diff --git a/.history/app_20220326215622.py b/.history/app_20220326215622.py new file mode 100644 index 0000000000000000000000000000000000000000..0aae55360740accb8e84bd9c2010b10d708f2eea --- /dev/null +++ b/.history/app_20220326215622.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANCleanv1-NoCE-C2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326225131.py b/.history/app_20220326225131.py new file mode 100644 index 0000000000000000000000000000000000000000..0aae55360740accb8e84bd9c2010b10d708f2eea --- /dev/null +++ b/.history/app_20220326225131.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANCleanv1-NoCE-C2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326225537.py b/.history/app_20220326225537.py new file mode 100644 index 0000000000000000000000000000000000000000..2da055353a652d24a7b8e9ad8af6c2b1c8a6dc3f --- /dev/null +++ b/.history/app_20220326225537.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANCleanv1-NoCE-C2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326225603.py b/.history/app_20220326225603.py new file mode 100644 index 0000000000000000000000000000000000000000..2e0a37be3ba26cc71d1a25ff33b06b64b6322c36 --- /dev/null +++ b/.history/app_20220326225603.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326225611.py b/.history/app_20220326225611.py new file mode 100644 index 0000000000000000000000000000000000000000..2e0a37be3ba26cc71d1a25ff33b06b64b6322c36 --- /dev/null +++ b/.history/app_20220326225611.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326225639.py b/.history/app_20220326225639.py new file mode 100644 index 0000000000000000000000000000000000000000..2e0a37be3ba26cc71d1a25ff33b06b64b6322c36 --- /dev/null +++ b/.history/app_20220326225639.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326230240.py b/.history/app_20220326230240.py new file mode 100644 index 0000000000000000000000000000000000000000..ae60e6422a44d557e34715b8015ac341a8cf9378 --- /dev/null +++ b/.history/app_20220326230240.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326230353.py b/.history/app_20220326230353.py new file mode 100644 index 0000000000000000000000000000000000000000..5d37ab7ae132e9c3e5e51c7f673e466ea67d9103 --- /dev/null +++ b/.history/app_20220326230353.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326230358.py b/.history/app_20220326230358.py new file mode 100644 index 0000000000000000000000000000000000000000..53e43ff27bdac92263729cc4b6cd03693a1ae8df --- /dev/null +++ b/.history/app_20220326230358.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326230538.py b/.history/app_20220326230538.py new file mode 100644 index 0000000000000000000000000000000000000000..94bdfdfa79b1d370f5f7b27bd0dc1d63a679a842 --- /dev/null +++ b/.history/app_20220326230538.py @@ -0,0 +1,68 @@ +import os +os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326231036.py b/.history/app_20220326231036.py new file mode 100644 index 0000000000000000000000000000000000000000..3cafe1a5f5e03ceb453c48fd3f4a26581551795d --- /dev/null +++ b/.history/app_20220326231036.py @@ -0,0 +1,68 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + + + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326232312.py b/.history/app_20220326232312.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220326232312.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326233155.py b/.history/app_20220326233155.py new file mode 100644 index 0000000000000000000000000000000000000000..ee4ef237741a7083b5b9eb309cdf6ac39a1a79f1 --- /dev/null +++ b/.history/app_20220326233155.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +torch.hub.download_url_to_file('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', 'lincoln.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220326233250.py b/.history/app_20220326233250.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220326233250.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327000906.py b/.history/app_20220327000906.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327000906.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001145.py b/.history/app_20220327001145.py new file mode 100644 index 0000000000000000000000000000000000000000..27131fe4690e351244fc597131fa13b43f88af22 --- /dev/null +++ b/.history/app_20220327001145.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-0]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001147.py b/.history/app_20220327001147.py new file mode 100644 index 0000000000000000000000000000000000000000..27131fe4690e351244fc597131fa13b43f88af22 --- /dev/null +++ b/.history/app_20220327001147.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-0]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001316.py b/.history/app_20220327001316.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220327001316.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001317.py b/.history/app_20220327001317.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220327001317.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001520.py b/.history/app_20220327001520.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327001520.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001522.py b/.history/app_20220327001522.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327001522.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001529.py b/.history/app_20220327001529.py new file mode 100644 index 0000000000000000000000000000000000000000..ed9ea8e7617ae6fc1f490a6b8ecab6fc2e625cbf --- /dev/null +++ b/.history/app_20220327001529.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001559.py b/.history/app_20220327001559.py new file mode 100644 index 0000000000000000000000000000000000000000..f184d1a5e77ddff91eafe37d37a88701e5ea9a94 --- /dev/null +++ b/.history/app_20220327001559.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001612.py b/.history/app_20220327001612.py new file mode 100644 index 0000000000000000000000000000000000000000..d057f3626e03f07ee3a3d9911acd2cdf7fe11a3b --- /dev/null +++ b/.history/app_20220327001612.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327001641.py b/.history/app_20220327001641.py new file mode 100644 index 0000000000000000000000000000000000000000..4a385df5c29fbaa0336f616d58162be93228e501 --- /dev/null +++ b/.history/app_20220327001641.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002459.py b/.history/app_20220327002459.py new file mode 100644 index 0000000000000000000000000000000000000000..7787cfdd3a110f00cfef2de05bb0b5efd8867957 --- /dev/null +++ b/.history/app_20220327002459.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002503.py b/.history/app_20220327002503.py new file mode 100644 index 0000000000000000000000000000000000000000..7787cfdd3a110f00cfef2de05bb0b5efd8867957 --- /dev/null +++ b/.history/app_20220327002503.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002645.py b/.history/app_20220327002645.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220327002645.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002649.py b/.history/app_20220327002649.py new file mode 100644 index 0000000000000000000000000000000000000000..78cbe6546ce3248b0cd20bc53523f286a9641536 --- /dev/null +++ b/.history/app_20220327002649.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002712.py b/.history/app_20220327002712.py new file mode 100644 index 0000000000000000000000000000000000000000..78cbe6546ce3248b0cd20bc53523f286a9641536 --- /dev/null +++ b/.history/app_20220327002712.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002809.py b/.history/app_20220327002809.py new file mode 100644 index 0000000000000000000000000000000000000000..78cbe6546ce3248b0cd20bc53523f286a9641536 --- /dev/null +++ b/.history/app_20220327002809.