Next (#502)
* Validate the overrides from facefusion.ini * Break down cli testing * Remove architecture lookup to support old driver * Remove architecture lookup to support old driver * Remove hwaccel auto * Respect the output video resolution * Bump next version * Full directml support (#501) * Introduce conditional thread management for DML support * Finish migration to thread helpers * Introduce dynamic frame colorizer sizes * Introduce dynamic frame colorizer sizes * Add 192x192 to frame colorizer * Fix async audio
This commit is contained in:
Henry Ruhs
@@ -1,6 +1,5 @@
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from typing import Any, Optional, List, Tuple
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from time import sleep
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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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@@ -13,12 +12,11 @@ from facefusion.face_store import get_static_faces, set_static_faces
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from facefusion.execution import apply_execution_provider_options
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from facefusion.download import conditional_download
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from facefusion.filesystem import resolve_relative_path, is_file
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from facefusion.thread_helper import thread_lock, thread_semaphore, conditional_thread_semaphore
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from facefusion.typing import VisionFrame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, BoundingBox, FaceLandmarkSet, FaceLandmark5, FaceLandmark68, Score, FaceScoreSet, Embedding
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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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THREAD_LOCK : threading.Lock = threading.Lock()
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MODELS : ModelSet =\
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{
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'face_detector_retinaface':
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@@ -85,7 +83,7 @@ def get_face_analyser() -> Any:
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face_detectors = {}
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face_landmarkers = {}
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with THREAD_LOCK:
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with thread_lock():
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while process_manager.is_checking():
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sleep(0.5)
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if FACE_ANALYSER is None:
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@@ -185,7 +183,7 @@ def detect_with_retinaface(vision_frame : VisionFrame, face_detector_size : str)
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score_list = []
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detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
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with THREAD_SEMAPHORE:
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with thread_semaphore():
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detections = face_detector.run(None,
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{
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face_detector.get_inputs()[0].name: detect_vision_frame
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@@ -227,7 +225,7 @@ def detect_with_scrfd(vision_frame : VisionFrame, face_detector_size : str) -> T
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score_list = []
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detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
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with THREAD_SEMAPHORE:
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with thread_semaphore():
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detections = face_detector.run(None,
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{
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face_detector.get_inputs()[0].name: detect_vision_frame
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@@ -266,7 +264,7 @@ def detect_with_yoloface(vision_frame : VisionFrame, face_detector_size : str) -
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score_list = []
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detect_vision_frame = prepare_detect_frame(temp_vision_frame, face_detector_size)
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with THREAD_SEMAPHORE:
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with thread_semaphore():
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detections = face_detector.run(None,
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{
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face_detector.get_inputs()[0].name: detect_vision_frame
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@@ -304,7 +302,7 @@ def detect_with_yunet(vision_frame : VisionFrame, face_detector_size : str) -> T
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face_detector.setInputSize((temp_vision_frame.shape[1], temp_vision_frame.shape[0]))
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face_detector.setScoreThreshold(facefusion.globals.face_detector_score)
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with THREAD_SEMAPHORE:
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with thread_semaphore():
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_, detections = face_detector.detect(temp_vision_frame)
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if numpy.any(detections):
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for detection in detections:
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@@ -380,10 +378,11 @@ def calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandma
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crop_vision_frame = crop_vision_frame / 127.5 - 1
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crop_vision_frame = crop_vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)
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crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0)
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embedding = face_recognizer.run(None,
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{
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face_recognizer.get_inputs()[0].name: crop_vision_frame
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})[0]
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with conditional_thread_semaphore(facefusion.globals.execution_providers):
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embedding = face_recognizer.run(None,
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{
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face_recognizer.get_inputs()[0].name: crop_vision_frame
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})[0]
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embedding = embedding.ravel()
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normed_embedding = embedding / numpy.linalg.norm(embedding)
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return embedding, normed_embedding
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@@ -399,10 +398,11 @@ def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : Boun
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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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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0
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face_landmark_68, face_heatmap = face_landmarker.run(None,
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{
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face_landmarker.get_inputs()[0].name: [ crop_vision_frame ]
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})
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with conditional_thread_semaphore(facefusion.globals.execution_providers):
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face_landmark_68, face_heatmap = face_landmarker.run(None,
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{
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face_landmarker.get_inputs()[0].name: [ crop_vision_frame ]
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})
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face_landmark_68 = face_landmark_68[:, :, :2][0] / 64
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face_landmark_68 = face_landmark_68.reshape(1, -1, 2) * 256
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face_landmark_68 = cv2.transform(face_landmark_68, cv2.invertAffineTransform(affine_matrix))
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@@ -416,10 +416,11 @@ def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandm
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face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5')
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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 = face_landmarker.run(None,
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{
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face_landmarker.get_inputs()[0].name: [ face_landmark_5 ]
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})[0][0]
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with conditional_thread_semaphore(facefusion.globals.execution_providers):
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face_landmark_68_5 = face_landmarker.run(None,
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{
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face_landmarker.get_inputs()[0].name: [ face_landmark_5 ]
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})[0][0]
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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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@@ -432,10 +433,11 @@ def detect_gender_age(temp_vision_frame : VisionFrame, bounding_box : BoundingBo
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (96, 96))
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crop_vision_frame = crop_vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)
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crop_vision_frame = numpy.expand_dims(crop_vision_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_vision_frame
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})[0][0]
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with conditional_thread_semaphore(facefusion.globals.execution_providers):
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prediction = gender_age.run(None,
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{
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gender_age.get_inputs()[0].name: crop_vision_frame
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})[0][0]
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gender = int(numpy.argmax(prediction[:2]))
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age = int(numpy.round(prediction[2] * 100))
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return gender, age
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