2.1.0 (#253)
* Operating system specific installer options * Update dependencies * Sorting before NMS according to the standard * Minor typing fix * Change the wording * Update preview.py (#222) Added a release listener to the preview frame slider, this will update the frame preview with the latest frame * Combine preview slider listener * Remove change listener * Introduce multi source (#223) * Implement multi source * Adjust face enhancer and face debugger to multi source * Implement multi source to UI * Implement multi source to UI part2 * Implement multi source to UI part3 * Implement multi source to UI part4 * Some cleanup * Add face occluder (#225) (#226) * Add face occluder (#225) * add face-occluder (commandline only) * review 1 * Update face_masker.py * Update face_masker.py * Add gui & fix typing * Minor naming cleanup * Minor naming cleanup part2 --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Update usage information * Fix averaged normed_embedding * Remove blur from face occluder, enable accelerators * Switch to RANSAC with 100 threshold * Update face_enhancer.py (#229) * Update face_debugger.py (#230) * Split utilities (#232) * Split utilities * Split utilities part2 * Split utilities part3 * Split utilities part4 * Some cleanup * Implement log level support (#233) * Implement log level support * Fix testing * Implement debug logger * Implement debug logger * Fix alignment offset (#235) * Update face_helper.py * fix 2 * Enforce virtual environment via installer * Enforce virtual environment via installer * Enforce virtual environment via installer * Enforce virtual environment via installer * Feat/multi process reference faces (#239) * Multi processing aware reference faces * First clean up and joining of files * Finalize the face store * Reduce similar face detection to one set, use __name__ for scopes in logger * Rename to face_occluder * Introduce ModelSet type * Improve webcam error handling * Prevent null pointer on is_image() and is_video() * Prevent null pointer on is_image() and is_video() * Fix find similar faces * Fix find similar faces * Fix process_images for face enhancer * Bunch of minor improvements * onnxruntime for ROCM under linux * Improve mask related naming * Fix falsy import * Fix typo * Feat/face parser refactoring (#247) * Face parser update (#244) * face-parser * Update face_masker.py * update debugger * Update globals.py * Update face_masker.py * Refactor code to split occlusion from region * fix (#246) * fix * fix debugger resolution * flip input to horizontal * Clean up UI * Reduce the regions to inside face only * Reduce the regions to inside face only --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * Fix enhancer, remove useless dest in add_argument() * Prevent unselect of the face_mask_regions via UI * Prepare next release * Shorten arguments that have choices and nargs * Add missing clear to face debugger --------- Co-authored-by: Mathias <github@feroc.de> Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
@@ -1,20 +1,21 @@
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from typing import Any, Optional, List, Dict, Tuple
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from typing import Any, Optional, List, Tuple
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import threading
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import cv2
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import numpy
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import onnxruntime
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import facefusion.globals
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from facefusion.face_cache import get_faces_cache, set_faces_cache
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from facefusion.download import conditional_download
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from facefusion.face_store import get_static_faces, set_static_faces
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from facefusion.face_helper import warp_face, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
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from facefusion.typing import Frame, Face, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelValue, Bbox, Kps, Score, Embedding
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from facefusion.utilities import resolve_relative_path, conditional_download
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from facefusion.filesystem import resolve_relative_path
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from facefusion.typing import Frame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, Bbox, Kps, Score, Embedding
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from facefusion.vision import resize_frame_dimension
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FACE_ANALYSER = None
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THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
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THREAD_LOCK : threading.Lock = threading.Lock()
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MODELS : Dict[str, ModelValue] =\
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MODELS : ModelSet =\
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{
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'face_detector_retinaface':
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{
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@@ -174,9 +175,13 @@ def detect_with_yunet(temp_frame : Frame, temp_frame_height : int, temp_frame_wi
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return bbox_list, kps_list, score_list
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def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face] :
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faces : List[Face] = []
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def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face]:
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faces = []
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if facefusion.globals.face_detector_score > 0:
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sort_indices = numpy.argsort(-numpy.array(score_list))
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bbox_list = [ bbox_list[index] for index in sort_indices ]
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kps_list = [ kps_list[index] for index in sort_indices ]
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score_list = [ score_list[index] for index in sort_indices ]
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keep_indices = apply_nms(bbox_list, 0.4)
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for index in keep_indices:
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bbox = bbox_list[index]
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@@ -198,7 +203,7 @@ def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], sc
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def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]:
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face_recognizer = get_face_analyser().get('face_recognizer')
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crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_v2', (112, 112))
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crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_112_v2', (112, 112))
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crop_frame = crop_frame.astype(numpy.float32) / 127.5 - 1
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crop_frame = crop_frame[:, :, ::-1].transpose(2, 0, 1)
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crop_frame = numpy.expand_dims(crop_frame, axis = 0)
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@@ -213,7 +218,7 @@ def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]
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def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
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gender_age = get_face_analyser().get('gender_age')
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crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_v2', (96, 96))
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crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_112_v2', (96, 96))
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crop_frame = numpy.expand_dims(crop_frame, axis = 0).transpose(0, 3, 1, 2).astype(numpy.float32)
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prediction = gender_age.run(None,
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{
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@@ -234,14 +239,38 @@ def get_one_face(frame : Frame, position : int = 0) -> Optional[Face]:
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return None
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def get_average_face(frames : List[Frame], position : int = 0) -> Optional[Face]:
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average_face = None
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faces = []
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embedding_list = []
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normed_embedding_list = []
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for frame in frames:
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face = get_one_face(frame, position)
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if face:
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faces.append(face)
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embedding_list.append(face.embedding)
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normed_embedding_list.append(face.normed_embedding)
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if faces:
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average_face = Face(
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bbox = faces[0].bbox,
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kps = faces[0].kps,
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score = faces[0].score,
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embedding = numpy.mean(embedding_list, axis = 0),
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normed_embedding = numpy.mean(normed_embedding_list, axis = 0),
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gender = faces[0].gender,
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age = faces[0].age
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)
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return average_face
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def get_many_faces(frame : Frame) -> List[Face]:
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try:
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faces_cache = get_faces_cache(frame)
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faces_cache = get_static_faces(frame)
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if faces_cache:
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faces = faces_cache
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else:
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faces = extract_faces(frame)
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set_faces_cache(frame, faces)
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set_static_faces(frame, faces)
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if facefusion.globals.face_analyser_order:
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faces = sort_by_order(faces, facefusion.globals.face_analyser_order)
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if facefusion.globals.face_analyser_age:
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@@ -253,18 +282,27 @@ def get_many_faces(frame : Frame) -> List[Face]:
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return []
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def find_similar_faces(frame : Frame, reference_face : Face, face_distance : float) -> List[Face]:
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def find_similar_faces(frame : Frame, reference_faces : FaceSet, face_distance : float) -> List[Face]:
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similar_faces : List[Face] = []
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many_faces = get_many_faces(frame)
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similar_faces = []
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if many_faces:
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for face in many_faces:
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if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
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current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
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if current_face_distance < face_distance:
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similar_faces.append(face)
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if reference_faces:
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for reference_set in reference_faces:
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if not similar_faces:
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for reference_face in reference_faces[reference_set]:
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for face in many_faces:
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if compare_faces(face, reference_face, face_distance):
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similar_faces.append(face)
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return similar_faces
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def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
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if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
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current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
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return current_face_distance < face_distance
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return False
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def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
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if order == 'left-right':
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return sorted(faces, key = lambda face: face.bbox[0])
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