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import cv2 |
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from loguru import logger |
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from lama_cleaner.helper import download_model |
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from lama_cleaner.plugins.base_plugin import BasePlugin |
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class RestoreFormerPlugin(BasePlugin): |
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name = "RestoreFormer" |
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def __init__(self, device, upscaler=None): |
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super().__init__() |
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from .gfpganer import MyGFPGANer |
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url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth" |
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model_md5 = "eaeeff6c4a1caa1673977cb374e6f699" |
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model_path = download_model(url, model_md5) |
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logger.info(f"RestoreFormer model path: {model_path}") |
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import facexlib |
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if hasattr(facexlib.detection.retinaface, "device"): |
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facexlib.detection.retinaface.device = device |
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self.face_enhancer = MyGFPGANer( |
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model_path=model_path, |
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upscale=1, |
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arch="RestoreFormer", |
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channel_multiplier=2, |
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device=device, |
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bg_upsampler=upscaler.model if upscaler is not None else None, |
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) |
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def __call__(self, rgb_np_img, files, form): |
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weight = 0.5 |
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bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR) |
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logger.info(f"RestoreFormer input shape: {bgr_np_img.shape}") |
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_, _, bgr_output = self.face_enhancer.enhance( |
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bgr_np_img, |
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has_aligned=False, |
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only_center_face=False, |
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paste_back=True, |
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weight=weight, |
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) |
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logger.info(f"RestoreFormer output shape: {bgr_output.shape}") |
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return bgr_output |
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def check_dep(self): |
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try: |
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import gfpgan |
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except ImportError: |
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return ( |
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"gfpgan is not installed, please install it first. pip install gfpgan" |
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) |
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