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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) | |