Next (#436)
* Rename landmark 5 variables * Mark as NEXT * Render tabs for multiple ui layout usage * Allow many face detectors at once, Add face detector tweaks * Remove face detector tweaks for now (kinda placebo) * Fix lint issues * Allow rendering the landmark-5 and landmark-5/68 via debugger * Fix naming * Convert face landmark based on confidence score * Convert face landmark based on confidence score * Add scrfd face detector model (#397) * Add scrfd face detector model * Switch to scrfd_2.5g.onnx model * Just some renaming * Downgrade OpenCV, Add SYSTEM_VERSION_COMPAT=0 for MacOS * Improve naming * prepare detect frame outside of semaphore * Feat/process manager (#399) * Minor naming * Introduce process manager to start and stop * Introduce process manager to start and stop * Introduce process manager to start and stop * Introduce process manager to start and stop * Introduce process manager to start and stop * Remove useless test for now * Avoid useless variables * Show stop once is_processing is True * Allow to stop ffmpeg processing too * Implement output image resolution (#403) * Implement output image resolution * Reorder code * Simplify output logic and therefore fix bug * Frame-enhancer-onnx (#404) * changes * changes * changes * changes * add models * update workflow * Some cleanup * Some cleanup * Feat/frame enhancer polishing (#410) * Some cleanup * Polish the frame enhancer * Frame Enhancer: Add more models, optimize processing * Minor changes * Improve readability of create_tile_frames and merge_tile_frames * We don't have enough models yet * Feat/face landmarker score (#413) * Introduce face landmarker score * Fix testing * Fix testing * Use release for score related sliders * Reduce face landmark fallbacks * Scores and landmarks in Face dict, Change color-theme in face debugger * Scores and landmarks in Face dict, Change color-theme in face debugger * Fix some naming * Add 8K support (for whatever reasons) * Fix testing * Using get() for face.landmarks * Introduce statistics * More statistics * Limit the histogram equalization * Enable queue() for default layout * Improve copy_image() * Fix error when switching detector model * Always set UI values with globals if possible * Use different logic for output image and output video resolutions * Enforce re-download if file size is off * Remove unused method * Remove unused method * Remove unused warning filter * Improved output path normalization (#419) * Handle some exceptions * Handle some exceptions * Cleanup * Prevent countless thread locks * Listen to user feedback * Fix webp edge case * Feat/cuda device detection (#424) * Introduce cuda device detection * Introduce cuda device detection * it's gtx * Move logic to run_nvidia_smi() * Finalize execution device naming * Finalize execution device naming * Merge execution_helper.py to execution.py * Undo lowercase of values * Undo lowercase of values * Finalize naming * Add missing entry to ini * fix lip_syncer preview (#426) * fix lip_syncer preview * change * Refresh preview on trim changes * Cleanup frame enhancers and remove useless scale in merge_video() (#428) * Keep lips over the whole video once lip syncer is enabled (#430) * Keep lips over the whole video once lip syncer is enabled * changes * changes * Fix spacing * Use empty audio frame on silence * Use empty audio frame on silence * Fix ConfigParser encoding (#431) facefusion.ini is UTF8 encoded but config.py doesn't specify encoding which results in corrupted entries when non english characters are used. Affected entries: source_paths target_path output_path * Adjust spacing * Improve the GTX 16 series detection * Use general exception to catch ParseError * Use general exception to catch ParseError * Host frame enhancer models4 * Use latest onnxruntime * Minor changes in benchmark UI * Different approach to cancel ffmpeg process * Add support for amd amf encoders (#433) * Add amd_amf encoders * remove -rc cqp from amf encoder parameters * Improve terminal output, move success messages to debug mode * Improve terminal output, move success messages to debug mode * Minor update * Minor update * onnxruntime 1.17.1 matches cuda 12.2 * Feat/improved scaling (#435) * Prevent useless temp upscaling, Show resolution and fps in terminal output * Remove temp frame quality * Remove temp frame quality * Tiny cleanup * Default back to png for temp frames, Remove pix_fmt from frame extraction due mjpeg error * Fix inswapper fallback by onnxruntime * Fix inswapper fallback by major onnxruntime * Fix inswapper fallback by major onnxruntime * Add testing for vision restrict methods * Fix left / right face mask regions, add left-ear and right-ear * Flip right and left again * Undo ears - does not work with box mask * Prepare next release * Fix spacing * 100% quality when using jpg for temp frames * Use span_kendata_x4 as default as of speed * benchmark optimal tile and pad * Undo commented out code * Add real_esrgan_x4_fp16 model * Be strict when using many face detectors --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> Co-authored-by: aldemoth <159712934+aldemoth@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
GitHub
parent
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commit
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2
.github/workflows/ci.yml
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.github/workflows/ci.yml
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@ -30,6 +30,6 @@ jobs:
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uses: actions/setup-python@v2
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with:
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python-version: '3.10'
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- run: python install.py --torch cpu --onnxruntime default --skip-venv
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- run: python install.py --onnxruntime default --skip-venv
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- run: pip install pytest
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- run: pytest
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13
README.md
13
README.md
@ -54,12 +54,13 @@ 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 in which the face analyser detects faces.
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--face-analyser-age {child,teen,adult,senior} filter the detected faces based on their age
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--face-analyser-gender {female,male} filter the detected faces based on their gender
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--face-detector-model {retinaface,yoloface,yunet} choose the model responsible for detecting the face
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--face-detector-model {many,retinaface,scrfd,yoloface,yunet} choose the model responsible for detecting the face
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--face-detector-size FACE_DETECTOR_SIZE specify the size of the frame provided to the face detector
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--face-detector-score [0.0-1.0] filter the detected faces base on the confidence score
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--face-landmarker-score [0.0-1.0] filter the detected landmarks base on the confidence score
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face selector:
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--face-selector-mode {reference,one,many} use reference based tracking with simple matching
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--face-selector-mode {many,one,reference} use reference based tracking or simple matching
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--reference-face-position REFERENCE_FACE_POSITION specify the position used to create the reference face
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--reference-face-distance [0.0-1.5] specify the desired similarity between the reference face and target face
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--reference-frame-number REFERENCE_FRAME_NUMBER specify the frame used to create the reference face
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@ -74,12 +75,12 @@ frame extraction:
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--trim-frame-start TRIM_FRAME_START specify the the start frame of the target video
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--trim-frame-end TRIM_FRAME_END specify the the end frame of the target video
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--temp-frame-format {bmp,jpg,png} specify the temporary resources format
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--temp-frame-quality [0-100] specify the temporary resources quality
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--keep-temp keep the temporary resources after processing
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output creation:
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--output-image-quality [0-100] specify the image quality which translates to the compression factor
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--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc} specify the encoder use for the video compression
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--output-image-resolution OUTPUT_IMAGE_RESOLUTION specify the image output resolution based on the target image
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--output-video-encoder {libx264,libx265,libvpx-vp9,h264_nvenc,hevc_nvenc,h264_amf,hevc_amf} specify the encoder use for the video compression
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--output-video-preset {ultrafast,superfast,veryfast,faster,fast,medium,slow,slower,veryslow} balance fast video processing and video file size
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--output-video-quality [0-100] specify the video quality which translates to the compression factor
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--output-video-resolution OUTPUT_VIDEO_RESOLUTION specify the video output resolution based on the target video
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@ -88,11 +89,11 @@ output creation:
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frame processors:
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--frame-processors FRAME_PROCESSORS [FRAME_PROCESSORS ...] load a single or multiple frame processors. (choices: face_debugger, face_enhancer, face_swapper, frame_enhancer, lip_syncer, ...)
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--face-debugger-items FACE_DEBUGGER_ITEMS [FACE_DEBUGGER_ITEMS ...] load a single or multiple frame processors (choices: bounding-box, landmark-5, landmark-68, face-mask, score, age, gender)
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--face-debugger-items FACE_DEBUGGER_ITEMS [FACE_DEBUGGER_ITEMS ...] load a single or multiple frame processors (choices: bounding-box, face-landmark-5, face-landmark-5/68, face-landmark-68, face-mask, face-detector-score, face-landmarker-score, age, gender)
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--face-enhancer-model {codeformer,gfpgan_1.2,gfpgan_1.3,gfpgan_1.4,gpen_bfr_256,gpen_bfr_512,restoreformer_plus_plus} choose the model responsible for enhancing the face
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--face-enhancer-blend [0-100] blend the enhanced into the previous face
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--face-swapper-model {blendswap_256,inswapper_128,inswapper_128_fp16,simswap_256,simswap_512_unofficial,uniface_256} choose the model responsible for swapping the face
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--frame-enhancer-model {real_esrgan_x2plus,real_esrgan_x4plus,real_esrnet_x4plus} choose the model responsible for enhancing the frame
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--frame-enhancer-model {lsdir_x4,nomos8k_sc_x4,real_esrgan_x4,span_kendata_x4} choose the model responsible for enhancing the frame
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--frame-enhancer-blend [0-100] blend the enhanced into the previous frame
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--lip-syncer-model {wav2lip_gan} choose the model responsible for syncing the lips
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@ -24,6 +24,7 @@ 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_landmarker_score =
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[face_selector]
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face_selector_mode =
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@ -41,11 +42,11 @@ face_mask_regions =
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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_image_resolution =
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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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@ -11,11 +11,16 @@ from facefusion.typing import Fps, Audio, Spectrogram, AudioFrame
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def get_audio_frame(audio_path : str, fps : Fps, frame_number : int = 0) -> Optional[AudioFrame]:
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if is_audio(audio_path):
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audio_frames = read_static_audio(audio_path, fps)
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if frame_number < len(audio_frames):
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if frame_number in range(len(audio_frames)):
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return audio_frames[frame_number]
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return None
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def create_empty_audio_frame() -> AudioFrame:
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audio_frame = numpy.zeros((80, 16), dtype = numpy.int16)
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return audio_frame
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@lru_cache(maxsize = None)
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def read_static_audio(audio_path : str, fps : Fps) -> Optional[List[AudioFrame]]:
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if is_audio(audio_path):
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@ -9,26 +9,29 @@ face_analyser_ages : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior
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face_analyser_genders : List[FaceAnalyserGender] = [ 'female', 'male' ]
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face_detector_set : Dict[FaceDetectorModel, List[str]] =\
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{
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'retinaface': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
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'many': [ '640x640' ],
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'retinaface': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
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'scrfd': [ '160x160', '320x320', '480x480', '512x512', '640x640' ],
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'yoloface': [ '640x640' ],
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'yunet': [ '160x160', '320x320', '480x480', '512x512', '640x640', '768x768', '960x960', '1024x1024' ]
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}
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face_selector_modes : List[FaceSelectorMode] = [ 'reference', 'one', 'many' ]
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face_selector_modes : List[FaceSelectorMode] = [ 'many', 'one', 'reference' ]
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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] = [ 'bmp', 'jpg', 'png' ]
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output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc' ]
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output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf' ]
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output_video_presets : List[OutputVideoPreset] = [ 'ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow' ]
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video_template_sizes : List[int] = [ 240, 360, 480, 540, 720, 1080, 1440, 2160 ]
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image_template_sizes : List[float] = [ 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4 ]
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video_template_sizes : List[int] = [ 240, 360, 480, 540, 720, 1080, 1440, 2160, 4320 ]
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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_landmarker_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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@ -1,4 +1,4 @@
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from typing import List, Any
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from typing import List, Any, Tuple
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import numpy
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@ -16,3 +16,12 @@ def create_float_range(start : float, stop : float, step : float) -> List[float]
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def get_first(__list__ : Any) -> Any:
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return next(iter(__list__), None)
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def extract_major_version(version : str) -> Tuple[int, int]:
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versions = version.split('.')
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if len(versions) > 1:
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return int(versions[0]), int(versions[1])
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if len(versions) == 1:
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return int(versions[0]), 0
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return 0, 0
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@ -12,7 +12,7 @@ def get_config() -> ConfigParser:
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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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CONFIG.read(config_path, encoding = 'utf-8')
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return CONFIG
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@ -9,7 +9,7 @@ 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 VisionFrame, ModelValue, Fps
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from facefusion.execution_helper import apply_execution_provider_options
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from facefusion.execution 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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@ -62,23 +62,23 @@ def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
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return False
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def prepare_frame(vision_frame : VisionFrame) -> VisionFrame:
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vision_frame = cv2.resize(vision_frame, (224, 224)).astype(numpy.float32)
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vision_frame -= numpy.array([ 104, 117, 123 ]).astype(numpy.float32)
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vision_frame = numpy.expand_dims(vision_frame, axis = 0)
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return vision_frame
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def analyse_frame(vision_frame : VisionFrame) -> bool:
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content_analyser = get_content_analyser()
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vision_frame = prepare_frame(vision_frame)
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probability = content_analyser.run(None,
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{
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'input:0': vision_frame
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content_analyser.get_inputs()[0].name: vision_frame
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})[0][0][1]
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return probability > PROBABILITY_LIMIT
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def prepare_frame(vision_frame : VisionFrame) -> VisionFrame:
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vision_frame = cv2.resize(vision_frame, (224, 224)).astype(numpy.float32)
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vision_frame -= numpy.array([ 104, 117, 123 ]).astype(numpy.float32)
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vision_frame = numpy.expand_dims(vision_frame, axis = 0)
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return vision_frame
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@lru_cache(maxsize = None)
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def analyse_image(image_path : str) -> bool:
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frame = read_image(image_path)
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@ -4,32 +4,31 @@ os.environ['OMP_NUM_THREADS'] = '1'
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import signal
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import sys
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import time
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import warnings
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import shutil
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import numpy
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import onnxruntime
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from time import sleep
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from time import sleep, time
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from argparse import ArgumentParser, HelpFormatter
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import facefusion.choices
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import facefusion.globals
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from facefusion.face_analyser import get_one_face, get_average_face
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from facefusion.face_store import get_reference_faces, append_reference_face
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from facefusion import face_analyser, face_masker, content_analyser, config, metadata, logger, wording
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from facefusion import face_analyser, face_masker, content_analyser, config, process_manager, metadata, logger, wording
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from facefusion.content_analyser import analyse_image, analyse_video
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from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
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from facefusion.common_helper import create_metavar, get_first
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from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
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from facefusion.execution import encode_execution_providers, decode_execution_providers
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from facefusion.normalizer import normalize_output_path, normalize_padding, normalize_fps
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from facefusion.memory import limit_system_memory
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from facefusion.statistics import conditional_log_statistics
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from facefusion.filesystem import list_directory, get_temp_frame_paths, create_temp, move_temp, clear_temp, is_image, is_video, filter_audio_paths
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from facefusion.ffmpeg import extract_frames, compress_image, merge_video, restore_audio, replace_audio
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from facefusion.vision import get_video_frame, read_image, read_static_images, pack_resolution, detect_video_resolution, detect_video_fps, create_video_resolutions
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from facefusion.ffmpeg import extract_frames, merge_video, copy_image, finalize_image, restore_audio, replace_audio
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from facefusion.vision import read_image, read_static_images, detect_image_resolution, restrict_video_fps, create_image_resolutions, get_video_frame, detect_video_resolution, detect_video_fps, restrict_video_resolution, restrict_image_resolution, create_video_resolutions, pack_resolution, unpack_resolution
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onnxruntime.set_default_logger_severity(3)
