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import threading
from typing import Any
import insightface
import roop.globals
from roop.typing import Frame, Face
import cv2
import numpy as np
from roop.capturer import get_video_frame
from roop.utilities import resolve_relative_path, conditional_download
FACE_ANALYSER = None
THREAD_LOCK_ANALYSER = threading.Lock()
THREAD_LOCK_SWAPPER = threading.Lock()
FACE_SWAPPER = None
def get_face_analyser() -> Any:
global FACE_ANALYSER
with THREAD_LOCK_ANALYSER:
if FACE_ANALYSER is None:
if roop.globals.CFG.force_cpu:
print('Forcing CPU for Face Analysis')
FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'])
else:
FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640) if roop.globals.default_det_size else (320,320))
return FACE_ANALYSER
def get_first_face(frame: Frame) -> Any:
try:
faces = get_face_analyser().get(frame)
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:
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:
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)
if faces is None:
return face_data
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
def get_face_swapper() -> Any:
global FACE_SWAPPER
with THREAD_LOCK_SWAPPER:
if FACE_SWAPPER is None:
model_path = resolve_relative_path('../models/inswapper_128.onnx')
FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
return FACE_SWAPPER
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../models')
conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
return True
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
def face_offset_top(face: Face, offset):
smallestmin = np.min(face.landmark_2d_106, 1)
smallest = smallestmin[1]
face['bbox'][1] += offset
face['bbox'][3] += offset
lm106 = face.landmark_2d_106
add = np.full_like(lm106, [0, offset])
face['landmark_2d_106'] = lm106 + add
return face
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