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002819.py b/.history/app_20220327002819.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220327002819.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327002904.py b/.history/app_20220327002904.py new file mode 100644 index 0000000000000000000000000000000000000000..0a38d76ce2ad23d2334dcc1d23d9094842aa1493 --- /dev/null +++ b/.history/app_20220327002904.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003334.py b/.history/app_20220327003334.py new file mode 100644 index 0000000000000000000000000000000000000000..abd115559e4ffebd017f0b2a68c8828c7d78ba1e --- /dev/null +++ b/.history/app_20220327003334.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003336.py b/.history/app_20220327003336.py new file mode 100644 index 0000000000000000000000000000000000000000..7787cfdd3a110f00cfef2de05bb0b5efd8867957 --- /dev/null +++ b/.history/app_20220327003336.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003455.py b/.history/app_20220327003455.py new file mode 100644 index 0000000000000000000000000000000000000000..38234670a7723f4e5edc275d1fd766fb46d7709d --- /dev/null +++ b/.history/app_20220327003455.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[1][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003506.py b/.history/app_20220327003506.py new file mode 100644 index 0000000000000000000000000000000000000000..df673ec9965e60183b387f1a966ef6101adfb251 --- /dev/null +++ b/.history/app_20220327003506.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_img , restored_faces= restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[2][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003524.py b/.history/app_20220327003524.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc3d3af5bf03865eecdfba9ceecc61752e1d526 --- /dev/null +++ b/.history/app_20220327003524.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[2][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003525.py b/.history/app_20220327003525.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc3d3af5bf03865eecdfba9ceecc61752e1d526 --- /dev/null +++ b/.history/app_20220327003525.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[2][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003535.py b/.history/app_20220327003535.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc3d3af5bf03865eecdfba9ceecc61752e1d526 --- /dev/null +++ b/.history/app_20220327003535.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[2][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003604.py b/.history/app_20220327003604.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327003604.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003733.py b/.history/app_20220327003733.py new file mode 100644 index 0000000000000000000000000000000000000000..8fdb9054bfd335671e21b4cc935d7824900e0443 --- /dev/null +++ b/.history/app_20220327003733.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-2]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003750.py b/.history/app_20220327003750.py new file mode 100644 index 0000000000000000000000000000000000000000..8fdb9054bfd335671e21b4cc935d7824900e0443 --- /dev/null +++ b/.history/app_20220327003750.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-2]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003753.py b/.history/app_20220327003753.py new file mode 100644 index 0000000000000000000000000000000000000000..8fdb9054bfd335671e21b4cc935d7824900e0443 --- /dev/null +++ b/.history/app_20220327003753.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-2]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327003759.py b/.history/app_20220327003759.py new file mode 100644 index 0000000000000000000000000000000000000000..3f315245492e6f6b795ba7b838bdc07a99f816c0 --- /dev/null +++ b/.history/app_20220327003759.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-3]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327004133.py b/.history/app_20220327004133.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327004133.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327004134.py b/.history/app_20220327004134.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327004134.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327004219.py b/.history/app_20220327004219.py new file mode 100644 index 0000000000000000000000000000000000000000..ed58c5e4509e8d73173deacd2716986623b3f65e --- /dev/null +++ b/.history/app_20220327004219.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327004403.py b/.history/app_20220327004403.py new file mode 100644 index 0000000000000000000000000000000000000000..377ca322982c7a78de83e7f10cebc9a17fbf979f --- /dev/null +++ b/.history/app_20220327004403.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img) + restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327004416.py b/.history/app_20220327004416.py new file mode 100644 index 0000000000000000000000000000000000000000..ed58c5e4509e8d73173deacd2716986623b3f65e --- /dev/null +++ b/.history/app_20220327004416.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327005406.py b/.history/app_20220327005406.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327005406.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327005407.py b/.history/app_20220327005407.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327005407.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327005511.py b/.history/app_20220327005511.py new file mode 100644 index 0000000000000000000000000000000000000000..43c7248137807e6458b0e62c42481571795ea9de --- /dev/null +++ b/.history/app_20220327005511.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) \ No newline at end of file diff --git a/.history/app_20220327005804.py b/.history/app_20220327005804.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a873859cd3071baa4ddbf117f248d0263e330f --- /dev/null +++ b/.history/app_20220327005804.py @@ -0,0 +1,71 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0])[:,:,::-1] + +return Image.fromarray(restored_faces[0])[:,:,::-1] +return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327005808.py b/.history/app_20220327005808.py new file mode 100644 index 0000000000000000000000000000000000000000..064650f6f979099bd0716a12c0a67eea333f2b6d --- /dev/null +++ b/.history/app_20220327005808.py @@ -0,0 +1,71 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0])[:,:,::-1] + +# return Image.fromarray(restored_faces[0])[:,:,::-1] +# return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327005902.py b/.history/app_20220327005902.py new file mode 100644 index 0000000000000000000000000000000000000000..6af36c182fb3ba129a54038a9c1a493d71ede8b7 --- /dev/null +++ b/.history/app_20220327005902.py @@ -0,0 +1,71 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +# return Image.fromarray(restored_faces[0])[:,:,::-1] +# return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327005912.py b/.history/app_20220327005912.py new file mode 100644 index 0000000000000000000000000000000000000000..6af36c182fb3ba129a54038a9c1a493d71ede8b7 --- /dev/null +++ b/.history/app_20220327005912.py @@ -0,0 +1,71 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +# return Image.fromarray(restored_faces[0])[:,:,::-1] +# return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327005914.py b/.history/app_20220327005914.py new file mode 100644 index 0000000000000000000000000000000000000000..6af36c182fb3ba129a54038a9c1a493d71ede8b7 --- /dev/null +++ b/.history/app_20220327005914.py @@ -0,0 +1,71 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +# return Image.fromarray(restored_faces[0])[:,:,::-1] +# return Image.fromarray(restored_faces[0][:,:,::-1]) + + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327005938.py b/.history/app_20220327005938.py new file mode 100644 index 0000000000000000000000000000000000000000..a349a305ffad302adaf14d0cf03ce569a4819766 --- /dev/null +++ b/.history/app_20220327005938.py @@ -0,0 +1,67 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327010010.py b/.history/app_20220327010010.py new file mode 100644 index 0000000000000000000000000000000000000000..a349a305ffad302adaf14d0cf03ce569a4819766 --- /dev/null +++ b/.history/app_20220327010010.py @@ -0,0 +1,67 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327010540.py b/.history/app_20220327010540.py new file mode 100644 index 0000000000000000000000000000000000000000..9c1aefb86cf313dd373c989b64fedf89f8ca7c3c --- /dev/null +++ b/.history/app_20220327010540.