Files changed (1) hide show
  1. app.py +4 -142
app.py CHANGED
@@ -1,145 +1,7 @@
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- import os
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-
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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.srvgg_arch import SRVGGNetCompact
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- from gfpgan.utils import GFPGANer
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- from realesrgan.utils import RealESRGANer
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-
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- os.system("pip freeze")
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- # download weights
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- if not os.path.exists('realesr-general-x4v3.pth'):
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- os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
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- if not os.path.exists('GFPGANv1.2.pth'):
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- os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
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- if not os.path.exists('GFPGANv1.3.pth'):
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- os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
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- if not os.path.exists('GFPGANv1.4.pth'):
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- os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
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- if not os.path.exists('RestoreFormer.pth'):
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- os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
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- if not os.path.exists('CodeFormer.pth'):
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- os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
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-
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- torch.hub.download_url_to_file(
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- 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
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- 'lincoln.jpg')
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- torch.hub.download_url_to_file(
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- 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
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- 'AI-generate.jpg')
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- torch.hub.download_url_to_file(
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- 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
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- 'Blake_Lively.jpg')
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- torch.hub.download_url_to_file(
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- 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
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- '10045.png')
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-
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- # background enhancer with RealESRGAN
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- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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- model_path = 'realesr-general-x4v3.pth'
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- half = True if torch.cuda.is_available() else False
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- upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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-
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- os.makedirs('output', exist_ok=True)
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-
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-
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- # def inference(img, version, scale, weight):
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- def inference(img, version, scale):
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- # weight /= 100
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- print(img, version, scale)
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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: # for gray inputs
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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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-
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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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-
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- if version == 'v1.2':
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- face_enhancer = GFPGANer(
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- model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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- elif version == 'v1.3':
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- face_enhancer = GFPGANer(
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- model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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- elif version == 'v1.4':
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- face_enhancer = GFPGANer(
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- model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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- elif version == 'RestoreFormer':
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- face_enhancer = GFPGANer(
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- model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
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- # elif version == 'CodeFormer':
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- # face_enhancer = GFPGANer(
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- # model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
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-
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- try:
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- # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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- except RuntimeError as error:
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- print('Error', error)
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-
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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': # RGBA images should be saved in png format
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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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-
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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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-
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-
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- title = "GFPGAN: Practical Face Restoration Algorithm"
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- description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
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- It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
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- To use it, simply upload your image.<br>
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- If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
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- """
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- article = r"""
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-
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- [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
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- [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
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- [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
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- If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
 
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- <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
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- <center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
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- """
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- demo = gr.Interface(
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- inference, [
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- gr.inputs.Image(type="filepath", label="Input"),
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- # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
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- gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", default='v1.4', label='version'),
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- gr.inputs.Number(label="Rescaling factor", default=2),
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- # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
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- ], [
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- gr.outputs.Image(type="numpy", label="Output (The whole image)"),
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- gr.outputs.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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- # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
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- # ['10045.png', 'v1.4', 2, 50]]).launch()
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- examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
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- ['10045.png', 'v1.4', 2]])
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- demo.queue(concurrency_count=4)
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- demo.launch()
 
 
 
 
1
  import gradio as gr
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ def greet(name):
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+ return "Hello " + name + "!!"
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+ iface = gr.Interface(fn=greet, inputs="text", outputs="text")
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+ iface.launch()