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warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
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warnings.filterwarnings('ignore', category = UserWarning, module = 'torchvision')
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def cli() -> None:
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@ -63,6 +62,7 @@ def cli() -> None:
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group_face_analyser.add_argument('--face-detector-model', help = wording.get('help.face_detector_model'), default = config.get_str_value('face_analyser.face_detector_model', 'yoloface'), choices = facefusion.choices.face_detector_set.keys())
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group_face_analyser.add_argument('--face-detector-size', help = wording.get('help.face_detector_size'), default = config.get_str_value('face_analyser.face_detector_size', '640x640'))
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group_face_analyser.add_argument('--face-detector-score', help = wording.get('help.face_detector_score'), 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))
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group_face_analyser.add_argument('--face-landmarker-score', help = wording.get('help.face_landmarker_score'), type = float, default = config.get_float_value('face_analyser.face_landmarker_score', '0.5'), choices = facefusion.choices.face_landmarker_score_range, metavar = create_metavar(facefusion.choices.face_landmarker_score_range))
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# face selector
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group_face_selector = program.add_argument_group('face selector')
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group_face_selector.add_argument('--face-selector-mode', help = wording.get('help.face_selector_mode'), default = config.get_str_value('face_selector.face_selector_mode', 'reference'), choices = facefusion.choices.face_selector_modes)
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@ -79,12 +79,12 @@ def cli() -> None:
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group_frame_extraction = program.add_argument_group('frame extraction')
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group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('help.trim_frame_start'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_start'))
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group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('help.trim_frame_end'), type = int, default = facefusion.config.get_int_value('frame_extraction.trim_frame_end'))
|
||||
group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('help.temp_frame_format'), 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('help.temp_frame_quality'), 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('--temp-frame-format', help = wording.get('help.temp_frame_format'), default = config.get_str_value('frame_extraction.temp_frame_format', 'png'), choices = facefusion.choices.temp_frame_formats)
|
||||
group_frame_extraction.add_argument('--keep-temp', help = wording.get('help.keep_temp'), 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('help.output_image_quality'), 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-image-resolution', help = wording.get('help.output_image_resolution'), default = config.get_str_value('output_creation.output_image_resolution'))
|
||||
group_output_creation.add_argument('--output-video-encoder', help = wording.get('help.output_video_encoder'), 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('help.output_video_preset'), 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('help.output_video_quality'), 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))
|
||||
@ -111,7 +111,7 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
# general
|
||||
facefusion.globals.source_paths = args.source_paths
|
||||
facefusion.globals.target_path = args.target_path
|
||||
facefusion.globals.output_path = normalize_output_path(facefusion.globals.source_paths, facefusion.globals.target_path, args.output_path)
|
||||
facefusion.globals.output_path = args.output_path
|
||||
# misc
|
||||
facefusion.globals.skip_download = args.skip_download
|
||||
facefusion.globals.headless = args.headless
|
||||
@ -133,6 +133,7 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
else:
|
||||
facefusion.globals.face_detector_size = '640x640'
|
||||
facefusion.globals.face_detector_score = args.face_detector_score
|
||||
facefusion.globals.face_landmarker_score = args.face_landmarker_score
|
||||
# face selector
|
||||
facefusion.globals.face_selector_mode = args.face_selector_mode
|
||||
facefusion.globals.reference_face_position = args.reference_face_position
|
||||
@ -147,20 +148,26 @@ def apply_args(program : ArgumentParser) -> None:
|
||||
facefusion.globals.trim_frame_start = args.trim_frame_start
|
||||
facefusion.globals.trim_frame_end = args.trim_frame_end
|
||||
facefusion.globals.temp_frame_format = args.temp_frame_format
|
||||
facefusion.globals.temp_frame_quality = args.temp_frame_quality
|
||||
facefusion.globals.keep_temp = args.keep_temp
|
||||
# output creation
|
||||
facefusion.globals.output_image_quality = args.output_image_quality
|
||||
if is_image(args.target_path):
|
||||
output_image_resolution = detect_image_resolution(args.target_path)
|
||||
output_image_resolutions = create_image_resolutions(output_image_resolution)
|
||||
if args.output_image_resolution in output_image_resolutions:
|
||||
facefusion.globals.output_image_resolution = args.output_image_resolution
|
||||
else:
|
||||
facefusion.globals.output_image_resolution = pack_resolution(output_image_resolution)
|
||||
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
|
||||
if is_video(args.target_path):
|
||||
target_video_resolutions = create_video_resolutions(args.target_path)
|
||||
if args.output_video_resolution in target_video_resolutions:
|
||||
output_video_resolution = detect_video_resolution(args.target_path)
|
||||
output_video_resolutions = create_video_resolutions(output_video_resolution)
|
||||
if args.output_video_resolution in output_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)
|
||||
facefusion.globals.output_video_resolution = pack_resolution(output_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
|
||||
@ -196,6 +203,9 @@ def run(program : ArgumentParser) -> None:
|
||||
|
||||
|
||||
def destroy() -> None:
|
||||
process_manager.stop()
|
||||
while process_manager.is_processing():
|
||||
sleep(0.5)
|
||||
if facefusion.globals.target_path:
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
sys.exit(0)
|
||||
@ -212,7 +222,7 @@ def pre_check() -> bool:
|
||||
|
||||
|
||||
def conditional_process() -> None:
|
||||
start_time = time.time()
|
||||
start_time = time()
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
while not frame_processor_module.post_check():
|
||||
logger.disable()
|
||||
@ -247,28 +257,43 @@ def conditional_append_reference_faces() -> None:
|
||||
|
||||
|
||||
def process_image(start_time : float) -> None:
|
||||
normed_output_path = normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
if analyse_image(facefusion.globals.target_path):
|
||||
return
|
||||
shutil.copy2(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
# process frame
|
||||
# copy image
|
||||
process_manager.start()
|
||||
temp_image_resolution = pack_resolution(restrict_image_resolution(facefusion.globals.target_path, unpack_resolution(facefusion.globals.output_image_resolution)))
|
||||
logger.info(wording.get('copying_image').format(resolution = temp_image_resolution), __name__.upper())
|
||||
if copy_image(facefusion.globals.target_path, normed_output_path, temp_image_resolution):
|
||||
logger.debug(wording.get('copying_image_succeed'), __name__.upper())
|
||||
else:
|
||||
logger.error(wording.get('copying_image_failed'), __name__.upper())
|
||||
return
|
||||
# process image
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
logger.info(wording.get('processing'), frame_processor_module.NAME)
|
||||
frame_processor_module.process_image(facefusion.globals.source_paths, facefusion.globals.output_path, facefusion.globals.output_path)
|
||||
frame_processor_module.process_image(facefusion.globals.source_paths, normed_output_path, normed_output_path)
|
||||
frame_processor_module.post_process()
|
||||
# compress image
|
||||
if compress_image(facefusion.globals.output_path):
|
||||
logger.info(wording.get('compressing_image_succeed'), __name__.upper())
|
||||
if is_process_stopping():
|
||||
return
|
||||
# finalize image
|
||||
logger.info(wording.get('finalizing_image').format(resolution = facefusion.globals.output_image_resolution), __name__.upper())
|
||||
if finalize_image(normed_output_path, facefusion.globals.output_image_resolution):
|
||||
logger.debug(wording.get('finalizing_image_succeed'), __name__.upper())
|
||||
else:
|
||||
logger.warn(wording.get('compressing_image_skipped'), __name__.upper())
|
||||
logger.warn(wording.get('finalizing_image_skipped'), __name__.upper())
|
||||
# validate image
|
||||
if is_image(facefusion.globals.output_path):
|
||||
seconds = '{:.2f}'.format((time.time() - start_time) % 60)
|
||||
if is_image(normed_output_path):
|
||||
seconds = '{:.2f}'.format((time() - start_time) % 60)
|
||||
logger.info(wording.get('processing_image_succeed').format(seconds = seconds), __name__.upper())
|
||||
conditional_log_statistics()
|
||||
else:
|
||||
logger.error(wording.get('processing_image_failed'), __name__.upper())
|
||||
process_manager.end()
|
||||
|
||||
|
||||
def process_video(start_time : float) -> None:
|
||||
normed_output_path = normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
if analyse_video(facefusion.globals.target_path, facefusion.globals.trim_frame_start, facefusion.globals.trim_frame_end):
|
||||
return
|
||||
# clear temp
|
||||
@ -278,47 +303,75 @@ def process_video(start_time : float) -> None:
|
||||
logger.debug(wording.get('creating_temp'), __name__.upper())
|
||||
create_temp(facefusion.globals.target_path)
|
||||
# extract frames
|
||||
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
|
||||
process_manager.start()
|
||||
temp_video_resolution = pack_resolution(restrict_video_resolution(facefusion.globals.target_path, unpack_resolution(facefusion.globals.output_video_resolution)))
|
||||
temp_video_fps = restrict_video_fps(facefusion.globals.target_path, facefusion.globals.output_video_fps)
|
||||
logger.info(wording.get('extracting_frames').format(resolution = temp_video_resolution, fps = temp_video_fps), __name__.upper())
|
||||
if extract_frames(facefusion.globals.target_path, temp_video_resolution, temp_video_fps):
|
||||
logger.debug(wording.get('extracting_frames_succeed'), __name__.upper())
|
||||
else:
|
||||
if is_process_stopping():
|
||||
return
|
||||
logger.error(wording.get('extracting_frames_failed'), __name__.upper())
|
||||
return
|
||||
# process frames
|
||||
temp_frame_paths = get_temp_frame_paths(facefusion.globals.target_path)
|
||||
if temp_frame_paths:
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
logger.info(wording.get('processing'), frame_processor_module.NAME)
|
||||
frame_processor_module.process_video(facefusion.globals.source_paths, temp_frame_paths)
|
||||
frame_processor_module.post_process()
|
||||
if is_process_stopping():
|
||||
return
|
||||
else:
|
||||
logger.error(wording.get('temp_frames_not_found'), __name__.upper())
|
||||
return
|
||||
# merge video
|
||||
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_resolution, facefusion.globals.output_video_fps):
|
||||
logger.info(wording.get('merging_video').format(resolution = facefusion.globals.output_video_resolution, fps = facefusion.globals.output_video_fps), __name__.upper())
|
||||
if merge_video(facefusion.globals.target_path, facefusion.globals.output_video_resolution, facefusion.globals.output_video_fps):
|
||||
logger.debug(wording.get('merging_video_succeed'), __name__.upper())
|
||||
else:
|
||||
if is_process_stopping():
|
||||
return
|
||||
logger.error(wording.get('merging_video_failed'), __name__.upper())
|
||||
return
|
||||
# handle audio
|
||||
if facefusion.globals.skip_audio:
|
||||
logger.info(wording.get('skipping_audio'), __name__.upper())
|
||||
move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
move_temp(facefusion.globals.target_path, normed_output_path)
|
||||
else:
|
||||
if 'lip_syncer' in facefusion.globals.frame_processors:
|
||||
source_audio_path = get_first(filter_audio_paths(facefusion.globals.source_paths))
|
||||
if source_audio_path and replace_audio(facefusion.globals.target_path, source_audio_path, facefusion.globals.output_path):
|
||||
logger.info(wording.get('restoring_audio_succeed'), __name__.upper())
|
||||
if source_audio_path and replace_audio(facefusion.globals.target_path, source_audio_path, normed_output_path):
|
||||
logger.debug(wording.get('restoring_audio_succeed'), __name__.upper())
|
||||
else:
|
||||
if is_process_stopping():
|
||||
return
|
||||
logger.warn(wording.get('restoring_audio_skipped'), __name__.upper())
|
||||
move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
move_temp(facefusion.globals.target_path, normed_output_path)
|
||||
else:
|
||||
if restore_audio(facefusion.globals.target_path, facefusion.globals.output_path, facefusion.globals.output_video_fps):
|
||||
logger.info(wording.get('restoring_audio_succeed'), __name__.upper())
|
||||
if restore_audio(facefusion.globals.target_path, normed_output_path, facefusion.globals.output_video_fps):
|
||||
logger.debug(wording.get('restoring_audio_succeed'), __name__.upper())
|
||||
else:
|
||||
if is_process_stopping():
|
||||
return
|
||||
logger.warn(wording.get('restoring_audio_skipped'), __name__.upper())
|
||||
move_temp(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
move_temp(facefusion.globals.target_path, normed_output_path)
|
||||
# clear temp
|
||||
logger.debug(wording.get('clearing_temp'), __name__.upper())
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
# validate video
|
||||
if is_video(facefusion.globals.output_path):
|
||||
seconds = '{:.2f}'.format((time.time() - start_time))
|
||||
if is_video(normed_output_path):
|
||||
seconds = '{:.2f}'.format((time() - start_time))
|
||||
logger.info(wording.get('processing_video_succeed').format(seconds = seconds), __name__.upper())
|
||||
conditional_log_statistics()
|
||||
else:
|
||||
logger.error(wording.get('processing_video_failed'), __name__.upper())
|
||||
process_manager.end()
|
||||
|
||||
|
||||
def is_process_stopping() -> bool:
|
||||
if process_manager.is_stopping():
|
||||
process_manager.end()
|
||||
logger.info(wording.get('processing_stopped'), __name__.upper())
|
||||
return process_manager.is_pending()
|
||||
|
||||
@ -32,6 +32,9 @@ def conditional_download(download_directory_path : str, urls : List[str]) -> Non
|
||||
if is_file(download_file_path):
|
||||
current = os.path.getsize(download_file_path)
|
||||
progress.update(current - progress.n)
|
||||
if not is_download_done(url, download_file_path):
|
||||
os.remove(download_file_path)
|
||||
conditional_download(download_directory_path, [ url] )
|
||||
|
||||
|
||||
@lru_cache(maxsize = None)
|
||||
|
||||
97
facefusion/execution.py
Normal file
97
facefusion/execution.py
Normal file
@ -0,0 +1,97 @@
|
||||
from typing import List, Any
|
||||
from functools import lru_cache
|
||||
import subprocess
|
||||
import xml.etree.ElementTree as ElementTree
|
||||
import onnxruntime
|
||||
|
||||
from facefusion.typing import ExecutionDevice, ValueAndUnit
|
||||
|
||||
|
||||
def encode_execution_providers(execution_providers : List[str]) -> List[str]:
|
||||
return [ execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers ]
|
||||
|
||||
|
||||
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 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': 'EXHAUSTIVE' if use_exhaustive() else 'DEFAULT'
|
||||
}))
|
||||
else:
|
||||
execution_providers_with_options.append(execution_provider)
|
||||
return execution_providers_with_options
|
||||
|
||||
|
||||
def use_exhaustive() -> bool:
|
||||
execution_devices = detect_static_execution_devices()
|
||||
product_names = ('GeForce GTX 1630', 'GeForce GTX 1650', 'GeForce GTX 1660')
|
||||
|
||||
return any(execution_device.get('product').get('name').startswith(product_names) for execution_device in execution_devices)
|
||||
|
||||
|
||||
def run_nvidia_smi() -> subprocess.Popen[bytes]:
|
||||
commands = [ 'nvidia-smi', '--query', '--xml-format' ]
|
||||
return subprocess.Popen(commands, stdout = subprocess.PIPE)
|
||||
|
||||
|
||||
@lru_cache(maxsize = None)
|
||||
def detect_static_execution_devices() -> List[ExecutionDevice]:
|
||||
return detect_execution_devices()
|
||||
|
||||
|
||||
def detect_execution_devices() -> List[ExecutionDevice]:
|
||||
execution_devices : List[ExecutionDevice] = []
|
||||
try:
|
||||
output, _ = run_nvidia_smi().communicate()
|
||||
root_element = ElementTree.fromstring(output)
|
||||
except Exception:
|
||||
root_element = ElementTree.Element('xml')
|
||||
|
||||
for gpu_element in root_element.findall('gpu'):
|
||||
execution_devices.append(
|
||||
{
|
||||
'driver_version': root_element.find('driver_version').text,
|
||||
'framework':
|
||||
{
|
||||
'name': 'CUDA',
|
||||
'version': root_element.find('cuda_version').text,
|
||||
},
|
||||
'product':
|
||||
{
|
||||
'vendor': 'NVIDIA',
|
||||
'name': gpu_element.find('product_name').text.replace('NVIDIA ', ''),
|
||||
'architecture': gpu_element.find('product_architecture').text,
|
||||
},
|
||||
'video_memory':
|
||||
{
|
||||
'total': create_value_and_unit(gpu_element.find('fb_memory_usage/total').text),
|
||||
'free': create_value_and_unit(gpu_element.find('fb_memory_usage/free').text)
|
||||
},
|
||||
'utilization':
|
||||
{
|
||||
'gpu': create_value_and_unit(gpu_element.find('utilization/gpu_util').text),
|
||||
'memory': create_value_and_unit(gpu_element.find('utilization/memory_util').text)
|
||||
}
|
||||
})
|
||||
return execution_devices
|
||||
|
||||
|
||||
def create_value_and_unit(text : str) -> ValueAndUnit:
|
||||
value, unit = text.split()
|
||||
value_and_unit : ValueAndUnit =\
|
||||
{
|
||||
'value': value,
|
||||
'unit': unit
|
||||
}
|
||||
|
||||
return value_and_unit
|
||||
@ -1,37 +0,0 @@
|
||||
from typing import Any, List
|
||||
import onnxruntime
|
||||
|
||||
|
||||
def encode_execution_providers(execution_providers : List[str]) -> List[str]:
|
||||
return [ execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers ]
|
||||
|
||||
|
||||
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 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 :
|
||||
return 'cuda'
|
||||
if 'OpenVINOExecutionProvider' in execution_providers:
|
||||
return 'mkl'
|
||||
return 'cpu'
|
||||
@ -8,10 +8,10 @@ import facefusion.globals
|
||||
from facefusion.common_helper import get_first
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender
|
||||
from facefusion.face_store import get_static_faces, set_static_faces
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.execution import apply_execution_provider_options
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.typing import VisionFrame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, BoundingBox, FaceLandmarkSet, FaceLandmark5, FaceLandmark68, Score, Embedding
|
||||
from facefusion.typing import VisionFrame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, BoundingBox, FaceLandmarkSet, FaceLandmark5, FaceLandmark68, Score, FaceScoreSet, Embedding
|
||||
from facefusion.vision import resize_frame_resolution, unpack_resolution
|
||||
|
||||
FACE_ANALYSER = None
|
||||
@ -24,6 +24,11 @@ MODELS : ModelSet =\
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/retinaface_10g.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
|
||||
},
|
||||
'face_detector_scrfd':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/scrfd_2.5g.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/scrfd_2.5g.onnx')
|
||||
},
|
||||
'face_detector_yoloface':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/yoloface_8n.onnx',
|
||||
@ -70,14 +75,21 @@ MODELS : ModelSet =\
|
||||
def get_face_analyser() -> Any:
|
||||
global FACE_ANALYSER
|
||||
|
||||
face_detectors = {}
|
||||
with THREAD_LOCK:
|
||||
if FACE_ANALYSER is None:
|
||||
if facefusion.globals.face_detector_model == 'retinaface':
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'retinaface' ]:
|
||||
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 == 'yoloface':
|
||||
face_detectors['retinaface'] = face_detector
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
|
||||
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_scrfd').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
face_detectors['scrfd'] = face_detector
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
|
||||
face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_yoloface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
if facefusion.globals.face_detector_model == 'yunet':
|
||||
face_detectors['yoloface'] = face_detector
|
||||
if facefusion.globals.face_detector_model in [ 'yunet' ]:
|
||||
face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
|
||||
face_detectors['yunet'] = face_detector
|
||||
if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
|
||||
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':
|
||||
@ -90,7 +102,7 @@ def get_face_analyser() -> Any:
|
||||
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,
|
||||
'face_detectors': face_detectors,
|
||||
'face_recognizer': face_recognizer,
|
||||
'face_landmarker': face_landmarker,
|
||||
'gender_age': gender_age
|
||||
@ -110,6 +122,7 @@ def pre_check() -> bool:
|
||||
model_urls =\
|
||||
[
|
||||
MODELS.get('face_detector_retinaface').get('url'),
|
||||
MODELS.get('face_detector_scrfd').get('url'),
|
||||
MODELS.get('face_detector_yoloface').get('url'),
|
||||
MODELS.get('face_detector_yunet').get('url'),
|
||||
MODELS.get('face_recognizer_arcface_blendswap').get('url'),
|
||||
@ -124,22 +137,23 @@ def pre_check() -> bool:
|
||||
|
||||
|
||||
def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
|
||||
face_detector = get_face_analyser().get('face_detector')
|
||||
face_detector = get_face_analyser().get('face_detectors').get('retinaface')
|
||||
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, face_detector_width, face_detector_height)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
|
||||
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
|
||||
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
|
||||
feature_strides = [ 8, 16, 32 ]
|
||||
feature_map_channel = 3
|
||||
anchor_total = 2
|
||||
bounding_box_list = []
|
||||
face_landmark5_list = []
|
||||
face_landmark_5_list = []
|
||||
score_list = []
|
||||
|
||||
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
|
||||
with THREAD_SEMAPHORE:
|
||||
detections = face_detector.run(None,
|
||||
{
|
||||
face_detector.get_inputs()[0].name: prepare_detect_frame(temp_vision_frame, face_detector_size)
|
||||
face_detector.get_inputs()[0].name: detect_vision_frame
|
||||
})
|
||||
for index, feature_stride in enumerate(feature_strides):
|
||||
keep_indices = numpy.where(detections[index] >= facefusion.globals.face_detector_score)[0]
|
||||
@ -157,27 +171,70 @@ def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str)
|
||||
bounding_box[2] * ratio_width,
|
||||
bounding_box[3] * ratio_height
|
||||
]))
|
||||
for face_landmark5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
|
||||
face_landmark5_list.append(face_landmark5 * [ ratio_width, ratio_height ])
|
||||
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
|
||||
face_landmark_5_list.append(face_landmark_5 * [ ratio_width, ratio_height ])
|
||||
for score in detections[index][keep_indices]:
|
||||
score_list.append(score[0])
|
||||
return bounding_box_list, face_landmark5_list, score_list
|
||||
return bounding_box_list, face_landmark_5_list, score_list
|
||||
|
||||
|
||||
def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
|
||||
face_detector = get_face_analyser().get('face_detector')
|
||||
def detect_with_scrfd(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
|
||||
face_detector = get_face_analyser().get('face_detectors').get('scrfd')
|
||||
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, face_detector_width, face_detector_height)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
|
||||
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
|
||||
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
|
||||
feature_strides = [ 8, 16, 32 ]
|
||||
feature_map_channel = 3
|
||||
anchor_total = 2
|
||||
bounding_box_list = []
|
||||
face_landmark5_list = []
|
||||
face_landmark_5_list = []