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011441.py b/.history/app_20220327011441.py new file mode 100644 index 0000000000000000000000000000000000000000..71424e872d2e107eb59e6f07fa365f00b95b4514 --- /dev/null +++ b/.history/app_20220327011441.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_img[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011453.py b/.history/app_20220327011453.py new file mode 100644 index 0000000000000000000000000000000000000000..9e81a34b4ff93769f5ef21a39d841d559f7c41e2 --- /dev/null +++ b/.history/app_20220327011453.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_faces[0][:,:,::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011459.py b/.history/app_20220327011459.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b1195110153fc1057059ac04749712a932992f --- /dev/null +++ b/.history/app_20220327011459.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011500.py b/.history/app_20220327011500.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b1195110153fc1057059ac04749712a932992f --- /dev/null +++ b/.history/app_20220327011500.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011610.py b/.history/app_20220327011610.py new file mode 100644 index 0000000000000000000000000000000000000000..9a97b7af52dc5000bdbdc9c4cfec96d00aa77418 --- /dev/null +++ b/.history/app_20220327011610.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[0]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011824.py b/.history/app_20220327011824.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac3389565340fbc6d1bf90c7915ded561e17ba0 --- /dev/null +++ b/.history/app_20220327011824.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327011825.py b/.history/app_20220327011825.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac3389565340fbc6d1bf90c7915ded561e17ba0 --- /dev/null +++ b/.history/app_20220327011825.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012402.py b/.history/app_20220327012402.py new file mode 100644 index 0000000000000000000000000000000000000000..bac12b405186135515e78ef6807e23410338b289 --- /dev/null +++ b/.history/app_20220327012402.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012404.py b/.history/app_20220327012404.py new file mode 100644 index 0000000000000000000000000000000000000000..9a97b7af52dc5000bdbdc9c4cfec96d00aa77418 --- /dev/null +++ b/.history/app_20220327012404.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[0]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012413.py b/.history/app_20220327012413.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b1195110153fc1057059ac04749712a932992f --- /dev/null +++ b/.history/app_20220327012413.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012427.py b/.history/app_20220327012427.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b1195110153fc1057059ac04749712a932992f --- /dev/null +++ b/.history/app_20220327012427.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012813.py b/.history/app_20220327012813.py new file mode 100644 index 0000000000000000000000000000000000000000..7dde4bd0e50c69167b02076e340460af16bc8402 --- /dev/null +++ b/.history/app_20220327012813.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.COLOR_BGR2RGB) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012814.py b/.history/app_20220327012814.py new file mode 100644 index 0000000000000000000000000000000000000000..7dde4bd0e50c69167b02076e340460af16bc8402 --- /dev/null +++ b/.history/app_20220327012814.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.COLOR_BGR2RGB) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012910.py b/.history/app_20220327012910.py new file mode 100644 index 0000000000000000000000000000000000000000..7dde4bd0e50c69167b02076e340460af16bc8402 --- /dev/null +++ b/.history/app_20220327012910.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.COLOR_BGR2RGB) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012916.py b/.history/app_20220327012916.py new file mode 100644 index 0000000000000000000000000000000000000000..7dde4bd0e50c69167b02076e340460af16bc8402 --- /dev/null +++ b/.history/app_20220327012916.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.COLOR_BGR2RGB) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327012920.py b/.history/app_20220327012920.py new file mode 100644 index 0000000000000000000000000000000000000000..7dde4bd0e50c69167b02076e340460af16bc8402 --- /dev/null +++ b/.history/app_20220327012920.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.COLOR_BGR2RGB) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013050.py b/.history/app_20220327013050.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b1195110153fc1057059ac04749712a932992f --- /dev/null +++ b/.history/app_20220327013050.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013113.py b/.history/app_20220327013113.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013113.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013210.py b/.history/app_20220327013210.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013210.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013228.py b/.history/app_20220327013228.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013228.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013229.py b/.history/app_20220327013229.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013229.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013230.py b/.history/app_20220327013230.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013230.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327013232.py b/.history/app_20220327013232.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2ed6d85dbde28e85d6d8de19a60c493da4f002 --- /dev/null +++ b/.history/app_20220327013232.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True) + + diff --git a/.history/app_20220327091732.py b/.history/app_20220327091732.py new file mode 100644 index 0000000000000000000000000000000000000000..7966ea0a7ccf97c62743edf6e0a9cd6227e8c933 --- /dev/null +++ b/.history/app_20220327091732.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +import warnings +warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327091836.py b/.history/app_20220327091836.py new file mode 100644 index 0000000000000000000000000000000000000000..178b8422c1ee131426a480e3bf5a28bad390f7e3 --- /dev/null +++ b/.history/app_20220327091836.py @@ -0,0 +1,63 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327092438.py b/.history/app_20220327092438.py new file mode 100644 index 0000000000000000000000000000000000000000..9150516b0d3b0562c237a653ce62ebc059a9a013 --- /dev/null +++ b/.history/app_20220327092438.py @@ -0,0 +1,63 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "GFP-GAN" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327092527.py b/.history/app_20220327092527.py new file mode 100644 index 0000000000000000000000000000000000000000..7367130a3e5d66605e1f576a08222fde244104be --- /dev/null +++ b/.history/app_20220327092527.py @@ -0,0 +1,63 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" +description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" +article = "

