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from chain_img_processor import ChainImgProcessor, ChainImgPlugin
from roop.face_helper import get_one_face, get_many_faces, swap_face
import os
from roop.utilities import compute_cosine_distance
modname = os.path.basename(__file__)[:-3] # calculating modname
# start function
def start(core:ChainImgProcessor):
manifest = { # plugin settings
"name": "Faceswap", # name
"version": "1.0", # version
"default_options": {
"swap_mode": "selected",
"max_distance": 0.65, # max distance to detect face similarity
},
"img_processor": {
"faceswap": Faceswap
}
}
return manifest
def start_with_options(core:ChainImgProcessor, manifest:dict):
pass
class Faceswap(ChainImgPlugin):
def init_plugin(self):
pass
def process(self, frame, params:dict):
if not "input_face_datas" in params or len(params["input_face_datas"]) < 1:
params["face_detected"] = False
return frame
temp_frame = frame
params["face_detected"] = True
params["processed_faces"] = []
if params["swap_mode"] == "first":
face = get_one_face(frame)
if face is None:
params["face_detected"] = False
return frame
params["processed_faces"].append(face)
frame = swap_face(params["input_face_datas"][0], face, frame)
return frame
else:
faces = get_many_faces(frame)
if(len(faces) < 1):
params["face_detected"] = False
return frame
dist_threshold = params["face_distance_threshold"]
if params["swap_mode"] == "all":
for sf in params["input_face_datas"]:
for face in faces:
params["processed_faces"].append(face)
temp_frame = swap_face(sf, face, temp_frame)
return temp_frame
elif params["swap_mode"] == "selected":
for i,tf in enumerate(params["target_face_datas"]):
for face in faces:
if compute_cosine_distance(tf.embedding, face.embedding) <= dist_threshold:
temp_frame = swap_face(params["input_face_datas"][i], face, temp_frame)
params["processed_faces"].append(face)
break
elif params["swap_mode"] == "all_female" or params["swap_mode"] == "all_male":
gender = 'F' if params["swap_mode"] == "all_female" else 'M'
face_found = False
for face in faces:
if face.sex == gender:
face_found = True
if face_found:
params["processed_faces"].append(face)
temp_frame = swap_face(params["input_face_datas"][0], face, temp_frame)
face_found = False
return temp_frame
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