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from chain_img_processor import ChainImgProcessor, ChainImgPlugin
import os
import gfpgan
import threading
from PIL import Image
from numpy import asarray
import cv2
from roop.utilities import resolve_relative_path, conditional_download
modname = os.path.basename(__file__)[:-3] # calculating modname
model_gfpgan = None
THREAD_LOCK_GFPGAN = threading.Lock()
# start function
def start(core:ChainImgProcessor):
manifest = { # plugin settings
"name": "GFPGAN", # name
"version": "1.4", # version
"default_options": {},
"img_processor": {
"gfpgan": GFPGAN
}
}
return manifest
def start_with_options(core:ChainImgProcessor, manifest:dict):
pass
class GFPGAN(ChainImgPlugin):
def init_plugin(self):
global model_gfpgan
if model_gfpgan is None:
model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
model_gfpgan = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=self.device) # type: ignore[attr-defined]
def process(self, frame, params:dict):
import copy
global model_gfpgan
if model_gfpgan is None:
return frame
if "face_detected" in params:
if not params["face_detected"]:
return frame
# don't touch original
temp_frame = copy.copy(frame)
if "processed_faces" in params:
for face in params["processed_faces"]:
start_x, start_y, end_x, end_y = map(int, face['bbox'])
padding_x = int((end_x - start_x) * 0.5)
padding_y = int((end_y - start_y) * 0.5)
start_x = max(0, start_x - padding_x)
start_y = max(0, start_y - padding_y)
end_x = max(0, end_x + padding_x)
end_y = max(0, end_y + padding_y)
temp_face = temp_frame[start_y:end_y, start_x:end_x]
if temp_face.size:
with THREAD_LOCK_GFPGAN:
_, _, temp_face = model_gfpgan.enhance(
temp_face,
paste_back=True
)
temp_frame[start_y:end_y, start_x:end_x] = temp_face
else:
with THREAD_LOCK_GFPGAN:
_, _, temp_frame = model_gfpgan.enhance(
temp_frame,
paste_back=True
)
if not "blend_ratio" in params:
return temp_frame
temp_frame = Image.blend(Image.fromarray(frame), Image.fromarray(temp_frame), params["blend_ratio"])
return asarray(temp_frame)
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