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327092728.py b/.history/app_20220327092728.py new file mode 100644 index 0000000000000000000000000000000000000000..dfc311232833ab0cc1dc8d7bc111303f1ff3bbea --- /dev/null +++ b/.history/app_20220327092728.py @@ -0,0 +1,63 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" +description = "上传老照片，点击Submit，稍等片刻，右侧Output框将照片另存为即可。" +article = "

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" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093003.py b/.history/app_20220327093003.py new file mode 100644 index 0000000000000000000000000000000000000000..c7577a69049892b3c11b6172f750a51803225ddf --- /dev/null +++ b/.history/app_20220327093003.py @@ -0,0 +1,65 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" +description = "上传老照片，点击Submit，稍等片刻，右侧Output框将照片另存为即可。" +article = "

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" + +article = "

" +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093110.py b/.history/app_20220327093110.py new file mode 100644 index 0000000000000000000000000000000000000000..b0ac24f16737c9539a2eed7ff977a20ecf461ea4 --- /dev/null +++ b/.history/app_20220327093110.py @@ -0,0 +1,64 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" +description = "上传老照片，点击Submit，稍等片刻，右侧Output框将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093305.py b/.history/app_20220327093305.py new file mode 100644 index 0000000000000000000000000000000000000000..91419c3a8ab057476c4d69ae57d4a60e67fab442 --- /dev/null +++ b/.history/app_20220327093305.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output框将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093322.py b/.history/app_20220327093322.py new file mode 100644 index 0000000000000000000000000000000000000000..9f55fd67fc52747ea6994ee7a4efbd44cda9a7ad --- /dev/null +++ b/.history/app_20220327093322.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093351.py b/.history/app_20220327093351.py new file mode 100644 index 0000000000000000000000000000000000000000..370def80c8bd8735d2eba49646ed3386e190bb5b --- /dev/null +++ b/.history/app_20220327093351.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093352.py b/.history/app_20220327093352.py new file mode 100644 index 0000000000000000000000000000000000000000..370def80c8bd8735d2eba49646ed3386e190bb5b --- /dev/null +++ b/.history/app_20220327093352.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093419.py b/.history/app_20220327093419.py new file mode 100644 index 0000000000000000000000000000000000000000..1f85e709731e90f7b288389da3dd978a8196bb9e --- /dev/null +++ b/.history/app_20220327093419.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093421.py b/.history/app_20220327093421.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f6d806358f15ac7768e960fdbf29399937ac2c --- /dev/null +++ b/.history/app_20220327093421.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/app_20220327093426.py b/.history/app_20220327093426.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f6d806358f15ac7768e960fdbf29399937ac2c --- /dev/null +++ b/.history/app_20220327093426.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/.history/inference_gfpgan_20220326215622.py b/.history/inference_gfpgan_20220326215622.py new file mode 100644 index 0000000000000000000000000000000000000000..a426cfc7b9e67aef84e0f3c0666e09d875ebb222 --- /dev/null +++ b/.history/inference_gfpgan_20220326215622.py @@ -0,0 +1,116 @@ +import argparse +import cv2 +import glob +import numpy as np +import os +import torch +from basicsr.utils import imwrite + +from gfpgan import GFPGANer + + +def main(): + """Inference demo for GFPGAN. + """ + parser = argparse.ArgumentParser() + parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') + parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') + parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') + parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth') + parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') + parser.add_argument( + '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') + parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') + parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') + parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') + parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') + parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images') + parser.add_argument('--save_root', type=str, default='results', help='Path to save root') + parser.add_argument( + '--ext', + type=str, + default='auto', + help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') + args = parser.parse_args() + + args = parser.parse_args() + if args.test_path.endswith('/'): + args.test_path = args.test_path[:-1] + os.makedirs(args.save_root, exist_ok=True) + + # background upsampler + if args.bg_upsampler == 'realesrgan': + if not torch.cuda.is_available(): # CPU + import warnings + warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') + bg_upsampler = None + else: + from basicsr.archs.rrdbnet_arch import RRDBNet + from realesrgan import RealESRGANer + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) + bg_upsampler = RealESRGANer( + scale=2, + model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', + model=model, + tile=args.bg_tile, + tile_pad=10, + pre_pad=0, + half=True) # need to set False in CPU mode + else: + bg_upsampler = None + # set up GFPGAN restorer + restorer = GFPGANer( + model_path=args.model_path, + upscale=args.upscale, + arch=args.arch, + channel_multiplier=args.channel, + bg_upsampler=bg_upsampler) + + img_list = sorted(glob.glob(os.path.join(args.test_path, '*'))) + for img_path in img_list: + # read image + img_name = os.path.basename(img_path) + print(f'Processing {img_name} ...') + basename, ext = os.path.splitext(img_name) + input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) + + # restore faces and background if necessary + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back) + + # save faces + for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): + # save cropped face + save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png') + imwrite(cropped_face, save_crop_path) + # save restored face + if args.suffix is not None: + save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' + else: + save_face_name = f'{basename}_{idx:02d}.png' + save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name) + imwrite(restored_face, save_restore_path) + # save comparison image + cmp_img = np.concatenate((cropped_face, restored_face), axis=1) + imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png')) + + # save restored img + if restored_img is not None: + if args.ext == 'auto': + extension = ext[1:] + else: + extension = args.ext + + if args.suffix is not None: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', + f'{basename}_{args.suffix}.