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| import cv2 | |
| from PIL import Image | |
| import glob | |
| import os | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from basicsr.utils.download_util import load_file_from_url | |
| from realesrgan import RealESRGANer | |
| from realesrgan.archs.srvgg_arch import SRVGGNetCompact | |
| def realEsrgan(model_name="RealESRGAN_x4plus_anime_6B", | |
| model_path = None, | |
| input_dir = 'inputs', | |
| output_dir = 'results', | |
| denoise_strength = 0.5, | |
| outscale = 4, | |
| suffix = 'out', | |
| tile = 200, | |
| tile_pad = 10, | |
| pre_pad = 0, | |
| face_enhance = True, | |
| alpha_upsampler = 'realsrgan', | |
| out_ext = 'auto', | |
| fp32 = True, | |
| gpu_id = None, | |
| ): | |
| # determine models according to model names | |
| model_name = model_name.split('.')[0] | |
| if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'] | |
| elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'] | |
| elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'] | |
| elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) | |
| netscale = 2 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'] | |
| elif model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size) | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') | |
| netscale = 4 | |
| file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth'] | |
| elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size) | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') | |
| netscale = 4 | |
| file_url = [ | |
| 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth', | |
| 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth' | |
| ] | |
| # determine model paths | |
| if model_path is None: | |
| model_path = os.path.join('weights', model_name + '.pth') | |
| if not os.path.isfile(model_path): | |
| ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| for url in file_url: | |
| # model_path will be updated | |
| model_path = load_file_from_url( | |
| url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None) | |
| # use dni to control the denoise strength | |
| dni_weight = None | |
| if model_name == 'realesr-general-x4v3' and denoise_strength != 1: | |
| wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3') | |
| model_path = [model_path, wdn_model_path] | |
| dni_weight = [denoise_strength, 1 - denoise_strength] | |
| # restorer | |
| upsampler = RealESRGANer( | |
| scale=netscale, | |
| model_path=model_path, | |
| dni_weight=dni_weight, | |
| model=model, | |
| tile=tile, | |
| tile_pad=tile_pad, | |
| pre_pad=pre_pad, | |
| half=not fp32, | |
| gpu_id=gpu_id) | |
| if face_enhance: # Use GFPGAN for face enhancement | |
| from gfpgan import GFPGANer | |
| face_enhancer = GFPGANer( | |
| model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth', | |
| upscale=outscale, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=upsampler) | |
| os.makedirs(output_dir, exist_ok=True) | |
| if os.path.isfile(input_dir): | |
| paths = [input_dir] | |
| else: | |
| paths = sorted(glob.glob(os.path.join(input_dir, '*'))) | |
| Imgs = [] | |
| for idx, path in enumerate(paths): | |
| imgname, extension = os.path.splitext(os.path.basename(path)) | |
| print(f'Scaling x{outscale}:', path) | |
| img = cv2.imread(path, cv2.IMREAD_UNCHANGED) | |
| if len(img.shape) == 3 and img.shape[2] == 4: | |
| img_mode = 'RGBA' | |
| else: | |
| img_mode = None | |
| try: | |
| if face_enhance: | |
| _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
| else: | |
| output, _ = upsampler.enhance(img, outscale=outscale) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| print('If you encounter CUDA or RAM out of memory, try to set --tile with a smaller number.') | |
| else: | |
| if out_ext == 'auto': | |
| extension = extension[1:] | |
| else: | |
| extension = out_ext | |
| if img_mode == 'RGBA': # RGBA images should be saved in png format | |
| extension = 'png' | |
| if suffix == '': | |
| save_path = os.path.join(output_dir, f'{imgname}.{extension}') | |
| else: | |
| save_path = os.path.join(output_dir, f'{imgname}_{suffix}.{extension}') | |
| cv2.imwrite(save_path, output) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| img = Image.fromarray(img) | |
| Imgs.append(img) | |
| return Imgs | |