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import threading
from typing import Any
import insightface
import roop.globals
from roop.typing import Frame
import cv2
from PIL import Image
from roop.capturer import get_video_frame
FACE_ANALYSER = None
THREAD_LOCK = threading.Lock()
def get_face_analyser() -> Any:
global FACE_ANALYSER
with THREAD_LOCK:
if FACE_ANALYSER is None:
FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
return FACE_ANALYSER
def get_first_face(frame: Frame) -> Any:
faces = get_face_analyser().get(frame)
try:
return min(faces, key=lambda x: x.bbox[0])
# return sorted(faces, reverse=True, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[0]
except ValueError:
return None
def get_all_faces(frame: Frame) -> Any:
try:
faces = get_face_analyser().get(frame)
return sorted(faces, key = lambda x : x.bbox[0])
except IndexError:
return None
def extract_face_images(source_filename, video_info):
face_data = []
source_image = None
if video_info[0]:
frame = get_video_frame(source_filename, video_info[1])
if frame is not None:
source_image = frame
else:
return face_data
else:
source_image = cv2.imread(source_filename)
faces = get_all_faces(source_image)
i = 0
for face in faces:
(startX, startY, endX, endY) = face['bbox'].astype("int")
face_temp = source_image[startY:endY, startX:endX]
if face_temp.size < 1:
continue
i += 1
face_data.append([face, face_temp])
return face_data |