|
||||
score_list = []
|
||||
|
||||
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
|
||||
with THREAD_SEMAPHORE:
|
||||
detections = face_detector.run(None,
|
||||
{
|
||||
face_detector.get_inputs()[0].name: prepare_detect_frame(temp_vision_frame, face_detector_size)
|
||||
face_detector.get_inputs()[0].name: detect_vision_frame
|
||||
})
|
||||
for index, feature_stride in enumerate(feature_strides):
|
||||
keep_indices = numpy.where(detections[index] >= facefusion.globals.face_detector_score)[0]
|
||||
if keep_indices.any():
|
||||
stride_height = face_detector_height // feature_stride
|
||||
stride_width = face_detector_width // feature_stride
|
||||
anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
|
||||
bounding_box_raw = detections[index + feature_map_channel] * feature_stride
|
||||
face_landmark_5_raw = detections[index + feature_map_channel * 2] * feature_stride
|
||||
for bounding_box in distance_to_bounding_box(anchors, bounding_box_raw)[keep_indices]:
|
||||
bounding_box_list.append(numpy.array(
|
||||
[
|
||||
bounding_box[0] * ratio_width,
|
||||
bounding_box[1] * ratio_height,
|
||||
bounding_box[2] * ratio_width,
|
||||
bounding_box[3] * ratio_height
|
||||
]))
|
||||
for face_landmark_5 in distance_to_face_landmark_5(anchors, face_landmark_5_raw)[keep_indices]:
|
||||
face_landmark_5_list.append(face_landmark_5 * [ ratio_width, ratio_height ])
|
||||
for score in detections[index][keep_indices]:
|
||||
score_list.append(score[0])
|
||||
return bounding_box_list, face_landmark_5_list, score_list
|
||||
|
||||
|
||||
def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
|
||||
face_detector = get_face_analyser().get('face_detectors').get('yoloface')
|
||||
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
|
||||
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
|
||||
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
|
||||
bounding_box_list = []
|
||||
face_landmark_5_list = []
|
||||
score_list = []
|
||||
|
||||
detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
|
||||
with THREAD_SEMAPHORE:
|
||||
detections = face_detector.run(None,
|
||||
{
|
||||
face_detector.get_inputs()[0].name: detect_vision_frame
|
||||
})
|
||||
detections = numpy.squeeze(detections).T
|
||||
bounding_box_raw, score_raw, face_landmark_5_raw = numpy.split(detections, [ 4, 5 ], axis = 1)
|
||||
@ -195,26 +252,26 @@ def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -
|
||||
face_landmark_5_raw[:, 0::3] = (face_landmark_5_raw[:, 0::3]) * ratio_width
|
||||
face_landmark_5_raw[:, 1::3] = (face_landmark_5_raw[:, 1::3]) * ratio_height
|
||||
for face_landmark_5 in face_landmark_5_raw:
|
||||
face_landmark5_list.append(numpy.array(face_landmark_5.reshape(-1, 3)[:, :2]))
|
||||
face_landmark_5_list.append(numpy.array(face_landmark_5.reshape(-1, 3)[:, :2]))
|
||||
score_list = score_raw.ravel().tolist()
|
||||
return bounding_box_list, face_landmark5_list, score_list
|
||||
return bounding_box_list, face_landmark_5_list, score_list
|
||||
|
||||
|
||||
def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> Tuple[List[BoundingBox], List[FaceLandmark5], List[Score]]:
|
||||
face_detector = get_face_analyser().get('face_detector')
|
||||
face_detector = get_face_analyser().get('face_detectors').get('yunet')
|
||||
face_detector_width, face_detector_height = unpack_resolution(face_detector_size)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, face_detector_width, face_detector_height)
|
||||
temp_vision_frame = resize_frame_resolution(vision_frame, (face_detector_width, face_detector_height))
|
||||
ratio_height = vision_frame.shape[0] / temp_vision_frame.shape[0]
|
||||
ratio_width = vision_frame.shape[1] / temp_vision_frame.shape[1]
|
||||
bounding_box_list = []
|
||||
face_landmark5_list = []
|
||||
face_landmark_5_list = []
|
||||
score_list = []
|
||||
|
||||
face_detector.setInputSize((temp_vision_frame.shape[1], temp_vision_frame.shape[0]))
|
||||
face_detector.setScoreThreshold(facefusion.globals.face_detector_score)
|
||||
with THREAD_SEMAPHORE:
|
||||
_, detections = face_detector.detect(temp_vision_frame)
|
||||
if detections.any():
|
||||
if numpy.any(detections):
|
||||
for detection in detections:
|
||||
bounding_box_list.append(numpy.array(
|
||||
[
|
||||
@ -223,9 +280,9 @@ def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> T
|
||||
(detection[0] + detection[2]) * ratio_width,
|
||||
(detection[1] + detection[3]) * ratio_height
|
||||
]))
|
||||
face_landmark5_list.append(detection[4:14].reshape((5, 2)) * [ ratio_width, ratio_height ])
|
||||
face_landmark_5_list.append(detection[4:14].reshape((5, 2)) * [ ratio_width, ratio_height ])
|
||||
score_list.append(detection[14])
|
||||
return bounding_box_list, face_landmark5_list, score_list
|
||||
return bounding_box_list, face_landmark_5_list, score_list
|
||||
|
||||
|
||||
def prepare_detect_frame(temp_vision_frame : VisionFrame, face_detector_size : str) -> VisionFrame:
|
||||
@ -237,30 +294,41 @@ def prepare_detect_frame(temp_vision_frame : VisionFrame, face_detector_size : s
|
||||
return detect_vision_frame
|
||||
|
||||
|
||||
def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBox], face_landmark5_list : List[FaceLandmark5], score_list : List[Score]) -> List[Face]:
|
||||
def create_faces(vision_frame : VisionFrame, bounding_box_list : List[BoundingBox], face_landmark_5_list : List[FaceLandmark5], score_list : List[Score]) -> List[Face]:
|
||||
faces = []
|
||||
if facefusion.globals.face_detector_score > 0:
|
||||
sort_indices = numpy.argsort(-numpy.array(score_list))
|
||||
bounding_box_list = [ bounding_box_list[index] for index in sort_indices ]
|
||||
face_landmark5_list = [ face_landmark5_list[index] for index in sort_indices ]
|
||||
face_landmark_5_list = [face_landmark_5_list[index] for index in sort_indices]
|
||||
score_list = [ score_list[index] for index in sort_indices ]
|
||||
keep_indices = apply_nms(bounding_box_list, 0.4)
|
||||
iou_threshold = 0.1 if facefusion.globals.face_detector_model == 'many' else 0.4
|
||||
keep_indices = apply_nms(bounding_box_list, iou_threshold)
|
||||
for index in keep_indices:
|
||||
bounding_box = bounding_box_list[index]
|
||||
face_landmark_68 = detect_face_landmark_68(vision_frame, bounding_box)
|
||||
landmark : FaceLandmarkSet =\
|
||||
face_landmark_5_68 = face_landmark_5_list[index]
|
||||
face_landmark_68 = None
|
||||
face_landmark_68_score = 0.0
|
||||
if facefusion.globals.face_landmarker_score > 0:
|
||||
face_landmark_68, face_landmark_68_score = detect_face_landmark_68(vision_frame, bounding_box)
|
||||
if face_landmark_68_score > facefusion.globals.face_landmarker_score:
|
||||
face_landmark_5_68 = convert_face_landmark_68_to_5(face_landmark_68)
|
||||
landmarks : FaceLandmarkSet =\
|
||||
{
|
||||
'5': face_landmark5_list[index],
|
||||
'5/68': convert_face_landmark_68_to_5(face_landmark_68),
|
||||
'5': face_landmark_5_list[index],
|
||||
'5/68': face_landmark_5_68,
|
||||
'68': face_landmark_68
|
||||
}
|
||||
score = score_list[index]
|
||||
embedding, normed_embedding = calc_embedding(vision_frame, landmark['5/68'])
|
||||
scores : FaceScoreSet = \
|
||||
{
|
||||
'detector': score_list[index],
|
||||
'landmarker': face_landmark_68_score
|
||||
}
|
||||
embedding, normed_embedding = calc_embedding(vision_frame, landmarks.get('5/68'))
|
||||
gender, age = detect_gender_age(vision_frame, bounding_box)
|
||||
faces.append(Face(
|
||||
bounding_box = bounding_box,
|
||||
landmark = landmark,
|
||||
score = score,
|
||||
landmarks = landmarks,
|
||||
scores = scores,
|
||||
embedding = embedding,
|
||||
normed_embedding = normed_embedding,
|
||||
gender = gender,
|
||||
@ -284,21 +352,27 @@ def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandma
|
||||
return embedding, normed_embedding
|
||||
|
||||
|
||||
def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> FaceLandmark68:
|
||||
def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[FaceLandmark68, Score]:
|
||||
face_landmarker = get_face_analyser().get('face_landmarker')
|
||||
scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max()
|
||||
translation = (256 - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
|
||||
crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (256, 256))
|
||||
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_RGB2Lab)
|
||||
if numpy.mean(crop_vision_frame[:, :, 0]) < 30:
|
||||
crop_vision_frame[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_vision_frame[:, :, 0])
|
||||
crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_Lab2RGB)
|
||||
crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
|
||||
face_landmark_68 = face_landmarker.run(None,
|
||||
face_landmark_68, face_heatmap = face_landmarker.run(None,
|
||||
{
|
||||
face_landmarker.get_inputs()[0].name: [ crop_vision_frame ]
|
||||
})[0]
|
||||
})
|
||||
face_landmark_68 = face_landmark_68[:, :, :2][0] / 64
|
||||
face_landmark_68 = face_landmark_68.reshape(1, -1, 2) * 256
|
||||
face_landmark_68 = cv2.transform(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
|
||||
face_landmark_68 = face_landmark_68.reshape(-1, 2)
|
||||
return face_landmark_68
|
||||
face_landmark_68_score = numpy.amax(face_heatmap, axis = (2, 3))
|
||||
face_landmark_68_score = numpy.mean(face_landmark_68_score)
|
||||
return face_landmark_68, face_landmark_68_score
|
||||
|
||||
|
||||
def detect_gender_age(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[int, int]:
|
||||
@ -344,8 +418,8 @@ def get_average_face(vision_frames : List[VisionFrame], position : int = 0) -> O
|
||||
first_face = get_first(faces)
|
||||
average_face = Face(
|
||||
bounding_box = first_face.bounding_box,
|
||||
landmark = first_face.landmark,
|
||||
score = first_face.score,
|
||||
landmarks = first_face.landmarks,
|
||||
scores = first_face.scores,
|
||||
embedding = numpy.mean(embedding_list, axis = 0),
|
||||
normed_embedding = numpy.mean(normed_embedding_list, axis = 0),
|
||||
gender = first_face.gender,
|
||||
@ -361,15 +435,32 @@ def get_many_faces(vision_frame : VisionFrame) -> List[Face]:
|
||||
if faces_cache:
|
||||
faces = faces_cache
|
||||
else:
|
||||
if facefusion.globals.face_detector_model == 'retinaface':
|
||||
bounding_box_list, face_landmark5_list, score_list = detect_with_retinaface(vision_frame, facefusion.globals.face_detector_size)
|
||||
faces = create_faces(vision_frame, bounding_box_list, face_landmark5_list, score_list)
|
||||
if facefusion.globals.face_detector_model == 'yoloface':
|
||||
bounding_box_list, face_landmark5_list, score_list = detect_with_yoloface(vision_frame, facefusion.globals.face_detector_size)
|
||||
faces = create_faces(vision_frame, bounding_box_list, face_landmark5_list, score_list)
|
||||
if facefusion.globals.face_detector_model == 'yunet':
|
||||
bounding_box_list, face_landmark5_list, score_list = detect_with_yunet(vision_frame, facefusion.globals.face_detector_size)
|
||||
faces = create_faces(vision_frame, bounding_box_list, face_landmark5_list, score_list)
|
||||
bounding_box_list = []
|
||||
face_landmark_5_list = []
|
||||
score_list = []
|
||||
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'retinaface']:
|
||||
bounding_box_list_retinaface, face_landmark_5_list_retinaface, score_list_retinaface = detect_with_retinaface(vision_frame, facefusion.globals.face_detector_size)
|
||||
bounding_box_list.extend(bounding_box_list_retinaface)
|
||||
face_landmark_5_list.extend(face_landmark_5_list_retinaface)
|
||||
score_list.extend(score_list_retinaface)
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'scrfd' ]:
|
||||
bounding_box_list_scrfd, face_landmark_5_list_scrfd, score_list_scrfd = detect_with_scrfd(vision_frame, facefusion.globals.face_detector_size)
|
||||
bounding_box_list.extend(bounding_box_list_scrfd)
|
||||
face_landmark_5_list.extend(face_landmark_5_list_scrfd)
|
||||
score_list.extend(score_list_scrfd)
|
||||
if facefusion.globals.face_detector_model in [ 'many', 'yoloface' ]:
|
||||
bounding_box_list_yoloface, face_landmark_5_list_yoloface, score_list_yoloface = detect_with_yoloface(vision_frame, facefusion.globals.face_detector_size)
|
||||
bounding_box_list.extend(bounding_box_list_yoloface)
|
||||
face_landmark_5_list.extend(face_landmark_5_list_yoloface)
|
||||
score_list.extend(score_list_yoloface)
|
||||
if facefusion.globals.face_detector_model in [ 'yunet' ]:
|
||||
bounding_box_list_yunet, face_landmark_5_list_yunet, score_list_yunet = detect_with_yunet(vision_frame, facefusion.globals.face_detector_size)
|
||||
bounding_box_list.extend(bounding_box_list_yunet)
|
||||
face_landmark_5_list.extend(face_landmark_5_list_yunet)
|
||||
score_list.extend(score_list_yunet)
|
||||
if bounding_box_list and face_landmark_5_list and score_list:
|
||||
faces = create_faces(vision_frame, bounding_box_list, face_landmark_5_list, score_list)
|
||||
if faces:
|
||||
set_static_faces(vision_frame, faces)
|
||||
if facefusion.globals.face_analyser_order:
|
||||
@ -422,9 +513,9 @@ def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
|
||||
if order == 'large-small':
|
||||
return sorted(faces, key = lambda face: (face.bounding_box[2] - face.bounding_box[0]) * (face.bounding_box[3] - face.bounding_box[1]), reverse = True)
|
||||
if order == 'best-worst':
|
||||
return sorted(faces, key = lambda face: face.score, reverse = True)
|
||||
return sorted(faces, key = lambda face: face.scores.get('detector'), reverse = True)
|
||||
if order == 'worst-best':
|
||||
return sorted(faces, key = lambda face: face.score)
|
||||
return sorted(faces, key = lambda face: face.scores.get('detector'))
|
||||
return faces
|
||||
|
||||
|
||||
|
||||
@ -1,12 +1,12 @@
|
||||
from typing import Any, Dict, Tuple, List
|
||||
from typing import Any, Tuple, List
|
||||
from cv2.typing import Size
|
||||
from functools import lru_cache
|
||||
import cv2
|
||||
import numpy
|
||||
|
||||
from facefusion.typing import BoundingBox, FaceLandmark5, FaceLandmark68, VisionFrame, Mask, Matrix, Translation, Template, FaceAnalyserAge, FaceAnalyserGender
|
||||
from facefusion.typing import BoundingBox, FaceLandmark5, FaceLandmark68, VisionFrame, Mask, Matrix, Translation, WarpTemplate, WarpTemplateSet, FaceAnalyserAge, FaceAnalyserGender
|
||||
|
||||
TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
|
||||
WARP_TEMPLATES : WarpTemplateSet =\
|
||||
{
|
||||
'arcface_112_v1': numpy.array(
|
||||
[
|
||||
@ -43,16 +43,16 @@ TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
|
||||
}
|
||||
|
||||
|
||||
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, template : Template, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
|
||||
normed_template = TEMPLATES.get(template) * crop_size
|
||||
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
|
||||
def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
|
||||
normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size
|
||||
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
|
||||
crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
|
||||
return crop_vision_frame, affine_matrix
|
||||
|
||||
|
||||
def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
|
||||
source_points = numpy.array([[bounding_box[0], bounding_box[1]], [bounding_box[2], bounding_box[1]], [bounding_box[0], bounding_box[3]]], dtype = numpy.float32)
|
||||
target_points = numpy.array([[ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ]], dtype = numpy.float32)
|
||||
source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ], dtype = numpy.float32)
|
||||
target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ], dtype = numpy.float32)
|
||||
affine_matrix = cv2.getAffineTransform(source_points, target_points)
|
||||
if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]:
|
||||
interpolation_method = cv2.INTER_AREA
|
||||
@ -63,7 +63,7 @@ def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : Bo
|
||||
|
||||
|
||||
def warp_face_by_translation(temp_vision_frame : VisionFrame, translation : Translation, scale : float, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
|
||||
affine_matrix = numpy.array([[ scale, 0, translation[0] ], [ 0, scale, translation[1] ]])
|
||||
affine_matrix = numpy.array([ [ scale, 0, translation[0] ], [ 0, scale, translation[1] ] ])
|
||||
crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size)
|
||||
return crop_vision_frame, affine_matrix
|
||||
|
||||
@ -89,7 +89,7 @@ def create_static_anchors(feature_stride : int, anchor_total : int, stride_heigh
|
||||
return anchors
|
||||
|
||||
|
||||
def create_bounding_box_from_landmark(face_landmark_68 : FaceLandmark68) -> BoundingBox:
|
||||
def create_bounding_box_from_face_landmark_68(face_landmark_68 : FaceLandmark68) -> BoundingBox:
|
||||
min_x, min_y = numpy.min(face_landmark_68, axis = 0)
|
||||
max_x, max_y = numpy.max(face_landmark_68, axis = 0)
|
||||
bounding_box = numpy.array([ min_x, min_y, max_x, max_y ]).astype(numpy.int16)
|
||||
@ -113,12 +113,14 @@ def distance_to_face_landmark_5(points : numpy.ndarray[Any, Any], distance : num
|
||||
|
||||
|
||||
def convert_face_landmark_68_to_5(landmark_68 : FaceLandmark68) -> FaceLandmark5:
|
||||
left_eye = numpy.mean(landmark_68[36:42], axis = 0)
|
||||
right_eye = numpy.mean(landmark_68[42:48], axis = 0)
|
||||
nose = landmark_68[30]
|
||||
left_mouth_end = landmark_68[48]
|
||||
right_mouth_end = landmark_68[54]
|
||||
face_landmark_5 = numpy.array([ left_eye, right_eye, nose, left_mouth_end, right_mouth_end ])
|
||||
face_landmark_5 = numpy.array(
|
||||
[
|
||||
numpy.mean(landmark_68[36:42], axis = 0),
|
||||
numpy.mean(landmark_68[42:48], axis = 0),
|
||||
landmark_68[30],
|
||||
landmark_68[48],
|
||||
landmark_68[54]
|
||||
])
|
||||
return face_landmark_5
|
||||
|
||||
|
||||
|
||||
@ -8,7 +8,7 @@ import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion.typing import FaceLandmark68, VisionFrame, Mask, Padding, FaceMaskRegion, ModelSet
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.execution import apply_execution_provider_options
|
||||
from facefusion.filesystem import resolve_relative_path
|
||||
from facefusion.download import conditional_download
|
||||
|
||||
|
||||
@ -1,57 +1,55 @@
|
||||
from typing import List, Optional
|
||||
import subprocess
|
||||
import filetype
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import logger
|
||||
from facefusion import process_manager
|
||||
from facefusion.typing import OutputVideoPreset, Fps, AudioBuffer
|
||||
from facefusion.filesystem import get_temp_frames_pattern, get_temp_output_video_path
|
||||
|
||||
|
||||
def run_ffmpeg(args : List[str]) -> bool:
|
||||
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
|
||||
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'quiet' ]
|
||||
commands.extend(args)
|
||||
try:
|
||||
subprocess.run(commands, stderr = subprocess.PIPE, check = True)
|
||||
return True
|
||||
except subprocess.CalledProcessError as exception:
|
||||
logger.debug(exception.stderr.decode().strip(), __name__.upper())
|
||||
return False
|
||||
process = subprocess.Popen(commands, stdout = subprocess.PIPE)
|
||||
|
||||
while process_manager.is_processing():
|
||||
try:
|
||||
return process.wait(timeout = 0.5) == 0
|
||||
except subprocess.TimeoutExpired:
|
||||
continue
|
||||
return process.returncode == 0
|
||||
|
||||
|
||||
def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
|
||||
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
|
||||
commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'quiet' ]
|
||||
commands.extend(args)
|
||||
return subprocess.Popen(commands, stdin = subprocess.PIPE, stdout = subprocess.PIPE)
|
||||
return subprocess.Popen(commands, stdout = subprocess.PIPE)
|
||||
|
||||
|
||||
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))
|
||||
def extract_frames(target_path : str, temp_video_resolution : str, temp_video_fps : Fps) -> bool:
|
||||
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' ]
|
||||
commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', '0' ]
|
||||
|
||||
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) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',scale=' + str(temp_video_resolution) + ',fps=' + str(temp_video_fps) ])
|
||||
elif trim_frame_start is not None:
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',scale=' + str(temp_video_resolution) + ',fps=' + str(temp_video_fps) ])
|
||||
elif trim_frame_end is not None:
|
||||
commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',scale=' + str(temp_video_resolution) + ',fps=' + str(temp_video_fps) ])
|
||||
else:
|
||||
commands.extend([ '-vf', 'scale=' + str(video_resolution) + ',fps=' + str(video_fps) ])
|
||||
commands.extend([ '-vf', 'scale=' + str(temp_video_resolution) + ',fps=' + str(temp_video_fps) ])
|
||||
commands.extend([ '-vsync', '0', temp_frames_pattern ])
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def compress_image(output_path : str) -> bool:
|
||||
output_image_compression = round(31 - (facefusion.globals.output_image_quality * 0.31))
|
||||
commands = [ '-hwaccel', 'auto', '-i', output_path, '-q:v', str(output_image_compression), '-y', output_path ]
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def merge_video(target_path : str, video_resolution : str, video_fps : Fps) -> bool:
|
||||
def merge_video(target_path : str, output_video_resolution : str, output_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', '-s', str(video_resolution), '-r', str(video_fps), '-i', temp_frames_pattern, '-c:v', facefusion.globals.output_video_encoder ]
|
||||
commands = [ '-hwaccel', 'auto', '-s', str(output_video_resolution), '-r', str(output_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), '-preset', facefusion.globals.output_video_preset ])
|
||||
@ -61,29 +59,46 @@ def merge_video(target_path : str, video_resolution : str, video_fps : Fps) -> b
|
||||
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), '-preset', map_nvenc_preset(facefusion.globals.output_video_preset) ])
|
||||
if facefusion.globals.output_video_encoder in [ 'h264_amf', 'hevc_amf' ]:
|
||||
output_video_compression = round(51 - (facefusion.globals.output_video_quality * 0.51))
|
||||
commands.extend([ '-qp_i', str(output_video_compression), '-qp_p', str(output_video_compression), '-quality', map_amf_preset(facefusion.globals.output_video_preset) ])
|
||||
commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def read_audio_buffer(target_path : str, sample_rate : int, channel_total : int) -> Optional[AudioBuffer]:
|
||||
commands = [ '-i', target_path, '-vn', '-f', 's16le', '-acodec', 'pcm_s16le', '-ar', str(sample_rate), '-ac', str(channel_total), '-' ]
|
||||
def copy_image(target_path : str, output_path : str, temp_image_resolution : str) -> bool:
|
||||
is_webp = filetype.guess_mime(target_path) == 'image/webp'
|
||||
temp_image_compression = 100 if is_webp else 0
|
||||
commands = [ '-i', target_path, '-q:v', str(temp_image_compression), '-vf', 'scale=' + str(temp_image_resolution), '-y', output_path ]
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def finalize_image(output_path : str, output_image_resolution : str) -> bool:
|
||||
output_image_compression = round(31 - (facefusion.globals.output_image_quality * 0.31))
|
||||
commands = [ '-i', output_path, '-q:v', str(output_image_compression), '-vf', 'scale=' + str(output_image_resolution), '-y', output_path ]
|
||||
return run_ffmpeg(commands)
|
||||
|
||||
|
||||
def read_audio_buffer(target_path : str, sample_rate : int, total_channel : int) -> Optional[AudioBuffer]:
|
||||
commands = [ '-i', target_path, '-vn', '-f', 's16le', '-acodec', 'pcm_s16le', '-ar', str(sample_rate), '-ac', str(total_channel), '-' ]
|
||||
process = open_ffmpeg(commands)
|
||||
audio_buffer, error = process.communicate()
|
||||
audio_buffer, _ = process.communicate()
|
||||
if process.returncode == 0:
|
||||
return audio_buffer
|
||||
return None
|
||||
|
||||
|
||||
def restore_audio(target_path : str, output_path : str, video_fps : Fps) -> bool:
|
||||
def restore_audio(target_path : str, output_path : str, output_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 / video_fps
|
||||
start_time = trim_frame_start / output_video_fps
|
||||
commands.extend([ '-ss', str(start_time) ])
|
||||
if trim_frame_end is not None:
|
||||
end_time = trim_frame_end / video_fps
|
||||
end_time = trim_frame_end / output_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)
|
||||
@ -111,3 +126,13 @@ def map_nvenc_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
|
||||
if output_video_preset == 'veryslow':
|
||||
return 'p7'
|
||||
return None
|
||||
|
||||
|
||||
def map_amf_preset(output_video_preset : OutputVideoPreset) -> Optional[str]:
|
||||
if output_video_preset in [ 'ultrafast', 'superfast', 'veryfast' ]:
|
||||
return 'speed'
|
||||
if output_video_preset in [ 'faster', 'fast', 'medium' ]:
|
||||
return 'balanced'
|
||||
if output_video_preset in [ 'slow', 'slower', 'veryslow' ]:
|
||||
return 'quality'
|
||||
return None
|
||||
|
||||
@ -49,7 +49,7 @@ def clear_temp(target_path : str) -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
parent_directory_path = os.path.dirname(temp_directory_path)
|
||||
if not facefusion.globals.keep_temp and is_directory(temp_directory_path):
|
||||
shutil.rmtree(temp_directory_path)
|
||||
shutil.rmtree(temp_directory_path, ignore_errors = True)
|
||||
if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
|
||||
os.rmdir(parent_directory_path)
|
||||
|
||||
|
||||
@ -24,6 +24,7 @@ face_analyser_gender : Optional[FaceAnalyserGender] = None
|
||||
face_detector_model : Optional[FaceDetectorModel] = None
|
||||
face_detector_size : Optional[str] = None
|
||||
face_detector_score : Optional[float] = None
|
||||
face_landmarker_score : Optional[float] = None
|
||||
face_recognizer_model : Optional[FaceRecognizerModel] = None
|
||||
# face selector
|
||||
face_selector_mode : Optional[FaceSelectorMode] = None
|
||||
@ -39,10 +40,10 @@ face_mask_regions : Optional[List[FaceMaskRegion]] = None
|
||||
trim_frame_start : Optional[int] = None
|
||||
trim_frame_end : Optional[int] = None
|
||||
temp_frame_format : Optional[TempFrameFormat] = None
|
||||
temp_frame_quality : Optional[int] = None
|
||||
keep_temp : Optional[bool] = None
|
||||
# output creation
|
||||
output_image_quality : Optional[int] = None
|
||||
output_image_resolution : Optional[str] = None
|
||||
output_video_encoder : Optional[OutputVideoEncoder] = None
|
||||
output_video_preset : Optional[OutputVideoPreset] = None
|
||||
output_video_quality : Optional[int] = None
|
||||
|
||||
@ -9,26 +9,17 @@ from argparse import ArgumentParser, HelpFormatter
|
||||
|
||||
from facefusion import metadata, wording
|
||||
|
||||
TORCH : Dict[str, str] =\
|
||||
{
|
||||
'default': 'default',
|
||||
'cpu': 'cpu'
|
||||
}
|
||||
ONNXRUNTIMES : Dict[str, Tuple[str, str]] = {}
|
||||
|
||||
if platform.system().lower() == 'darwin':
|
||||
ONNXRUNTIMES['default'] = ('onnxruntime', '1.17.0')
|
||||
ONNXRUNTIMES['default'] = ('onnxruntime', '1.17.1')
|
||||
else:
|
||||
ONNXRUNTIMES['default'] = ('onnxruntime', '1.16.3')
|
||||
if platform.system().lower() == 'linux' or platform.system().lower() == 'windows':
|
||||
TORCH['cuda-12.1'] = 'cu121'
|
||||
TORCH['cuda-11.8'] = 'cu118'
|
||||
ONNXRUNTIMES['cuda-12.1'] = ('onnxruntime-gpu', '1.17.0')
|
||||
ONNXRUNTIMES['cuda-12.2'] = ('onnxruntime-gpu', '1.17.1')
|
||||
ONNXRUNTIMES['cuda-11.8'] = ('onnxruntime-gpu', '1.16.3')
|
||||
ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.16.0')
|
||||
if platform.system().lower() == 'linux':
|
||||
TORCH['rocm-5.4.2'] = 'rocm5.4.2'
|
||||
TORCH['rocm-5.6'] = 'rocm5.6'
|
||||
ONNXRUNTIMES['rocm-5.4.2'] = ('onnxruntime-rocm', '1.16.3')
|
||||
ONNXRUNTIMES['rocm-5.6'] = ('onnxruntime-rocm', '1.16.3')
|
||||
if platform.system().lower() == 'windows':
|
||||
@ -37,7 +28,6 @@ if platform.system().lower() == 'windows':
|
||||
|
||||
def cli() -> None:
|
||||
program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 130))
|
||||
program.add_argument('--torch', help = wording.get('help.install_dependency').format(dependency = 'torch'), choices = TORCH.keys())
|
||||
program.add_argument('--onnxruntime', help = wording.get('help.install_dependency').format(dependency = 'onnxruntime'), choices = ONNXRUNTIMES.keys())
|
||||
program.add_argument('--skip-venv', help = wording.get('help.skip_venv'), action = 'store_true')
|
||||
program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
|
||||
@ -52,29 +42,21 @@ def run(program : ArgumentParser) -> None:
|
||||
os.environ['SYSTEM_VERSION_COMPAT'] = '0'
|
||||
if not args.skip_venv:
|
||||
os.environ['PIP_REQUIRE_VIRTUALENV'] = '1'
|
||||
if args.torch and args.onnxruntime:
|
||||
if args.onnxruntime:
|
||||
answers =\
|
||||
{
|
||||
'torch': args.torch,
|
||||
'onnxruntime': args.onnxruntime
|
||||
}
|
||||
else:
|
||||
answers = inquirer.prompt(
|
||||
[
|
||||
inquirer.List('torch', message = wording.get('help.install_dependency').format(dependency = 'torch'), choices = list(TORCH.keys())),
|
||||
inquirer.List('onnxruntime', message = wording.get('help.install_dependency').format(dependency = 'onnxruntime'), choices = list(ONNXRUNTIMES.keys()))
|
||||
])
|
||||
if answers:
|
||||
torch = answers['torch']
|
||||
torch_wheel = TORCH[torch]
|
||||
onnxruntime = answers['onnxruntime']
|
||||
onnxruntime_name, onnxruntime_version = ONNXRUNTIMES[onnxruntime]
|
||||
|
||||
subprocess.call([ 'pip', 'uninstall', 'torch', '-y', '-q' ])
|
||||
if torch_wheel == 'default':
|
||||
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, '--force-reinstall' ])
|
||||
subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--force-reinstall' ])
|
||||
if onnxruntime == 'rocm-5.4.2' or onnxruntime == 'rocm-5.6':
|
||||
if python_id in [ 'cp39', 'cp310', 'cp311' ]:
|
||||
rocm_version = onnxruntime.replace('-', '')
|
||||
|
||||
@ -2,7 +2,7 @@ METADATA =\
|
||||
{
|
||||
'name': 'FaceFusion',
|
||||
'description': 'Next generation face swapper and enhancer',
|
||||
'version': '2.3.0',
|
||||
'version': '2.4.0',
|
||||
'license': 'MIT',
|
||||
'author': 'Henry Ruhs',
|
||||
'url': 'https://facefusion.io'
|
||||
|
||||
@ -1,25 +1,24 @@
|
||||
from typing import List, Optional
|
||||
import hashlib
|
||||
import os
|
||||
|
||||
from facefusion.filesystem import is_file, is_directory
|
||||
import facefusion.globals
|
||||
from facefusion.filesystem import is_directory
|
||||
from facefusion.typing import Padding, Fps
|
||||
|
||||
|
||||
def normalize_output_path(source_paths : List[str], target_path : str, output_path : str) -> Optional[str]:
|
||||
if is_file(target_path) and is_directory(output_path):
|
||||
def normalize_output_path(target_path : Optional[str], output_path : Optional[str]) -> Optional[str]:
|
||||
if target_path and output_path:
|
||||
target_name, target_extension = os.path.splitext(os.path.basename(target_path))
|
||||
if source_paths and is_file(source_paths[0]):
|
||||
source_name, _ = os.path.splitext(os.path.basename(source_paths[0]))
|
||||
return os.path.join(output_path, source_name + '-' + target_name + target_extension)
|
||||
return os.path.join(output_path, target_name + target_extension)
|
||||
if is_file(target_path) and output_path:
|
||||
_, target_extension = os.path.splitext(os.path.basename(target_path))
|
||||
if is_directory(output_path):
|
||||
output_hash = hashlib.sha1(str(facefusion.globals.__dict__).encode('utf-8')).hexdigest()[:8]
|
||||
output_name = target_name + '-' + output_hash
|
||||
return os.path.join(output_path, output_name + target_extension)
|
||||
output_name, output_extension = os.path.splitext(os.path.basename(output_path))
|
||||
output_directory_path = os.path.dirname(output_path)
|
||||
if is_directory(output_directory_path) and output_extension:
|
||||
return os.path.join(output_directory_path, output_name + target_extension)
|
||||
return None
|
||||
return output_path
|
||||
return None
|
||||
|
||||
|
||||
def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:
|
||||
|
||||
45
facefusion/process_manager.py
Normal file
45
facefusion/process_manager.py
Normal file
@ -0,0 +1,45 @@
|
||||
from typing import Generator, List
|
||||
|
||||
from facefusion.typing import QueuePayload, ProcessState
|
||||
|
||||
PROCESS_STATE : ProcessState = 'pending'
|
||||
|
||||
|
||||
def get_process_state() -> ProcessState:
|
||||
return PROCESS_STATE
|
||||
|
||||
|
||||
def set_process_state(process_state : ProcessState) -> None:
|
||||
global PROCESS_STATE
|
||||
|
||||
PROCESS_STATE = process_state
|
||||
|
||||
|
||||
def is_processing() -> bool:
|
||||
return get_process_state() == 'processing'
|
||||
|
||||
|
||||
def is_stopping() -> bool:
|
||||
return get_process_state() == 'stopping'
|
||||
|
||||
|
||||
def is_pending() -> bool:
|
||||
return get_process_state() == 'pending'
|
||||
|
||||
|
||||
def start() -> None:
|
||||
set_process_state('processing')
|
||||
|
||||
|
||||
def stop() -> None:
|
||||
set_process_state('stopping')
|
||||
|
||||
|
||||
def end() -> None:
|
||||
set_process_state('pending')
|
||||
|
||||
|
||||
def manage(queue_payloads : List[QueuePayload]) -> Generator[QueuePayload, None, None]:
|
||||
for query_payload in queue_payloads:
|
||||
if is_processing():
|
||||
yield query_payload
|
||||
@ -3,10 +3,10 @@ from typing import List
|
||||
from facefusion.common_helper import create_int_range
|
||||
from facefusion.processors.frame.typings import FaceDebuggerItem, FaceEnhancerModel, FaceSwapperModel, FrameEnhancerModel, LipSyncerModel
|
||||
|
||||
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'landmark-5', 'landmark-68', 'face-mask', 'score', 'age', 'gender' ]
|
||||
face_debugger_items : List[FaceDebuggerItem] = [ 'bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender' ]
|
||||
face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus' ]
|
||||
face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256' ]
|
||||
frame_enhancer_models : List[FrameEnhancerModel] = [ 'real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus' ]
|
||||
frame_enhancer_models : List[FrameEnhancerModel] = [ 'lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'span_kendata_x4' ]
|
||||
lip_syncer_models : List[LipSyncerModel] = [ 'wav2lip_gan' ]
|
||||
|
||||
face_enhancer_blend_range : List[int] = create_int_range(0, 100, 1)
|
||||
|
||||
@ -8,8 +8,8 @@ from typing import Any, List
|
||||
from tqdm import tqdm
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion.typing import Process_Frames, QueuePayload
|
||||
from facefusion.execution_helper import encode_execution_providers
|
||||
from facefusion.typing import ProcessFrames, QueuePayload
|
||||
from facefusion.execution import encode_execution_providers
|
||||
from facefusion import logger, wording
|
||||
|
||||
FRAME_PROCESSORS_MODULES : List[ModuleType] = []
|
||||
@ -67,7 +67,7 @@ def clear_frame_processors_modules() -> None:
|
||||
FRAME_PROCESSORS_MODULES = []
|
||||
|
||||
|
||||
def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str], process_frames : Process_Frames) -> None:
|
||||
def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str], process_frames : ProcessFrames) -> None:
|
||||
queue_payloads = create_queue_payloads(temp_frame_paths)
|
||||
with tqdm(total = len(queue_payloads), desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = facefusion.globals.log_level in [ 'warn', 'error' ]) as progress:
|
||||
progress.set_postfix(
|
||||
@ -109,8 +109,8 @@ def create_queue_payloads(temp_frame_paths : List[str]) -> List[QueuePayload]:
|
||||
for frame_number, frame_path in enumerate(temp_frame_paths):
|
||||
frame_payload : QueuePayload =\
|
||||
{
|
||||
'frame_number' : frame_number,
|
||||
'frame_path' : frame_path
|
||||
'frame_number': frame_number,
|
||||
'frame_path': frame_path
|
||||
}
|
||||
queue_payloads.append(frame_payload)
|
||||
return queue_payloads
|
||||
|
||||
@ -5,13 +5,13 @@ import numpy
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import config, wording
|
||||
from facefusion import config, process_manager, wording
|
||||
from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
|
||||
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, categorize_age, categorize_gender
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, VisionFrame, Update_Process, ProcessMode, QueuePayload
|
||||
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, QueuePayload
|
||||
from facefusion.vision import read_image, read_static_image, write_image
|
||||
from facefusion.processors.frame.typings import FaceDebuggerInputs
|
||||
from facefusion.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
|
||||
@ -36,7 +36,7 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = config.get_str_list('frame_processors.face_debugger_items', 'landmark-5 face-mask'), choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
|
||||
program.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = config.get_str_list('frame_processors.face_debugger_items', 'face-landmark-5 face-mask'), choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
|
||||
|
||||
|
||||
def apply_args(program : ArgumentParser) -> None:
|
||||
@ -70,13 +70,15 @@ def post_process() -> None:
|
||||
def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
primary_color = (0, 0, 255)
|
||||
secondary_color = (0, 255, 0)
|
||||
tertiary_color = (255, 255, 0)
|
||||
bounding_box = target_face.bounding_box.astype(numpy.int32)
|
||||
temp_vision_frame = temp_vision_frame.copy()
|
||||
has_face_landmark_5_fallback = numpy.array_equal(target_face.landmarks.get('5'), target_face.landmarks.get('5/68'))
|
||||
|
||||
if 'bounding-box' in frame_processors_globals.face_debugger_items:
|
||||
cv2.rectangle(temp_vision_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), secondary_color, 2)
|
||||
cv2.rectangle(temp_vision_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), primary_color, 2)
|
||||
if 'face-mask' in frame_processors_globals.face_debugger_items:
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark['5/68'], 'arcface_128_v2', (512, 512))
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), 'arcface_128_v2', (512, 512))
|
||||
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
|
||||
temp_size = temp_vision_frame.shape[:2][::-1]
|
||||
crop_mask_list = []
|
||||
@ -95,30 +97,38 @@ def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFra
|
||||
inverse_vision_frame = cv2.threshold(inverse_vision_frame, 100, 255, cv2.THRESH_BINARY)[1]
|
||||
inverse_vision_frame[inverse_vision_frame > 0] = 255
|
||||
inverse_contours = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
|
||||
cv2.drawContours(temp_vision_frame, inverse_contours, -1, primary_color, 2)
|
||||
if bounding_box[3] - bounding_box[1] > 60 and bounding_box[2] - bounding_box[0] > 60:
|
||||
cv2.drawContours(temp_vision_frame, inverse_contours, -1, tertiary_color if has_face_landmark_5_fallback else secondary_color, 2)
|
||||
if 'face-landmark-5' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('5')):
|
||||
face_landmark_5 = target_face.landmarks.get('5').astype(numpy.int32)
|
||||
for index in range(face_landmark_5.shape[0]):
|
||||
cv2.circle(temp_vision_frame, (face_landmark_5[index][0], face_landmark_5[index][1]), 3, primary_color, -1)
|
||||
if 'face-landmark-5/68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('5/68')):
|
||||
face_landmark_5_68 = target_face.landmarks.get('5/68').astype(numpy.int32)
|
||||
for index in range(face_landmark_5_68.shape[0]):
|
||||
cv2.circle(temp_vision_frame, (face_landmark_5_68[index][0], face_landmark_5_68[index][1]), 3, tertiary_color if has_face_landmark_5_fallback else secondary_color, -1)
|
||||
if 'face-landmark-68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')):
|
||||
face_landmark_68 = target_face.landmarks.get('68').astype(numpy.int32)
|
||||
for index in range(face_landmark_68.shape[0]):
|
||||
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, secondary_color, -1)
|
||||
if bounding_box[3] - bounding_box[1] > 50 and bounding_box[2] - bounding_box[0] > 50:
|
||||
top = bounding_box[1]
|
||||
left = bounding_box[0] + 20
|
||||
if 'landmark-5' in frame_processors_globals.face_debugger_items:
|
||||
face_landmark_5 = target_face.landmark['5/68'].astype(numpy.int32)
|
||||
for index in range(face_landmark_5.shape[0]):
|
||||
cv2.circle(temp_vision_frame, (face_landmark_5[index][0], face_landmark_5[index][1]), 3, primary_color, -1)
|
||||
if 'landmark-68' in frame_processors_globals.face_debugger_items:
|
||||
face_landmark_68 = target_face.landmark['68'].astype(numpy.int32)
|
||||
for index in range(face_landmark_68.shape[0]):
|
||||
cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, secondary_color, -1)
|
||||
if 'score' in frame_processors_globals.face_debugger_items:
|
||||
face_score_text = str(round(target_face.score, 2))
|
||||
left = bounding_box[0] - 20
|
||||
if 'face-detector-score' in frame_processors_globals.face_debugger_items:
|
||||
face_score_text = str(round(target_face.scores.get('detector'), 2))
|
||||
top = top + 20
|
||||
cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
|
||||
cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2)
|
||||
if 'face-landmarker-score' in frame_processors_globals.face_debugger_items:
|
||||
face_score_text = str(round(target_face.scores.get('landmarker'), 2))
|
||||
top = top + 20
|
||||
cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, tertiary_color if has_face_landmark_5_fallback else secondary_color, 2)
|
||||
if 'age' in frame_processors_globals.face_debugger_items:
|
||||
face_age_text = categorize_age(target_face.age)
|
||||
top = top + 20
|
||||
cv2.putText(temp_vision_frame, face_age_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
|
||||
cv2.putText(temp_vision_frame, face_age_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2)
|
||||
if 'gender' in frame_processors_globals.face_debugger_items:
|
||||
face_gender_text = categorize_gender(target_face.gender)
|
||||
top = top + 20
|
||||
cv2.putText(temp_vision_frame, face_gender_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
|
||||
cv2.putText(temp_vision_frame, face_gender_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
@ -127,50 +137,50 @@ def get_reference_frame(source_face : Face, target_face : Face, temp_vision_fram
|
||||
|
||||
|
||||
def process_frame(inputs : FaceDebuggerInputs) -> VisionFrame:
|
||||
reference_faces = inputs['reference_faces']
|
||||
target_vision_frame = inputs['target_vision_frame']
|
||||
reference_faces = inputs.get('reference_faces')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
|
||||
if 'reference' in facefusion.globals.face_selector_mode:
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = debug_face(similar_face, target_vision_frame)
|
||||
if 'one' in facefusion.globals.face_selector_mode:
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = debug_face(target_face, target_vision_frame)
|
||||
if 'many' in facefusion.globals.face_selector_mode:
|
||||
if facefusion.globals.face_selector_mode == 'many':
|
||||
many_faces = get_many_faces(target_vision_frame)
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
target_vision_frame = debug_face(target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'one':
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = debug_face(target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'reference':
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = debug_face(similar_face, target_vision_frame)
|
||||
return target_vision_frame
|
||||
|
||||
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : Update_Process) -> None:
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
|
||||
for queue_payload in queue_payloads:
|
||||
for queue_payload in process_manager.manage(queue_payloads):
|
||||
target_vision_path = queue_payload['frame_path']
|
||||
target_vision_frame = read_image(target_vision_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(target_vision_path, result_frame)
|
||||
write_image(target_vision_path, output_vision_frame)
|
||||
update_progress()
|
||||
|
||||
|
||||
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
target_vision_frame = read_static_image(target_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(output_path, result_frame)
|
||||
write_image(output_path, output_vision_frame)
|
||||
|
||||
|
||||
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
||||
|
||||
@ -7,14 +7,15 @@ import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion import config, process_manager, logger, wording
|
||||
from facefusion.face_analyser import get_many_faces, clear_face_analyser, find_similar_faces, get_one_face
|
||||
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, clear_face_occluder
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, paste_back
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion.execution import apply_execution_provider_options
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.typing import Face, VisionFrame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.common_helper import create_metavar
|
||||
from facefusion.filesystem import is_file, is_image, is_video, resolve_relative_path
|
||||
from facefusion.download import conditional_download, is_download_done
|
||||
@ -150,7 +151,7 @@ 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
|
||||
if mode == 'output' and not facefusion.globals.output_path:
|
||||
if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
|
||||
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
@ -169,7 +170,7 @@ def post_process() -> None:
|
||||
def enhance_face(target_face: Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_template = get_options('model').get('template')
|
||||
model_size = get_options('model').get('size')
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark['5/68'], model_template, model_size)
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), model_template, model_size)
|
||||
box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, (0, 0, 0, 0))
|
||||
crop_mask_list =\
|
||||
[
|
||||
@ -230,50 +231,50 @@ def get_reference_frame(source_face : Face, target_face : Face, temp_vision_fram
|
||||
|
||||
|
||||
def process_frame(inputs : FaceEnhancerInputs) -> VisionFrame:
|
||||
reference_faces = inputs['reference_faces']
|
||||
target_vision_frame = inputs['target_vision_frame']
|
||||
reference_faces = inputs.get('reference_faces')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
|
||||
if 'reference' in facefusion.globals.face_selector_mode:
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = enhance_face(similar_face, target_vision_frame)
|
||||
if 'one' in facefusion.globals.face_selector_mode:
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = enhance_face(target_face, target_vision_frame)
|
||||
if 'many' in facefusion.globals.face_selector_mode:
|
||||
if facefusion.globals.face_selector_mode == 'many':
|
||||
many_faces = get_many_faces(target_vision_frame)
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
target_vision_frame = enhance_face(target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'one':
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = enhance_face(target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'reference':
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = enhance_face(similar_face, target_vision_frame)
|
||||
return target_vision_frame
|
||||
|
||||
|
||||
def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : Update_Process) -> None:
|
||||
def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
|
||||
for queue_payload in queue_payloads:
|
||||
for queue_payload in process_manager.manage(queue_payloads):
|
||||
target_vision_path = queue_payload['frame_path']
|
||||
target_vision_frame = read_image(target_vision_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(target_vision_path, result_frame)
|
||||
write_image(target_vision_path, output_vision_frame)
|
||||
update_progress()
|
||||
|
||||
|
||||
def process_image(source_path : str, target_path : str, output_path : str) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
target_vision_frame = read_static_image(target_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(output_path, result_frame)
|
||||
write_image(output_path, output_vision_frame)
|
||||
|
||||
|
||||
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
||||
|
||||
@ -8,14 +8,16 @@ from onnx import numpy_helper
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion import config, process_manager, logger, wording
|
||||
from facefusion.execution 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_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, paste_back
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.common_helper import extract_major_version
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, Embedding, VisionFrame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.typing import Face, Embedding, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.filesystem import is_file, is_image, has_image, is_video, filter_image_paths, resolve_relative_path
|
||||
from facefusion.download import conditional_download, is_download_done
|
||||
from facefusion.vision import read_image, read_static_image, read_static_images, write_image
|
||||
@ -144,7 +146,8 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
if onnxruntime.__version__ == '1.17.0':
|
||||
onnxruntime_version = extract_major_version(onnxruntime.__version__)
|
||||
if onnxruntime_version > (1, 16):
|
||||
face_swapper_model_fallback = 'inswapper_128'
|
||||
else:
|
||||
face_swapper_model_fallback = 'inswapper_128_fp16'
|
||||
@ -197,7 +200,7 @@ 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