{extension}') + else: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') + imwrite(restored_img, save_restore_path) + + print(f'Results are in the [{args.save_root}] folder.') + + +if __name__ == '__main__': + main() diff --git a/.history/inference_gfpgan_20220326225636.py b/.history/inference_gfpgan_20220326225636.py new file mode 100644 index 0000000000000000000000000000000000000000..ac814a50ead170ab69f32c1714bd445c6c5baf17 --- /dev/null +++ b/.history/inference_gfpgan_20220326225636.py @@ -0,0 +1,116 @@ +import argparse +import cv2 +import glob +import numpy as np +import os +import torch +from basicsr.utils import imwrite + +from gfpgan import GFPGANer + + +def main(): + """Inference demo for GFPGAN. + """ + parser = argparse.ArgumentParser() + parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') + parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') + parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') + parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.3.pth') + parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') + parser.add_argument( + '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') + parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') + parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') + parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') + parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') + parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images') + parser.add_argument('--save_root', type=str, default='results', help='Path to save root') + parser.add_argument( + '--ext', + type=str, + default='auto', + help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') + args = parser.parse_args() + + args = parser.parse_args() + if args.test_path.endswith('/'): + args.test_path = args.test_path[:-1] + os.makedirs(args.save_root, exist_ok=True) + + # background upsampler + if args.bg_upsampler == 'realesrgan': + if not torch.cuda.is_available(): # CPU + import warnings + warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') + bg_upsampler = None + else: + from basicsr.archs.rrdbnet_arch import RRDBNet + from realesrgan import RealESRGANer + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) + bg_upsampler = RealESRGANer( + scale=2, + model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', + model=model, + tile=args.bg_tile, + tile_pad=10, + pre_pad=0, + half=True) # need to set False in CPU mode + else: + bg_upsampler = None + # set up GFPGAN restorer + restorer = GFPGANer( + model_path=args.model_path, + upscale=args.upscale, + arch=args.arch, + channel_multiplier=args.channel, + bg_upsampler=bg_upsampler) + + img_list = sorted(glob.glob(os.path.join(args.test_path, '*'))) + for img_path in img_list: + # read image + img_name = os.path.basename(img_path) + print(f'Processing {img_name} ...') + basename, ext = os.path.splitext(img_name) + input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) + + # restore faces and background if necessary + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back) + + # save faces + for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): + # save cropped face + save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png') + imwrite(cropped_face, save_crop_path) + # save restored face + if args.suffix is not None: + save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' + else: + save_face_name = f'{basename}_{idx:02d}.png' + save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name) + imwrite(restored_face, save_restore_path) + # save comparison image + cmp_img = np.concatenate((cropped_face, restored_face), axis=1) + imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png')) + + # save restored img + if restored_img is not None: + if args.ext == 'auto': + extension = ext[1:] + else: + extension = args.ext + + if args.suffix is not None: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', + f'{basename}_{args.suffix}.{extension}') + else: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') + imwrite(restored_img, save_restore_path) + + print(f'Results are in the [{args.save_root}] folder.') + + +if __name__ == '__main__': + main() diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..e1194256d1855311e28595972f5fa0d8b4fa9b88 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // 使用 IntelliSense 了解相关属性。 + // 悬停以查看现有属性的描述。 + // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: 当前文件", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true + } + ] +} \ No newline at end of file diff --git a/Frida.jpg b/Frida.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5e686ed4437899cea526ad43308a9cdae58706ec Binary files /dev/null and b/Frida.jpg differ diff --git a/Henry.jpg b/Henry.jpg new file mode 100644 index 0000000000000000000000000000000000000000..762ff15a7810e010e8f186948e406fa25200e388 Binary files /dev/null and b/Henry.jpg differ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..24384c0728442ace20d180d784cb3ea714413923 --- /dev/null +++ b/LICENSE @@ -0,0 +1,351 @@ +Tencent is pleased to support the open source community by making GFPGAN available. + +Copyright (C) 2021 THL A29 Limited, a Tencent company. 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We list these here. + +Name: Numpydoc +Files: doc/sphinxext/numpydoc/* +License: BSD-2-Clause + For details, see doc/sphinxext/LICENSE.txt + +Name: scipy-sphinx-theme +Files: doc/scipy-sphinx-theme/* +License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0 + For details, see doc/scipy-sphinx-theme/LICENSE.txt + +Name: lapack-lite +Files: numpy/linalg/lapack_lite/* +License: BSD-3-Clause + For details, see numpy/linalg/lapack_lite/LICENSE.txt + +Name: tempita +Files: tools/npy_tempita/* +License: MIT + For details, see tools/npy_tempita/license.txt + +Name: dragon4 +Files: numpy/core/src/multiarray/dragon4.c +License: MIT + For license text, see numpy/core/src/multiarray/dragon4.c + + + +Open Source Software licensed under the MIT license: +--------------------------------------------- +1. facexlib +Copyright (c) 2020 Xintao Wang + +2. opencv-python +Copyright (c) Olli-Pekka Heinisuo +Please note that only files in cv2 package are used. + + +Terms of the MIT License: +--------------------------------------------- +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + + +Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein: +--------------------------------------------- +1. tqdm +Copyright (c) 2013 noamraph + +`tqdm` is a product of collaborative work. +Unless otherwise stated, all authors (see commit logs) retain copyright +for their respective work, and release the work under the MIT licence +(text below). + +Exceptions or notable authors are listed below +in reverse chronological order: + +* files: * + MPLv2.0 2015-2020 (c) Casper da Costa-Luis + [casperdcl](https://github.com/casperdcl). +* files: tqdm/_tqdm.py + MIT 2016 (c) [PR #96] on behalf of Google Inc. +* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore + MIT 2013 (c) Noam Yorav-Raphael, original author. + +[PR #96]: https://github.com/tqdm/tqdm/pull/96 + + +Mozilla Public Licence (MPL) v. 2.0 - Exhibit A +----------------------------------------------- + +This Source Code Form is subject to the terms of the +Mozilla Public License, v. 2.0. +If a copy of the MPL was not distributed with this file, +You can obtain one at https://mozilla.org/MPL/2.0/. + + +MIT License (MIT) +----------------- + +Copyright (c) 2013 noamraph + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..bcaa7179b82f6f0eebace30fa7e4ebea88408f52 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,8 @@ +include assets/* +include inputs/* +include scripts/*.py +include inference_gfpgan.py +include VERSION +include LICENSE +include requirements.txt +include gfpgan/weights/README.md diff --git a/PaperModel.md b/PaperModel.md new file mode 100644 index 0000000000000000000000000000000000000000..aec81d31de56df74c19ae840d44ad2b2a1f06d28 --- /dev/null +++ b/PaperModel.md @@ -0,0 +1,76 @@ +# Installation + +We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.
