3.0.0 (#748)
* Cleanup after age modifier PR * Cleanup after age modifier PR * Use OpenVino 2024.2.0 for installer * Prepare 3.0.0 for installer * Fix benchmark suite, Introduce sync_item() for state manager * Fix lint * Render slide preview also in lower res * Lower thread and queue count to avoid false usage * Fix spacing * Feat/jobs UI (#627) * Jobs UI part1 * Change naming * Jobs UI part2 * Jobs UI part3 * Jobs UI part4 * Jobs UI part4 * Jobs UI part5 * Jobs UI part6 * Jobs UI part7 * Jobs UI part8 * Jobs UI part9 * Jobs UI part10 * Jobs UI part11 * Jobs UI part12 * Fix rebase * Jobs UI part13 * Jobs UI part14 * Jobs UI part15 * changes (#626) * Remove useless ui registration * Remove useless ui registration * move job_list.py replace [0] with get_first() * optimize imports * fix date None problem add test job list * Jobs UI part16 * Jobs UI part17 * Jobs UI part18 * Jobs UI part19 * Jobs UI part20 * Jobs UI part21 * Jobs UI part22 * move job_list_options * Add label to job status checkbox group * changes * changes --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Update some dependencies * UI helper to convert 'none' * validate job (#628) * changes * changes * add test * changes * changes * Minor adjustments * Replace is_json with is_file * Handle empty and invalid json in job_list * Handle empty and invalid json in job_list * Handle empty and invalid json in job_list * Work on the job manager UI * Cosmetic changes on common helper * Just make it work for now * Just make it work for now * Just make it work for now * Streamline the step index lookups * Hide footer * Simplify instant runner * Simplify instant runner UI and job manager UI * Fix empty step choices * Fix empty step choices * Fix none values in UI * Rework on benchmark (add warmup) and job list * Improve ValueAndUnit * Add step 1 of x output * Cosmetic changes on the UI * Fix invalid job file names * Update preview * Introducing has_step() and sorting out insert behaviour * Introducing has_step() and sorting out insert behaviour * Add [ none ] to some job id dropdowns * Make updated dropdown values kinda perfect * Make updated dropdown values kinda perfect * Fix testing * Minor improvement on UI * Fix false config lookup * Remove TensorRT as our models are not made for it * Feat/cli commands second try rev2 (#640) * Refactor CLI to commands * Refactor CLI to commands part2 * Refactor CLI to commands part3 * Refactor CLI to commands part4 * Rename everything to facefusion.py * Refactor CLI to commands part5 * Refactor CLI to commands part6 * Adjust testing * Fix lint * Fix lint * Fix lint * Refactor CLI to commands part7 * Extend State typing * Fix false config lookup, adjust logical orders * Move away from passing program part1 * Move away from passing program part2 * Move away from passing program part3 * Fix lint * Move away from passing program part4 * ui-args update * ui-args update * ui-args update * temporary type fix * Move away from passing program part5 * remove unused * creates args.py * Move away from passing program part6 * Move away from passing program part7 --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Minor optimizations * Update commands in README * Fix job-retry command * Fix multi runs via UI * add more job keys * Cleanup codebase * One method to create inference session (#641) * One method to create inference session * Remove warnings, as there are none * Remember job id during processing * Fix face masker config block * Change wording * Prevent age modifier from using CoreML * add expression restorer (#642) * add expression restorer * fix import * fix lint * changes * changes * changes * Host the final model for expression restorer * Insert step on the given index * UI workover (#644) * UI workover part1 * Introduce ComponentOptions * Only set Media components to None when visibility changes * Clear static faces and reference faces between step processing * Minor changes * Minor changes * Fix testing * Enable test_sanitize_path_for_windows (#646) * Dynamic download during job processing (#647) * Fix face masker UI * Rename run-headless to headless-run * Feat/split frame processor UI (#649) * Split frame processor UI * Split frame processor UI part3, Refactor get_model_initializer * Split frame processor UI part4 * Feat/rename frame processors (#651) * Rename frame processors * Rename frame processors part2 * Fix imports Conflicts: facefusion/uis/layouts/benchmark.py facefusion/uis/layouts/default.py * Fix imports * Cosmetic changes * Fix multi threading for ROCm * Change temp frames pattern * Adjust terminal help * remove expression restorer (#653) * Expression restorer as processor (#655) * add expression restorer * changes * Cleanup code * Add TensorRT support back * Add TensorRT support back * Add TensorRT support back * changes (#656) * Change minor wording * Fix face enhancer slider * Add more typing * Fix expression-restorer when using trim (#659) * changes * changes * Rework/model and inference pool part2 (#660) * Rework on model and inference pool * Introduce inference sources and pools part1 * Introduce inference sources and pools part2 * Introduce inference sources and pools part3 * Introduce inference sources and pools part4 * Introduce inference sources and pools part5 * Introduce inference sources and pools part6 * Introduce inference sources and pools part6 * Introduce inference sources and pools part6 * Introduce inference sources and pools part7 * Introduce inference sources and pools part7 * Introduce inference sources and pools part8 * Introduce inference sources and pools part9 * Introduce inference sources and pools part10 * Introduce inference sources and pools part11 * Introduce inference sources and pools part11 * Introduce inference sources and pools part11 * Introduce inference sources and pools part12 * Reorganize the face masker UI * Fix trim in UI * Feat/hashed sources (#668) * Introduce source helper * Remove post_check() and just use process_manager * Remove post_check() part2 * Add hash based downloads * Add hash based downloads part2 * Add hash based downloads part3 * Add hash based downloads part4 * Add hash based downloads part5 * Add hash based downloads part6 * Add hash based downloads part7 * Add hash based downloads part7 * Add hash based downloads part8 * Remove print * Prepare 3.0.0 release * Fix UI * Release the check when really done * Update inputs for live portrait * Update to 3.0.0 releases, extend download postfix * Move files to the right place * Logging for the hash and source validation * Changing logic to handle corrupt sources * Fix typo * Use names over get_inputs(), Remove set_options() call * Age modifier now works for CoreML too * Update age_modifier.py * Add video encoder h264_videotoolbox and hevc_videotoolbox * Face editor add eye gaze & remove open factor sliders (#670) * changes * add eye gaze * changes * cleanup * add eyebrow control * changes * changes * Feat/terminal UI (#671) * Introduce terminal to the UI * Introduce terminal to the UI part2 * Introduce terminal to the UI part2 * Introduce terminal to the UI part2 * Calc range step to avoid weird values * Use Sequence for ranges * Use Sequence for ranges * changes (#673) * Use Sequence for ranges * Finalize terminal UI * Finalize terminal UI * Webcam cosmetics, Fix normalize fps to