Next (#318)
* renaming and restructuring (#282) * Renaming and restructuring * Renaming and restructuring * Renaming and restructuring * Fix gender detection * Implement distance to face debugger * Implement distance to face debugger part2 * Implement distance to face debugger part3 * Mark as next * Fix reference when face_debugger comes first * Use official onnxruntime nightly * CUDA on steroids * CUDA on steroids * Add some testing * Set inswapper_128_fp16 as default * Feat/block until post check (#292) * Block until download is done * Introduce post_check() * Fix webcam * Update dependencies * Add --force-reinstall to installer * Introduce config ini (#298) * Introduce config ini * Fix output video encoder * Revert help listings back to commas, Move SSL hack to download.py * Introduce output-video-preset which defaults to veryfast * Mapping for nvenc encoders * Rework on events and non-blocking UI * Add fast bmp to temp_frame_formats * Add fast bmp to temp_frame_formats * Show total processing time on success * Show total processing time on success * Show total processing time on success * Move are_images, is_image and is_video back to filesystem * Fix some spacings * Pissing everyone of by renaming stuff * Fix seconds output * feat/video output fps (#312) * added output fps slider, removed 'keep fps' option (#311) * added output fps slider, removed 'keep fps' option * now uses passed fps instead of global fps for ffmpeg * fps values are now floats instead of ints * fix previous commit * removed default value from fps slider this is so we can implement a dynamic default value later * Fix seconds output * Some cleanup --------- Co-authored-by: Ran Shaashua <47498956+ranshaa05@users.noreply.github.com> * Allow 0.01 steps for fps * Make fps unregulated * Make fps unregulated * Remove distance from face debugger again (does not work) * Fix gender age * Fix gender age * Hotfix benchmark suite * Warp face normalize (#313) * use normalized kp templates * Update face_helper.py * My 50 cents to warp_face() --------- Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com> * face-swapper-weight (#315) * Move prepare_crop_frame and normalize_crop_frame out of apply_swap * Fix UI bug with different range * feat/output video resolution (#316) * Introduce detect_video_resolution, Rename detect_fps to detect_video_fps * Add calc_video_resolution_range * Make output resolution work, does not auto-select yet * Make output resolution work, does not auto-select yet * Try to keep the origin resolution * Split code into more fragments * Add pack/unpack resolution * Move video_template_sizes to choices * Improve create_video_resolutions * Reword benchmark suite * Optimal speed for benchmark * Introduce different video memory strategies, rename max_memory to max… (#317) * Introduce different video memory strategies, rename max_memory to max_system_memory * Update readme * Fix limit_system_memory call * Apply video_memory_strategy to face debugger * Limit face swapper weight to 3.0 * Remove face swapper weight due bad render outputs * Show/dide logic for output video preset * fix uint8 conversion * Fix whitespace * Finalize layout and update preview * Fix multi renders on face debugger * Restore less restrictive rendering of preview and stream * Fix block mode for model downloads * Add testing * Cosmetic changes * Enforce valid fps and resolution via CLI * Empty config * Cosmetics on args processing * Memory workover (#319) * Cosmetics on args processing * Fix for MacOS * Rename all max_ to _limit * More fixes * Update preview * Fix whitespace --------- Co-authored-by: Ran Shaashua <47498956+ranshaa05@users.noreply.github.com> Co-authored-by: Harisreedhar <46858047+harisreedhar@users.noreply.github.com>
This commit is contained in:
Henry Ruhs
@@ -7,10 +7,11 @@ import onnxruntime
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import facefusion.globals
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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.execution_helper import apply_execution_provider_options
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from facefusion.face_helper import warp_face_by_kps, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
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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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from facefusion.vision import resize_frame_resolution, unpack_resolution
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FACE_ANALYSER = None
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THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
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@@ -56,16 +57,16 @@ def get_face_analyser() -> Any:
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with THREAD_LOCK:
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if FACE_ANALYSER is None:
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if facefusion.globals.face_detector_model == 'retinaface':
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face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = facefusion.globals.execution_providers)
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face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_detector_model == 'yunet':
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face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
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if facefusion.globals.face_recognizer_model == 'arcface_blendswap':
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = facefusion.globals.execution_providers)
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_recognizer_model == 'arcface_inswapper':
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = facefusion.globals.execution_providers)
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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if facefusion.globals.face_recognizer_model == 'arcface_simswap':
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = facefusion.globals.execution_providers)
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gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = facefusion.globals.execution_providers)
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face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = apply_execution_provider_options(facefusion.globals.execution_providers))
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FACE_ANALYSER =\
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{
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'face_detector': face_detector,
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@@ -96,10 +97,10 @@ def pre_check() -> bool:
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return True
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def extract_faces(frame: Frame) -> List[Face]:
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face_detector_width, face_detector_height = map(int, facefusion.globals.face_detector_size.split('x'))
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def extract_faces(frame : Frame) -> List[Face]:
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face_detector_width, face_detector_height = unpack_resolution(facefusion.globals.face_detector_size)
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frame_height, frame_width, _ = frame.shape
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temp_frame = resize_frame_dimension(frame, face_detector_width, face_detector_height)
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temp_frame = resize_frame_resolution(frame, face_detector_width, face_detector_height)
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temp_frame_height, temp_frame_width, _ = temp_frame.shape
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ratio_height = frame_height / temp_frame_height
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ratio_width = frame_width / temp_frame_width
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@@ -135,7 +136,7 @@ def detect_with_retinaface(temp_frame : Frame, temp_frame_height : int, temp_fra
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stride_height = face_detector_height // feature_stride
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stride_width = face_detector_width // feature_stride
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anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
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bbox_raw = (detections[index + feature_map_channel] * feature_stride)
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bbox_raw = detections[index + feature_map_channel] * feature_stride
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kps_raw = detections[index + feature_map_channel * 2] * feature_stride
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for bbox in distance_to_bbox(anchors, bbox_raw)[keep_indices]:
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bbox_list.append(numpy.array(
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@@ -188,7 +189,7 @@ def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], sc
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kps = kps_list[index]
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score = score_list[index]
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embedding, normed_embedding = calc_embedding(frame, kps)
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gender, age = detect_gender_age(frame, kps)
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gender, age = detect_gender_age(frame, bbox)
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faces.append(Face(
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bbox = bbox,
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kps = kps,
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@@ -203,7 +204,7 @@ 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_112_v2', (112, 112))
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crop_frame, matrix = warp_face_by_kps(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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@@ -216,10 +217,15 @@ def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]
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return embedding, normed_embedding
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def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
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def detect_gender_age(frame : Frame, bbox : Bbox) -> 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_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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bbox = bbox.reshape(2, -1)
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scale = 64 / numpy.subtract(*bbox[::-1]).max()
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translation = 48 - bbox.sum(axis = 0) * 0.5 * scale
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affine_matrix = numpy.array([[ scale, 0, translation[0] ], [ 0, scale, translation[1] ]])
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crop_frame = cv2.warpAffine(frame, affine_matrix, (96, 96))
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crop_frame = crop_frame.astype(numpy.float32)[:, :, ::-1].transpose(2, 0, 1)
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crop_frame = numpy.expand_dims(crop_frame, axis = 0)
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prediction = gender_age.run(None,
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{
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gender_age.get_inputs()[0].name: crop_frame
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@@ -297,10 +303,14 @@ def find_similar_faces(frame : Frame, reference_faces : FaceSet, face_distance :
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def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
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current_face_distance = calc_face_distance(face, reference_face)
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return current_face_distance < face_distance
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def calc_face_distance(face : Face, reference_face : Face) -> float:
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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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return 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
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return 0
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def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
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