+If you want want to use the original model in our paper, please follow the instructions below. + +1. Clone repo + + ```bash + git clone https://github.com/xinntao/GFPGAN.git + cd GFPGAN + ``` + +1. Install dependent packages + + As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**. + You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details. + + **Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues) + + ```bash + # Install basicsr - https://github.com/xinntao/BasicSR + # We use BasicSR for both training and inference + pip install basicsr + + # Install facexlib - https://github.com/xinntao/facexlib + # We use face detection and face restoration helper in the facexlib package + pip install facexlib + + pip install -r requirements.txt + python setup.py develop + + # remember to set BASICSR_JIT=True before your running commands + ``` + + **Option 2: Compile extensions during installation** (For those need to train/inference for many times) + + ```bash + # Install basicsr - https://github.com/xinntao/BasicSR + # We use BasicSR for both training and inference + # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient + # Add -vvv for detailed log prints + BASICSR_EXT=True pip install basicsr -vvv + + # Install facexlib - https://github.com/xinntao/facexlib + # We use face detection and face restoration helper in the facexlib package + pip install facexlib + + pip install -r requirements.txt + python setup.py develop + ``` + +## :zap: Quick Inference + +Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) + +```bash +wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models +``` + +- Option 1: Load extensions just-in-time(JIT) + + ```bash + BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 + + # for aligned images + BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned + ``` + +- Option 2: Have successfully compiled extensions during installation + + ```bash + python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 + + # for aligned images + python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned + ``` diff --git a/README.md b/README.md index a7913225e11bfab4e8ee3b5dcb5c2303bc700ad9..c01224a69f399e92e1f8160f1f14d4e0fff04692 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,33 @@ --- -title: GFPGAN V1.3 Whole Image -emoji: 💩 -colorFrom: pink -colorTo: green +title: GFPGAN +emoji: 📚 +colorFrom: green +colorTo: blue sdk: gradio -sdk_version: 2.8.14 app_file: app.py pinned: false -license: mit --- -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference +# Configuration + +`title`: _string_ +Display title for the Space + +`emoji`: _string_ +Space emoji (emoji-only character allowed) + +`colorFrom`: _string_ +Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) + +`colorTo`: _string_ +Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) + +`sdk`: _string_ +Can be either `gradio` or `streamlit` + +`app_file`: _string_ +Path to your main application file (which contains either `gradio` or `streamlit` Python code). +Path is relative to the root of the repository. + +`pinned`: _boolean_ +Whether the Space stays on top of your list. diff --git a/VERSION b/VERSION new file mode 100644 index 0000000000000000000000000000000000000000..abd410582dea1b6dcb53bcfd93921df71212b778 --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +0.2.4 diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f6d806358f15ac7768e960fdbf29399937ac2c --- /dev/null +++ b/app.py @@ -0,0 +1,66 @@ +import os +#os.system("pip install gfpgan") + +#os.system("pip freeze") +#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .") +import random +import gradio as gr +from PIL import Image +import torch +# torch.hub.download_url_to_file('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', 'lincoln.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg') +# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg') + + +import cv2 +import glob +import numpy as np +from basicsr.utils import imwrite +from gfpgan import GFPGANer + +bg_upsampler = None + + + +# set up GFPGAN restorer +restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=bg_upsampler) + + +def inference(img): + input_img = cv2.imread(img, cv2.IMREAD_COLOR) + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=False, only_center_face=False, paste_back=True) + + #return Image.fromarray(restored_faces[0][:,:,::-1]) + return Image.fromarray(restored_img[:, :, ::-1]) + +title = "让美好回忆更清晰" + + +description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。" +article = "

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" + +gr.Interface( + inference, + [gr.inputs.Image(type="filepath", label="Input")], + gr.outputs.Image(type="pil", label="Output"), + title=title, + description=description, + article=article, + examples=[ + ['lincoln.jpg'], + ['einstein.png'], + ['edison.jpg'], + ['Henry.jpg'], + ['Frida.jpg'] + ] + ).launch(enable_queue=True,cache_examples=True,share=True) + + diff --git a/edison.jpg b/edison.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1627bb5c70d7349284872a46e94277c6dff50c05 Binary files /dev/null and b/edison.jpg differ diff --git a/einstein.png b/einstein.png new file mode 100644 index 0000000000000000000000000000000000000000..e6df2b9df924ca88e5f2f802306d39a620149baf Binary files /dev/null and b/einstein.png differ diff --git a/experiments/pretrained_models/GFPGANv1.3.pth b/experiments/pretrained_models/GFPGANv1.3.pth new file mode 100644 index 0000000000000000000000000000000000000000..1da748a3ef84ff85dd2c77c836f222aae22b007e --- /dev/null +++ b/experiments/pretrained_models/GFPGANv1.3.