|
||||
if mode == 'output' and not facefusion.globals.output_path:
|
||||
if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
|
||||
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
@ -218,7 +221,7 @@ def post_process() -> None:
|
||||
def swap_face(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
model_template = get_options('model').get('template')
|
||||
model_size = get_options('model').get('size')
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark['5/68'], model_template, model_size)
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), model_template, model_size)
|
||||
crop_mask_list = []
|
||||
|
||||
if 'box' in facefusion.globals.face_mask_types:
|
||||
@ -259,9 +262,9 @@ def prepare_source_frame(source_face : Face) -> VisionFrame:
|
||||
model_type = get_options('model').get('type')
|
||||
source_vision_frame = read_static_image(facefusion.globals.source_paths[0])
|
||||
if model_type == 'blendswap':
|
||||
source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmark['5/68'], 'arcface_112_v2', (112, 112))
|
||||
source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmarks.get('5/68'), 'arcface_112_v2', (112, 112))
|
||||
if model_type == 'uniface':
|
||||
source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmark['5/68'], 'ffhq_512', (256, 256))
|
||||
source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmarks.get('5/68'), 'ffhq_512', (256, 256))
|
||||
source_vision_frame = source_vision_frame[:, :, ::-1] / 255.0
|
||||
source_vision_frame = source_vision_frame.transpose(2, 0, 1)
|
||||
source_vision_frame = numpy.expand_dims(source_vision_frame, axis = 0).astype(numpy.float32)
|
||||
@ -301,42 +304,42 @@ def get_reference_frame(source_face : Face, target_face : Face, temp_vision_fram
|
||||
|
||||
|
||||
def process_frame(inputs : FaceSwapperInputs) -> VisionFrame:
|
||||
reference_faces = inputs['reference_faces']
|
||||
source_face = inputs['source_face']
|
||||
target_vision_frame = inputs['target_vision_frame']
|
||||
reference_faces = inputs.get('reference_faces')
|
||||
source_face = inputs.get('source_face')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
|
||||
if 'reference' in facefusion.globals.face_selector_mode:
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = swap_face(source_face, similar_face, target_vision_frame)
|
||||
if 'one' in facefusion.globals.face_selector_mode:
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = swap_face(source_face, target_face, target_vision_frame)
|
||||
if 'many' in facefusion.globals.face_selector_mode:
|
||||
if facefusion.globals.face_selector_mode == 'many':
|
||||
many_faces = get_many_faces(target_vision_frame)
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
target_vision_frame = swap_face(source_face, target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'one':
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = swap_face(source_face, target_face, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'reference':
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = swap_face(source_face, similar_face, target_vision_frame)
|
||||
return target_vision_frame
|
||||
|
||||
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : Update_Process) -> None:
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
source_frames = read_static_images(source_paths)
|
||||
source_face = get_average_face(source_frames)
|
||||
|
||||
for queue_payload in queue_payloads:
|
||||
for queue_payload in process_manager.manage(queue_payloads):
|
||||
target_vision_path = queue_payload['frame_path']
|
||||
target_vision_frame = read_image(target_vision_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'source_face': source_face,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(target_vision_path, result_frame)
|
||||
write_image(target_vision_path, output_vision_frame)
|
||||
update_progress()
|
||||
|
||||
|
||||
@ -345,13 +348,13 @@ def process_image(source_paths : List[str], target_path : str, output_path : str
|
||||
source_frames = read_static_images(source_paths)
|
||||
source_face = get_average_face(source_frames)
|
||||
target_vision_frame = read_static_image(target_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'source_face': source_face,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(output_path, result_frame)
|
||||
write_image(output_path, output_vision_frame)
|
||||
|
||||
|
||||
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
||||
|
||||
@ -2,46 +2,63 @@ from typing import Any, List, Literal, Optional
|
||||
from argparse import ArgumentParser
|
||||
import threading
|
||||
import cv2
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from realesrgan import RealESRGANer
|
||||
import numpy
|
||||
import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion import config, process_manager, logger, wording
|
||||
from facefusion.face_analyser import clear_face_analyser
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, VisionFrame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.execution import apply_execution_provider_options
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
|
||||
from facefusion.common_helper import create_metavar
|
||||
from facefusion.execution_helper import map_torch_backend
|
||||
from facefusion.filesystem import is_file, resolve_relative_path
|
||||
from facefusion.filesystem import is_file, resolve_relative_path, is_image, is_video
|
||||
from facefusion.download import conditional_download, is_download_done
|
||||
from facefusion.vision import read_image, read_static_image, write_image
|
||||
from facefusion.vision import read_image, read_static_image, write_image, merge_tile_frames, create_tile_frames
|
||||
from facefusion.processors.frame.typings import FrameEnhancerInputs
|
||||
from facefusion.processors.frame import globals as frame_processors_globals
|
||||
from facefusion.processors.frame import choices as frame_processors_choices
|
||||
|
||||
FRAME_PROCESSOR = None
|
||||
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
|
||||
THREAD_LOCK : threading.Lock = threading.Lock()
|
||||
NAME = __name__.upper()
|
||||
MODELS : ModelSet =\
|
||||
{
|
||||
'real_esrgan_x2plus':
|
||||
'lsdir_x4':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x2plus.pth',
|
||||
'path': resolve_relative_path('../.assets/models/real_esrgan_x2plus.pth'),
|
||||
'scale': 2
|
||||
},
|
||||
'real_esrgan_x4plus':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4plus.pth',
|
||||
'path': resolve_relative_path('../.assets/models/real_esrgan_x4plus.pth'),
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/lsdir_x4.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/lsdir_x4.onnx'),
|
||||
'size': (128, 8, 2),
|
||||
'scale': 4
|
||||
},
|
||||
'real_esrnet_x4plus':
|
||||
'nomos8k_sc_x4':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrnet_x4plus.pth',
|
||||
'path': resolve_relative_path('../.assets/models/real_esrnet_x4plus.pth'),
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/nomos8k_sc_x4.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/nomos8k_sc_x4.onnx'),
|
||||
'size': (128, 8, 2),
|
||||
'scale': 4
|
||||
},
|
||||
'real_esrgan_x4':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/real_esrgan_x4.onnx'),
|
||||
'size': (128, 8, 2),
|
||||
'scale': 4
|
||||
},
|
||||
'real_esrgan_x4_fp16':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/real_esrgan_x4_fp16.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/real_esrgan_x4_fp16.onnx'),
|
||||
'size': (128, 8, 2),
|
||||
'scale': 4
|
||||
},
|
||||
'span_kendata_x4':
|
||||
{
|
||||
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/span_kendata_x4.onnx',
|
||||
'path': resolve_relative_path('../.assets/models/span_kendata_x4.onnx'),
|
||||
'size': (128, 8, 2),
|
||||
'scale': 4
|
||||
}
|
||||
}
|
||||
@ -54,17 +71,7 @@ def get_frame_processor() -> Any:
|
||||
with THREAD_LOCK:
|
||||
if FRAME_PROCESSOR is None:
|
||||
model_path = get_options('model').get('path')
|
||||
model_scale = get_options('model').get('scale')
|
||||
FRAME_PROCESSOR = RealESRGANer(
|
||||
model_path = model_path,
|
||||
model = RRDBNet(
|
||||
num_in_ch = 3,
|
||||
num_out_ch = 3,
|
||||
scale = model_scale
|
||||
),
|
||||
device = map_torch_backend(facefusion.globals.execution_providers),
|
||||
scale = model_scale
|
||||
)
|
||||
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_providers))
|
||||
return FRAME_PROCESSOR
|
||||
|
||||
|
||||
@ -92,7 +99,7 @@ def set_options(key : Literal['model'], value : Any) -> None:
|
||||
|
||||
|
||||
def register_args(program : ArgumentParser) -> None:
|
||||
program.add_argument('--frame-enhancer-model', help = wording.get('help.frame_enhancer_model'), 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-model', help = wording.get('help.frame_enhancer_model'), default = config.get_str_value('frame_processors.frame_enhancer_model', 'span_kendata_x4'), choices = frame_processors_choices.frame_enhancer_models)
|
||||
program.add_argument('--frame-enhancer-blend', help = wording.get('help.frame_enhancer_blend'), 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))
|
||||
|
||||
|
||||
@ -123,7 +130,10 @@ def post_check() -> bool:
|
||||
|
||||
|
||||
def pre_process(mode : ProcessMode) -> bool:
|
||||
if mode == 'output' and not facefusion.globals.output_path:
|
||||
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
|
||||
if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
|
||||
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
@ -139,12 +149,36 @@ def post_process() -> None:
|
||||
|
||||
|
||||
def enhance_frame(temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
with THREAD_SEMAPHORE:
|
||||
paste_vision_frame, _ = get_frame_processor().enhance(temp_vision_frame)
|
||||
temp_vision_frame = blend_frame(temp_vision_frame, paste_vision_frame)
|
||||
frame_processor = get_frame_processor()
|
||||
size = get_options('model').get('size')
|
||||
scale = get_options('model').get('scale')
|
||||
temp_height, temp_width = temp_vision_frame.shape[:2]
|
||||
tile_vision_frames, pad_width, pad_height = create_tile_frames(temp_vision_frame, size)
|
||||
|
||||
for index, tile_vision_frame in enumerate(tile_vision_frames):
|
||||
tile_vision_frame = frame_processor.run(None,
|
||||
{
|
||||
frame_processor.get_inputs()[0].name : prepare_tile_frame(tile_vision_frame)
|
||||
})[0]
|
||||
tile_vision_frames[index] = normalize_tile_frame(tile_vision_frame)
|
||||
merge_vision_frame = merge_tile_frames(tile_vision_frames, temp_width * scale, temp_height * scale, pad_width * scale, pad_height * scale, (size[0] * scale, size[1] * scale, size[2] * scale))
|
||||
temp_vision_frame = blend_frame(temp_vision_frame, merge_vision_frame)
|
||||
return temp_vision_frame
|
||||
|
||||
|
||||
def prepare_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
|
||||
vision_tile_frame = numpy.expand_dims(vision_tile_frame[:,:,::-1], axis = 0)
|
||||
vision_tile_frame = vision_tile_frame.transpose(0, 3, 1, 2)
|
||||
vision_tile_frame = vision_tile_frame.astype(numpy.float32) / 255
|
||||
return vision_tile_frame
|
||||
|
||||
|
||||
def normalize_tile_frame(vision_tile_frame : VisionFrame) -> VisionFrame:
|
||||
vision_tile_frame = vision_tile_frame.transpose(0, 2, 3, 1).squeeze(0) * 255
|
||||
vision_tile_frame = vision_tile_frame.clip(0, 255).astype(numpy.uint8)[:,:,::-1]
|
||||
return vision_tile_frame
|
||||
|
||||
|
||||
def blend_frame(temp_vision_frame : VisionFrame, paste_vision_frame : VisionFrame) -> VisionFrame:
|
||||
frame_enhancer_blend = 1 - (frame_processors_globals.frame_enhancer_blend / 100)
|
||||
temp_vision_frame = cv2.resize(temp_vision_frame, (paste_vision_frame.shape[1], paste_vision_frame.shape[0]))
|
||||
@ -157,29 +191,29 @@ def get_reference_frame(source_face : Face, target_face : Face, temp_vision_fram
|
||||
|
||||
|
||||
def process_frame(inputs : FrameEnhancerInputs) -> VisionFrame:
|
||||
target_vision_frame = inputs['target_vision_frame']
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
return enhance_frame(target_vision_frame)
|
||||
|
||||
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : Update_Process) -> None:
|
||||
for queue_payload in queue_payloads:
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
|
||||
for queue_payload in process_manager.manage(queue_payloads):
|
||||
target_vision_path = queue_payload['frame_path']
|
||||
target_vision_frame = read_image(target_vision_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(target_vision_path, result_frame)
|
||||
write_image(target_vision_path, output_vision_frame)
|
||||
update_progress()
|
||||
|
||||
|
||||
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
|
||||
target_vision_frame = read_static_image(target_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(output_path, result_frame)
|
||||
write_image(output_path, output_vision_frame)
|
||||
|
||||
|
||||
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
||||
|
||||
@ -7,17 +7,18 @@ import onnxruntime
|
||||
|
||||
import facefusion.globals
|
||||
import facefusion.processors.frame.core as frame_processors
|
||||
from facefusion import config, logger, wording
|
||||
from facefusion.execution_helper import apply_execution_provider_options
|
||||
from facefusion import config, process_manager, logger, wording
|
||||
from facefusion.execution import apply_execution_provider_options
|
||||
from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
|
||||
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, create_mouth_mask, clear_face_occluder, clear_face_parser
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_bounding_box, paste_back, create_bounding_box_from_landmark
|
||||
from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_bounding_box, paste_back, create_bounding_box_from_face_landmark_68
|
||||
from facefusion.face_store import get_reference_faces
|
||||
from facefusion.content_analyser import clear_content_analyser
|
||||
from facefusion.typing import Face, VisionFrame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, AudioFrame, QueuePayload
|
||||
from facefusion.normalizer import normalize_output_path
|
||||
from facefusion.typing import Face, VisionFrame, UpdateProcess, ProcessMode, ModelSet, OptionsWithModel, AudioFrame, QueuePayload
|
||||
from facefusion.filesystem import is_file, has_audio, resolve_relative_path
|
||||
from facefusion.download import conditional_download, is_download_done
|
||||
from facefusion.audio import read_static_audio, get_audio_frame
|
||||
from facefusion.audio import read_static_audio, get_audio_frame, create_empty_audio_frame
|
||||
from facefusion.filesystem import is_image, is_video, filter_audio_paths
|
||||
from facefusion.common_helper import get_first
|
||||
from facefusion.vision import read_image, write_image, read_static_image
|
||||
@ -109,7 +110,7 @@ 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
|
||||
if mode == 'output' and not facefusion.globals.output_path:
|
||||
if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
|
||||
logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
|
||||
return False
|
||||
return True
|
||||
@ -129,23 +130,24 @@ def post_process() -> None:
|
||||
|
||||
def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_frame : VisionFrame) -> VisionFrame:
|
||||
frame_processor = get_frame_processor()
|
||||
crop_mask_list = []
|
||||
temp_audio_frame = prepare_audio_frame(temp_audio_frame)
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark['5/68'], 'ffhq_512', (512, 512))
|
||||
face_landmark_68 = cv2.transform(target_face.landmark['68'].reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
|
||||
bounding_box = create_bounding_box_from_landmark(face_landmark_68)
|
||||
bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125
|
||||
mouth_mask = create_mouth_mask(face_landmark_68)
|
||||
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), 'ffhq_512', (512, 512))
|
||||
if numpy.any(target_face.landmarks.get('68')):
|
||||
face_landmark_68 = cv2.transform(target_face.landmarks.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
|
||||
bounding_box = create_bounding_box_from_face_landmark_68(face_landmark_68)
|
||||
bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125
|
||||
mouth_mask = create_mouth_mask(face_landmark_68)
|
||||
crop_mask_list.append(mouth_mask)
|
||||
else:
|
||||
bounding_box = target_face.bounding_box
|
||||
box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding)
|
||||
crop_mask_list =\
|
||||
[
|
||||
mouth_mask,
|
||||
box_mask
|
||||
]
|
||||
crop_mask_list.append(box_mask)
|
||||
|
||||
if 'occlusion' in facefusion.globals.face_mask_types:
|
||||
occlusion_mask = create_occlusion_mask(crop_vision_frame)
|
||||
crop_mask_list.append(occlusion_mask)
|
||||
close_vision_frame, closeup_matrix = warp_face_by_bounding_box(crop_vision_frame, bounding_box, (96, 96))
|
||||
close_vision_frame, close_matrix = warp_face_by_bounding_box(crop_vision_frame, bounding_box, (96, 96))
|
||||
close_vision_frame = prepare_crop_frame(close_vision_frame)
|
||||
close_vision_frame = frame_processor.run(None,
|
||||
{
|
||||
@ -153,7 +155,7 @@ def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_fram
|
||||
'target': close_vision_frame
|
||||
})[0]
|
||||
crop_vision_frame = normalize_crop_frame(close_vision_frame)
|
||||
crop_vision_frame = cv2.warpAffine(crop_vision_frame, cv2.invertAffineTransform(closeup_matrix), (512, 512), borderMode = cv2.BORDER_REPLICATE)
|
||||
crop_vision_frame = cv2.warpAffine(crop_vision_frame, cv2.invertAffineTransform(close_matrix), (512, 512), borderMode = cv2.BORDER_REPLICATE)
|
||||
crop_mask = numpy.minimum.reduce(crop_mask_list)
|
||||
paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
|
||||
return paste_vision_frame
|
||||
@ -188,60 +190,59 @@ def get_reference_frame(source_face : Face, target_face : Face, temp_vision_fram
|
||||
|
||||
|
||||
def process_frame(inputs : LipSyncerInputs) -> VisionFrame:
|
||||
reference_faces = inputs['reference_faces']
|
||||
source_audio_frame = inputs['source_audio_frame']
|
||||
target_vision_frame = inputs['target_vision_frame']
|
||||
is_source_audio_frame = isinstance(source_audio_frame, numpy.ndarray) and source_audio_frame.any()
|
||||
reference_faces = inputs.get('reference_faces')
|
||||
source_audio_frame = inputs.get('source_audio_frame')
|
||||
target_vision_frame = inputs.get('target_vision_frame')
|
||||
|
||||
if 'reference' in facefusion.globals.face_selector_mode:
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces and is_source_audio_frame:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = sync_lip(similar_face, source_audio_frame, target_vision_frame)
|
||||
if 'one' in facefusion.globals.face_selector_mode:
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face and is_source_audio_frame:
|
||||
target_vision_frame = sync_lip(target_face, source_audio_frame, target_vision_frame)
|
||||
if 'many' in facefusion.globals.face_selector_mode:
|
||||
if facefusion.globals.face_selector_mode == 'many':
|
||||
many_faces = get_many_faces(target_vision_frame)
|
||||
if many_faces and is_source_audio_frame:
|
||||
if many_faces:
|
||||
for target_face in many_faces:
|
||||
target_vision_frame = sync_lip(target_face, source_audio_frame, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'one':
|
||||
target_face = get_one_face(target_vision_frame)
|
||||
if target_face:
|
||||
target_vision_frame = sync_lip(target_face, source_audio_frame, target_vision_frame)
|
||||
if facefusion.globals.face_selector_mode == 'reference':
|
||||
similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
|
||||
if similar_faces:
|
||||
for similar_face in similar_faces:
|
||||
target_vision_frame = sync_lip(similar_face, source_audio_frame, target_vision_frame)
|
||||
return target_vision_frame
|
||||
|
||||
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : Update_Process) -> None:
|
||||
def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProcess) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
source_audio_path = get_first(filter_audio_paths(source_paths))
|
||||
target_video_fps = facefusion.globals.output_video_fps
|
||||
|
||||
for queue_payload in queue_payloads:
|
||||
for queue_payload in process_manager.manage(queue_payloads):
|
||||
frame_number = queue_payload['frame_number']
|
||||
target_vision_path = queue_payload['frame_path']
|
||||
source_audio_frame = get_audio_frame(source_audio_path, target_video_fps, frame_number)
|
||||
source_audio_frame = get_audio_frame(source_audio_path, facefusion.globals.output_video_fps, frame_number)
|
||||
if not numpy.any(source_audio_frame):
|
||||
source_audio_frame = create_empty_audio_frame()
|
||||
target_vision_frame = read_image(target_vision_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'source_audio_frame': source_audio_frame,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(target_vision_path, result_frame)
|
||||
write_image(target_vision_path, output_vision_frame)
|
||||
update_progress()
|
||||
|
||||
|
||||
def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
|
||||
reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
|
||||
source_audio_path = get_first(filter_audio_paths(source_paths))
|
||||
source_audio_frame = get_audio_frame(source_audio_path, 25)
|
||||
source_audio_frame = create_empty_audio_frame()
|
||||
target_vision_frame = read_static_image(target_path)
|
||||
result_frame = process_frame(
|
||||
output_vision_frame = process_frame(
|
||||
{
|
||||
'reference_faces': reference_faces,
|
||||
'source_audio_frame': source_audio_frame,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
write_image(output_path, result_frame)
|
||||
write_image(output_path, output_vision_frame)
|
||||
|
||||
|
||||
def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
|
||||
|
||||
@ -2,10 +2,10 @@ from typing import Literal, TypedDict
|
||||
|
||||
from facefusion.typing import Face, FaceSet, AudioFrame, VisionFrame
|
||||
|
||||
FaceDebuggerItem = Literal['bounding-box', 'landmark-5', 'landmark-68', 'face-mask', 'score', 'age', 'gender']
|
||||
FaceDebuggerItem = Literal['bounding-box', 'face-landmark-5', 'face-landmark-5/68', 'face-landmark-68', 'face-mask', 'face-detector-score', 'face-landmarker-score', 'age', 'gender']
|
||||
FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer_plus_plus']
|
||||
FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial', 'uniface_256']
|
||||
FrameEnhancerModel = Literal['real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus']
|
||||
FrameEnhancerModel = Literal['lsdir_x4', 'nomos8k_sc_x4', 'real_esrgan_x4', 'real_esrgan_x4_fp16', 'span_kendata_x4']
|
||||
LipSyncerModel = Literal['wav2lip_gan']
|
||||
|
||||
FaceDebuggerInputs = TypedDict('FaceDebuggerInputs',
|
||||
|
||||
51
facefusion/statistics.py
Normal file
51
facefusion/statistics.py
Normal file
@ -0,0 +1,51 @@
|
||||
from typing import Any, Dict
|
||||
import numpy
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion.face_store import FACE_STORE
|
||||
from facefusion.typing import FaceSet
|
||||
from facefusion import logger
|
||||
|
||||
|
||||
def create_statistics(static_faces : FaceSet) -> Dict[str, Any]:
|
||||
face_detector_score_list = []
|
||||
face_landmarker_score_list = []
|
||||
statistics =\
|
||||
{
|
||||
'min_face_detector_score': 0,
|
||||
'min_face_landmarker_score': 0,
|
||||
'max_face_detector_score': 0,
|
||||
'max_face_landmarker_score': 0,
|
||||
'average_face_detector_score': 0,
|
||||
'average_face_landmarker_score': 0,
|
||||
'total_face_landmark_5_fallbacks': 0,
|
||||
'total_frames_with_faces': 0,
|
||||
'total_faces': 0
|
||||
}
|
||||
|
||||
for faces in static_faces.values():
|
||||
statistics['total_frames_with_faces'] = statistics.get('total_frames_with_faces') + 1
|
||||
for face in faces:
|
||||
statistics['total_faces'] = statistics.get('total_faces') + 1
|
||||
face_detector_score_list.append(face.scores.get('detector'))
|
||||
face_landmarker_score_list.append(face.scores.get('landmarker'))
|
||||
if numpy.array_equal(face.landmarks.get('5'), face.landmarks.get('5/68')):
|
||||
statistics['total_face_landmark_5_fallbacks'] = statistics.get('total_face_landmark_5_fallbacks') + 1
|
||||
|
||||
if face_detector_score_list:
|
||||
statistics['min_face_detector_score'] = round(min(face_detector_score_list), 2)
|
||||
statistics['max_face_detector_score'] = round(max(face_detector_score_list), 2)
|
||||
statistics['average_face_detector_score'] = round(numpy.mean(face_detector_score_list), 2)
|
||||
if face_landmarker_score_list:
|
||||
statistics['min_face_landmarker_score'] = round(min(face_landmarker_score_list), 2)
|
||||
statistics['max_face_landmarker_score'] = round(max(face_landmarker_score_list), 2)
|
||||
statistics['average_face_landmarker_score'] = round(numpy.mean(face_landmarker_score_list), 2)
|