accept int * Cosmetic changes * Finalize terminal UI * Rename leftover typings * Fix wording * Fix rounding in metavar * Fix rounding in metavar * Rename to face classifier * Face editor lip moves (#677) * changes * changes * changes * Fix rounding in metavar * Rename to face classifier * changes * changes * update naming --------- Co-authored-by: henryruhs <info@henryruhs.com> * Fix wording * Feat/many landmarker + face analyser breakdown (#678) * Basic multi landmarker integration * Simplify some method names * Break into face_detector and face_landmarker * Fix cosmetics * Fix testing * Break into face_attributor and face_recognizer * Clear them all * Clear them all * Rename to face classifier * Rename to face classifier * Fix testing * Fix stuff * Add face landmarker model to UI * Add face landmarker model to UI part2 * Split the config * Split the UI * Improvement from code review * Improvement from code review * Validate args also for sub parsers * Remove clear of processors in process step * Allow finder control for the face editor * Fix lint * Improve testing performance * Remove unused file, Clear processors from the UI before job runs * Update the installer * Uniform set handler for swapper and detector in the UI * Fix example urls * Feat/inference manager (#684) * Introduce inference manager * Migrate all to inference manager * clean ini * Introduce app context based inference pools * Fix lint * Fix typing * Adjust layout * Less border radius * Rename app context names * Fix/live portrait directml (#691) * changes (#690) * Adjust naming * Use our assets release * Adjust naming --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Add caches to gitignore * Update dependencies and drop CUDA 11.8 support (#693) * Update dependencies and drop CUDA 11.8 support * Play save and keep numpy 1.x.x * Improve TensorRT optimization * changes * changes * changes * changes * changes * changes * changes * changes * changes * Reuse inference sessions (#696) * Fix force-download command * Refactor processors to forward() (#698) * Install tensorrt when selecting cuda * Minor changes * Use latest numpy * Fix limit system memory * Implement forward() for every inference (#699) * Implement forward() for every inference * Implement forward() for every inference * Implement forward() for every inference * Implement forward() for every inference * changes * changes * changes * changes * Feat/fairface (#710) * Replace gender_age model with fair face (#709) * changes * changes * changes * age dropdown to range-slider * Cleanup code * Cleanup code --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Extend installer to set library paths for cuda and tensorrt (#707) * Extend installer to set library paths for cuda and tensorrt * Add refresh of conda env * Remove invalid commands * Set the conda env according to operating system * Update for ROCm 6.2 * fix installer * Aktualisieren von installer.py * Add missing face selector keys * Try to keep original LD_LIBRARY_PATH * windows support installer * Final touch to the installer * Remove spaces * Simplidy collect_model_downloads() * Fix force download for once and forever * Housekeeping (#715) * changes * changes * changes * Fix performance part1 * Fix mixed states (#689) * Fix mixed states * Add missing sync for job args * Move UnionStateXXX to base typing * Undo * Remove UnionStateXXX * Fix app context performance lookup (#717) * Restore performance for inswapper * Mover upper() to the logger * Undo debugging * Move TensorRT installation to docs * Sort out log level typing, Add log level UI dropdown (#719) * Fix inference pool part1 * Validate conda library paths existence * Default face selector order to large-small * Fix inference pool context according to execution provider (#720) * Fix app context under Windows * CUDA and TensorRT update for the installer * Remove concept of static processor modules * Revert false commit * Change event order makes a difference * Fix multi model context in inference pool (#721) * Fix multi model context in inference pool * Fix multi model context in inference pool part2 * Use latest gradio to avoid fastapi bug * Rework on the Windows Installer * Use embedding converter (#724) * changes (#723) * Upload models to official assets repo --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Rework on the Windows Installer part2 * Resolve subprocess calls (#726) * Experiment * Resolve subprocess calls to cover edge cases like broken PATH * Adjust wording * Simplify code * Rework on the Windows Installer part3 * Rework on the Windows Installer part4 * Numpy fix for older onnxruntime * changes (#729) * Add space * Add MacOS installer * Use favicon * Fix disabled logger * Layout polishing (#731) * Update dependencies, Adjust many face landmarker logic * Cosmetics changes * Should be button * Introduce randomized action button * Fix update of lip syncer and expression restorer * Stop sharing inference session this prevents flushing VRAM * Fix test * Fix urls * Prepare release * Vanish inquirer * Sticky preview does not work on portrait images * Sticky preview only for landscape images and videos * remove gradio tunnel env * Change wording and deeplinks * increase peppa landmark score offset * Change wording * Graceful exit install.py * Just adding a required * Cannot use the exit_helper * Rename our model * Change color of face-landmark-68/5 * Limit liveportrait (#739) * changes * changes * changes * Cleanup * Cleanup --------- Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com> Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * limit expression restorer * change expression restorer 0-100 range * Use 256x icon * changes * changes * changes * changes * Limit face editor rotation (#745) * changes (#743) * Finish euler methods --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Use different coveralls badge * Move about wording * Shorten scope in the logger * changes * changes * Shorten scope in the logger * fix typo * Simplify the arcface converter names * Update preview --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> Co-authored-by: harisreedhar <h4harisreedhar.s.s@gmail.com>
This commit is contained in:
Henry Ruhs
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facefusion/face_landmarker.py
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217
facefusion/face_landmarker.py
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from typing import Tuple
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import cv2
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import numpy
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from facefusion import inference_manager, state_manager
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from facefusion.download import conditional_download_hashes, conditional_download_sources
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from facefusion.face_helper import create_rotated_matrix_and_size, estimate_matrix_by_face_landmark_5, transform_points, warp_face_by_translation
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from facefusion.filesystem import resolve_relative_path
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from facefusion.thread_helper import conditional_thread_semaphore
