_models : List[FrameEnhancerModel] = [ 'real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus' ]
|
||||
|
||||
face_enhancer_blend_range : List[int] = numpy.arange(0, 101, 1).tolist()
|
||||
frame_enhancer_blend_range : List[int] = numpy.arange(0, 101, 1).tolist()
|
||||
|
||||
face_debugger_items : List[FaceDebuggerItem] = [ 'bbox', 'kps', 'face-mask', 'score' ]
|
||||
|
||||
face_enhancer_blend_range : List[int] = create_int_range(0, 100, 1)
|
||||
frame_enhancer_blend_range : List[int] = create_int_range(0, 100, 1)
|
||||
|
||||
|
||||
@ -21,13 +21,14 @@ FRAME_PROCESSORS_METHODS =\
|
||||
'register_args',
|
||||
'apply_args',
|
||||
'pre_check',
|
||||
'post_check',
|
||||
'pre_process',
|
||||
'post_process',
|
||||
'get_reference_frame',
|
||||
'process_frame',
|
||||
'process_frames',
|
||||
'process_image',
|
||||
'process_video',
|
||||
'post_process'
|
||||
'process_video'
|
||||
]
|
||||
|
||||
|
||||
@ -38,10 +39,12 @@ def load_frame_processor_module(frame_processor : str) -> Any:
|
||||
if not hasattr(frame_processor_module, method_name):
|
||||
raise NotImplementedError
|
||||
except ModuleNotFoundError as exception:
|
||||
logger.error(wording.get('frame_processor_not_loaded').format(frame_processor = frame_processor), __name__.upper())
|
||||
logger.debug(exception.msg, __name__.upper())
|
||||
sys.exit(wording.get('frame_processor_not_loaded').format(frame_processor = frame_processor))
|
||||
sys.exit(1)
|
||||
except NotImplementedError:
|
||||
sys.exit(wording.get('frame_processor_not_implemented').format(frame_processor = frame_processor))
|
||||
logger.error(wording.get('frame_processor_not_implemented').format(frame_processor = frame_processor), __name__.upper())
|
||||
sys.exit(1)
|
||||
return frame_processor_module
|
||||
|
||||
|
||||
@ -73,11 +76,11 @@ def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str],
|
||||
})
|
||||
with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
|
||||
futures = []
|
||||
queue_temp_frame_paths : Queue[str] = create_queue(temp_frame_paths)
|
||||
queue_frame_paths : Queue[str] = create_queue(temp_frame_paths)
|
||||
queue_per_future = max(len(temp_frame_paths) // facefusion.globals.execution_thread_count * facefusion.globals.execution_queue_count, 1)
|
||||
while not queue_temp_frame_paths.empty():
|
||||
payload_temp_frame_paths = pick_queue(queue_temp_frame_paths, queue_per_future)
|
||||
future = executor.submit(process_frames, source_paths, payload_temp_frame_paths, progress.update)
|
||||
while not queue_frame_paths.empty():
|
||||
submit_frame_paths = pick_queue(queue_frame_paths, queue_per_future)
|
||||
future = executor.submit(process_frames, source_paths, submit_frame_paths, progress.update)
|
||||
futures.append(future)
|
||||
for future_done in as_completed(futures):
|
||||
future_done.result()
|
||||
|
||||
@ -5,13 +5,13 @@ import numpy
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import wording
|
||||
from facefusion import config, wording
|
||||
from facefusion.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode
|
||||
from facefusion.vision import read_image, read_static_image, read_static_images, write_image
|
||||
from facefusion.face_helper import warp_face
|
||||
from facefusion.face_helper import warp_face_by_kps
|
||||
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
|
||||
from facefusion.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
|
||||
|
||||
@ -35,7 +35,7 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = [ 'kps', 'face-mask' ], choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
|
||||
program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = config.get_str_list('frame_processors.face_debugger_items', 'kps face-mask'), choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
|
||||
|
||||
|
||||
def apply_args(program : ArgumentParser) -> None:
|
||||
@ -47,27 +47,34 @@ def pre_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def post_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
clear_frame_processor()
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
clear_face_parser()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
|
||||
clear_frame_processor()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict':
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
clear_face_parser()
|
||||
|
||||
|
||||
def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
|
||||
def debug_face(source_face : Face, target_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
|
||||
primary_color = (0, 0, 255)
|
||||
secondary_color = (0, 255, 0)
|
||||
bounding_box = target_face.bbox.astype(numpy.int32)
|
||||
temp_frame = temp_frame.copy()
|
||||
if 'bbox' in frame_processors_globals.face_debugger_items:
|
||||
cv2.rectangle(temp_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), secondary_color, 2)
|
||||
if 'face-mask' in frame_processors_globals.face_debugger_items:
|
||||
crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, 'arcface_128_v2', (128, 512))
|
||||
crop_frame, affine_matrix = warp_face_by_kps(temp_frame, target_face.kps, 'arcface_128_v2', (512, 512))
|
||||
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
|
||||
temp_frame_size = temp_frame.shape[:2][::-1]
|
||||
crop_mask_list = []
|
||||
@ -80,9 +87,9 @@ def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fr
|
||||
crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
|
||||
crop_mask = (crop_mask * 255).astype(numpy.uint8)
|
||||
inverse_mask_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size)
|
||||
inverse_mask_frame_edges = cv2.threshold(inverse_mask_frame, 100, 255, cv2.THRESH_BINARY)[1]
|
||||
inverse_mask_frame_edges[inverse_mask_frame_edges > 0] = 255
|
||||
inverse_mask_contours = cv2.findContours(inverse_mask_frame_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
|
||||
inverse_mask_frame = cv2.threshold(inverse_mask_frame, 100, 255, cv2.THRESH_BINARY)[1]
|
||||
inverse_mask_frame[inverse_mask_frame > 0] = 255
|
||||
inverse_mask_contours = cv2.findContours(inverse_mask_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
|
||||
cv2.drawContours(temp_frame, inverse_mask_contours, -1, primary_color, 2)
|
||||
if bounding_box[3] - bounding_box[1] > 60 and bounding_box[2] - bounding_box[0] > 60:
|
||||
if 'kps' in frame_processors_globals.face_debugger_items:
|
||||
@ -90,9 +97,9 @@ def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fr
|
||||
for index in range(kps.shape[0]):
|
||||
cv2.circle(temp_frame, (kps[index][0], kps[index][1]), 3, primary_color, -1)
|
||||
if 'score' in frame_processors_globals.face_debugger_items:
|
||||
score_text = str(round(target_face.score, 2))
|
||||
score_position = (bounding_box[0] + 10, bounding_box[1] + 20)
|
||||
cv2.putText(temp_frame, score_text, score_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
|
||||
face_score_text = str(round(target_face.score, 2))
|
||||
face_score_position = (bounding_box[0] + 10, bounding_box[1] + 20)
|
||||
cv2.putText(temp_frame, face_score_text, face_score_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
|
||||
return temp_frame
|
||||
|
||||
|
||||
@ -105,16 +112,16 @@ def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Fr
|
||||
similar_faces = find_similar_faces(temp_frame, reference_faces, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
temp_frame = debug_face(source_face, similar_face, temp_frame)
|
||||
temp_frame = debug_face(source_face, similar_face, reference_faces, temp_frame)
|
||||
if 'one' in facefusion.globals.face_selector_mode:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = debug_face(source_face, target_face, temp_frame)
|
||||
temp_frame = debug_face(source_face, target_face, None, temp_frame)
|
||||
if 'many' in facefusion.globals.face_selector_mode:
|
||||
many_faces = get_many_faces(temp_frame)
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
temp_frame = debug_face(source_face, target_face, temp_frame)
|
||||
temp_frame = debug_face(source_face, target_face, None, temp_frame)
|
||||
return temp_frame
|
||||
|
||||
|
||||
|
||||
@ -7,9 +7,10 @@ import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import logger, wording
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion.face_analyser import get_many_faces, clear_face_analyser, find_similar_faces, get_one_face
|
||||
from facefusion.face_helper import warp_face, paste_back
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.face_helper import warp_face_by_kps, paste_back
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
|
||||
@ -60,7 +61,7 @@ MODELS : ModelSet =\
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'),
|
||||
'template': 'arcface_128_v2',
|
||||
'size': (128, 256)
|
||||
'size': (256, 256)
|
||||
},
|
||||
'gpen_bfr_512':
|
||||
{
|
||||
@ -86,7 +87,7 @@ def get_frame_processor() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FRAME_PROCESSOR is None:
|
||||
model_path = get_options('model').get('path')
|
||||
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
|
||||
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
return FRAME_PROCESSOR
|
||||
|
||||
|
||||
@ -114,8 +115,8 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
|
||||
program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))
|
||||
program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), default = config.get_str_value('frame_processors.face_enhancer_model', 'gfpgan_1.4'), choices = frame_processors_choices.face_enhancer_models)
|
||||
program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = config.get_int_value('frame_processors.face_enhancer_blend', '80'), choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))
|
||||
|
||||
|
||||
def apply_args(program : ArgumentParser) -> None:
|
||||
@ -132,7 +133,7 @@ def pre_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
def post_check() -> bool:
|
||||
model_url = get_options('model').get('url')
|
||||
model_path = get_options('model').get('path')
|
||||
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
|
||||
@ -141,6 +142,10 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
elif not is_file(model_path):
|
||||
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
|