||||
return statistics
|
||||
|
||||
|
||||
def conditional_log_statistics() -> None:
|
||||
if facefusion.globals.log_level == 'debug':
|
||||
statistics = create_statistics(FACE_STORE.get('static_faces'))
|
||||
|
||||
for name, value in statistics.items():
|
||||
logger.debug(str(name) + ': ' + str(value), __name__.upper())
|
||||
@ -12,12 +12,17 @@ FaceLandmarkSet = TypedDict('FaceLandmarkSet',
|
||||
'68' : FaceLandmark68 # type: ignore[valid-type]
|
||||
})
|
||||
Score = float
|
||||
FaceScoreSet = TypedDict('FaceScoreSet',
|
||||
{
|
||||
'detector' : Score,
|
||||
'landmarker' : Score
|
||||
})
|
||||
Embedding = numpy.ndarray[Any, Any]
|
||||
Face = namedtuple('Face',
|
||||
[
|
||||
'bounding_box',
|
||||
'landmark',
|
||||
'score',
|
||||
'landmarks',
|
||||
'scores',
|
||||
'embedding',
|
||||
'normed_embedding',
|
||||
'gender',
|
||||
@ -29,6 +34,7 @@ FaceStore = TypedDict('FaceStore',
|
||||
'static_faces' : FaceSet,
|
||||
'reference_faces': FaceSet
|
||||
})
|
||||
|
||||
VisionFrame = numpy.ndarray[Any, Any]
|
||||
Mask = numpy.ndarray[Any, Any]
|
||||
Matrix = numpy.ndarray[Any, Any]
|
||||
@ -43,29 +49,32 @@ Fps = float
|
||||
Padding = Tuple[int, int, int, int]
|
||||
Resolution = Tuple[int, int]
|
||||
|
||||
ProcessState = Literal['processing', 'stopping', 'pending']
|
||||
QueuePayload = TypedDict('QueuePayload',
|
||||
{
|
||||
'frame_number' : int,
|
||||
'frame_path' : str
|
||||
})
|
||||
Update_Process = Callable[[], None]
|
||||
Process_Frames = Callable[[List[str], List[QueuePayload], Update_Process], None]
|
||||
UpdateProcess = Callable[[], None]
|
||||
ProcessFrames = Callable[[List[str], List[QueuePayload], UpdateProcess], None]
|
||||
|
||||
Template = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
|
||||
WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
|
||||
WarpTemplateSet = Dict[WarpTemplate, numpy.ndarray[Any, Any]]
|
||||
ProcessMode = Literal['output', 'preview', 'stream']
|
||||
|
||||
LogLevel = Literal['error', 'warn', 'info', 'debug']
|
||||
VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
|
||||
FaceSelectorMode = Literal['reference', 'one', 'many']
|
||||
FaceSelectorMode = Literal['many', 'one', 'reference']
|
||||
FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
|
||||
FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
|
||||
FaceAnalyserGender = Literal['female', 'male']
|
||||
FaceDetectorModel = Literal['retinaface', 'yoloface', 'yunet']
|
||||
FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yoloface', 'yunet']
|
||||
FaceDetectorTweak = Literal['low-luminance', 'high-luminance']
|
||||
FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap', 'arcface_uniface']
|
||||
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', 'bmp']
|
||||
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
|
||||
OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']
|
||||
OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
|
||||
|
||||
ModelValue = Dict[str, Any]
|
||||
@ -74,3 +83,38 @@ OptionsWithModel = TypedDict('OptionsWithModel',
|
||||
{
|
||||
'model' : ModelValue
|
||||
})
|
||||
|
||||
ValueAndUnit = TypedDict('ValueAndUnit',
|
||||
{
|
||||
'value' : str,
|
||||
'unit' : str
|
||||
})
|
||||
ExecutionDeviceFramework = TypedDict('ExecutionDeviceFramework',
|
||||
{
|
||||
'name' : str,
|
||||
'version' : str
|
||||
})
|
||||
ExecutionDeviceProduct = TypedDict('ExecutionDeviceProduct',
|
||||
{
|
||||
'vendor' : str,
|
||||
'name' : str,
|
||||
'architecture' : str,
|
||||
})
|
||||
ExecutionDeviceVideoMemory = TypedDict('ExecutionDeviceVideoMemory',
|
||||
{
|
||||
'total' : ValueAndUnit,
|
||||
'free' : ValueAndUnit
|
||||
})
|
||||
ExecutionDeviceUtilization = TypedDict('ExecutionDeviceUtilization',
|
||||
{
|
||||
'gpu' : ValueAndUnit,
|
||||
'memory' : ValueAndUnit
|
||||
})
|
||||
ExecutionDevice = TypedDict('ExecutionDevice',
|
||||
{
|
||||
'driver_version' : str,
|
||||
'framework' : ExecutionDeviceFramework,
|
||||
'product' : ExecutionDeviceProduct,
|
||||
'video_memory' : ExecutionDeviceVideoMemory,
|
||||
'utilization' : ExecutionDeviceUtilization
|
||||
})
|
||||
|
||||
@ -42,3 +42,17 @@
|
||||
grid-template-columns: repeat(var(--grid-cols), minmax(5em, 1fr));
|
||||
grid-template-rows: repeat(var(--grid-rows), minmax(5em, 1fr));
|
||||
}
|
||||
|
||||
:root:root:root .tab-nav > button
|
||||
{
|
||||
border: unset;
|
||||
border-bottom: 0.125rem solid transparent;
|
||||
font-size: 1.125em;
|
||||
margin: 0.5rem 1rem;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
:root:root:root .tab-nav > button.selected
|
||||
{
|
||||
border-bottom: 0.125rem solid;
|
||||
}
|
||||
|
||||
@ -1,17 +1,16 @@
|
||||
from typing import Any, Optional, List, Dict, Generator
|
||||
import time
|
||||
from time import sleep, perf_counter
|
||||
import tempfile
|
||||
import statistics
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion import process_manager, wording
|
||||
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, 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
|
||||
|
||||
@ -70,6 +69,7 @@ def render() -> None:
|
||||
def listen() -> None:
|
||||
benchmark_runs_checkbox_group = get_ui_component('benchmark_runs_checkbox_group')
|
||||
benchmark_cycles_slider = get_ui_component('benchmark_cycles_slider')
|
||||
|
||||
if benchmark_runs_checkbox_group and benchmark_cycles_slider:
|
||||
BENCHMARK_START_BUTTON.click(start, inputs = [ benchmark_runs_checkbox_group, benchmark_cycles_slider ], outputs = BENCHMARK_RESULTS_DATAFRAME)
|
||||
BENCHMARK_CLEAR_BUTTON.click(clear, outputs = BENCHMARK_RESULTS_DATAFRAME)
|
||||
@ -77,10 +77,13 @@ 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.output_path = tempfile.gettempdir()
|
||||
facefusion.globals.face_landmarker_score = 0
|
||||
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 = []
|
||||
target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
|
||||
|
||||
if target_paths:
|
||||
pre_process()
|
||||
for target_path in target_paths:
|
||||
@ -103,16 +106,16 @@ def post_process() -> None:
|
||||
def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
|
||||
process_times = []
|
||||
total_fps = 0.0
|
||||
facefusion.globals.target_path = target_path
|
||||
video_frame_total = count_video_frame_total(facefusion.globals.target_path)
|
||||
output_video_resolution = detect_video_resolution(facefusion.globals.target_path)
|
||||
facefusion.globals.output_video_resolution = pack_resolution(output_video_resolution)
|
||||
facefusion.globals.output_video_fps = detect_video_fps(facefusion.globals.target_path)
|
||||
|
||||
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()
|
||||
start_time = perf_counter()
|
||||
conditional_process()
|
||||
end_time = time.perf_counter()
|
||||
end_time = perf_counter()
|
||||
process_time = end_time - start_time
|
||||
total_fps += video_frame_total / process_time
|
||||
process_times.append(process_time)
|
||||
@ -132,6 +135,8 @@ def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
|
||||
|
||||
|
||||
def clear() -> gradio.Dataframe:
|
||||
while process_manager.is_processing():
|
||||
sleep(0.5)
|
||||
if facefusion.globals.target_path:
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
return gradio.Dataframe(value = None)
|
||||
|
||||
@ -6,7 +6,7 @@ import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.face_analyser import clear_face_analyser
|
||||
from facefusion.processors.frame.core import clear_frame_processors_modules
|
||||
from facefusion.execution_helper import encode_execution_providers, decode_execution_providers
|
||||
from facefusion.execution import encode_execution_providers, decode_execution_providers
|
||||
|
||||
EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
|
||||
|
||||
@ -28,7 +28,6 @@ def listen() -> None:
|
||||
def update_execution_providers(execution_providers : List[str]) -> gradio.CheckboxGroup:
|
||||
clear_face_analyser()
|
||||
clear_frame_processors_modules()
|
||||
if not execution_providers:
|
||||
execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
|
||||
execution_providers = execution_providers or encode_execution_providers(onnxruntime.get_available_providers())
|
||||
facefusion.globals.execution_providers = decode_execution_providers(execution_providers)
|
||||
return gradio.CheckboxGroup(value = execution_providers)
|
||||
|
||||
@ -11,18 +11,20 @@ from facefusion.uis.core import register_ui_component
|
||||
FACE_ANALYSER_ORDER_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_ANALYSER_AGE_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_ANALYSER_GENDER_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_DETECTOR_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_DETECTOR_SIZE_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_DETECTOR_SCORE_SLIDER : Optional[gradio.Slider] = None
|
||||
FACE_DETECTOR_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
FACE_LANDMARKER_SCORE_SLIDER : Optional[gradio.Slider] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global FACE_ANALYSER_ORDER_DROPDOWN
|
||||
global FACE_ANALYSER_AGE_DROPDOWN
|
||||
global FACE_ANALYSER_GENDER_DROPDOWN
|
||||
global FACE_DETECTOR_MODEL_DROPDOWN
|
||||
global FACE_DETECTOR_SIZE_DROPDOWN
|
||||
global FACE_DETECTOR_SCORE_SLIDER
|
||||
global FACE_DETECTOR_MODEL_DROPDOWN
|
||||
global FACE_LANDMARKER_SCORE_SLIDER
|
||||
|
||||
face_detector_size_dropdown_args : Dict[str, Any] =\
|
||||
{
|
||||
@ -53,19 +55,28 @@ def render() -> None:
|
||||
value = facefusion.globals.face_detector_model
|
||||
)
|
||||
FACE_DETECTOR_SIZE_DROPDOWN = gradio.Dropdown(**face_detector_size_dropdown_args)
|
||||
FACE_DETECTOR_SCORE_SLIDER = gradio.Slider(
|
||||
label = wording.get('uis.face_detector_score_slider'),
|
||||
value = facefusion.globals.face_detector_score,
|
||||
step = facefusion.choices.face_detector_score_range[1] - facefusion.choices.face_detector_score_range[0],
|
||||
minimum = facefusion.choices.face_detector_score_range[0],
|
||||
maximum = facefusion.choices.face_detector_score_range[-1]
|
||||
)
|
||||
with gradio.Row():
|
||||
FACE_DETECTOR_SCORE_SLIDER = gradio.Slider(
|
||||
label = wording.get('uis.face_detector_score_slider'),
|
||||
value = facefusion.globals.face_detector_score,
|
||||
step = facefusion.choices.face_detector_score_range[1] - facefusion.choices.face_detector_score_range[0],
|
||||
minimum = facefusion.choices.face_detector_score_range[0],
|
||||
maximum = facefusion.choices.face_detector_score_range[-1]
|
||||
)
|
||||
FACE_LANDMARKER_SCORE_SLIDER = gradio.Slider(
|
||||
label = wording.get('uis.face_landmarker_score_slider'),
|
||||
value = facefusion.globals.face_landmarker_score,
|
||||
step = facefusion.choices.face_landmarker_score_range[1] - facefusion.choices.face_landmarker_score_range[0],
|
||||
minimum = facefusion.choices.face_landmarker_score_range[0],
|
||||
maximum = facefusion.choices.face_landmarker_score_range[-1]
|
||||
)
|
||||
register_ui_component('face_analyser_order_dropdown', FACE_ANALYSER_ORDER_DROPDOWN)
|
||||
register_ui_component('face_analyser_age_dropdown', FACE_ANALYSER_AGE_DROPDOWN)
|
||||
register_ui_component('face_analyser_gender_dropdown', FACE_ANALYSER_GENDER_DROPDOWN)
|
||||
register_ui_component('face_detector_model_dropdown', FACE_DETECTOR_MODEL_DROPDOWN)
|
||||
register_ui_component('face_detector_size_dropdown', FACE_DETECTOR_SIZE_DROPDOWN)
|
||||
register_ui_component('face_detector_score_slider', FACE_DETECTOR_SCORE_SLIDER)
|
||||
register_ui_component('face_landmarker_score_slider', FACE_LANDMARKER_SCORE_SLIDER)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
@ -74,7 +85,8 @@ def listen() -> None:
|
||||
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, outputs = 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)
|
||||
FACE_DETECTOR_SCORE_SLIDER.release(update_face_detector_score, inputs = FACE_DETECTOR_SCORE_SLIDER)
|
||||
FACE_LANDMARKER_SCORE_SLIDER.release(update_face_landmarker_score, inputs = FACE_LANDMARKER_SCORE_SLIDER)
|
||||
|
||||
|
||||
def update_face_analyser_order(face_analyser_order : FaceAnalyserOrder) -> None:
|
||||
@ -91,9 +103,10 @@ def update_face_analyser_gender(face_analyser_gender : FaceAnalyserGender) -> No
|
||||
|
||||
def update_face_detector_model(face_detector_model : FaceDetectorModel) -> gradio.Dropdown:
|
||||
facefusion.globals.face_detector_model = face_detector_model
|
||||
facefusion.globals.face_detector_size = '640x640'
|
||||
if facefusion.globals.face_detector_size in facefusion.choices.face_detector_set[face_detector_model]:
|
||||
return gradio.Dropdown(value = '640x640', choices = facefusion.choices.face_detector_set[face_detector_model])
|
||||
return gradio.Dropdown(value = '640x640', choices = [ '640x640' ])
|
||||
return gradio.Dropdown(value = facefusion.globals.face_detector_size, choices = facefusion.choices.face_detector_set[face_detector_model])
|
||||
return gradio.Dropdown(value = facefusion.globals.face_detector_size, choices = [ facefusion.globals.face_detector_size ])
|
||||
|
||||
|
||||
def update_face_detector_size(face_detector_size : str) -> None:
|
||||
@ -102,3 +115,7 @@ def update_face_detector_size(face_detector_size : str) -> None:
|
||||
|
||||
def update_face_detector_score(face_detector_score : float) -> None:
|
||||
facefusion.globals.face_detector_score = face_detector_score
|
||||
|
||||
|
||||
def update_face_landmarker_score(face_landmarker_score : float) -> None:
|
||||
facefusion.globals.face_landmarker_score = face_landmarker_score
|
||||
|
||||
@ -100,12 +100,10 @@ def listen() -> None:
|
||||
|
||||
|
||||
def update_face_mask_type(face_mask_types : List[FaceMaskType]) -> Tuple[gradio.CheckboxGroup, gradio.Group, gradio.CheckboxGroup]:
|
||||
if not face_mask_types:
|
||||
face_mask_types = facefusion.choices.face_mask_types
|
||||
facefusion.globals.face_mask_types = face_mask_types
|
||||
facefusion.globals.face_mask_types = face_mask_types or facefusion.choices.face_mask_types
|
||||
has_box_mask = 'box' in face_mask_types
|
||||
has_region_mask = 'region' in face_mask_types
|
||||
return gradio.CheckboxGroup(value = face_mask_types), gradio.Group(visible = has_box_mask), gradio.CheckboxGroup(visible = has_region_mask)
|
||||
return gradio.CheckboxGroup(value = facefusion.globals.face_mask_types), gradio.Group(visible = has_box_mask), gradio.CheckboxGroup(visible = has_region_mask)
|
||||
|
||||
|
||||
def update_face_mask_blur(face_mask_blur : float) -> None:
|
||||
@ -117,7 +115,5 @@ def update_face_mask_padding(face_mask_padding_top : int, face_mask_padding_righ
|
||||
|
||||
|
||||
def update_face_mask_regions(face_mask_regions : List[FaceMaskRegion]) -> gradio.CheckboxGroup:
|
||||
if not face_mask_regions:
|
||||
face_mask_regions = facefusion.choices.face_mask_regions
|
||||
facefusion.globals.face_mask_regions = face_mask_regions
|
||||
return gradio.CheckboxGroup(value = face_mask_regions)
|
||||
facefusion.globals.face_mask_regions = face_mask_regions or facefusion.choices.face_mask_regions
|
||||
return gradio.CheckboxGroup(value = facefusion.globals.face_mask_regions)
|
||||
|
||||
@ -23,7 +23,7 @@ def render() -> None:
|
||||
global REFERENCE_FACE_POSITION_GALLERY
|
||||
global REFERENCE_FACE_DISTANCE_SLIDER
|
||||
|
||||
reference_face_gallery_args: Dict[str, Any] =\
|
||||
reference_face_gallery_args : Dict[str, Any] =\
|
||||
{
|
||||
'label': wording.get('uis.reference_face_gallery'),
|
||||
'object_fit': 'cover',
|
||||
@ -85,7 +85,8 @@ def listen() -> None:
|
||||
[
|
||||
'face_detector_model_dropdown',
|
||||
'face_detector_size_dropdown',
|
||||
'face_detector_score_slider'
|
||||
'face_detector_score_slider',
|
||||
'face_landmarker_score_slider'
|
||||
]
|
||||
for component_name in change_two_component_names:
|
||||
component = get_ui_component(component_name)
|
||||
@ -98,15 +99,15 @@ def listen() -> None:
|
||||
|
||||
|
||||
def update_face_selector_mode(face_selector_mode : FaceSelectorMode) -> Tuple[gradio.Gallery, gradio.Slider]:
|
||||
if face_selector_mode == 'reference':
|
||||
facefusion.globals.face_selector_mode = face_selector_mode
|
||||
return gradio.Gallery(visible = True), gradio.Slider(visible = True)
|
||||
if face_selector_mode == 'one':
|
||||
facefusion.globals.face_selector_mode = face_selector_mode
|
||||
return gradio.Gallery(visible = False), gradio.Slider(visible = False)
|
||||
if face_selector_mode == 'many':
|
||||
facefusion.globals.face_selector_mode = face_selector_mode
|
||||
return gradio.Gallery(visible = False), gradio.Slider(visible = False)
|
||||
if face_selector_mode == 'one':
|
||||
facefusion.globals.face_selector_mode = face_selector_mode
|
||||
return gradio.Gallery(visible = False), gradio.Slider(visible = False)
|
||||
if face_selector_mode == 'reference':
|
||||
facefusion.globals.face_selector_mode = face_selector_mode
|
||||
return gradio.Gallery(visible = True), gradio.Slider(visible = True)
|
||||
|
||||
|
||||
def clear_and_update_reference_face_position(event : gradio.SelectData) -> gradio.Gallery:
|
||||
|
||||
@ -32,7 +32,7 @@ def update_frame_processors(frame_processors : List[str]) -> gradio.CheckboxGrou
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
if not frame_processor_module.pre_check():
|
||||
return gradio.CheckboxGroup()
|
||||
return gradio.CheckboxGroup(value = frame_processors, choices = sort_frame_processors(frame_processors))
|
||||
return gradio.CheckboxGroup(value = facefusion.globals.frame_processors, choices = sort_frame_processors(facefusion.globals.frame_processors))
|
||||
|
||||
|
||||
def sort_frame_processors(frame_processors : List[str]) -> list[str]:
|
||||
|
||||
@ -113,7 +113,7 @@ def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> gradi
|
||||
face_enhancer_module.clear_frame_processor()
|
||||
face_enhancer_module.set_options('model', face_enhancer_module.MODELS[face_enhancer_model])
|
||||
if face_enhancer_module.pre_check():
|
||||
return gradio.Dropdown(value = face_enhancer_model)
|
||||
return gradio.Dropdown(value = frame_processors_globals.face_enhancer_model)
|
||||
return gradio.Dropdown()
|
||||
|
||||
|
||||
@ -135,7 +135,7 @@ def update_face_swapper_model(face_swapper_model : FaceSwapperModel) -> gradio.D
|
||||
face_swapper_module.clear_frame_processor()
|
||||
face_swapper_module.set_options('model', face_swapper_module.MODELS[face_swapper_model])
|
||||
if face_swapper_module.pre_check():
|
||||
return gradio.Dropdown(value = face_swapper_model)
|
||||
return gradio.Dropdown(value = frame_processors_globals.face_swapper_model)
|
||||
return gradio.Dropdown()
|
||||
|
||||
|
||||
@ -145,7 +145,7 @@ def update_frame_enhancer_model(frame_enhancer_model : FrameEnhancerModel) -> gr
|
||||
frame_enhancer_module.clear_frame_processor()
|
||||
frame_enhancer_module.set_options('model', frame_enhancer_module.MODELS[frame_enhancer_model])
|
||||
if frame_enhancer_module.pre_check():
|
||||
return gradio.Dropdown(value = frame_enhancer_model)
|
||||
return gradio.Dropdown(value = frame_processors_globals.frame_enhancer_model)
|
||||
return gradio.Dropdown()
|
||||
|
||||
|
||||
@ -159,5 +159,5 @@ def update_lip_syncer_model(lip_syncer_model : LipSyncerModel) -> gradio.Dropdow
|
||||
lip_syncer_module.clear_frame_processor()
|
||||
lip_syncer_module.set_options('model', lip_syncer_module.MODELS[lip_syncer_model])
|
||||
if lip_syncer_module.pre_check():
|
||||
return gradio.Dropdown(value = lip_syncer_model)
|
||||
return gradio.Dropdown(value = frame_processors_globals.lip_syncer_model)
|
||||
return gradio.Dropdown()
|
||||
|
||||
@ -1,24 +1,27 @@
|
||||
from typing import Tuple, Optional
|
||||
from time import sleep
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion import process_manager, wording
|
||||
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.uis.core import get_ui_component
|
||||
from facefusion.filesystem import clear_temp, is_image, is_video
|
||||
|
||||
OUTPUT_IMAGE : Optional[gradio.Image] = None
|
||||
OUTPUT_VIDEO : Optional[gradio.Video] = None
|
||||
OUTPUT_START_BUTTON : Optional[gradio.Button] = None
|
||||
OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
|
||||
OUTPUT_STOP_BUTTON : Optional[gradio.Button] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global OUTPUT_IMAGE
|
||||
global OUTPUT_VIDEO
|
||||
global OUTPUT_START_BUTTON
|
||||
global OUTPUT_STOP_BUTTON
|
||||
global OUTPUT_CLEAR_BUTTON
|
||||
|
||||
OUTPUT_IMAGE = gradio.Image(
|
||||
@ -33,6 +36,12 @@ def render() -> None:
|
||||
variant = 'primary',
|
||||
size = 'sm'
|
||||
)
|
||||
OUTPUT_STOP_BUTTON = gradio.Button(
|
||||
value = wording.get('uis.stop_button'),
|
||||
variant = 'primary',
|
||||
size = 'sm',
|
||||
visible = False
|
||||
)
|
||||
OUTPUT_CLEAR_BUTTON = gradio.Button(
|
||||
value = wording.get('uis.clear_button'),
|
||||
size = 'sm'
|
||||
@ -42,23 +51,38 @@ def render() -> None:
|
||||
def listen() -> None:
|
||||
output_path_textbox = get_ui_component('output_path_textbox')
|
||||
if output_path_textbox:
|
||||
OUTPUT_START_BUTTON.click(start, inputs = output_path_textbox, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
|
||||
OUTPUT_START_BUTTON.click(start, outputs = [ OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
|
||||
OUTPUT_START_BUTTON.click(process, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO, OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
|
||||
OUTPUT_STOP_BUTTON.click(stop, outputs = [ OUTPUT_START_BUTTON, OUTPUT_STOP_BUTTON ])
|
||||
OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
|
||||
|
||||
|
||||
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)
|
||||
def start() -> Tuple[gradio.Button, gradio.Button]:
|
||||
while not process_manager.is_processing():
|
||||
sleep(0.5)
|
||||
return gradio.Button(visible = False), gradio.Button(visible = True)
|
||||
|
||||
|
||||
def process() -> Tuple[gradio.Image, gradio.Video, gradio.Button, gradio.Button]:
|
||||
normed_output_path = normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path)
|
||||
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)
|
||||
if is_video(facefusion.globals.output_path):
|
||||
return gradio.Image(value = None, visible = False), gradio.Video(value = facefusion.globals.output_path, visible = True)
|
||||
return gradio.Image(), gradio.Video()
|
||||
if is_image(normed_output_path):
|
||||
return gradio.Image(value = normed_output_path, visible = True), gradio.Video(value = None, visible = False), gradio.Button(visible = True), gradio.Button(visible = False)
|
||||
if is_video(normed_output_path):
|
||||
return gradio.Image(value = None, visible = False), gradio.Video(value = normed_output_path, visible = True), gradio.Button(visible = True), gradio.Button(visible = False)
|
||||
return gradio.Image(value = None), gradio.Video(value = None), gradio.Button(visible = True), gradio.Button(visible = False)
|
||||
|
||||
|
||||
def stop() -> Tuple[gradio.Button, gradio.Button]:
|
||||
process_manager.stop()
|
||||
return gradio.Button(visible = True), gradio.Button(visible = False)
|
||||
|
||||
|
||||
def clear() -> Tuple[gradio.Image, gradio.Video]:
|
||||
while process_manager.is_processing():
|
||||
sleep(0.5)
|
||||
if facefusion.globals.target_path:
|
||||
clear_temp(facefusion.globals.target_path)
|
||||
return gradio.Image(value = None), gradio.Video(value = None)
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
from typing import Optional, Tuple, List
|
||||
import tempfile
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
@ -9,10 +8,11 @@ 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
|
||||
from facefusion.vision import detect_image_resolution, create_image_resolutions, detect_video_fps, detect_video_resolution, create_video_resolutions, pack_resolution
|
||||
|
||||
OUTPUT_PATH_TEXTBOX : Optional[gradio.Textbox] = None
|
||||
OUTPUT_IMAGE_QUALITY_SLIDER : Optional[gradio.Slider] = None
|
||||
OUTPUT_IMAGE_RESOLUTION_DROPDOWN : Optional[gradio.Dropdown] = 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
|
||||
@ -23,15 +23,25 @@ OUTPUT_VIDEO_FPS_SLIDER : Optional[gradio.Slider] = None
|
||||
def render() -> None:
|
||||
global OUTPUT_PATH_TEXTBOX
|
||||
global OUTPUT_IMAGE_QUALITY_SLIDER
|
||||
global OUTPUT_IMAGE_RESOLUTION_DROPDOWN
|
||||
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_image_resolutions = []
|
||||
output_video_resolutions = []
|
||||
if is_image(facefusion.globals.target_path):
|
||||
output_image_resolution = detect_image_resolution(facefusion.globals.target_path)
|
||||
output_image_resolutions = create_image_resolutions(output_image_resolution)
|
||||
if is_video(facefusion.globals.target_path):
|
||||
output_video_resolution = detect_video_resolution(facefusion.globals.target_path)
|
||||
output_video_resolutions = create_video_resolutions(output_video_resolution)
|
||||
facefusion.globals.output_path = facefusion.globals.output_path or '.'
