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import os |
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import shutil |
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import cv2 |
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import gradio as gr |
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import torch |
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from basicsr.archs.rrdbnet_arch import RRDBNet |
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from gfpgan.utils import GFPGANer |
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from realesrgan.utils import RealESRGANer |
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if not os.path.exists('model_zoo/real/RealESRGAN_x4plus.pth'): |
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P model_zoo/real") |
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if not os.path.exists('model_zoo/real/RealESRGAN_x2plus.pth'): |
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -P model_zoo/real") |
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if not os.path.exists('model_zoo/gan/GFPGANv1.4.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P model_zoo/gan") |
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if not os.path.exists('model_zoo/gan/RestoreFormer.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P model_zoo/gan") |
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def inference(img, version, scale, enhance_face): |
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if scale <= 2: |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) |
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netscale = 2 |
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model_path = 'model_zoo/real/RealESRGAN_x2plus.pth' |
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else: |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) |
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model_path = 'model_zoo/real/RealESRGAN_x4plus.pth' |
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netscale = 4 |
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print(model_path) |
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tile = 400 if torch.cuda.is_available() else 0 |
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dni_weight = None |
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upsampler = RealESRGANer( |
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scale=netscale, |
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model_path=model_path, |
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dni_weight=dni_weight, |
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model=model, |
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tile=tile, |
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tile_pad=10, |
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pre_pad=0, |
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half=False, |
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gpu_id=None) |
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if enhance_face: |
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if version == 'RestoreFormer': |
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face_enhancer = GFPGANer( |
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model_path='model_zoo/gan/RestoreFormer.pth', upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) |
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else: |
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face_enhancer = GFPGANer( |
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model_path='model_zoo/gan/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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os.makedirs('output', exist_ok=True) |
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if scale > 4: |
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scale = 4 |
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try: |
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extension = os.path.splitext(os.path.basename(str(img)))[1] |
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED) |
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if len(img.shape) == 3 and img.shape[2] == 4: |
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img_mode = 'RGBA' |
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elif len(img.shape) == 2: |
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img_mode = None |
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
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else: |
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img_mode = None |
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h, w = img.shape[0:2] |
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if h < 300: |
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) |
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try: |
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if enhance_face: |
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) |
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else: |
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output, _ = upsampler.enhance(img, outscale=scale) |
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except RuntimeError as error: |
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print('Error', error) |
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try: |
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if scale != 2: |
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 |
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h, w = img.shape[0:2] |
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) |
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except Exception as error: |
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print('wrong scale input.', error) |
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if img_mode == 'RGBA': |
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extension = 'png' |
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else: |
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extension = 'jpg' |
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save_path = f'output/out.{extension}' |
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cv2.imwrite(save_path, output) |
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) |
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return output, save_path |
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except Exception as error: |
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print('global exception', error) |
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return None, None |
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def clean_folder(folder): |
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for filename in os.listdir(folder): |
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file_path = os.path.join(folder, filename) |
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try: |
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if os.path.isfile(file_path) or os.path.islink(file_path): |
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os.unlink(file_path) |
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elif os.path.isdir(file_path): |
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shutil.rmtree(file_path) |
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except Exception as e: |
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print('Failed to delete %s. Reason: %s' % (file_path, e)) |
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title = "Real Esrgan Restore Ai Face Restoration by appsgenz.com" |
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description = r""" |
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""" |
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article = r""" |
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π |
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""" |
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reminiApp = gr.Interface( |
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inference, [ |
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gr.Image(type="filepath", label="Input"), |
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gr.Radio(['v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version GFPGAN. Note that it work when enable Enhance faces '), |
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gr.Number(label="Rescaling factor", value=1), |
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gr.Checkbox(label="Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes", value=True), |
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], [ |
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gr.Image(type="numpy", label="Output (The whole image)"), |
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gr.File(label="Download the output image") |
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], |
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title=title, |
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description=description, |
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article=article) |
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reminiApp.queue(concurrency_count=4) |
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reminiApp.launch(share=False) |