||||
logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
@ -151,18 +156,19 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
clear_frame_processor()
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
|
||||
clear_frame_processor()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict':
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
|
||||
|
||||
def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
|
||||
frame_processor = get_frame_processor()
|
||||
def enhance_face(target_face: Face, temp_frame : Frame) -> Frame:
|
||||
model_template = get_options('model').get('template')
|
||||
model_size = get_options('model').get('size')
|
||||
crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
|
||||
crop_frame, affine_matrix = warp_face_by_kps(temp_frame, target_face.kps, model_template, model_size)
|
||||
crop_mask_list =\
|
||||
[
|
||||
create_static_box_mask(crop_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, (0, 0, 0, 0))
|
||||
@ -170,14 +176,7 @@ def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
|
||||
if 'occlusion' in facefusion.globals.face_mask_types:
|
||||
crop_mask_list.append(create_occlusion_mask(crop_frame))
|
||||
crop_frame = prepare_crop_frame(crop_frame)
|
||||
frame_processor_inputs = {}
|
||||
for frame_processor_input in frame_processor.get_inputs():
|
||||
if frame_processor_input.name == 'input':
|
||||
frame_processor_inputs[frame_processor_input.name] = crop_frame
|
||||
if frame_processor_input.name == 'weight':
|
||||
frame_processor_inputs[frame_processor_input.name] = numpy.array([ 1 ], dtype = numpy.double)
|
||||
with THREAD_SEMAPHORE:
|
||||
crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
|
||||
crop_frame = apply_enhance(crop_frame)
|
||||
crop_frame = normalize_crop_frame(crop_frame)
|
||||
crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
|
||||
paste_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
|
||||
@ -185,6 +184,21 @@ def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
|
||||
return temp_frame
|
||||
|
||||
|
||||
def apply_enhance(crop_frame : Frame) -> Frame:
|
||||
frame_processor = get_frame_processor()
|
||||
frame_processor_inputs = {}
|
||||
|
||||
for frame_processor_input in frame_processor.get_inputs():
|
||||
if frame_processor_input.name == 'input':
|
||||
frame_processor_inputs[frame_processor_input.name] = crop_frame
|
||||
if frame_processor_input.name == 'weight':
|
||||
weight = numpy.array([ 1 ], dtype = numpy.double)
|
||||
frame_processor_inputs[frame_processor_input.name] = weight
|
||||
with THREAD_SEMAPHORE:
|
||||
crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
|
||||
return crop_frame
|
||||
|
||||
|
||||
def prepare_crop_frame(crop_frame : Frame) -> Frame:
|
||||
crop_frame = crop_frame[:, :, ::-1] / 255.0
|
||||
crop_frame = (crop_frame - 0.5) / 0.5
|
||||
@ -207,7 +221,7 @@ def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
|
||||
return temp_frame
|
||||
|
||||
|
||||
def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Optional[Frame]:
|
||||
def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
|
||||
return enhance_face(target_face, temp_frame)
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from typing import Any, List, Literal, Optional
|
||||
from argparse import ArgumentParser
|
||||
import platform
|
||||
import threading
|
||||
import numpy
|
||||
import onnx
|
||||
@ -8,9 +9,10 @@ from onnx import numpy_helper
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import logger, wording
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
|
||||
from facefusion.face_helper import warp_face, paste_back
|
||||
from facefusion.face_helper import warp_face_by_kps, paste_back
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, Embedding
|
||||
@ -33,7 +35,7 @@ MODELS : ModelSet =\
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendswap_256.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/blendswap_256.onnx'),
|
||||
'template': 'ffhq_512',
|
||||
'size': (512, 256),
|
||||
'size': (256, 256),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
},
|
||||
@ -63,7 +65,7 @@ MODELS : ModelSet =\
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/simswap_256.onnx'),
|
||||
'template': 'arcface_112_v1',
|
||||
'size': (112, 256),
|
||||
'size': (256, 256),
|
||||
'mean': [ 0.485, 0.456, 0.406 ],
|
||||
'standard_deviation': [ 0.229, 0.224, 0.225 ]
|
||||
},
|
||||
@ -73,7 +75,7 @@ MODELS : ModelSet =\
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'),
|
||||
'template': 'arcface_112_v1',
|
||||
'size': (112, 512),
|
||||
'size': (512, 512),
|
||||
'mean': [ 0.0, 0.0, 0.0 ],
|
||||
'standard_deviation': [ 1.0, 1.0, 1.0 ]
|
||||
}
|
||||
@ -87,7 +89,7 @@ def get_frame_processor() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FRAME_PROCESSOR is None:
|
||||
model_path = get_options('model').get('path')
|
||||
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
|
||||
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
return FRAME_PROCESSOR
|
||||
|
||||
|
||||
@ -132,7 +134,11 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models)
|
||||
if platform.system().lower() == 'darwin':
|
||||
face_swapper_model_fallback = 'inswapper_128'
|
||||
else:
|
||||
face_swapper_model_fallback = 'inswapper_128_fp16'
|
||||
program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), default = config.get_str_value('frame_processors.face_swapper_model', face_swapper_model_fallback), choices = frame_processors_choices.face_swapper_models)
|
||||
|
||||
|
||||
def apply_args(program : ArgumentParser) -> None:
|
||||
@ -154,7 +160,7 @@ def pre_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
def post_check() -> bool:
|
||||
model_url = get_options('model').get('url')
|
||||
model_path = get_options('model').get('path')
|
||||
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
|
||||
@ -163,6 +169,10 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
elif not is_file(model_path):
|
||||
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
if not are_images(facefusion.globals.source_paths):
|
||||
logger.error(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
@ -180,28 +190,42 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
clear_frame_processor()
|
||||
clear_model_matrix()
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
clear_face_parser()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
|
||||
clear_frame_processor()
|
||||
clear_model_matrix()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict':
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
clear_face_occluder()
|
||||
clear_face_parser()
|
||||
|
||||
|
||||
def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
|
||||
frame_processor = get_frame_processor()
|
||||
model_template = get_options('model').get('template')
|
||||
model_size = get_options('model').get('size')
|
||||
model_type = get_options('model').get('type')
|
||||
crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
|
||||
crop_frame, affine_matrix = warp_face_by_kps(temp_frame, target_face.kps, model_template, model_size)
|
||||
crop_mask_list = []
|
||||
|
||||
if 'box' in facefusion.globals.face_mask_types:
|
||||
crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding))
|
||||
if 'occlusion' in facefusion.globals.face_mask_types:
|
||||
crop_mask_list.append(create_occlusion_mask(crop_frame))
|
||||
crop_frame = prepare_crop_frame(crop_frame)
|
||||
crop_frame = apply_swap(source_face, crop_frame)
|
||||
crop_frame = normalize_crop_frame(crop_frame)
|
||||
if 'region' in facefusion.globals.face_mask_types:
|
||||
crop_mask_list.append(create_region_mask(crop_frame, facefusion.globals.face_mask_regions))
|
||||
crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
|
||||
temp_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def apply_swap(source_face : Face, crop_frame : Frame) -> Frame:
|
||||
frame_processor = get_frame_processor()
|
||||
model_type = get_options('model').get('type')
|
||||
frame_processor_inputs = {}
|
||||
|
||||
for frame_processor_input in frame_processor.get_inputs():
|
||||
if frame_processor_input.name == 'source':
|
||||
if model_type == 'blendswap':
|
||||
@ -211,17 +235,12 @@ def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Fra
|
||||
if frame_processor_input.name == 'target':
|
||||
frame_processor_inputs[frame_processor_input.name] = crop_frame
|
||||
crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
|
||||
crop_frame = normalize_crop_frame(crop_frame)
|
||||
if 'region' in facefusion.globals.face_mask_types:
|
||||
crop_mask_list.append(create_region_mask(crop_frame, facefusion.globals.face_mask_regions))
|
||||
crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
|
||||
temp_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
|
||||
return temp_frame
|
||||
return crop_frame
|
||||
|
||||
|
||||
def prepare_source_frame(source_face : Face) -> Frame:
|
||||
source_frame = read_static_image(facefusion.globals.source_paths[0])
|
||||
source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_112_v2', (112, 112))
|
||||
source_frame, _ = warp_face_by_kps(source_frame, source_face.kps, 'arcface_112_v2', (112, 112))
|
||||
source_frame = source_frame[:, :, ::-1] / 255.0
|
||||
source_frame = source_frame.transpose(2, 0, 1)
|
||||
source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32)
|
||||
@ -252,7 +271,7 @@ def prepare_crop_frame(crop_frame : Frame) -> Frame:
|
||||
def normalize_crop_frame(crop_frame : Frame) -> Frame:
|
||||
crop_frame = crop_frame.transpose(1, 2, 0)
|
||||
crop_frame = (crop_frame * 255.0).round()
|
||||
crop_frame = crop_frame[:, :, ::-1].astype(numpy.uint8)
|
||||
crop_frame = crop_frame[:, :, ::-1]
|
||||
return crop_frame
|
||||
|
||||
|
||||
|
||||
@ -7,12 +7,12 @@ from realesrgan import RealESRGANer
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import logger, wording
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion.face_analyser import clear_face_analyser
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
|
||||
from facefusion.common_helper import create_metavar
|
||||
from facefusion.execution_helper import map_device
|
||||