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from facefusion.typing import Angle, BoundingBox, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame
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MODEL_SET : ModelSet =\
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{
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'2dfan4':
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{
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'hashes':
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{
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'2dfan4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.hash',
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'path': resolve_relative_path('../.assets/models/2dfan4.hash')
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}
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},
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'sources':
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{
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'2dfan4':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/2dfan4.onnx',
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'path': resolve_relative_path('../.assets/models/2dfan4.onnx')
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}
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},
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'size': (256, 256)
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},
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'peppa_wutz':
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{
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'hashes':
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{
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'peppa_wutz':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.hash',
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'path': resolve_relative_path('../.assets/models/peppa_wutz.hash')
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}
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},
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'sources':
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{
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'peppa_wutz':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/peppa_wutz.onnx',
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'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx')
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}
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},
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'size': (256, 256)
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},
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'fan_68_5':
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{
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'hashes':
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{
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'fan_68_5':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.hash',
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'path': resolve_relative_path('../.assets/models/fan_68_5.hash')
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}
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},
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'sources':
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{
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'fan_68_5':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/fan_68_5.onnx',
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'path': resolve_relative_path('../.assets/models/fan_68_5.onnx')
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}
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}
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}
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}
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def get_inference_pool() -> InferencePool:
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_, model_sources = collect_model_downloads()
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model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
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return inference_manager.get_inference_pool(model_context, model_sources)
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def clear_inference_pool() -> None:
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model_context = __name__ + '.' + state_manager.get_item('face_landmarker_model')
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inference_manager.clear_inference_pool(model_context)
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def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
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model_hashes =\
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{
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'fan_68_5': MODEL_SET.get('fan_68_5').get('hashes').get('fan_68_5')
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}
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model_sources =\
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{
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'fan_68_5': MODEL_SET.get('fan_68_5').get('sources').get('fan_68_5')
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}
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if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
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model_hashes['2dfan4'] = MODEL_SET.get('2dfan4').get('hashes').get('2dfan4')
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model_sources['2dfan4'] = MODEL_SET.get('2dfan4').get('sources').get('2dfan4')
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if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
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model_hashes['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('hashes').get('peppa_wutz')
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model_sources['peppa_wutz'] = MODEL_SET.get('peppa_wutz').get('sources').get('peppa_wutz')
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return model_hashes, model_sources
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def pre_check() -> bool:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_hashes, model_sources = collect_model_downloads()
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return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
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def detect_face_landmarks(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
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face_landmark_2dfan4 = None
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face_landmark_peppa_wutz = None
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face_landmark_score_2dfan4 = 0.0
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face_landmark_score_peppa_wutz = 0.0
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if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]:
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face_landmark_2dfan4, face_landmark_score_2dfan4 = detect_with_2dfan4(vision_frame, bounding_box, face_angle)
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if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]:
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face_landmark_peppa_wutz, face_landmark_score_peppa_wutz = detect_with_peppa_wutz(vision_frame, bounding_box, face_angle)
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if face_landmark_score_2dfan4 > face_landmark_score_peppa_wutz - 0.2:
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return face_landmark_2dfan4, face_landmark_score_2dfan4
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return face_landmark_peppa_wutz, face_landmark_score_peppa_wutz
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def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox, face_angle: Angle) -> Tuple[FaceLandmark68, Score]:
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model_size = MODEL_SET.get('2dfan4').get('size')
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scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
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translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
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rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, model_size)
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crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size)
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crop_vision_frame = conditional_optimize_contrast(crop_vision_frame)
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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
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face_landmark_68, face_heatmap = forward_with_2dfan4(crop_vision_frame)
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face_landmark_68 = face_landmark_68[:, :, :2][0] / 64 * 256
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix))
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
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face_landmark_score_68 = numpy.amax(face_heatmap, axis = (2, 3))
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face_landmark_score_68 = numpy.mean(face_landmark_score_68)
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face_landmark_score_68 = numpy.interp(face_landmark_score_68, [ 0, 0.9 ], [ 0, 1 ])
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return face_landmark_68, face_landmark_score_68
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def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]:
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model_size = MODEL_SET.get('peppa_wutz').get('size')
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scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None)
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translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5
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rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size)
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, model_size)
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crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size)
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crop_vision_frame = conditional_optimize_contrast(crop_vision_frame)
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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
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crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0)
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prediction = forward_with_peppa_wutz(crop_vision_frame)
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face_landmark_68 = prediction.reshape(-1, 3)[:, :2] / 64 * model_size[0]
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix))
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
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face_landmark_score_68 = prediction.reshape(-1, 3)[:, 2].mean()
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face_landmark_score_68 = numpy.interp(face_landmark_score_68, [ 0, 0.95 ], [ 0, 1 ])
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return face_landmark_68, face_landmark_score_68
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def conditional_optimize_contrast(crop_vision_frame : VisionFrame) -> VisionFrame:
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crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_RGB2Lab)
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if numpy.mean(crop_vision_frame[:, :, 0]) < 30: # type:ignore[arg-type]
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crop_vision_frame[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_vision_frame[:, :, 0])
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crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_Lab2RGB)
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return crop_vision_frame
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def estimate_face_landmark_68_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
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affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (1, 1))
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face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
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face_landmark_68_5 = forward_fan_68_5(face_landmark_5)
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face_landmark_68_5 = cv2.transform(face_landmark_68_5.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2)
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return face_landmark_68_5
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def forward_with_2dfan4(crop_vision_frame : VisionFrame) -> Tuple[Prediction, Prediction]:
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face_landmarker = get_inference_pool().get('2dfan4')
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with conditional_thread_semaphore():
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prediction = face_landmarker.run(None,
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{
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'input': [ crop_vision_frame ]
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})
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return prediction
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def forward_with_peppa_wutz(crop_vision_frame : VisionFrame) -> Prediction:
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face_landmarker = get_inference_pool().get('peppa_wutz')
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with conditional_thread_semaphore():
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prediction = face_landmarker.run(None,
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{
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'input': crop_vision_frame
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})[0]
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return prediction
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def forward_fan_68_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
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face_landmarker = get_inference_pool().get('fan_68_5')
|
||||
|
||||
with conditional_thread_semaphore():
|
||||
face_landmark_68_5 = face_landmarker.run(None,
|
||||
{
|
||||
'input': [ face_landmark_5 ]
|
||||
})[0][0]
|
||||
|
||||
return face_landmark_68_5
|
||||
Reference in New Issue
Block a user