|
||||
OUTPUT_PATH_TEXTBOX = gradio.Textbox(
|
||||
label = wording.get('uis.output_path_textbox'),
|
||||
value = facefusion.globals.output_path or tempfile.gettempdir(),
|
||||
value = facefusion.globals.output_path,
|
||||
max_lines = 1
|
||||
)
|
||||
OUTPUT_IMAGE_QUALITY_SLIDER = gradio.Slider(
|
||||
@ -42,6 +52,12 @@ def render() -> None:
|
||||
maximum = facefusion.choices.output_image_quality_range[-1],
|
||||
visible = is_image(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_IMAGE_RESOLUTION_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('uis.output_image_resolution_dropdown'),
|
||||
choices = output_image_resolutions,
|
||||
value = facefusion.globals.output_image_resolution,
|
||||
visible = is_image(facefusion.globals.target_path)
|
||||
)
|
||||
OUTPUT_VIDEO_ENCODER_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('uis.output_video_encoder_dropdown'),
|
||||
choices = facefusion.choices.output_video_encoders,
|
||||
@ -64,7 +80,7 @@ def render() -> None:
|
||||
)
|
||||
OUTPUT_VIDEO_RESOLUTION_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('uis.output_video_resolution_dropdown'),
|
||||
choices = create_video_resolutions(facefusion.globals.target_path),
|
||||
choices = output_video_resolutions,
|
||||
value = facefusion.globals.output_video_resolution,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
@ -83,6 +99,7 @@ def render() -> None:
|
||||
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_IMAGE_RESOLUTION_DROPDOWN.change(update_output_image_resolution, inputs = OUTPUT_IMAGE_RESOLUTION_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)
|
||||
@ -97,19 +114,22 @@ 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_PRESET_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER, OUTPUT_VIDEO_RESOLUTION_DROPDOWN, OUTPUT_VIDEO_FPS_SLIDER ])
|
||||
getattr(component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_IMAGE_RESOLUTION_DROPDOWN, 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.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider]:
|
||||
def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, 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.Dropdown(visible = False), gradio.Slider(visible = False), gradio.Dropdown(visible = False, value = None, choices = None), gradio.Slider(visible = False, value = None)
|
||||
output_image_resolution = detect_image_resolution(facefusion.globals.target_path)
|
||||
output_image_resolutions = create_image_resolutions(output_image_resolution)
|
||||
facefusion.globals.output_image_resolution = pack_resolution(output_image_resolution)
|
||||
return gradio.Slider(visible = True), gradio.Dropdown(visible = True, value = facefusion.globals.output_image_resolution, choices = output_image_resolutions), 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):
|
||||
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)
|
||||
output_video_resolution = detect_video_resolution(facefusion.globals.target_path)
|
||||
output_video_resolutions = create_video_resolutions(output_video_resolution)
|
||||
facefusion.globals.output_video_resolution = pack_resolution(output_video_resolution)
|
||||
facefusion.globals.output_video_fps = detect_video_fps(facefusion.globals.target_path)
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Dropdown(visible = True), gradio.Dropdown(visible = True), gradio.Slider(visible = True), gradio.Dropdown(visible = True, value = facefusion.globals.output_video_resolution, choices = output_video_resolutions), gradio.Slider(visible = True, value = facefusion.globals.output_video_fps)
|
||||
return gradio.Slider(visible = False), gradio.Dropdown(visible = False, value = None, choices = None), 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:
|
||||
@ -120,6 +140,10 @@ def update_output_image_quality(output_image_quality : int) -> None:
|
||||
facefusion.globals.output_image_quality = output_image_quality
|
||||
|
||||
|
||||
def update_output_image_resolution(output_image_resolution : str) -> None:
|
||||
facefusion.globals.output_image_resolution = output_image_resolution
|
||||
|
||||
|
||||
def update_output_video_encoder(output_video_encoder: OutputVideoEncoder) -> None:
|
||||
facefusion.globals.output_video_encoder = output_video_encoder
|
||||
|
||||
|
||||
@ -2,10 +2,11 @@ from typing import Any, Dict, List, Optional
|
||||
from time import sleep
|
||||
import cv2
|
||||
import gradio
|
||||
import numpy
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import wording, logger
|
||||
from facefusion.audio import get_audio_frame
|
||||
from facefusion.audio import get_audio_frame, create_empty_audio_frame
|
||||
from facefusion.common_helper import get_first
|
||||
from facefusion.core import conditional_append_reference_faces
|
||||
from facefusion.face_analyser import get_average_face, clear_face_analyser
|
||||
@ -26,12 +27,12 @@ def render() -> None:
|
||||
global PREVIEW_IMAGE
|
||||
global PREVIEW_FRAME_SLIDER
|
||||
|
||||
preview_image_args: Dict[str, Any] =\
|
||||
preview_image_args : Dict[str, Any] =\
|
||||
{
|
||||
'label': wording.get('uis.preview_image'),
|
||||
'interactive': False
|
||||
}
|
||||
preview_frame_slider_args: Dict[str, Any] =\
|
||||
preview_frame_slider_args : Dict[str, Any] =\
|
||||
{
|
||||
'label': wording.get('uis.preview_frame_slider'),
|
||||
'step': 1,
|
||||
@ -46,6 +47,8 @@ def render() -> None:
|
||||
source_audio_path = get_first(filter_audio_paths(facefusion.globals.source_paths))
|
||||
if source_audio_path and facefusion.globals.output_video_fps:
|
||||
source_audio_frame = get_audio_frame(source_audio_path, facefusion.globals.output_video_fps, facefusion.globals.reference_frame_number)
|
||||
if not numpy.any(source_audio_frame):
|
||||
source_audio_frame = create_empty_audio_frame()
|
||||
else:
|
||||
source_audio_frame = None
|
||||
if is_image(facefusion.globals.target_path):
|
||||
@ -97,6 +100,8 @@ def listen() -> None:
|
||||
'face_debugger_items_checkbox_group',
|
||||
'face_enhancer_blend_slider',
|
||||
'frame_enhancer_blend_slider',
|
||||
'trim_frame_start_slider',
|
||||
'trim_frame_end_slider',
|
||||
'face_selector_mode_dropdown',
|
||||
'reference_face_distance_slider',
|
||||
'face_mask_types_checkbox_group',
|
||||
@ -124,7 +129,8 @@ def listen() -> None:
|
||||
'lip_syncer_model_dropdown',
|
||||
'face_detector_model_dropdown',
|
||||
'face_detector_size_dropdown',
|
||||
'face_detector_score_slider'
|
||||
'face_detector_score_slider',
|
||||
'face_landmarker_score_slider'
|
||||
]
|
||||
for component_name in change_two_component_names:
|
||||
component = get_ui_component(component_name)
|
||||
@ -153,10 +159,14 @@ def update_preview_image(frame_number : int = 0) -> gradio.Image:
|
||||
source_face = get_average_face(source_frames)
|
||||
source_audio_path = get_first(filter_audio_paths(facefusion.globals.source_paths))
|
||||
if source_audio_path and facefusion.globals.output_video_fps:
|
||||
source_audio_frame = get_audio_frame(source_audio_path, facefusion.globals.output_video_fps, facefusion.globals.reference_frame_number)
|
||||
reference_audio_frame_number = facefusion.globals.reference_frame_number
|
||||
if facefusion.globals.trim_frame_start:
|
||||
reference_audio_frame_number -= facefusion.globals.trim_frame_start
|
||||
source_audio_frame = get_audio_frame(source_audio_path, facefusion.globals.output_video_fps, reference_audio_frame_number)
|
||||
if not numpy.any(source_audio_frame):
|
||||
source_audio_frame = create_empty_audio_frame()
|
||||
else:
|
||||
source_audio_frame = None
|
||||
|
||||
if is_image(facefusion.globals.target_path):
|
||||
target_vision_frame = read_static_image(facefusion.globals.target_path)
|
||||
preview_vision_frame = process_preview_frame(reference_faces, source_face, source_audio_frame, target_vision_frame)
|
||||
@ -178,7 +188,7 @@ def update_preview_frame_slider() -> gradio.Slider:
|
||||
|
||||
|
||||
def process_preview_frame(reference_faces : FaceSet, source_face : Face, source_audio_frame : AudioFrame, target_vision_frame : VisionFrame) -> VisionFrame:
|
||||
target_vision_frame = resize_frame_resolution(target_vision_frame, 640, 640)
|
||||
target_vision_frame = resize_frame_resolution(target_vision_frame, (640, 640))
|
||||
if analyse_frame(target_vision_frame):
|
||||
return cv2.GaussianBlur(target_vision_frame, (99, 99), 0)
|
||||
for frame_processor in facefusion.globals.frame_processors:
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import Optional, Tuple
|
||||
from typing import Optional
|
||||
import gradio
|
||||
|
||||
import facefusion.globals
|
||||
@ -9,12 +9,10 @@ from facefusion.filesystem import is_video
|
||||
from facefusion.uis.core import get_ui_component
|
||||
|
||||
TEMP_FRAME_FORMAT_DROPDOWN : Optional[gradio.Dropdown] = None
|
||||
TEMP_FRAME_QUALITY_SLIDER : Optional[gradio.Slider] = None
|
||||
|
||||
|
||||
def render() -> None:
|
||||
global TEMP_FRAME_FORMAT_DROPDOWN
|
||||
global TEMP_FRAME_QUALITY_SLIDER
|
||||
|
||||
TEMP_FRAME_FORMAT_DROPDOWN = gradio.Dropdown(
|
||||
label = wording.get('uis.temp_frame_format_dropdown'),
|
||||
@ -22,34 +20,22 @@ def render() -> None:
|
||||
value = facefusion.globals.temp_frame_format,
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
TEMP_FRAME_QUALITY_SLIDER = gradio.Slider(
|
||||
label = wording.get('uis.temp_frame_quality_slider'),
|
||||
value = facefusion.globals.temp_frame_quality,
|
||||
step = facefusion.choices.temp_frame_quality_range[1] - facefusion.choices.temp_frame_quality_range[0],
|
||||
minimum = facefusion.choices.temp_frame_quality_range[0],
|
||||
maximum = facefusion.choices.temp_frame_quality_range[-1],
|
||||
visible = is_video(facefusion.globals.target_path)
|
||||
)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
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:
|
||||
for method in [ 'upload', 'change', 'clear' ]:
|
||||
getattr(target_video, method)(remote_update, outputs = [ TEMP_FRAME_FORMAT_DROPDOWN, TEMP_FRAME_QUALITY_SLIDER ])
|
||||
getattr(target_video, method)(remote_update, outputs = TEMP_FRAME_FORMAT_DROPDOWN)
|
||||
|
||||
|
||||
def remote_update() -> Tuple[gradio.Dropdown, gradio.Slider]:
|
||||
def remote_update() -> gradio.Dropdown:
|
||||
if is_video(facefusion.globals.target_path):
|
||||
return gradio.Dropdown(visible = True), gradio.Slider(visible = True)
|
||||
return gradio.Dropdown(visible = False), gradio.Slider(visible = False)
|
||||
return gradio.Dropdown(visible = True)
|
||||
return gradio.Dropdown(visible = False)
|
||||
|
||||
|
||||
def update_temp_frame_format(temp_frame_format : TempFrameFormat) -> None:
|
||||
facefusion.globals.temp_frame_format = temp_frame_format
|
||||
|
||||
|
||||
def update_temp_frame_quality(temp_frame_quality : int) -> None:
|
||||
facefusion.globals.temp_frame_quality = temp_frame_quality
|
||||
|
||||
@ -5,7 +5,7 @@ import facefusion.globals
|
||||
from facefusion import wording
|
||||
from facefusion.vision import count_video_frame_total
|
||||
from facefusion.filesystem import is_video
|
||||
from facefusion.uis.core import get_ui_component
|
||||
from facefusion.uis.core import get_ui_component, register_ui_component
|
||||
|
||||
TRIM_FRAME_START_SLIDER : Optional[gradio.Slider] = None
|
||||
TRIM_FRAME_END_SLIDER : Optional[gradio.Slider] = None
|
||||
@ -42,6 +42,8 @@ def render() -> None:
|
||||
with gradio.Row():
|
||||
TRIM_FRAME_START_SLIDER = gradio.Slider(**trim_frame_start_slider_args)
|
||||
TRIM_FRAME_END_SLIDER = gradio.Slider(**trim_frame_end_slider_args)
|
||||
register_ui_component('trim_frame_start_slider', TRIM_FRAME_START_SLIDER)
|
||||
register_ui_component('trim_frame_end_slider', TRIM_FRAME_END_SLIDER)
|
||||
|
||||
|
||||
def listen() -> None:
|
||||
|
||||
@ -11,7 +11,9 @@ from tqdm import tqdm
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import logger, wording
|
||||
from facefusion.audio import create_empty_audio_frame
|
||||
from facefusion.content_analyser import analyse_stream
|
||||
from facefusion.filesystem import filter_image_paths
|
||||
from facefusion.typing import VisionFrame, Face, Fps
|
||||
from facefusion.face_analyser import get_average_face
|
||||
from facefusion.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
|
||||
@ -92,9 +94,11 @@ def listen() -> None:
|
||||
def start(webcam_mode : WebcamMode, webcam_resolution : str, webcam_fps : Fps) -> Generator[VisionFrame, 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_image_paths = filter_image_paths(facefusion.globals.source_paths)
|
||||
source_frames = read_static_images(source_image_paths)
|
||||
source_face = get_average_face(source_frames)
|
||||
stream = None
|
||||
|
||||
if webcam_mode in [ 'udp', 'v4l2' ]:
|
||||
stream = open_stream(webcam_mode, webcam_resolution, webcam_fps) # type: ignore[arg-type]
|
||||
webcam_width, webcam_height = unpack_resolution(webcam_resolution)
|
||||
@ -150,6 +154,7 @@ def stop() -> gradio.Image:
|
||||
|
||||
|
||||
def process_stream_frame(source_face : Face, target_vision_frame : VisionFrame) -> VisionFrame:
|
||||
source_audio_frame = create_empty_audio_frame()
|
||||
for frame_processor_module in get_frame_processors_modules(facefusion.globals.frame_processors):
|
||||
logger.disable()
|
||||
if frame_processor_module.pre_process('stream'):
|
||||
@ -157,8 +162,7 @@ def process_stream_frame(source_face : Face, target_vision_frame : VisionFrame)
|
||||
target_vision_frame = frame_processor_module.process_frame(
|
||||
{
|
||||
'source_face': source_face,
|
||||
'reference_faces': None,
|
||||
'source_audio_frame': None,
|
||||
'source_audio_frame': source_audio_frame,
|
||||
'target_vision_frame': target_vision_frame
|
||||
})
|
||||
return target_vision_frame
|
||||
|
||||
@ -28,7 +28,7 @@ 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.error(wording.get('ui_layout_not_loaded').format(ui_layout = ui_layout), __name__.upper())
|
||||
logger.debug(exception.msg, __name__.upper())
|
||||
sys.exit(1)
|
||||
except NotImplementedError:
|
||||
@ -58,12 +58,18 @@ def register_ui_component(name : ComponentName, component: Component) -> None:
|
||||
|
||||
|
||||
def launch() -> None:
|
||||
ui_layouts_total = len(facefusion.globals.ui_layouts)
|
||||
with gradio.Blocks(theme = get_theme(), css = get_css(), title = metadata.get('name') + ' ' + metadata.get('version')) as ui:
|
||||
for ui_layout in facefusion.globals.ui_layouts:
|
||||
ui_layout_module = load_ui_layout_module(ui_layout)
|
||||
if ui_layout_module.pre_render():
|
||||
ui_layout_module.render()
|
||||
ui_layout_module.listen()
|
||||
if ui_layouts_total > 1:
|
||||
with gradio.Tab(ui_layout):
|
||||
ui_layout_module.render()
|
||||
ui_layout_module.listen()
|
||||
else:
|
||||
ui_layout_module.render()
|
||||
ui_layout_module.listen()
|
||||
|
||||
for ui_layout in facefusion.globals.ui_layouts:
|
||||
ui_layout_module = load_ui_layout_module(ui_layout)
|
||||
|
||||
@ -75,4 +75,4 @@ def listen() -> None:
|
||||
|
||||
|
||||
def run(ui : gradio.Blocks) -> None:
|
||||
ui.launch(show_api = False, quiet = True)
|
||||
ui.queue(concurrency_count = 4).launch(show_api = False, quiet = True)
|
||||
|
||||
@ -10,6 +10,8 @@ ComponentName = Literal\
|
||||
'target_image',
|
||||
'target_video',
|
||||
'preview_frame_slider',
|
||||
'trim_frame_start_slider',
|
||||
'trim_frame_end_slider',
|
||||
'face_selector_mode_dropdown',
|
||||
'reference_face_position_gallery',
|
||||
'reference_face_distance_slider',
|
||||
@ -19,6 +21,7 @@ ComponentName = Literal\
|
||||
'face_detector_model_dropdown',
|
||||
'face_detector_size_dropdown',
|
||||
'face_detector_score_slider',
|
||||
'face_landmarker_score_slider',
|
||||
'face_mask_types_checkbox_group',
|
||||
'face_mask_blur_slider',
|
||||
'face_mask_padding_top_slider',
|
||||
|
||||
@ -1,12 +1,55 @@
|
||||
from typing import Optional, List, Tuple
|
||||
from functools import lru_cache
|
||||
import cv2
|
||||
import numpy
|
||||
from cv2.typing import Size
|
||||
|
||||
from facefusion.typing import VisionFrame, Resolution
|
||||
from facefusion.choices import video_template_sizes
|
||||
from facefusion.typing import VisionFrame, Resolution, Fps
|
||||
from facefusion.choices import image_template_sizes, video_template_sizes
|
||||
from facefusion.filesystem import is_image, is_video
|
||||
|
||||
|
||||
@lru_cache(maxsize = 128)
|
||||
def read_static_image(image_path : str) -> Optional[VisionFrame]:
|
||||
return read_image(image_path)
|
||||
|
||||
|
||||
def read_static_images(image_paths : List[str]) -> Optional[List[VisionFrame]]:
|
||||
frames = []
|
||||
if image_paths:
|
||||
for image_path in image_paths:
|
||||
frames.append(read_static_image(image_path))
|
||||
return frames
|
||||
|
||||
|
||||
def read_image(image_path : str) -> Optional[VisionFrame]:
|
||||
if is_image(image_path):
|
||||
return cv2.imread(image_path)
|
||||
return None
|
||||
|
||||
|
||||
def write_image(image_path : str, vision_frame : VisionFrame) -> bool:
|
||||
if image_path:
|
||||
return cv2.imwrite(image_path, vision_frame)
|
||||
return False
|
||||
|
||||
|
||||
def detect_image_resolution(image_path : str) -> Optional[Resolution]:
|
||||
if is_image(image_path):
|
||||
image = read_image(image_path)
|
||||
height, width = image.shape[:2]
|
||||
return width, height
|
||||
return None
|
||||
|
||||
|
||||
def restrict_image_resolution(image_path : str, resolution : Resolution) -> Resolution:
|
||||
if is_image(image_path):
|
||||
image_resolution = detect_image_resolution(image_path)
|
||||
if image_resolution < resolution:
|
||||
return image_resolution
|
||||
return resolution
|
||||
|
||||
|
||||
def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[VisionFrame]:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
@ -20,6 +63,21 @@ def get_video_frame(video_path : str, frame_number : int = 0) -> Optional[Vision
|
||||
return None
|
||||
|
||||
|
||||
def create_image_resolutions(resolution : Resolution) -> List[str]:
|
||||
resolutions = []
|
||||
temp_resolutions = []
|
||||
|
||||
if resolution:
|
||||
width, height = resolution
|
||||
temp_resolutions.append(normalize_resolution(resolution))
|
||||
for template_size in image_template_sizes:
|
||||
temp_resolutions.append(normalize_resolution((width * template_size, height * template_size)))
|
||||
temp_resolutions = sorted(set(temp_resolutions))
|
||||
for temp_resolution in temp_resolutions:
|
||||
resolutions.append(pack_resolution(temp_resolution))
|
||||
return resolutions
|
||||
|
||||
|
||||
def count_video_frame_total(video_path : str) -> int:
|
||||
if is_video(video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
@ -40,35 +98,49 @@ def detect_video_fps(video_path : str) -> Optional[float]:
|
||||
return None
|
||||
|
||||
|
||||
def detect_video_resolution(video_path : str) -> Optional[Tuple[float, float]]:
|
||||
def restrict_video_fps(video_path : str, fps : Fps) -> Fps:
|
||||
if is_video(video_path):
|
||||
video_fps = detect_video_fps(video_path)
|
||||
if video_fps < fps:
|
||||
return video_fps
|
||||
return fps
|
||||
|
||||
|
||||
def detect_video_resolution(video_path : str) -> Optional[Resolution]:
|
||||
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 int(width), int(height)
|
||||
return None
|
||||
|
||||
|
||||
def create_video_resolutions(video_path : str) -> Optional[List[str]]:
|
||||
temp_resolutions = []
|
||||
video_resolutions = []
|
||||
video_resolution = detect_video_resolution(video_path)
|
||||
def restrict_video_resolution(video_path : str, resolution : Resolution) -> Resolution:
|
||||
if is_video(video_path):
|
||||
video_resolution = detect_video_resolution(video_path)
|
||||
if video_resolution < resolution:
|
||||
return video_resolution
|
||||
return resolution
|
||||
|
||||
if video_resolution:
|
||||
width, height = video_resolution
|
||||
temp_resolutions.append(normalize_resolution(video_resolution))
|
||||
|
||||
def create_video_resolutions(resolution : Resolution) -> List[str]:
|
||||
resolutions = []
|
||||
temp_resolutions = []
|
||||
|
||||
if resolution:
|
||||
width, height = resolution
|
||||
temp_resolutions.append(normalize_resolution(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
|
||||
for temp_resolution in temp_resolutions:
|
||||
resolutions.append(pack_resolution(temp_resolution))
|
||||
return resolutions
|
||||
|
||||
|
||||
def normalize_resolution(resolution : Tuple[float, float]) -> Resolution:
|
||||
@ -81,7 +153,7 @@ def normalize_resolution(resolution : Tuple[float, float]) -> Resolution:
|
||||
return 0, 0
|
||||
|
||||
|
||||
def pack_resolution(resolution : Tuple[float, float]) -> str:
|
||||
def pack_resolution(resolution : Resolution) -> str:
|
||||
width, height = normalize_resolution(resolution)
|
||||
return str(width) + 'x' + str(height)
|
||||
|
||||
@ -91,8 +163,9 @@ def unpack_resolution(resolution : str) -> Resolution:
|
||||
return width, height
|
||||
|
||||
|
||||
def resize_frame_resolution(vision_frame : VisionFrame, max_width : int, max_height : int) -> VisionFrame:
|
||||
def resize_frame_resolution(vision_frame : VisionFrame, max_resolution : Resolution) -> VisionFrame:
|
||||
height, width = vision_frame.shape[:2]
|
||||
max_width, max_height = max_resolution
|
||||
|
||||
if height > max_height or width > max_width:
|
||||
scale = min(max_height / height, max_width / width)
|
||||
@ -106,26 +179,40 @@ def normalize_frame_color(vision_frame : VisionFrame) -> VisionFrame:
|
||||
return cv2.cvtColor(vision_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
|
||||
@lru_cache(maxsize = 128)
|
||||
def read_static_image(image_path : str) -> Optional[VisionFrame]:
|
||||
return read_image(image_path)
|
||||
def create_tile_frames(vision_frame : VisionFrame, size : Size) -> Tuple[List[VisionFrame], int, int]:
|
||||
vision_frame = numpy.pad(vision_frame, ((size[1], size[1]), (size[1], size[1]), (0, 0)))
|
||||
tile_width = size[0] - 2 * size[2]
|
||||
pad_size_bottom = size[2] + tile_width - vision_frame.shape[0] % tile_width
|
||||
pad_size_right = size[2] + tile_width - vision_frame.shape[1] % tile_width
|
||||
pad_vision_frame = numpy.pad(vision_frame, ((size[2], pad_size_bottom), (size[2], pad_size_right), (0, 0)))
|
||||
pad_height, pad_width = pad_vision_frame.shape[:2]
|
||||