from facefusion.execution_helper import map_torch_backend
|
||||
from facefusion.filesystem import is_file, resolve_relative_path
|
||||
from facefusion.download import conditional_download, is_download_done
|
||||
from facefusion.vision import read_image, read_static_image, write_image
|
||||
@ -61,7 +61,7 @@ def get_frame_processor() -> Any:
|
||||
num_out_ch = 3,
|
||||
scale = model_scale
|
||||
),
|
||||
device = map_device(facefusion.globals.execution_providers),
|
||||
device = map_torch_backend(facefusion.globals.execution_providers),
|
||||
scale = model_scale
|
||||
)
|
||||
return FRAME_PROCESSOR
|
||||
@ -91,8 +91,8 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'real_esrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
|
||||
program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
|
||||
program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), default = config.get_str_value('frame_processors.frame_enhancer_model', 'real_esrgan_x2plus'), choices = frame_processors_choices.frame_enhancer_models)
|
||||
program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = config.get_int_value('frame_processors.frame_enhancer_blend', '80'), choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
|
||||
|
||||
|
||||
def apply_args(program : ArgumentParser) -> None:
|
||||
@ -109,7 +109,7 @@ def pre_check() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
def post_check() -> bool:
|
||||
model_url = get_options('model').get('url')
|
||||
model_path = get_options('model').get('path')
|
||||
if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
|
||||
@ -118,6 +118,10 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
elif not is_file(model_path):
|
||||
logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not facefusion.globals.output_path:
|
||||
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
@ -125,10 +129,12 @@ def pre_process(mode : ProcessMode) -> bool:
|
||||
|
||||
|
||||
def post_process() -> None:
|
||||
clear_frame_processor()
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
|
||||
clear_frame_processor()
|
||||
read_static_image.cache_clear()
|
||||
if facefusion.globals.video_memory_strategy == 'strict':
|
||||
clear_face_analyser()
|
||||
clear_content_analyser()
|
||||
|
||||
|
||||
def enhance_frame(temp_frame : Frame) -> Frame:
|
||||
|
||||
@ -3,5 +3,4 @@ from typing import Literal
|
||||
FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial']
|
||||
FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer']
|
||||
FrameEnhancerModel = Literal['real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus']
|
||||
|
||||
FaceDebuggerItem = Literal['bbox', 'kps', 'face-mask', 'score']
|
||||
FaceDebuggerItem = Literal['bbox', 'kps', 'face-mask', 'score', 'distance']
|
||||
|
||||
@ -25,13 +25,19 @@ FaceStore = TypedDict('FaceStore',
|
||||
Frame = numpy.ndarray[Any, Any]
|
||||
Mask = numpy.ndarray[Any, Any]
|
||||
Matrix = numpy.ndarray[Any, Any]
|
||||
|
||||
Fps = float
|
||||
Padding = Tuple[int, int, int, int]
|
||||
Resolution = Tuple[int, int]
|
||||
|
||||
Update_Process = Callable[[], None]
|
||||
Process_Frames = Callable[[List[str], List[str], Update_Process], None]
|
||||
LogLevel = Literal['error', 'warn', 'info', 'debug']
|
||||
|
||||
Template = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
|
||||
ProcessMode = Literal['output', 'preview', 'stream']
|
||||
|
||||
LogLevel = Literal['error', 'warn', 'info', 'debug']
|
||||
VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
|
||||
FaceSelectorMode = Literal['reference', 'one', 'many']
|
||||
FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
|
||||
FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
|
||||
@ -40,8 +46,9 @@ FaceDetectorModel = Literal['retinaface', 'yunet']
|
||||
FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap']
|
||||
FaceMaskType = Literal['box', 'occlusion', 'region']
|
||||
FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
|
||||
TempFrameFormat = Literal['jpg', 'png']
|
||||
TempFrameFormat = Literal['jpg', 'png', 'bmp']
|
||||
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
|
||||
OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
|
||||
|
||||
ModelValue = Dict[str, Any]
|
||||
ModelSet = Dict[str, ModelValue]
|
||||
|
||||
@ -2,6 +2,6 @@ from typing import List
|
||||
|
||||
from facefusion.uis.typing import WebcamMode
|
||||
|
||||
common_options : List[str] = [ 'keep-fps', 'keep-temp', 'skip-audio', 'skip-download' ]
|
||||
common_options : List[str] = [ 'keep-temp', 'skip-audio', 'skip-download' ]
|
||||
webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
|
||||
webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080', '2560x1440', '3840x2160' ]
|
||||
|
||||
@ -6,11 +6,11 @@ import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.face_analyser import get_face_analyser
|
||||
from facefusion.face_store import clear_static_faces
|
||||
from facefusion.processors.frame.core import get_frame_processors_modules
|
||||
from facefusion.vision import count_video_frame_total
|
||||
from facefusion.core import limit_resources, conditional_process
|
||||
from facefusion.vision import count_video_frame_total, detect_video_resolution, detect_video_fps, pack_resolution
|
||||
from facefusion.core import conditional_process
|
||||
from facefusion.memory import limit_system_memory
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.filesystem import clear_temp
|
||||
from facefusion.uis.core import get_ui_component
|
||||
@ -77,6 +77,8 @@ def listen() -> None:
|
||||
|
||||
def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
|
||||
facefusion.globals.source_paths = [ '.assets/examples/source.jpg' ]
|
||||
facefusion.globals.temp_frame_format = 'bmp'
|
||||
facefusion.globals.output_video_preset = 'ultrafast'
|
||||
target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
|
||||
benchmark_results = []
|
||||
if target_paths:
|
||||
@ -88,8 +90,8 @@ def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[
|
||||
|
||||
|
||||
def pre_process() -> None:
|
||||
limit_resources()
|
||||
get_face_analyser()
|
||||
if facefusion.globals.system_memory_limit > 0:
|
||||
limit_system_memory(facefusion.globals.system_memory_limit)
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
frame_processor_module.get_frame_processor()
|
||||
|
||||
@ -101,9 +103,12 @@ def post_process() -> None:
|
||||
def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
|
||||
process_times = []
|
||||
total_fps = 0.0
|
||||
for i in range(benchmark_cycles):
|
||||
for index in range(benchmark_cycles):
|
||||
facefusion.globals.target_path = target_path
|
||||
facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, tempfile.gettempdir())
|
||||
target_video_resolution = detect_video_resolution(facefusion.globals.target_path)
|
||||
facefusion.globals.output_video_resolution = pack_resolution(target_video_resolution)
|
||||
facefusion.globals.output_video_fps = detect_video_fps(facefusion.globals.target_path)
|
||||
video_frame_total = count_video_frame_total(facefusion.globals.target_path)
|
||||
start_time = time.perf_counter()
|
||||
conditional_process()
|
||||
|
||||
@ -20,7 +20,7 @@ def render() -> None:
|
||||
)
|
||||
BENCHMARK_CYCLES_SLIDER = gradio.Slider(
|
||||
label = wording.get('benchmark_cycles_slider_label'),
|
||||
value = 3,
|
||||
value = 5,
|
||||
step = 1,
|
||||
minimum = 1,
|
||||
maximum = 10
|
||||
|
||||
@ -12,8 +12,6 @@ def render() -> None:
|
||||
global COMMON_OPTIONS_CHECKBOX_GROUP
|
||||
|
||||
value = []
|
||||
if facefusion.globals.keep_fps:
|
||||
value.append('keep-fps')
|
||||
if facefusion.globals.keep_temp:
|
||||
value.append('keep-temp')
|
||||
if facefusion.globals.skip_audio:
|
||||
@ -32,7 +30,6 @@ def listen() -> None:
|
||||
|
||||
|
||||
def update(common_options : List[str]) -> None:
|
||||
facefusion.globals.keep_fps = 'keep-fps' in common_options
|
||||
facefusion.globals.keep_temp = 'keep-temp' in common_options
|
||||
facefusion.globals.skip_audio = 'skip-audio' in common_options
|
||||
facefusion.globals.skip_download = 'skip-download' in common_options
|
||||
|
||||
@ -66,11 +66,11 @@ def render() -> None:
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
FACE_ANALYSER_ORDER_DROPDOWN.select(update_face_analyser_order, inputs = FACE_ANALYSER_ORDER_DROPDOWN)
|
||||
FACE_ANALYSER_AGE_DROPDOWN.select(update_face_analyser_age, inputs = FACE_ANALYSER_AGE_DROPDOWN)
|
||||
FACE_ANALYSER_GENDER_DROPDOWN.select(update_face_analyser_gender, inputs = FACE_ANALYSER_GENDER_DROPDOWN)
|
||||
FACE_ANALYSER_ORDER_DROPDOWN.change(update_face_analyser_order, inputs = FACE_ANALYSER_ORDER_DROPDOWN)
|
||||
FACE_ANALYSER_AGE_DROPDOWN.change(update_face_analyser_age, inputs = FACE_ANALYSER_AGE_DROPDOWN)
|
||||
FACE_ANALYSER_GENDER_DROPDOWN.change(update_face_analyser_gender, inputs = FACE_ANALYSER_GENDER_DROPDOWN)
|
||||
FACE_DETECTOR_MODEL_DROPDOWN.change(update_face_detector_model, inputs = FACE_DETECTOR_MODEL_DROPDOWN)
|
||||
FACE_DETECTOR_SIZE_DROPDOWN.select(update_face_detector_size, inputs = FACE_DETECTOR_SIZE_DROPDOWN)
|
||||
FACE_DETECTOR_SIZE_DROPDOWN.change(update_face_detector_size, inputs = FACE_DETECTOR_SIZE_DROPDOWN)
|
||||
FACE_DETECTOR_SCORE_SLIDER.change(update_face_detector_score, inputs = FACE_DETECTOR_SCORE_SLIDER)
|
||||
|
||||
|
||||
|
||||
@ -7,9 +7,9 @@ import facefusion.choices
|
||||
from facefusion import wording
|
||||
from facefusion.face_store import clear_static_faces, clear_reference_faces
|
||||
from facefusion.vision import get_video_frame, read_static_image, normalize_frame_color
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
from facefusion.face_analyser import get_many_faces
|
||||
from facefusion.typing import Frame, FaceSelectorMode
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
from facefusion.uis.core import get_ui_component, register_ui_component
|
||||
from facefusion.uis.typing import ComponentName
|
||||
|
||||
@ -57,7 +57,7 @@ def render() -> None:
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
FACE_SELECTOR_MODE_DROPDOWN.select(update_face_selector_mode, inputs = FACE_SELECTOR_MODE_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