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c953a88f2727c85c3d9ae72e2bd4846bbaf59fe6972ad94130e23e7017524a70 +size 348632874 diff --git a/experiments/pretrained_models/README.md b/experiments/pretrained_models/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3401a5ca9b393e0033f58c5af8905961565826d9 --- /dev/null +++ b/experiments/pretrained_models/README.md @@ -0,0 +1,7 @@ +# Pre-trained Models and Other Data + +Download pre-trained models and other data. Put them in this folder. + +1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth) +1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth) +1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth) diff --git a/flagged/Input/0.png b/flagged/Input/0.png new file mode 100644 index 0000000000000000000000000000000000000000..f8525c77fee41dde161aaa97aa0dbe8273de0926 Binary files /dev/null and b/flagged/Input/0.png differ diff --git a/flagged/Output/0.png b/flagged/Output/0.png new file mode 100644 index 0000000000000000000000000000000000000000..9aba13fcd8f633d57a6031373600fad9938a99b0 Binary files /dev/null and b/flagged/Output/0.png differ diff --git a/flagged/log.csv b/flagged/log.csv new file mode 100644 index 0000000000000000000000000000000000000000..1be41cd4d59dcaf8952fff10aa0737c119d11945 --- /dev/null +++ b/flagged/log.csv @@ -0,0 +1,2 @@ +'Input','Output','timestamp' +'Input/0.png','Output/0.png','2022-03-27 00:06:24.080907' diff --git a/gfpgan/__init__.py b/gfpgan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94daaeebce5604d61999f0b1b354b9a9e299b991 --- /dev/null +++ b/gfpgan/__init__.py @@ -0,0 +1,7 @@ +# flake8: noqa +from .archs import * +from .data import * +from .models import * +from .utils import * + +# from .version import * diff --git a/gfpgan/__pycache__/__init__.cpython-38.pyc b/gfpgan/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8acebd66ae74e451ad7cae0615fe3c041be93513 Binary files /dev/null and b/gfpgan/__pycache__/__init__.cpython-38.pyc differ diff --git a/gfpgan/__pycache__/utils.cpython-38.pyc b/gfpgan/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3655da061f937b9065d3c53bbaf05292b5a79bd3 Binary files /dev/null and b/gfpgan/__pycache__/utils.cpython-38.pyc differ diff --git a/gfpgan/archs/__init__.py b/gfpgan/archs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bec5f17bfa38729b55f57cae8e40c27310db2b7b --- /dev/null +++ b/gfpgan/archs/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import arch modules for registry +# scan all the files that end with '_arch.py' under the archs folder +arch_folder = osp.dirname(osp.abspath(__file__)) +arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')] +# import all the arch modules +_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames] diff --git a/gfpgan/archs/__pycache__/__init__.cpython-38.pyc b/gfpgan/archs/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5db94a8811be1df53b74b6e1314bb71697850a82 Binary files /dev/null and b/gfpgan/archs/__pycache__/__init__.cpython-38.pyc differ diff --git a/gfpgan/archs/__pycache__/arcface_arch.cpython-38.pyc b/gfpgan/archs/__pycache__/arcface_arch.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c36e0d1b19fc7055d186b37d27a64b2dd58d8954 Binary files /dev/null and b/gfpgan/archs/__pycache__/arcface_arch.cpython-38.pyc differ diff --git a/gfpgan/archs/__pycache__/gfpganv1_arch.cpython-38.pyc b/gfpgan/archs/__pycache__/gfpganv1_arch.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0011a77424a29f8aa80fe2754a194529dc641755 Binary files /dev/null and b/gfpgan/archs/__pycache__/gfpganv1_arch.cpython-38.pyc differ diff --git a/gfpgan/archs/__pycache__/gfpganv1_clean_arch.cpython-38.pyc b/gfpgan/archs/__pycache__/gfpganv1_clean_arch.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6f281657c25991c31457d715763963b42c8bd503 Binary files /dev/null and b/gfpgan/archs/__pycache__/gfpganv1_clean_arch.cpython-38.pyc differ diff --git a/gfpgan/archs/__pycache__/stylegan2_clean_arch.cpython-38.pyc b/gfpgan/archs/__pycache__/stylegan2_clean_arch.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..76bb66068989be6d02805065301972abdc48faeb Binary files /dev/null and b/gfpgan/archs/__pycache__/stylegan2_clean_arch.cpython-38.pyc differ diff --git a/gfpgan/archs/arcface_arch.py b/gfpgan/archs/arcface_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..e6d3bd97f83334450bd78ad2c3b9871102a56b70 --- /dev/null +++ b/gfpgan/archs/arcface_arch.py @@ -0,0 +1,245 @@ +import torch.nn as nn +from basicsr.utils.registry import ARCH_REGISTRY + + +def conv3x3(inplanes, outplanes, stride=1): + """A simple wrapper for 3x3 convolution with padding. + + Args: + inplanes (int): Channel number of inputs. + outplanes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + """ + return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + """Basic residual block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class IRBlock(nn.Module): + """Improved residual block (IR Block) used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): + super(IRBlock, self).__init__() + self.bn0 = nn.BatchNorm2d(inplanes) + self.conv1 = conv3x3(inplanes, inplanes) + self.bn1 = nn.BatchNorm2d(inplanes) + self.prelu = nn.PReLU() + self.conv2 = conv3x3(inplanes, planes, stride) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.use_se = use_se + if self.use_se: + self.se = SEBlock(planes) + + def forward(self, x): + residual = x + out = self.bn0(x) + out = self.conv1(out) + out = self.bn1(out) + out = self.prelu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.use_se: + out = self.se(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.prelu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + expansion = 4 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class SEBlock(nn.Module): + """The squeeze-and-excitation block (SEBlock) used in the IRBlock. + + Args: + channel (int): Channel number of inputs. + reduction (int): Channel reduction ration. Default: 16. + """ + + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) # pool to 1x1 without spatial information + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel), + nn.Sigmoid()) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +@ARCH_REGISTRY.register() +class ResNetArcFace(nn.Module): + """ArcFace with ResNet architectures. + + Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. + + Args: + block (str): Block used in the ArcFace architecture. + layers (tuple(int)): Block numbers in each layer. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + def __init__(self, block, layers, use_se=True): + if block == 'IRBlock': + block = IRBlock + self.inplanes = 64 + self.use_se = use_se + super(ResNetArcFace, self).