row_range = range(size[2], pad_height - size[2], tile_width)
|
||||
col_range = range(size[2], pad_width - size[2], tile_width)
|
||||
tile_vision_frames = []
|
||||
|
||||
for row_vision_frame in row_range:
|
||||
top = row_vision_frame - size[2]
|
||||
bottom = row_vision_frame + size[2] + tile_width
|
||||
for column_vision_frame in col_range:
|
||||
left = column_vision_frame - size[2]
|
||||
right = column_vision_frame + size[2] + tile_width
|
||||
tile_vision_frames.append(pad_vision_frame[top:bottom, left:right, :])
|
||||
return tile_vision_frames, pad_width, pad_height
|
||||
|
||||
|
||||
def read_static_images(image_paths : List[str]) -> Optional[List[VisionFrame]]:
|
||||
frames = []
|
||||
if image_paths:
|
||||
for image_path in image_paths:
|
||||
frames.append(read_static_image(image_path))
|
||||
return frames
|
||||
def merge_tile_frames(tile_vision_frames : List[VisionFrame], temp_width : int, temp_height : int, pad_width : int, pad_height : int, size : Size) -> VisionFrame:
|
||||
merge_vision_frame = numpy.zeros((pad_height, pad_width, 3)).astype(numpy.uint8)
|
||||
tile_width = tile_vision_frames[0].shape[1] - 2 * size[2]
|
||||
tiles_per_row = min(pad_width // tile_width, len(tile_vision_frames))
|
||||
|
||||
|
||||
def read_image(image_path : str) -> Optional[VisionFrame]:
|
||||
if is_image(image_path):
|
||||
return cv2.imread(image_path)
|
||||
return None
|
||||
|
||||
|
||||
def write_image(image_path : str, frame : VisionFrame) -> bool:
|
||||
if image_path:
|
||||
return cv2.imwrite(image_path, frame)
|
||||
return False
|
||||
for index, tile_vision_frame in enumerate(tile_vision_frames):
|
||||
tile_vision_frame = tile_vision_frame[size[2]:-size[2], size[2]:-size[2]]
|
||||
row_index = index // tiles_per_row
|
||||
col_index = index % tiles_per_row
|
||||
top = row_index * tile_vision_frame.shape[0]
|
||||
bottom = top + tile_vision_frame.shape[0]
|
||||
left = col_index * tile_vision_frame.shape[1]
|
||||
right = left + tile_vision_frame.shape[1]
|
||||
merge_vision_frame[top:bottom, left:right, :] = tile_vision_frame
|
||||
merge_vision_frame = merge_vision_frame[size[1] : size[1] + temp_height, size[1]: size[1] + temp_width, :]
|
||||
return merge_vision_frame
|
||||
|
||||
@ -5,19 +5,27 @@ WORDING : Dict[str, Any] =\
|
||||
'python_not_supported': 'Python version is not supported, upgrade to {version} or higher',
|
||||
'ffmpeg_not_installed': 'FFMpeg is not installed',
|
||||
'creating_temp': 'Creating temporary resources',
|
||||
'extracting_frames_fps': 'Extracting frames with {video_fps} FPS',
|
||||
'extracting_frames': 'Extracting frames with a resolution of {resolution} and {fps} frames per second',
|
||||
'extracting_frames_succeed': 'Extracting frames succeed',
|
||||
'extracting_frames_failed': 'Extracting frames failed',
|
||||
'analysing': 'Analysing',
|
||||
'processing': 'Processing',
|
||||
'downloading': 'Downloading',
|
||||
'temp_frames_not_found': 'Temporary frames not found',
|
||||
'compressing_image_succeed': 'Compressing image succeed',
|
||||
'compressing_image_skipped': 'Compressing image skipped',
|
||||
'merging_video_fps': 'Merging video with {video_fps} FPS',
|
||||
'copying_image': 'Copying image with a resolution of {resolution}',
|
||||
'copying_image_succeed': 'Copying image succeed',
|
||||
'copying_image_failed': 'Copying image failed',
|
||||
'finalizing_image': 'Finalizing image with a resolution of {resolution}',
|
||||
'finalizing_image_succeed': 'Finalizing image succeed',
|
||||
'finalizing_image_skipped': 'Finalizing image skipped',
|
||||
'merging_video': 'Merging video with a resolution of {resolution} and {fps} frames per second',
|
||||
'merging_video_succeed': 'Merging video succeed',
|
||||
'merging_video_failed': 'Merging video failed',
|
||||
'skipping_audio': 'Skipping audio',
|
||||
'restoring_audio_succeed': 'Restoring audio succeed',
|
||||
'restoring_audio_skipped': 'Restoring audio skipped',
|
||||
'clearing_temp': 'Clearing temporary resources',
|
||||
'processing_stopped': 'Processing stopped',
|
||||
'processing_image_succeed': 'Processing to image succeed in {seconds} seconds',
|
||||
'processing_image_failed': 'Processing to image failed',
|
||||
'processing_video_succeed': 'Processing to video succeed in {seconds} seconds',
|
||||
@ -67,8 +75,9 @@ WORDING : Dict[str, Any] =\
|
||||
'face_detector_model': 'choose the model responsible for detecting the face',
|
||||
'face_detector_size': 'specify the size of the frame provided to the face detector',
|
||||
'face_detector_score': 'filter the detected faces base on the confidence score',
|
||||
'face_landmarker_score': 'filter the detected landmarks base on the confidence score',
|
||||
# face selector
|
||||
'face_selector_mode': 'use reference based tracking with simple matching',
|
||||
'face_selector_mode': 'use reference based tracking or simple matching',
|
||||
'reference_face_position': 'specify the position used to create the reference face',
|
||||
'reference_face_distance': 'specify the desired similarity between the reference face and target face',
|
||||
'reference_frame_number': 'specify the frame used to create the reference face',
|
||||
@ -81,10 +90,10 @@ WORDING : Dict[str, Any] =\
|
||||
'trim_frame_start': 'specify the the start frame of the target video',
|
||||
'trim_frame_end': 'specify the the end frame of the target video',
|
||||
'temp_frame_format': 'specify the temporary resources format',
|
||||
'temp_frame_quality': 'specify the temporary resources quality',
|
||||
'keep_temp': 'keep the temporary resources after processing',
|
||||
# output creation
|
||||
'output_image_quality': 'specify the image quality which translates to the compression factor',
|
||||
'output_image_resolution': 'specify the image output resolution based on the target image',
|
||||
'output_video_encoder': 'specify the encoder use for the video compression',
|
||||
'output_video_preset': 'balance fast video processing and video file size',
|
||||
'output_video_quality': 'specify the video quality which translates to the compression factor',
|
||||
@ -131,6 +140,7 @@ WORDING : Dict[str, Any] =\
|
||||
'face_detector_model_dropdown': 'FACE DETECTOR MODEL',
|
||||
'face_detector_size_dropdown': 'FACE DETECTOR SIZE',
|
||||
'face_detector_score_slider': 'FACE DETECTOR SCORE',
|
||||
'face_landmarker_score_slider': 'FACE LANDMARKER SCORE',
|
||||
# face masker
|
||||
'face_mask_types_checkbox_group': 'FACE MASK TYPES',
|
||||
'face_mask_blur_slider': 'FACE MASK BLUR',
|
||||
@ -161,6 +171,7 @@ WORDING : Dict[str, Any] =\
|
||||
# output options
|
||||
'output_path_textbox': 'OUTPUT PATH',
|
||||
'output_image_quality_slider': 'OUTPUT IMAGE QUALITY',
|
||||
'output_image_resolution_dropdown': 'OUTPUT IMAGE RESOLUTION',
|
||||
'output_video_encoder_dropdown': 'OUTPUT VIDEO ENCODER',
|
||||
'output_video_preset_dropdown': 'OUTPUT VIDEO PRESET',
|
||||
'output_video_quality_slider': 'OUTPUT VIDEO QUALITY',
|
||||
@ -175,7 +186,6 @@ WORDING : Dict[str, Any] =\
|
||||
'target_file': 'TARGET',
|
||||
# temp frame
|
||||
'temp_frame_format_dropdown': 'TEMP FRAME FORMAT',
|
||||
'temp_frame_quality_slider': 'TEMP FRAME QUALITY',
|
||||
# trim frame
|
||||
'trim_frame_start_slider': 'TRIM FRAME START',
|
||||
'trim_frame_end_slider': 'TRIM FRAME END',
|
||||
|
||||
@ -1,11 +1,9 @@
|
||||
basicsr==1.4.2
|
||||
filetype==1.2.0
|
||||
gradio==3.50.2
|
||||
numpy==1.26.2
|
||||
numpy==1.26.4
|
||||
onnx==1.15.0
|
||||
onnxruntime==1.16.3
|
||||
opencv-python==4.8.1.78
|
||||
psutil==5.9.6
|
||||
realesrgan==0.3.0
|
||||
torch==2.1.2
|
||||
tqdm==4.66.1
|
||||
psutil==5.9.8
|
||||
tqdm==4.66.2
|
||||
scipy==1.12.0
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from facefusion.common_helper import create_metavar, create_int_range, create_float_range
|
||||
from facefusion.common_helper import create_metavar, create_int_range, create_float_range, extract_major_version
|
||||
|
||||
|
||||
def test_create_metavar() -> None:
|
||||
@ -13,3 +13,9 @@ def test_create_int_range() -> None:
|
||||
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 ]
|
||||
|
||||
|
||||
def test_extract_major_version() -> None:
|
||||
assert extract_major_version('1') == (1, 0)
|
||||
assert extract_major_version('1.1') == (1, 1)
|
||||
assert extract_major_version('1.2.0') == (1, 2)
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from facefusion.execution_helper import encode_execution_providers, decode_execution_providers, apply_execution_provider_options, map_torch_backend
|
||||
from facefusion.execution import encode_execution_providers, decode_execution_providers, apply_execution_provider_options
|
||||
|
||||
|
||||
def test_encode_execution_providers() -> None:
|
||||
@ -19,8 +19,3 @@ def test_multiple_execution_providers() -> None:
|
||||
})
|
||||
]
|
||||
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'
|
||||
@ -21,14 +21,33 @@ def before_all() -> None:
|
||||
|
||||
@pytest.fixture(autouse = True)
|
||||
def before_each() -> None:
|
||||
facefusion.globals.face_detector_score = 0.5
|
||||
facefusion.globals.face_landmarker_score = 0.5
|
||||
facefusion.globals.face_recognizer_model = 'arcface_inswapper'
|
||||
clear_face_analyser()
|
||||
|
||||
|
||||
def test_get_one_face_with_retinaface() -> None:
|
||||
facefusion.globals.face_detector_model = 'retinaface'
|
||||
facefusion.globals.face_detector_size = '320x320'
|
||||
facefusion.globals.face_detector_score = 0.5
|
||||
facefusion.globals.face_recognizer_model = 'arcface_inswapper'
|
||||
|
||||
source_paths =\
|
||||
[
|
||||
'.assets/examples/source.jpg',
|
||||
'.assets/examples/source-80crop.jpg',
|
||||
'.assets/examples/source-70crop.jpg',
|
||||
'.assets/examples/source-60crop.jpg'
|
||||
]
|
||||
for source_path in source_paths:
|
||||
source_frame = read_static_image(source_path)
|
||||
face = get_one_face(source_frame)
|
||||
|
||||
assert isinstance(face, Face)
|
||||
|
||||
|
||||
def test_get_one_face_with_scrfd() -> None:
|
||||
facefusion.globals.face_detector_model = 'scrfd'
|
||||
facefusion.globals.face_detector_size = '640x640'
|
||||
|
||||
source_paths =\
|
||||
[
|
||||
@ -47,8 +66,6 @@ def test_get_one_face_with_retinaface() -> None:
|
||||
def test_get_one_face_with_yoloface() -> None:
|
||||
facefusion.globals.face_detector_model = 'yoloface'
|
||||
facefusion.globals.face_detector_size = '640x640'
|
||||
facefusion.globals.face_detector_score = 0.5
|
||||
facefusion.globals.face_recognizer_model = 'arcface_inswapper'
|
||||
|
||||
source_paths =\
|
||||
[
|
||||
@ -67,8 +84,6 @@ def test_get_one_face_with_yoloface() -> None:
|
||||
def test_get_one_face_with_yunet() -> None:
|
||||
facefusion.globals.face_detector_model = 'yunet'
|
||||
facefusion.globals.face_detector_size = '640x640'
|
||||
facefusion.globals.face_detector_score = 0.5
|
||||
facefusion.globals.face_recognizer_model = 'arcface_inswapper'
|
||||
|
||||
source_paths =\
|
||||
[
|
||||
|
||||
@ -3,6 +3,7 @@ import subprocess
|
||||
import pytest
|
||||
|
||||
import facefusion.globals
|
||||
from facefusion import process_manager
|
||||
from facefusion.filesystem import get_temp_directory_path, create_temp, clear_temp
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.ffmpeg import extract_frames, read_audio_buffer
|
||||
@ -10,6 +11,7 @@ from facefusion.ffmpeg import extract_frames, read_audio_buffer
|
||||
|
||||
@pytest.fixture(scope = 'module', autouse = True)
|
||||
def before_all() -> None:
|
||||
process_manager.start()
|
||||
conditional_download('.assets/examples',
|
||||
[
|
||||
'https://github.com/facefusion/facefusion-assets/releases/download/examples/source.jpg',
|
||||
@ -26,7 +28,6 @@ def before_all() -> None:
|
||||
def before_each() -> None:
|
||||
facefusion.globals.trim_frame_start = None
|
||||
facefusion.globals.trim_frame_end = None
|
||||
facefusion.globals.temp_frame_quality = 80
|
||||
facefusion.globals.temp_frame_format = 'jpg'
|
||||
|
||||
|
||||
@ -37,6 +38,7 @@ def test_extract_frames() -> None:
|
||||
'.assets/examples/target-240p-30fps.mp4',
|
||||
'.assets/examples/target-240p-60fps.mp4'
|
||||
]
|
||||
|
||||
for target_path in target_paths:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
@ -55,6 +57,7 @@ def test_extract_frames_with_trim_start() -> None:
|
||||
('.assets/examples/target-240p-30fps.mp4', 100),
|
||||
('.assets/examples/target-240p-60fps.mp4', 212)
|
||||
]
|
||||
|
||||
for target_path, frame_total in data_provider:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
@ -74,6 +77,7 @@ def test_extract_frames_with_trim_start_and_trim_end() -> None:
|
||||
('.assets/examples/target-240p-30fps.mp4', 100),
|
||||
('.assets/examples/target-240p-60fps.mp4', 50)
|
||||
]
|
||||
|
||||
for target_path, frame_total in data_provider:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
@ -92,6 +96,7 @@ def test_extract_frames_with_trim_end() -> None:
|
||||
('.assets/examples/target-240p-30fps.mp4', 100),
|
||||
('.assets/examples/target-240p-60fps.mp4', 50)
|
||||
]
|
||||
|
||||
for target_path, frame_total in data_provider:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
create_temp(target_path)
|
||||
|
||||
@ -4,17 +4,16 @@ from facefusion.normalizer import normalize_output_path, normalize_padding, norm
|
||||
|
||||
|
||||
def test_normalize_output_path() -> None:
|
||||
if platform.system().lower() != 'windows':
|
||||
assert normalize_output_path([ '.assets/examples/source.jpg' ], None, '.assets/examples/target-240p.mp4') == '.assets/examples/target-240p.mp4'
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/examples/target-240p.mp4') == '.assets/examples/target-240p.mp4'
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/examples') == '.assets/examples/target-240p.mp4'
|
||||
assert normalize_output_path([ '.assets/examples/source.jpg' ], '.assets/examples/target-240p.mp4', '.assets/examples') == '.assets/examples/source-target-240p.mp4'
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/examples/output.mp4') == '.assets/examples/output.mp4'
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/output.mov') == '.assets/output.mp4'
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/examples/invalid') is None
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', '.assets/invalid/output.mp4') is None
|
||||
assert normalize_output_path(None, '.assets/examples/target-240p.mp4', 'invalid') is None
|
||||
assert normalize_output_path([ '.assets/examples/source.jpg' ], '.assets/examples/target-240p.mp4', None) is None
|
||||
if platform.system().lower() == 'linux' or platform.system().lower() == 'darwin':
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/examples/target-240p.mp4') == '.assets/examples/target-240p.mp4'
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/examples').startswith('.assets/examples/target-240p')
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/examples').endswith('.mp4')
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/examples/output.mp4') == '.assets/examples/output.mp4'
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/examples/invalid') is None
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', '.assets/invalid/output.mp4') is None
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', 'invalid') is None
|
||||
assert normalize_output_path('.assets/examples/target-240p.mp4', None) is None
|
||||
assert normalize_output_path(None, '.assets/examples/output.mp4') is None
|
||||
|
||||
|
||||
def test_normalize_padding() -> None:
|
||||
|
||||
22
tests/test_process_manager.py
Normal file
22
tests/test_process_manager.py
Normal file
@ -0,0 +1,22 @@
|
||||
from facefusion.process_manager import set_process_state, is_processing, is_stopping, is_pending, start, stop, end
|
||||
|
||||
|
||||
def test_start() -> None:
|
||||
set_process_state('pending')
|
||||
start()
|
||||
|
||||
assert is_processing()
|
||||
|
||||
|
||||
def test_stop() -> None:
|
||||
set_process_state('processing')
|
||||
stop()
|
||||
|
||||
assert is_stopping()
|
||||
|
||||
|
||||
def test_end() -> None:
|
||||
set_process_state('processing')
|
||||
end()
|
||||
|
||||
assert is_pending()
|
||||
@ -2,7 +2,7 @@ import subprocess
|
||||
import pytest
|
||||
|
||||
from facefusion.download import conditional_download
|
||||
from facefusion.vision import get_video_frame, count_video_frame_total, detect_video_fps, detect_video_resolution, pack_resolution, unpack_resolution, create_video_resolutions
|
||||
from facefusion.vision import detect_image_resolution, restrict_image_resolution, create_image_resolutions, get_video_frame, count_video_frame_total, detect_video_fps, restrict_video_fps, detect_video_resolution, restrict_video_resolution, create_video_resolutions, normalize_resolution, pack_resolution, unpack_resolution
|
||||
|
||||
|
||||
@pytest.fixture(scope = 'module', autouse = True)
|
||||
@ -13,6 +13,10 @@ def before_all() -> None:
|
||||
'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', '-vframes', '1', '.assets/examples/target-240p.jpg' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-1080p.mp4', '-vframes', '1', '.assets/examples/target-1080p.jpg' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-240p.mp4', '-vframes', '1', '-vf', 'transpose=0', '.assets/examples/target-240p-90deg.jpg' ])
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-1080p.mp4', '-vframes', '1', '-vf', 'transpose=0', '.assets/examples/target-1080p-90deg.jpg' ])
|
||||
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' ])
|
||||
@ -20,6 +24,28 @@ def before_all() -> None:
|
||||
subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-1080p.mp4', '-vf', 'transpose=0', '.assets/examples/target-1080p-90deg.mp4' ])
|
||||
|
||||
|
||||
def test_detect_image_resolution() -> None:
|
||||
assert detect_image_resolution('.assets/examples/target-240p.jpg') == (426, 226)
|
||||
assert detect_image_resolution('.assets/examples/target-240p-90deg.jpg') == (226, 426)
|
||||
assert detect_image_resolution('.assets/examples/target-1080p.jpg') == (2048, 1080)
|
||||
assert detect_image_resolution('.assets/examples/target-1080p-90deg.jpg') == (1080, 2048)
|
||||
assert detect_image_resolution('invalid') is None
|
||||
|
||||
|
||||
def test_restrict_image_resolution() -> None:
|
||||
assert restrict_image_resolution('.assets/examples/target-1080p.jpg', (426, 226)) == (426, 226)
|
||||
assert restrict_image_resolution('.assets/examples/target-1080p.jpg', (2048, 1080)) == (2048, 1080)
|
||||
assert restrict_image_resolution('.assets/examples/target-1080p.jpg', (4096, 2160)) == (2048, 1080)
|
||||
|
||||
|
||||
def test_create_image_resolutions() -> None:
|
||||
assert create_image_resolutions((426, 226)) == [ '106x56', '212x112', '320x170', '426x226', '640x340', '852x452', '1064x564', '1278x678', '1492x792', '1704x904' ]
|
||||
assert create_image_resolutions((226, 426)) == [ '56x106', '112x212', '170x320', '226x426', '340x640', '452x852', '564x1064', '678x1278', '792x1492', '904x1704' ]
|
||||
assert create_image_resolutions((2048, 1080)) == [ '512x270', '1024x540', '1536x810', '2048x1080', '3072x1620', '4096x2160', '5120x2700', '6144x3240', '7168x3780', '8192x4320' ]
|
||||
assert create_image_resolutions((1080, 2048)) == [ '270x512', '540x1024', '810x1536', '1080x2048', '1620x3072', '2160x4096', '2700x5120', '3240x6144', '3780x7168', '4320x8192' ]
|
||||
assert create_image_resolutions(None) == []
|
||||
|
||||
|
||||
def test_get_video_frame() -> None:
|
||||
assert get_video_frame('.assets/examples/target-240p-25fps.mp4') is not None
|
||||
assert get_video_frame('invalid') is None
|
||||
@ -39,25 +65,45 @@ def test_detect_video_fps() -> None:
|
||||
assert detect_video_fps('invalid') is None
|
||||
|
||||
|
||||
def test_restrict_video_fps() -> None:
|
||||
assert restrict_video_fps('.assets/examples/target-1080p.mp4', 20.0) == 20.0
|
||||
assert restrict_video_fps('.assets/examples/target-1080p.mp4', 25.0) == 25.0
|
||||
assert restrict_video_fps('.assets/examples/target-1080p.mp4', 60.0) == 25.0
|
||||
|
||||
|
||||
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('.assets/examples/target-240p.mp4') == (426, 226)
|
||||
assert detect_video_resolution('.assets/examples/target-240p-90deg.mp4') == (226, 426)
|
||||
assert detect_video_resolution('.assets/examples/target-1080p.mp4') == (2048, 1080)
|
||||
assert detect_video_resolution('.assets/examples/target-1080p-90deg.mp4') == (1080, 2048)
|
||||
assert detect_video_resolution('invalid') is None
|
||||
|
||||
|
||||
def test_restrict_video_resolution() -> None:
|
||||
assert restrict_video_resolution('.assets/examples/target-1080p.mp4', (426, 226)) == (426, 226)
|
||||
assert restrict_video_resolution('.assets/examples/target-1080p.mp4', (2048, 1080)) == (2048, 1080)
|
||||
assert restrict_video_resolution('.assets/examples/target-1080p.mp4', (4096, 2160)) == (2048, 1080)
|
||||
|
||||
|
||||
def test_create_video_resolutions() -> None:
|
||||
assert create_video_resolutions((426, 226)) == [ '426x226', '452x240', '678x360', '904x480', '1018x540', '1358x720', '2036x1080', '2714x1440', '4072x2160', '8144x4320' ]
|
||||
assert create_video_resolutions((226, 426)) == [ '226x426', '240x452', '360x678', '480x904', '540x1018', '720x1358', '1080x2036', '1440x2714', '2160x4072', '4320x8144' ]
|
||||
assert create_video_resolutions((2048, 1080)) == [ '456x240', '682x360', '910x480', '1024x540', '1366x720', '2048x1080', '2730x1440', '4096x2160', '8192x4320' ]
|
||||
assert create_video_resolutions((1080, 2048)) == [ '240x456', '360x682', '480x910', '540x1024', '720x1366', '1080x2048', '1440x2730', '2160x4096', '4320x8192' ]
|
||||
assert create_video_resolutions(None) == []
|
||||
|
||||
|
||||
def test_normalize_resolution() -> None:
|
||||
assert normalize_resolution((2.5, 2.5)) == (2, 2)
|
||||
assert normalize_resolution((3.0, 3.0)) == (4, 4)
|
||||
assert normalize_resolution((6.5, 6.5)) == (6, 6)
|
||||
|
||||
|
||||
def test_pack_resolution() -> None:
|
||||
assert pack_resolution((1.0, 1.0)) == '0x0'
|
||||
assert pack_resolution((2.0, 2.0)) == '2x2'
|
||||
assert pack_resolution((1, 1)) == '0x0'
|
||||
assert pack_resolution((2, 2)) == '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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user