|
||||
FACE_SELECTOR_MODE_DROPDOWN.change(update_face_selector_mode, inputs = FACE_SELECTOR_MODE_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
|
||||
REFERENCE_FACE_POSITION_GALLERY.select(clear_and_update_reference_face_position)
|
||||
REFERENCE_FACE_DISTANCE_SLIDER.change(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
|
||||
multi_component_names : List[ComponentName] =\
|
||||
|
||||
@ -4,7 +4,7 @@ import gradio
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.processors.frame.core import load_frame_processor_module, clear_frame_processors_modules
|
||||
from facefusion.filesystem import list_module_names
|
||||
from facefusion.filesystem import list_directory
|
||||
from facefusion.uis.core import register_ui_component
|
||||
|
||||
FRAME_PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
|
||||
@ -36,5 +36,5 @@ def update_frame_processors(frame_processors : List[str]) -> gradio.CheckboxGrou
|
||||
|
||||
|
||||
def sort_frame_processors(frame_processors : List[str]) -> list[str]:
|
||||
available_frame_processors = list_module_names('facefusion/processors/frame/modules')
|
||||
available_frame_processors = list_directory('facefusion/processors/frame/modules')
|
||||
return sorted(available_frame_processors, key = lambda frame_processor : frame_processors.index(frame_processor) if frame_processor in frame_processors else len(frame_processors))
|
||||
|
||||
@ -56,7 +56,7 @@ def render() -> None:
|
||||
step = frame_processors_choices.frame_enhancer_blend_range[1] - frame_processors_choices.frame_enhancer_blend_range[0],
|
||||
minimum = frame_processors_choices.frame_enhancer_blend_range[0],
|
||||
maximum = frame_processors_choices.frame_enhancer_blend_range[-1],
|
||||
visible = 'face_enhancer' in facefusion.globals.frame_processors
|
||||
visible = 'frame_enhancer' in facefusion.globals.frame_processors
|
||||
)
|
||||
FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP = gradio.CheckboxGroup(
|
||||
label = wording.get('face_debugger_items_checkbox_group_label'),
|
||||
|
||||
@ -1,27 +0,0 @@
|
||||
from typing import Optional
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.choices
|
||||
from facefusion import wording
|
||||
|
||||
MAX_MEMORY_SLIDER : Optional[gradio.Slider] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global MAX_MEMORY_SLIDER
|
||||
|
||||
MAX_MEMORY_SLIDER = gradio.Slider(
|
||||
label = wording.get('max_memory_slider_label'),
|
||||
step = facefusion.choices.max_memory_range[1] - facefusion.choices.max_memory_range[0],
|
||||
minimum = facefusion.choices.max_memory_range[0],
|
||||
maximum = facefusion.choices.max_memory_range[-1]
|
||||
)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
MAX_MEMORY_SLIDER.change(update_max_memory, inputs = MAX_MEMORY_SLIDER)
|
||||
|
||||
|
||||
def update_max_memory(max_memory : int) -> None:
|
||||
facefusion.globals.max_memory = max_memory if max_memory > 0 else None
|
||||
41
facefusion/uis/components/memory.py
Normal file
41
facefusion/uis/components/memory.py
Normal file
@ -0,0 +1,41 @@
|
||||
from typing import Optional
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.choices
|
||||
from facefusion.typing import VideoMemoryStrategy
|
||||
from facefusion import wording
|
||||
|
||||
VIDEO_MEMORY_STRATEGY : Optional[gradio.Dropdown] = None
|
||||
SYSTEM_MEMORY_LIMIT_SLIDER : Optional[gradio.Slider] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global VIDEO_MEMORY_STRATEGY
|
||||
global SYSTEM_MEMORY_LIMIT_SLIDER
|
||||
|
||||
VIDEO_MEMORY_STRATEGY = gradio.Dropdown(
|
||||
label = wording.get('video_memory_strategy_dropdown_label'),
|
||||
choices = facefusion.choices.video_memory_strategies,
|
||||
value = facefusion.globals.video_memory_strategy
|
||||
)
|
||||
SYSTEM_MEMORY_LIMIT_SLIDER = gradio.Slider(
|
||||
label = wording.get('system_memory_limit_slider_label'),
|
||||
step =facefusion.choices.system_memory_limit_range[1] - facefusion.choices.system_memory_limit_range[0],
|
||||
minimum = facefusion.choices.system_memory_limit_range[0],
|
||||
maximum = facefusion.choices.system_memory_limit_range[-1],
|
||||
value = facefusion.globals.system_memory_limit
|
||||
)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
VIDEO_MEMORY_STRATEGY.change(update_video_memory_strategy, inputs = VIDEO_MEMORY_STRATEGY)
|
||||
SYSTEM_MEMORY_LIMIT_SLIDER.change(update_system_memory_limit, inputs = SYSTEM_MEMORY_LIMIT_SLIDER)
|
||||
|
||||
|
||||
def update_video_memory_strategy(video_memory_strategy : VideoMemoryStrategy) -> None:
|
||||
facefusion.globals.video_memory_strategy = video_memory_strategy
|
||||
|
||||
|
||||
def update_system_memory_limit(system_memory_limit : int) -> None:
|
||||
facefusion.globals.system_memory_limit = system_memory_limit
|
||||
@ -3,10 +3,11 @@ import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.core import limit_resources, conditional_process
|
||||
from facefusion.core import conditional_process
|
||||
from facefusion.memory import limit_system_memory
|
||||
from facefusion.uis.core import get_ui_component
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.filesystem import is_image, is_video, clear_temp
|
||||
from facefusion.filesystem import clear_temp, is_image, is_video
|
||||
|
||||
OUTPUT_IMAGE : Optional[gradio.Image] = None
|
||||
OUTPUT_VIDEO : Optional[gradio.Video] = None
|
||||
@ -47,7 +48,8 @@ def listen() -> None:
|
||||
|
||||
def start(output_path : str) -> Tuple[gradio.Image, gradio.Video]:
|
||||
facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, output_path)
|
||||
limit_resources()
|
||||
if facefusion.globals.system_memory_limit > 0:
|
||||
limit_system_memory(facefusion.globals.system_memory_limit)
|
||||
conditional_process()
|
||||
if is_image(facefusion.globals.output_path):
|
||||
return gradio.Image(value = facefusion.globals.output_path, visible = True), gradio.Video(value = None, visible = False)
|
||||
|
||||
@ -5,22 +5,29 @@ import gradio
|
||||
import facefusion.globals
|
||||
import facefusion.choices
|
||||
from facefusion import wording
|
||||
from facefusion.typing import OutputVideoEncoder
|
||||
from facefusion.typing import OutputVideoEncoder, OutputVideoPreset, Fps
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
from facefusion.uis.typing import ComponentName
|
||||
from facefusion.uis.core import get_ui_component, register_ui_component
|
||||
from facefusion.vision import detect_video_fps, create_video_resolutions, detect_video_resolution, pack_resolution
|
||||
|
||||
OUTPUT_PATH_TEXTBOX : Optional[gradio.Textbox] = None
|
||||
OUTPUT_IMAGE_QUALITY_SLIDER : Optional[gradio.Slider] = None
|
||||
OUTPUT_VIDEO_ENCODER_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
OUTPUT_VIDEO_PRESET_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
OUTPUT_VIDEO_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
OUTPUT_VIDEO_QUALITY_SLIDER : Optional[gradio.Slider] = None
|
||||
OUTPUT_VIDEO_FPS_SLIDER : Optional[gradio.Slider] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global OUTPUT_PATH_TEXTBOX
|
||||
global OUTPUT_IMAGE_QUALITY_SLIDER
|
||||
global OUTPUT_VIDEO_ENCODER_DROPDOWN
|
||||
global OUTPUT_VIDEO_PRESET_DROPDOWN
|
||||
global OUTPUT_VIDEO_RESOLUTION_DROPDOWN
|
||||
global OUTPUT_VIDEO_QUALITY_SLIDER
|
||||
global OUTPUT_VIDEO_FPS_SLIDER
|
||||
|
||||
OUTPUT_PATH_TEXTBOX = gradio.Textbox(
|
||||
label = wording.get('output_path_textbox_label'),
|
||||
@ -41,6 +48,12 @@ def render() -> None:
|
||||
value = facefusion.globals.output_video_encoder,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_VIDEO_PRESET_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('output_video_preset_dropdown_label'),
|
||||
choices = facefusion.choices.output_video_presets,
|
||||
value = facefusion.globals.output_video_preset,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_VIDEO_QUALITY_SLIDER = gradio.Slider(
|
||||
label = wording.get('output_video_quality_slider_label'),
|
||||
value = facefusion.globals.output_video_quality,
|
||||
@ -49,14 +62,31 @@ def render() -> None:
|
||||
maximum = facefusion.choices.output_video_quality_range[-1],
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_VIDEO_RESOLUTION_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('output_video_resolution_dropdown_label'),
|
||||
choices = create_video_resolutions(facefusion.globals.target_path),
|
||||
value = facefusion.globals.output_video_resolution,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_VIDEO_FPS_SLIDER = gradio.Slider(
|
||||
label = wording.get('output_video_fps_slider_label'),
|
||||
value = facefusion.globals.output_video_fps,
|
||||
step = 0.01,
|
||||
minimum = 1,
|
||||
maximum = 60,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
register_ui_component('output_path_textbox', OUTPUT_PATH_TEXTBOX)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
OUTPUT_PATH_TEXTBOX.change(update_output_path, inputs = OUTPUT_PATH_TEXTBOX)
|
||||
OUTPUT_IMAGE_QUALITY_SLIDER.change(update_output_image_quality, inputs = OUTPUT_IMAGE_QUALITY_SLIDER)
|
||||
OUTPUT_VIDEO_ENCODER_DROPDOWN.select(update_output_video_encoder, inputs = OUTPUT_VIDEO_ENCODER_DROPDOWN)
|
||||
OUTPUT_VIDEO_ENCODER_DROPDOWN.change(update_output_video_encoder, inputs = OUTPUT_VIDEO_ENCODER_DROPDOWN)
|
||||
OUTPUT_VIDEO_PRESET_DROPDOWN.change(update_output_video_preset, inputs = OUTPUT_VIDEO_PRESET_DROPDOWN)
|
||||
OUTPUT_VIDEO_QUALITY_SLIDER.change(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER)
|
||||
OUTPUT_VIDEO_RESOLUTION_DROPDOWN.change(update_output_video_resolution, inputs = OUTPUT_VIDEO_RESOLUTION_DROPDOWN)
|
||||
OUTPUT_VIDEO_FPS_SLIDER.change(update_output_video_fps, inputs = OUTPUT_VIDEO_FPS_SLIDER)
|
||||
multi_component_names : List[ComponentName] =\
|
||||
[
|
||||
'source_image',
|
||||
@ -67,15 +97,19 @@ def listen() -> None:
|
||||
component = get_ui_component(component_name)
|
||||
if component:
|
||||
for method in [ 'upload', 'change', 'clear' ]:
|
||||
getattr(component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER ])
|
||||
getattr(component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_FPS_SLIDER ])
|
||||
|
||||
|
||||
