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updated & improved code

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tfrymnn 2023-06-17 13:20:51 +02:00 committed by GitHub
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commit 1b1f5b882d
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1 changed files with 15 additions and 31 deletions
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46
roop/processors/frame/face_enhancer.py
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@ -6,7 +6,7 @@ import gfpgan
import roop.globals import roop.globals
import roop.processors.frame.core import roop.processors.frame.core
from roop.core import update_status from roop.core import update_status
from roop.face_analyser import get_one_face, get_many_faces from roop.face_analyser import get_one_face
from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
from PIL import Image from PIL import Image
from numpy import asarray from numpy import asarray
@ -19,7 +19,7 @@ NAME = 'ROOP.FACE-ENHANCER'
def pre_check() -> bool: def pre_check() -> bool:
download_directory_path = resolve_relative_path('../models') download_directory_path = resolve_relative_path('../models')
conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth']) conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/GFPGANv1.3.pth'])
return True return True
@ -37,58 +37,42 @@ def get_face_enhancer() -> None:
if FACE_ENHANCER is None: if FACE_ENHANCER is None:
model_path = resolve_relative_path('../models/GFPGANv1.3.pth') model_path = resolve_relative_path('../models/GFPGANv1.3.pth')
# todo: set models path https://github.com/TencentARC/GFPGAN/issues/399 # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
FACE_ENHANCER = gfpgan.GFPGANer( FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)
model_path=model_path,
channel_multiplier=2
)
return FACE_ENHANCER return FACE_ENHANCER
def enhance_face(source_face: Any, target_face: Any, temp_frame: Any) -> Any: def enhance_face(temp_frame: Any) -> Any:
THREAD_SEMAPHORE.acquire() with THREAD_SEMAPHORE:
if target_face: temp_frame_original = Image.fromarray(temp_frame)
temp_frame_original = temp_frame
_, _, temp_frame = get_face_enhancer().enhance( _, _, temp_frame = get_face_enhancer().enhance(
temp_frame, temp_frame,
paste_back=True paste_back=True
) )
temp_frame_original=Image.fromarray(temp_frame_original) temp_frame = Image.blend(temp_frame_original, Image.fromarray(temp_frame), 0.75)
temp_frame_original=temp_frame_original.resize([temp_frame_original.width*2,temp_frame_original.height*2]) return asarray(temp_frame)
temp_frame = Image.blend(temp_frame_original, Image.fromarray(temp_frame), 0.75)
temp_frame = asarray(temp_frame)
THREAD_SEMAPHORE.release()
return temp_frame
def process_frame(source_face: Any, temp_frame: Any) -> Any: def process_frame(source_face: Any, temp_frame: Any) -> Any:
if roop.globals.many_faces: target_face = get_one_face(temp_frame)
many_faces = get_many_faces(temp_frame) if target_face:
if many_faces: temp_frame = enhance_face(temp_frame)
for target_face in many_faces:
temp_frame = enhance_face(source_face, target_face, temp_frame)
else:
target_face = get_one_face(temp_frame)
if target_face:
temp_frame = enhance_face(source_face, target_face, temp_frame)
return temp_frame return temp_frame
def process_frames(source_path: str, temp_frame_paths: List[str], progress=None) -> None: def process_frames(source_path: str, temp_frame_paths: List[str], progress=None) -> None:
source_face = get_one_face(cv2.imread(source_path))
for temp_frame_path in temp_frame_paths: for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path) temp_frame = cv2.imread(temp_frame_path)
result = process_frame(source_face, temp_frame) result = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result) cv2.imwrite(temp_frame_path, result)
if progress: if progress:
progress.update(1) progress.update(1)
def process_image(source_path: str, target_path: str, output_path: str) -> None: def process_image(source_path: str, target_path: str, output_path: str) -> None:
source_face = get_one_face(cv2.imread(source_path))
target_frame = cv2.imread(target_path) target_frame = cv2.imread(target_path)
result = process_frame(source_face, target_frame) result = process_frame(None, target_frame)
cv2.imwrite(output_path, result) cv2.imwrite(output_path, result)
def process_video(source_path: str, temp_frame_paths: List[str]) -> None: def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames) roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)