def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, gradio.Slider]:
|
||||
def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider]:
|
||||
if is_image(facefusion.globals.target_path):
|
||||
return gradio.Slider(visible = True), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
|
||||
return gradio.Slider(visible = True), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False, value = None, choices = None), gradio.Slider(visible = False, value = None)
|
||||
if is_video(facefusion.globals.target_path):
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = True), gradio.Slider(visible = True)
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
|
||||
target_video_resolution = detect_video_resolution(facefusion.globals.target_path)
|
||||
output_video_resolution = pack_resolution(target_video_resolution)
|
||||
output_video_resolutions = create_video_resolutions(facefusion.globals.target_path)
|
||||
output_video_fps = detect_video_fps(facefusion.globals.target_path)
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = True), gradio.Dropdown(visible = True), gradio.Slider(visible = True), gradio.Dropdown(visible = True, value = output_video_resolution, choices = output_video_resolutions), gradio.Slider(visible = True, value = output_video_fps)
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False, value = None, choices = None), gradio.Slider(visible = False, value = None)
|
||||
|
||||
|
||||
def update_output_path(output_path : str) -> None:
|
||||
@ -90,5 +124,17 @@ def update_output_video_encoder(output_video_encoder: OutputVideoEncoder) -> Non
|
||||
facefusion.globals.output_video_encoder = output_video_encoder
|
||||
|
||||
|
||||
def update_output_video_preset(output_video_preset : OutputVideoPreset) -> None:
|
||||
facefusion.globals.output_video_preset = output_video_preset
|
||||
|
||||
|
||||
def update_output_video_quality(output_video_quality : int) -> None:
|
||||
facefusion.globals.output_video_quality = output_video_quality
|
||||
|
||||
|
||||
def update_output_video_resolution(output_video_resolution : str) -> None:
|
||||
facefusion.globals.output_video_resolution = output_video_resolution
|
||||
|
||||
|
||||
def update_output_video_fps(output_video_fps : Fps) -> None:
|
||||
facefusion.globals.output_video_fps = output_video_fps
|
||||
|
||||
@ -1,17 +1,18 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
from time import sleep
|
||||
import cv2
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion import wording, logger
|
||||
from facefusion.core import conditional_append_reference_faces
|
||||
from facefusion.face_store import clear_static_faces, get_reference_faces, clear_reference_faces
|
||||
from facefusion.typing import Frame, Face, FaceSet
|
||||
from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_dimension, read_static_image, read_static_images
|
||||
from facefusion.vision import get_video_frame, count_video_frame_total, normalize_frame_color, resize_frame_resolution, read_static_image, read_static_images
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
from facefusion.face_analyser import get_average_face, clear_face_analyser
|
||||
from facefusion.content_analyser import analyse_frame
|
||||
from facefusion.processors.frame.core import load_frame_processor_module
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
from facefusion.uis.typing import ComponentName
|
||||
from facefusion.uis.core import get_ui_component, register_ui_component
|
||||
|
||||
@ -94,9 +95,7 @@ def listen() -> None:
|
||||
change_one_component_names : List[ComponentName] =\
|
||||
[
|
||||
'face_debugger_items_checkbox_group',
|
||||
'face_enhancer_model_dropdown',
|
||||
'face_enhancer_blend_slider',
|
||||
'frame_enhancer_model_dropdown',
|
||||
'frame_enhancer_blend_slider',
|
||||
'face_selector_mode_dropdown',
|
||||
'reference_face_distance_slider',
|
||||
@ -115,7 +114,9 @@ def listen() -> None:
|
||||
change_two_component_names : List[ComponentName] =\
|
||||
[
|
||||
'frame_processors_checkbox_group',
|
||||
'face_enhancer_model_dropdown',
|
||||
'face_swapper_model_dropdown',
|
||||
'frame_enhancer_model_dropdown',
|
||||
'face_detector_model_dropdown',
|
||||
'face_detector_size_dropdown',
|
||||
'face_detector_score_slider'
|
||||
@ -130,22 +131,29 @@ def clear_and_update_preview_image(frame_number : int = 0) -> gradio.Image:
|
||||
clear_face_analyser()
|
||||
clear_reference_faces()
|
||||
clear_static_faces()
|
||||
sleep(0.5)
|
||||
return update_preview_image(frame_number)
|
||||
|
||||
|
||||
def update_preview_image(frame_number : int = 0) -> gradio.Image:
|
||||
for frame_processor in facefusion.globals.frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
while not frame_processor_module.post_check():
|
||||
logger.disable()
|
||||
sleep(0.5)
|
||||
logger.enable()
|
||||
conditional_append_reference_faces()
|
||||
source_frames = read_static_images(facefusion.globals.source_paths)
|
||||
source_face = get_average_face(source_frames)
|
||||
reference_face = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
if is_image(facefusion.globals.target_path):
|
||||
target_frame = read_static_image(facefusion.globals.target_path)
|
||||
preview_frame = process_preview_frame(source_face, reference_face, target_frame)
|
||||
preview_frame = process_preview_frame(source_face, reference_faces, target_frame)
|
||||
preview_frame = normalize_frame_color(preview_frame)
|
||||
return gradio.Image(value = preview_frame)
|
||||
if is_video(facefusion.globals.target_path):
|
||||
temp_frame = get_video_frame(facefusion.globals.target_path, frame_number)
|
||||
preview_frame = process_preview_frame(source_face, reference_face, temp_frame)
|
||||
preview_frame = process_preview_frame(source_face, reference_faces, temp_frame)
|
||||
preview_frame = normalize_frame_color(preview_frame)
|
||||
return gradio.Image(value = preview_frame)
|
||||
return gradio.Image(value = None)
|
||||
@ -159,12 +167,14 @@ def update_preview_frame_slider() -> gradio.Slider:
|
||||
|
||||
|
||||
def process_preview_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
|
||||
temp_frame = resize_frame_dimension(temp_frame, 640, 640)
|
||||
temp_frame = resize_frame_resolution(temp_frame, 640, 640)
|
||||
if analyse_frame(temp_frame):
|
||||
return cv2.GaussianBlur(temp_frame, (99, 99), 0)
|
||||
for frame_processor in facefusion.globals.frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
logger.disable()
|
||||
if frame_processor_module.pre_process('preview'):
|
||||
logger.enable()
|
||||
temp_frame = frame_processor_module.process_frame(
|
||||
source_face,
|
||||
reference_faces,
|
||||
|
||||
@ -33,7 +33,7 @@ def render() -> None:
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
TEMP_FRAME_FORMAT_DROPDOWN.select(update_temp_frame_format, inputs = TEMP_FRAME_FORMAT_DROPDOWN)
|
||||
TEMP_FRAME_FORMAT_DROPDOWN.change(update_temp_frame_format, inputs = TEMP_FRAME_FORMAT_DROPDOWN)
|
||||
TEMP_FRAME_QUALITY_SLIDER.change(update_temp_frame_quality, inputs = TEMP_FRAME_QUALITY_SLIDER)
|
||||
target_video = get_ui_component('target_video')
|
||||
if target_video:
|
||||
|
||||
@ -1,22 +1,23 @@
|
||||
from typing import Optional, Generator, Deque
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from collections import deque
|
||||
from typing import Optional, Generator, Deque, List
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import cv2
|
||||
import gradio
|
||||
from time import sleep
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from collections import deque
|
||||
from tqdm import tqdm
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import logger, wording
|
||||
from facefusion.content_analyser import analyse_stream
|
||||
from facefusion.typing import Frame, Face
|
||||
from facefusion.typing import Frame, Face, Fps
|
||||
from facefusion.face_analyser import get_average_face
|
||||
from facefusion.processors.frame.core import get_frame_processors_modules
|
||||
from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
|
||||
from facefusion.ffmpeg import open_ffmpeg
|
||||
from facefusion.vision import normalize_frame_color, read_static_images
|
||||
from facefusion.uis.typing import StreamMode, WebcamMode
|
||||
from facefusion.vision import normalize_frame_color, read_static_images, unpack_resolution
|
||||
from facefusion.uis.typing import StreamMode, WebcamMode, ComponentName
|
||||
from facefusion.uis.core import get_ui_component
|
||||
|
||||
WEBCAM_CAPTURE : Optional[cv2.VideoCapture] = None
|
||||
@ -73,28 +74,36 @@ def listen() -> None:
|
||||
if webcam_mode_radio and webcam_resolution_dropdown and webcam_fps_slider:
|
||||
start_event = WEBCAM_START_BUTTON.click(start, inputs = [ webcam_mode_radio, webcam_resolution_dropdown, webcam_fps_slider ], outputs = WEBCAM_IMAGE)
|
||||
WEBCAM_STOP_BUTTON.click(stop, cancels = start_event)
|
||||
source_image = get_ui_component('source_image')
|
||||
if source_image:
|
||||
for method in [ 'upload', 'change', 'clear' ]:
|
||||
getattr(source_image, method)(stop, cancels = start_event)
|
||||
change_two_component_names : List[ComponentName] =\
|
||||
[
|
||||
'frame_processors_checkbox_group',
|
||||
'face_swapper_model_dropdown',
|
||||
'face_enhancer_model_dropdown',
|
||||
'frame_enhancer_model_dropdown',
|
||||
'source_image'
|
||||
]
|
||||
for component_name in change_two_component_names:
|
||||
component = get_ui_component(component_name)
|
||||
if component:
|
||||
component.change(update, cancels = start_event)
|
||||
|
||||
|
||||
def start(webcam_mode : WebcamMode, resolution : str, fps : float) -> Generator[Frame, None, None]:
|
||||
def start(webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[Frame, None, None]:
|
||||
facefusion.globals.face_selector_mode = 'one'
|
||||
facefusion.globals.face_analyser_order = 'large-small'
|
||||
source_frames = read_static_images(facefusion.globals.source_paths)
|
||||
source_face = get_average_face(source_frames)
|
||||
stream = None
|
||||
if webcam_mode in [ 'udp', 'v4l2' ]:
|
||||
stream = open_stream(webcam_mode, resolution, fps) # type: ignore[arg-type]
|
||||
webcam_width, webcam_height = map(int, resolution.split('x'))
|
||||
stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) # type: ignore[arg-type]
|
||||
webcam_width, webcam_height = unpack_resolution(webcam_resolution)
|
||||
webcam_capture = get_webcam_capture()
|
||||
if webcam_capture and webcam_capture.isOpened():
|
||||
webcam_capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG')) # type: ignore[attr-defined]
|
||||
webcam_capture.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width)
|
||||
webcam_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height)
|
||||
webcam_capture.set(cv2.CAP_PROP_FPS, fps)
|
||||
for capture_frame in multi_process_capture(source_face, webcam_capture, fps):
|
||||
webcam_capture.set(cv2.CAP_PROP_FPS, webcam_fps)
|
||||
for capture_frame in multi_process_capture(source_face, webcam_capture, webcam_fps):
|
||||
if webcam_mode == 'inline':
|
||||
yield normalize_frame_color(capture_frame)
|
||||
else:
|
||||
@ -105,14 +114,14 @@ def start(webcam_mode : WebcamMode, resolution : str, fps : float) -> Generator[
|
||||
yield None
|
||||
|
||||
|
||||
def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture, fps : float) -> Generator[Frame, None, None]:
|
||||
def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture, webcam_fps : Fps) -> Generator[Frame, None, None]:
|
||||
with tqdm(desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
|
||||
with ThreadPoolExecutor(max_workers = facefusion.globals.execution_thread_count) as executor:
|
||||
futures = []
|
||||
deque_capture_frames : Deque[Frame] = deque()
|
||||
while webcam_capture and webcam_capture.isOpened():
|
||||
_, capture_frame = webcam_capture.read()
|
||||
if analyse_stream(capture_frame, fps):
|
||||
if analyse_stream(capture_frame, webcam_fps):
|
||||
return
|
||||
future = executor.submit(process_stream_frame, source_face, capture_frame)
|
||||
futures.append(future)
|
||||
@ -125,6 +134,15 @@ def multi_process_capture(source_face : Face, webcam_capture : cv2.VideoCapture,
|
||||
yield deque_capture_frames.popleft()
|
||||
|
||||
|
||||
def update() -> None:
|
||||
for frame_processor in facefusion.globals.frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
while not frame_processor_module.post_check():
|
||||
logger.disable()
|
||||
sleep(0.5)
|
||||
logger.enable()
|
||||
|
||||
|
||||
def stop() -> gradio.Image:
|
||||
clear_webcam_capture()
|
||||
return gradio.Image(value = None)
|
||||
@ -132,7 +150,9 @@ def stop() -> gradio.Image:
|
||||
|
||||
def process_stream_frame(source_face : Face, temp_frame : Frame) -> Frame:
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
logger.disable()
|
||||
if frame_processor_module.pre_process('stream'):
|
||||
logger.enable()
|
||||
temp_frame = frame_processor_module.process_frame(
|
||||
source_face,
|
||||
None,
|
||||
@ -141,8 +161,8 @@ def process_stream_frame(source_face : Face, temp_frame : Frame) -> Frame:
|
||||
return temp_frame
|
||||
|
||||
|
||||
def open_stream(stream_mode : StreamMode, resolution : str, fps : float) -> subprocess.Popen[bytes]:
|
||||
commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', resolution, '-r', str(fps), '-i', '-' ]
|
||||
def open_stream(stream_mode : StreamMode, stream_resolution : str, stream_fps : Fps) -> subprocess.Popen[bytes]:
|
||||
commands = [ '-f', 'rawvideo', '-pix_fmt', 'bgr24', '-s', stream_resolution, '-r', str(stream_fps), '-i', '-']
|
||||
if stream_mode == 'udp':
|
||||
commands.extend([ '-b:v', '2000k', '-f', 'mpegts', 'udp://localhost:27000?pkt_size=1316' ])
|
||||
if stream_mode == 'v4l2':
|
||||
|
||||
@ -28,10 +28,12 @@ def load_ui_layout_module(ui_layout : str) -> Any:
|
||||
if not hasattr(ui_layout_module, method_name):
|
||||
raise NotImplementedError
|
||||
except ModuleNotFoundError as exception:
|
||||
logger.error(wording.get('ui_layout_not_loaded').format(ui_layout=ui_layout), __name__.upper())
|
||||
logger.debug(exception.msg, __name__.upper())
|
||||
sys.exit(wording.get('ui_layout_not_loaded').format(ui_layout = ui_layout))
|
||||
sys.exit(1)
|
||||
except NotImplementedError:
|
||||
sys.exit(wording.get('ui_layout_not_implemented').format(ui_layout = ui_layout))
|
||||
logger.error(wording.get('ui_layout_not_implemented').format(ui_layout = ui_layout), __name__.upper())
|
||||
sys.exit(1)
|
||||
return ui_layout_module
|
||||
|
||||
|
||||
|
||||
@ -2,7 +2,7 @@ import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, limit_resources, benchmark_options, benchmark
|
||||
from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, memory, benchmark_options, benchmark
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
@ -40,7 +40,7 @@ def render() -> gradio.Blocks:
|
||||
execution_thread_count.render()
|
||||
execution_queue_count.render()
|
||||
with gradio.Blocks():
|
||||
limit_resources.render()
|
||||
memory.render()
|
||||
with gradio.Blocks():
|
||||
benchmark_options.render()
|
||||
with gradio.Column(scale = 5):
|
||||
@ -55,7 +55,7 @@ def listen() -> None:
|
||||
execution.listen()
|
||||
execution_thread_count.listen()
|
||||
execution_queue_count.listen()
|
||||
limit_resources.listen()
|
||||
memory.listen()
|
||||
benchmark.listen()
|
||||
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import gradio
|
||||
|
||||
from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, limit_resources, temp_frame, output_options, common_options, source, target, output, preview, trim_frame, face_analyser, face_selector, face_masker
|
||||
from facefusion.uis.components import about, frame_processors, frame_processors_options, execution, execution_thread_count, execution_queue_count, memory, temp_frame, output_options, common_options, source, target, output, preview, trim_frame, face_analyser, face_selector, face_masker
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
@ -25,13 +25,11 @@ def render() -> gradio.Blocks:
|
||||
execution_thread_count.render()
|
||||
execution_queue_count.render()
|
||||
with gradio.Blocks():
|
||||
limit_resources.render()
|
||||
memory.render()
|
||||
with gradio.Blocks():
|
||||
temp_frame.render()
|
||||
with gradio.Blocks():
|
||||
output_options.render()
|
||||
with gradio.Blocks():
|
||||
common_options.render()
|
||||
with gradio.Column(scale = 2):
|
||||
with gradio.Blocks():
|
||||
source.render()
|
||||
@ -50,6 +48,8 @@ def render() -> gradio.Blocks:
|
||||
face_masker.render()
|
||||
with gradio.Blocks():
|
||||
face_analyser.render()
|
||||
with gradio.Blocks():
|
||||
common_options.render()
|
||||
return layout
|
||||
|
||||
|
||||
@ -59,10 +59,9 @@ def listen() -> None:
|
||||
execution.listen()
|
||||
execution_thread_count.listen()
|
||||
execution_queue_count.listen()
|
||||
limit_resources.listen()
|
||||
memory.listen()
|
||||
temp_frame.listen()
|
||||
output_options.listen()
|
||||
common_options.listen()
|
||||
source.listen()
|
||||
target.listen()
|
||||
output.listen()
|
||||
@ -71,6 +70,7 @@ def listen() -> None:
|
||||
face_selector.listen()
|
||||
face_masker.listen()
|
||||
face_analyser.listen()
|
||||
common_options.listen()
|
||||
|
||||
|
||||
def run(ui : gradio.Blocks) -> None:
|
||||
|
||||
@ -39,5 +39,6 @@ ComponentName = Literal\
|
||||
'webcam_resolution_dropdown',
|
||||
'webcam_fps_slider'
|
||||
]
|
||||
|
||||
WebcamMode = Literal['inline', 'udp', 'v4l2']
|
||||
StreamMode = Literal['udp', 'v4l2']
|
||||
|
||||
@ -1,12 +1,14 @@
|
||||
from typing import Optional, List
|
||||
from typing import Optional, List, Tuple
|
||||
from functools import lru_cache
|
||||
import cv2
|
||||
|
||||
from facefusion.typing import Frame
|
||||
from facefusion.typing import Frame, Resolution
|
||||
from facefusion.choices import video_template_sizes
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
|
||||
|
||||
def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]:
|
||||
if video_path:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
if video_capture.isOpened():
|
||||
frame_total = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||
@ -18,16 +20,8 @@ def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Frame]
|
||||
return None
|
||||
|
||||
|
||||
def detect_fps(video_path : str) -> Optional[float]:
|
||||
if video_path:
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
if video_capture.isOpened():
|
||||
return video_capture.get(cv2.CAP_PROP_FPS)
|
||||
return None
|
||||
|
||||
|
||||
def count_video_frame_total(video_path : str) -> int:
|
||||
if video_path:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
if video_capture.isOpened():
|
||||
video_frame_total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
@ -36,12 +30,70 @@ def count_video_frame_total(video_path : str) -> int:
|
||||
return 0
|
||||
|
||||
|
||||
def normalize_frame_color(frame : Frame) -> Frame:
|
||||
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
def detect_video_fps(video_path : str) -> Optional[float]:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
if video_capture.isOpened():
|
||||
video_fps = video_capture.get(cv2.CAP_PROP_FPS)
|
||||
video_capture.release()
|
||||
return video_fps
|
||||
return None
|
||||
|
||||
|
||||
def resize_frame_dimension(frame : Frame, max_width : int, max_height : int) -> Frame:
|
||||
def detect_video_resolution(video_path : str) -> Optional[Tuple[float, float]]:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
if video_capture.isOpened():
|
||||
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
|
||||
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
|
||||
video_capture.release()
|
||||
return width, height
|
||||
return None
|
||||
|
||||
|
||||
def create_video_resolutions(video_path : str) -> Optional[List[str]]:
|
||||
temp_resolutions = []
|
||||
video_resolutions = []
|
||||
video_resolution = detect_video_resolution(video_path)
|
||||
|
||||
if video_resolution:
|
||||
width, height = video_resolution
|
||||
temp_resolutions.append(normalize_resolution(video_resolution))
|
||||
for template_size in video_template_sizes:
|
||||
if width > height:
|
||||
temp_resolutions.append(normalize_resolution((template_size * width / height, template_size)))
|
||||
else:
|
||||
temp_resolutions.append(normalize_resolution((template_size, template_size * height / width)))
|
||||
temp_resolutions = sorted(set(temp_resolutions))
|
||||
for temp in temp_resolutions:
|
||||
video_resolutions.append(pack_resolution(temp))
|
||||
return video_resolutions
|
||||
return None
|
||||
|
||||
|
||||
def normalize_resolution(resolution : Tuple[float, float]) -> Resolution:
|
||||
width, height = resolution
|
||||
|
||||
if width and height:
|
||||
normalize_width = round(width / 2) * 2
|
||||
normalize_height = round(height / 2) * 2
|
||||
return normalize_width, normalize_height
|
||||
return 0, 0
|
||||
|
||||
|
||||
def pack_resolution(resolution : Tuple[float, float]) -> str:
|
||||
width, height = normalize_resolution(resolution)
|
||||
return str(width) + 'x' + str(height)
|
||||
|
||||
|
||||
def unpack_resolution(resolution : str) -> Resolution:
|
||||
width, height = map(int, resolution.split('x'))
|
||||
return width, height
|
||||
|
||||
|
||||
def resize_frame_resolution(frame : Frame, max_width : int, max_height : int) -> Frame:
|
||||
height, width = frame.shape[:2]
|
||||
|
||||
if height > max_height or width > max_width:
|
||||
scale = min(max_height / height, max_width / width)
|
||||
new_width = int(width * scale)
|
||||
@ -50,6 +102,10 @@ def resize_frame_dimension(frame : Frame, max_width : int, max_height : int) ->
|
||||
return frame
|
||||
|
||||
|
||||
def normalize_frame_color(frame : Frame) -> Frame:
|
||||
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
|
||||
@lru_cache(maxsize = 128)
|
||||
def read_static_image(image_path : str) -> Optional[Frame]:
|
||||
return read_image(image_path)
|
||||
@ -64,7 +120,7 @@ def read_static_images(image_paths : List[str]) -> Optional[List[Frame]]:
|
||||
|
||||
|
||||
def read_image(image_path : str) -> Optional[Frame]:
|
||||
if image_path:
|
||||
if is_image(image_path):
|
||||
return cv2.imread(image_path)
|
||||
return None
|
||||
|
||||
|
||||
@ -12,7 +12,6 @@ WORDING =\
|
||||
'frame_processor_blend_help': 'specify the blend amount for the frame processor',
|
||||
'face_debugger_items_help': 'specify the face debugger items (choices: {choices})',
|
||||
'ui_layouts_help': 'choose from the available ui layouts (choices: {choices}, ...)',
|
||||
'keep_fps_help': 'preserve the frames per second (fps) of the target',
|
||||
'keep_temp_help': 'retain temporary frames after processing',
|
||||
'skip_audio_help': 'omit audio from the target',
|
||||
'face_analyser_order_help': 'specify the order used for the face analyser',
|
||||
@ -35,8 +34,12 @@ WORDING =\
|
||||
'temp_frame_quality_help': 'specify the image quality used for frame extraction',
|
||||
'output_image_quality_help': 'specify the quality used for the output image',
|
||||
'output_video_encoder_help': 'specify the encoder used for the output video',
|
||||
'output_video_preset_help': 'specify the preset used for the output video',
|
||||
'output_video_quality_help': 'specify the quality used for the output video',
|
||||
'max_memory_help': 'specify the maximum amount of ram to be used (in gb)',
|
||||
'output_video_resolution_help': 'specify the resolution used for the output video',
|
||||
'output_video_fps_help': 'specify the frames per second (fps) used for the output video',
|
||||
'video_memory_strategy_help': 'specify strategy to handle the video memory',
|
||||
'system_memory_limit_help': 'specify the amount (gb) of system memory to be used',
|
||||
'execution_providers_help': 'choose from the available execution providers (choices: {choices}, ...)',
|
||||
'execution_thread_count_help': 'specify the number of execution threads',
|
||||
'execution_queue_count_help': 'specify the number of execution queries',
|
||||
@ -44,22 +47,22 @@ WORDING =\
|
||||
'headless_help': 'run the program in headless mode',
|
||||
'log_level_help': 'choose from the available log levels',
|
||||
'creating_temp': 'Creating temporary resources',
|
||||
'extracting_frames_fps': 'Extracting frames with {fps} FPS',
|
||||
'extracting_frames_fps': 'Extracting frames with {video_fps} FPS',
|
||||
'analysing': 'Analysing',
|
||||
'processing': 'Processing',
|
||||
'downloading': 'Downloading',
|
||||
'temp_frames_not_found': 'Temporary frames not found',
|
||||
'compressing_image': 'Compressing image',
|
||||
'compressing_image_failed': 'Compressing image failed',
|
||||
'merging_video_fps': 'Merging video with {fps} FPS',
|
||||
'merging_video_fps': 'Merging video with {video_fps} FPS',
|
||||
'merging_video_failed': 'Merging video failed',
|
||||
'skipping_audio': 'Skipping audio',
|
||||
'restoring_audio': 'Restoring audio',
|
||||
'restoring_audio_skipped': 'Restoring audio skipped',
|
||||
'clearing_temp': 'Clearing temporary resources',
|
||||
'processing_image_succeed': 'Processing to image succeed',
|
||||
'processing_image_succeed': 'Processing to image succeed in {seconds} seconds',
|
||||
'processing_image_failed': 'Processing to image failed',
|
||||
'processing_video_succeed': 'Processing to video succeed',
|
||||
'processing_video_succeed': 'Processing to video succeed in {seconds} seconds',
|
||||
'processing_video_failed': 'Processing to video failed',
|
||||
'model_download_not_done': 'Download of the model is not done',
|
||||
'model_file_not_present': 'File of the model is not present',
|
||||
@ -98,12 +101,16 @@ WORDING =\
|
||||
'face_mask_padding_left_slider_label': 'FACE MASK PADDING LEFT',
|
||||
'face_mask_padding_right_slider_label': 'FACE MASK PADDING RIGHT',
|
||||
'face_mask_region_checkbox_group_label': 'FACE MASK REGIONS',
|
||||
'max_memory_slider_label': 'MAX MEMORY',
|
||||
'video_memory_strategy_dropdown_label': 'VIDEO MEMORY STRATEGY',
|
||||
'system_memory_limit_slider_label': 'SYSTEM MEMORY LIMIT',
|
||||
'output_image_or_video_label': 'OUTPUT',
|
||||
'output_path_textbox_label': 'OUTPUT PATH',
|
||||
'output_image_quality_slider_label': 'OUTPUT IMAGE QUALITY',
|
||||
'output_video_encoder_dropdown_label': 'OUTPUT VIDEO ENCODER',
|
||||
'output_video_preset_dropdown_label': 'OUTPUT VIDEO PRESET',
|
||||
'output_video_quality_slider_label': 'OUTPUT VIDEO QUALITY',
|
||||
'output_video_resolution_dropdown_label': 'OUTPUT VIDEO RESOLUTION',
|
||||
'output_video_fps_slider_label': 'OUTPUT VIDEO FPS',
|
||||
'preview_image_label': 'PREVIEW',
|
||||
'preview_frame_slider_label': 'PREVIEW FRAME',
|
||||
'frame_processors_checkbox_group_label': 'FRAME PROCESSORS',
|
||||
|
||||
@ -7,5 +7,5 @@ onnxruntime==1.16.3
|
||||
opencv-python==4.8.1.78
|
||||
psutil==5.9.6
|
||||
realesrgan==0.3.0
|
||||
torch==2.1.1
|
||||
torch==2.1.2
|
||||
tqdm==4.66.1
|
||||
|
||||
@ -2,7 +2,6 @@ import subprocess
|
||||
import sys
|
||||
import pytest
|
||||
|
||||
from facefusion import wording
|
||||
from facefusion.download import conditional_download
|
||||
|
||||
|
||||
@ -21,7 +20,7 @@ def test_image_to_image() -> None:
|
||||
run = subprocess.run(commands, stdout = subprocess.PIPE, stderr = subprocess.STDOUT)
|
||||
|
||||
assert run.returncode == 0
|
||||
assert wording.get('processing_image_succeed') in run.stdout.decode()
|
||||
assert 'image succeed' in run.stdout.decode()
|
||||
|
||||
|
||||
def test_image_to_video() -> None:
|
||||
@ -29,4 +28,4 @@ def test_image_to_video() -> None:
|
||||
run = subprocess.run(commands, stdout = subprocess.PIPE, stderr = subprocess.STDOUT)
|
||||
|
||||
assert run.returncode == 0
|
||||
assert wording.get('processing_video_succeed') in run.stdout.decode()
|
||||
assert 'video succeed' in run.stdout.decode()
|
||||
|
||||
@ -1,10 +1,15 @@
|
||||
from facefusion.common_helper import create_metavar, create_range
|
||||
from facefusion.common_helper import create_metavar, create_int_range, create_float_range
|
||||
|
||||
|
||||
def test_create_metavar() -> None:
|
||||
assert create_metavar([ 1, 2, 3, 4, 5 ]) == '[1-5]'
|
||||
|
||||
|
||||
def test_create_range() -> None:
|
||||
assert create_range(0.0, 1.0, 0.5) == [ 0.0, 0.5, 1.0 ]
|
||||
assert create_range(0.0, 0.2, 0.05) == [ 0.0, 0.05, 0.10, 0.15, 0.20 ]
|
||||
def test_create_int_range() -> None:
|
||||
assert create_int_range(0, 2, 1) == [ 0, 1, 2 ]
|
||||
assert create_float_range(0, 1, 1) == [ 0, 1 ]
|
||||
|
||||
|
||||
def test_create_float_range() -> None:
|
||||
assert create_float_range(0.0, 1.0, 0.5) == [ 0.0, 0.5, 1.0 ]
|
||||
assert create_float_range(0.0, 0.2, 0.05) == [ 0.0, 0.05, 0.10, 0.15, 0.20 ]
|
||||
|
||||
96
tests/test_config.py
Normal file
96
tests/test_config.py
Normal file
@ -0,0 +1,96 @@
|
||||
from configparser import ConfigParser
|
||||
import pytest
|
||||
|
||||
from facefusion import config
|
||||
|
||||
|
||||
@pytest.fixture(scope = 'module', autouse = True)
|
||||
def before_all() -> None:
|
||||
config.CONFIG = ConfigParser()
|
||||
config.CONFIG.read_dict(
|
||||
{
|
||||
'str':
|
||||
{
|
||||
'valid': 'a',
|
||||
'unset': ''
|
||||
},
|
||||
'int':
|
||||
{
|
||||
'valid': '1',
|
||||
'unset': ''
|
||||
},
|
||||
'float':
|
||||
{
|
||||
'valid': '1.0',
|
||||
'unset': ''
|
||||
},
|
||||
'bool':
|
||||
{
|
||||
'valid': 'True',
|
||||
'unset': ''
|
||||
},
|
||||
'str_list':
|
||||
{
|
||||
'valid': 'a b c',
|
||||
'unset': ''
|
||||
},
|
||||
'int_list':
|
||||
{
|
||||
'valid': '1 2 3',
|
||||
'unset': ''
|
||||
},
|
||||
'float_list':
|
||||
{
|
||||
'valid': '1.0 2.0 3.0',
|
||||
'unset': ''
|
||||
}
|
||||
})
|
||||
|
||||
|
||||
def test_get_str_value() -> None:
|
||||
assert config.get_str_value('str.valid') == 'a'
|
||||
assert config.get_str_value('str.unset', 'b') == 'b'
|
||||
assert config.get_str_value('str.unset') is None
|
||||
assert config.get_str_value('str.invalid') is None
|
||||
|
||||
|
||||
def test_get_int_value() -> None:
|
||||
assert config.get_int_value('int.valid') == 1
|
||||
assert config.get_int_value('int.unset', '1') == 1
|
||||
assert config.get_int_value('int.unset') is None
|
||||
assert config.get_int_value('int.invalid') is None
|
||||
|
||||
|
||||
def test_get_float_value() -> None:
|
||||
assert config.get_float_value('float.valid') == 1.0
|
||||
assert config.get_float_value('float.unset', '1.0') == 1.0
|
||||
assert config.get_float_value('float.unset') is None
|
||||
assert config.get_float_value('float.invalid') is None
|
||||
|
||||
|
||||
def test_get_bool_value() -> None:
|
||||
assert config.get_bool_value('bool.valid') is True
|
||||
assert config.get_bool_value('bool.unset', 'False') is False
|
||||
assert config.get_bool_value('bool.unset') is None
|
||||
assert config.get_bool_value('bool.invalid') is None
|
||||
|
||||
|
||||
def test_get_str_list() -> None:
|
||||
assert config.get_str_list('str_list.valid') == [ 'a', 'b', 'c' ]
|
||||
assert config.get_str_list('str_list.unset', 'c b a') == [ 'c', 'b', 'a' ]
|
||||
assert config.get_str_list('str_list.unset') is None
|
||||
assert config.get_str_list('str_list.invalid') is None
|
||||
|
||||
|
||||
def test_get_int_list() -> None:
|
||||
assert config.get_int_list('int_list.valid') == [ 1, 2, 3 ]
|
||||
assert config.get_int_list('int_list.unset', '3 2 1') == [ 3, 2, 1 ]
|
||||
assert config.get_int_list('int_list.unset') is None
|
||||
assert config.get_int_list('int_list.invalid') is None
|
||||
|
||||
|
||||
def test_get_float_list() -> None:
|
||||
assert config.get_float_list('float_list.valid') == [ 1.0, 2.0, 3.0 ]
|
||||
assert config.get_float_list('float_list.unset', '3.0 2.0 1.0') == [ 3.0, 2.0, 1.0 ]
|
||||
assert config.get_float_list('float_list.unset') is None
|
||||
assert config.get_float_list('float_list.invalid') is None
|
||||
@ -1,4 +1,4 @@
|
||||
from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
|
||||
from facefusion.execution_helper import encode_execution_providers, decode_execution_providers, apply_execution_provider_options, map_torch_backend
|
||||
|
||||
|
||||
def test_encode_execution_providers() -> None:
|
||||
@ -7,3 +7,20 @@ def test_encode_execution_providers() -> None:
|
||||
|
||||
def test_decode_execution_providers() -> None:
|
||||
assert decode_execution_providers([ 'cpu' ]) == [ 'CPUExecutionProvider' ]
|
||||
|
||||
|
||||
def test_multiple_execution_providers() -> None:
|
||||
execution_provider_with_options =\
|
||||
[
|
||||
'CPUExecutionProvider',
|
||||
('CUDAExecutionProvider',
|
||||
{
|
||||
'cudnn_conv_algo_search': 'DEFAULT'
|
||||
})
|
||||
]
|
||||
assert apply_execution_provider_options([ 'CPUExecutionProvider', 'CUDAExecutionProvider' ]) == execution_provider_with_options
|
||||
|
||||
|
||||
def test_map_device() -> None:
|
||||
assert map_torch_backend([ 'CPUExecutionProvider' ]) == 'cpu'
|
||||
assert map_torch_backend([ 'CPUExecutionProvider', 'CUDAExecutionProvider' ]) == 'cuda'
|
||||
|
||||
@ -39,7 +39,7 @@ def test_extract_frames() -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
|
||||
assert extract_frames(target_path, 30.0) is True
|
||||
assert extract_frames(target_path, '452x240', 30.0) is True
|
||||
assert len(glob.glob1(temp_directory_path, '*.jpg')) == 324
|
||||
|
||||
clear_temp(target_path)
|
||||
@ -57,7 +57,7 @@ def test_extract_frames_with_trim_start() -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
|
||||
assert extract_frames(target_path, 30.0) is True
|
||||
assert extract_frames(target_path, '452x240', 30.0) is True
|
||||
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
|
||||
|
||||
clear_temp(target_path)
|
||||
@ -76,7 +76,7 @@ def test_extract_frames_with_trim_start_and_trim_end() -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
|
||||
assert extract_frames(target_path, 30.0) is True
|
||||
assert extract_frames(target_path, '452x240', 30.0) is True
|
||||
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
|
||||
|
||||
clear_temp(target_path)
|
||||
@ -94,7 +94,7 @@ def test_extract_frames_with_trim_end() -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
|
||||
assert extract_frames(target_path, 30.0) is True
|
||||
assert extract_frames(target_path, '426x240', 30.0) is True
|
||||
assert len(glob.glob1(temp_directory_path, '*.jpg')) == frame_total
|
||||
|
||||
clear_temp(target_path)
|
||||
|
||||
@ -1,4 +1,15 @@
|
||||
from facefusion.filesystem import is_file, is_directory, is_image, are_images, is_video
|
||||
import pytest
|
||||
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.filesystem import is_file, is_directory, is_image, are_images, is_video, list_directory
|
||||
|
||||
|
||||
@pytest.fixture(scope = 'module', autouse = True)
|
||||
def before_all() -> None:
|
||||
conditional_download('.assets/examples',
|
||||
[
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.jpg'
|
||||
])
|
||||
|
||||
|
||||
def test_is_file() -> None:
|
||||
@ -29,3 +40,9 @@ def test_is_video() -> None:
|
||||
assert is_video('.assets/examples/target-240p.mp4') is True
|
||||
assert is_video('.assets/examples/source.jpg') is False
|
||||
assert is_video('invalid') is False
|
||||
|
||||
|
||||
def test_list_directory() -> None:
|
||||
assert list_directory('.assets/examples')
|
||||
assert list_directory('.assets/examples/source.jpg') is None
|
||||
assert list_directory('invalid') is None
|
||||
|
||||
9
tests/test_memory.py
Normal file
9
tests/test_memory.py
Normal file
@ -0,0 +1,9 @@
|
||||
import platform
|
||||
|
||||
from facefusion.memory import limit_system_memory
|
||||
|
||||
|
||||
def test_limit_system_memory() -> None:
|
||||
assert limit_system_memory(4) is True
|
||||
if platform.system().lower() == 'darwin' or platform.system().lower() == 'linux':
|
||||
assert limit_system_memory(1024) is False
|
||||
@ -1,6 +1,6 @@
|
||||
import platform
|
||||
|
||||
from facefusion.normalizer import normalize_output_path, normalize_padding
|
||||
from facefusion.normalizer import normalize_output_path, normalize_padding, normalize_fps
|
||||
|
||||
|
||||
def test_normalize_output_path() -> None:
|
||||
@ -23,3 +23,10 @@ def test_normalize_padding() -> None:
|
||||
assert normalize_padding([ 1, 2 ]) == (1, 2, 1, 2)
|
||||
assert normalize_padding([ 1, 2, 3 ]) == (1, 2, 3, 2)
|
||||
assert normalize_padding(None) is None
|
||||
|
||||
|
||||
def test_normalize_fps() -> None:
|
||||
assert normalize_fps(0.0) == 1.0
|
||||
assert normalize_fps(25.0) == 25.0
|
||||
assert normalize_fps(61.0) == 60.0
|
||||
assert normalize_fps(None) is None
|
||||
|
||||
@ -2,7 +2,7 @@ import subprocess
|
||||
import pytest
|
||||
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.vision import get_video_frame, detect_fps, count_video_frame_total
|
||||
from facefusion.vision import get_video_frame, count_video_frame_total, detect_video_fps, detect_video_resolution, pack_resolution, unpack_resolution, create_video_resolutions
|
||||
|
||||
|
||||
@pytest.fixture(scope = 'module', autouse = True)
|
||||
@ -10,11 +10,14 @@ def before_all() -> None:
|
||||
conditional_download('.assets/examples',
|
||||
[
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.jpg',
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-240p.mp4'
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-240p.mp4',
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/target-1080p.mp4'
|
||||
])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=25', '.assets/examples/target-240p-25fps.mp4' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=30', '.assets/examples/target-240p-30fps.mp4' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'fps=60', '.assets/examples/target-240p-60fps.mp4' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vf', 'transpose=0', '.assets/examples/target-240p-90deg.mp4' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-1080p.mp4', '-vf', 'transpose=0', '.assets/examples/target-1080p-90deg.mp4' ])
|
||||
|
||||
|
||||
def test_get_video_frame() -> None:
|
||||
@ -22,15 +25,39 @@ def test_get_video_frame() -> None:
|
||||
assert get_video_frame('invalid') is None
|
||||
|
||||
|
||||
def test_detect_fps() -> None:
|
||||
assert detect_fps('.assets/examples/target-240p-25fps.mp4') == 25.0
|
||||
assert detect_fps('.assets/examples/target-240p-30fps.mp4') == 30.0
|
||||
assert detect_fps('.assets/examples/target-240p-60fps.mp4') == 60.0
|
||||
assert detect_fps('invalid') is None
|
||||
|
||||
|
||||
def test_count_video_frame_total() -> None:
|
||||
assert count_video_frame_total('.assets/examples/target-240p-25fps.mp4') == 270
|
||||
assert count_video_frame_total('.assets/examples/target-240p-30fps.mp4') == 324
|
||||
assert count_video_frame_total('.assets/examples/target-240p-60fps.mp4') == 648
|
||||
assert count_video_frame_total('invalid') == 0
|
||||
|
||||
|
||||
def test_detect_video_fps() -> None:
|
||||
assert detect_video_fps('.assets/examples/target-240p-25fps.mp4') == 25.0
|
||||
assert detect_video_fps('.assets/examples/target-240p-30fps.mp4') == 30.0
|
||||
assert detect_video_fps('.assets/examples/target-240p-60fps.mp4') == 60.0
|
||||
assert detect_video_fps('invalid') is None
|
||||
|
||||
|
||||
def test_detect_video_resolution() -> None:
|
||||
assert detect_video_resolution('.assets/examples/target-240p.mp4') == (426.0, 226.0)
|
||||
assert detect_video_resolution('.assets/examples/target-1080p.mp4') == (2048.0, 1080.0)
|
||||
assert detect_video_resolution('invalid') is None
|
||||
|
||||
|
||||
def test_pack_resolution() -> None:
|
||||
assert pack_resolution((1.0, 1.0)) == '0x0'
|
||||
assert pack_resolution((2.0, 2.0)) == '2x2'
|
||||
|
||||
|
||||
def test_unpack_resolution() -> None:
|
||||
assert unpack_resolution('0x0') == (0, 0)
|
||||
assert unpack_resolution('2x2') == (2, 2)
|
||||
|
||||
|
||||
def test_create_video_resolutions() -> None:
|
||||
assert create_video_resolutions('.assets/examples/target-240p.mp4') == [ '426x226', '452x240', '678x360', '904x480', '1018x540', '1358x720', '2036x1080', '2714x1440', '4072x2160' ]
|
||||
assert create_video_resolutions('.assets/examples/target-240p-90deg.mp4') == [ '226x426', '240x452', '360x678', '480x904', '540x1018', '720x1358', '1080x2036', '1440x2714', '2160x4072' ]
|
||||
assert create_video_resolutions('.assets/examples/target-1080p.mp4') == [ '456x240', '682x360', '910x480', '1024x540', '1366x720', '2048x1080', '2730x1440', '4096x2160' ]
|
||||
assert create_video_resolutions('.assets/examples/target-1080p-90deg.mp4') == [ '240x456', '360x682', '480x910', '540x1024', '720x1366', '1080x2048', '1440x2730', '2160x4096' ]
|
||||
assert create_video_resolutions('invalid') is None
|
||||
|
||||
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