__init__() + + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.prelu = nn.PReLU() + self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.bn4 = nn.BatchNorm2d(512) + self.dropout = nn.Dropout() + self.fc5 = nn.Linear(512 * 8 * 8, 512) + self.bn5 = nn.BatchNorm1d(512) + + # initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, num_blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se)) + self.inplanes = planes + for _ in range(1, num_blocks): + layers.append(block(self.inplanes, planes, use_se=self.use_se)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.prelu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.bn4(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.fc5(x) + x = self.bn5(x) + + return x diff --git a/gfpgan/archs/gfpganv1_arch.py b/gfpgan/archs/gfpganv1_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..e092b4f7633dece505e5cd3bac4a482df3746654 --- /dev/null +++ b/gfpgan/archs/gfpganv1_arch.py @@ -0,0 +1,439 @@ +import math +import random +import torch +from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, + StyleGAN2Generator) +from basicsr.ops.fused_act import FusedLeakyReLU +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + + +class StyleGAN2GeneratorSFT(StyleGAN2Generator): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__(self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False): + super(StyleGAN2GeneratorSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow) + self.sft_half = sft_half + + def forward(self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorSFT. + + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ConvUpLayer(nn.Module): + """Convolutional upsampling layer. It uses bilinear upsampler + Conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + activate=True): + super(ConvUpLayer, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + # self.scale is used to scale the convolution weights, which is related to the common initializations. + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) + + if bias and not activate: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + # activation + if activate: + if bias: + self.activation = FusedLeakyReLU(out_channels) + else: + self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + + def forward(self, x): + # bilinear upsample + out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) + # conv + out = F.conv2d( + out, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + # activation + if self.activation is not None: + out = self.activation(out) + return out + + +class ResUpBlock(nn.Module): + """Residual block with upsampling. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels): + super(ResUpBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True) + self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +@ARCH_REGISTRY.register() +class GFPGANv1(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False): + + super(GFPGANv1, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + '4': int(512 * unet_narrow), + '8': int(512 * unet_narrow), + '16': int(512 * unet_narrow), + '32': int(512 * unet_narrow), + '64': int(256 * channel_multiplier * unet_narrow), + '128': int(128 * channel_multiplier * unet_narrow), + '256': int(64 * channel_multiplier * unet_narrow), + '512': int(32 * channel_multiplier * unet_narrow), + '1024': int(16 * channel_multiplier * unet_narrow) + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2**(int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True) + + # downsample + in_channels = channels[f'{first_out_size}'] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel)) + in_channels = out_channels + + self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True) + + # upsample + in_channels = channels['4'] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1))) + self.condition_shift.append( + nn.Sequential( + EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0), + ScaledLeakyReLU(0.2), + EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0))) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANv1. + + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder([style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise) + + return image, out_rgbs + + +@ARCH_REGISTRY.register() +class FacialComponentDiscriminator(nn.Module): + """Facial component (eyes, mouth, noise) discriminator used in GFPGAN. + """ + + def __init__(self): + super(FacialComponentDiscriminator, self).__init__() + # It now uses a VGG-style architectrue with fixed model size + self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True) + self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) + + def forward(self, x, return_feats=False): + """Forward function for FacialComponentDiscriminator. + + Args: + x (Tensor): Input images. + return_feats (bool): Whether to return intermediate features. Default: False. + """ + feat = self.conv1(x) + feat = self.conv3(self.conv2(feat)) + rlt_feats = [] + if return_feats: + rlt_feats.append(feat.clone()) + feat = self.conv5(self.conv4(feat)) + if return_feats: + rlt_feats.append(feat.clone()) + out = self.final_conv(feat) + + if return_feats: + return out, rlt_feats + else: + return out, None diff --git a/gfpgan/archs/gfpganv1_clean_arch.py b/gfpgan/archs/gfpganv1_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..eb2e15d288bf0ad641034ed58d5dab37b0baabb3 --- /dev/null +++ b/gfpgan/archs/gfpganv1_clean_arch.py @@ -0,0 +1,324 @@ +import math +import random +import torch +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + +from .stylegan2_clean_arch import StyleGAN2GeneratorClean + + +class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False): + super(StyleGAN2GeneratorCSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow) + self.sft_half = sft_half + + def forward(self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorCSFT. + + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ResBlock(nn.Module): + """Residual block with bilinear upsampling/downsampling. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. + """ + + def __init__(self, in_channels, out_channels, mode='down'): + super(ResBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) + self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) + if mode == 'down': + self.scale_factor = 0.5 + elif mode == 'up': + self.scale_factor = 2 + + def forward(self, x): + out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) + # upsample/downsample + out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) + out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) + # skip + x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) + skip = self.skip(x) + out = out + skip + return out + + +@ARCH_REGISTRY.register() +class GFPGANv1Clean(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + + num_mlp (int): Layer number of MLP style layers. Default: 8. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False): + + super(GFPGANv1Clean, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + '4': int(512 * unet_narrow), + '8': int(512 * unet_narrow), + '16': int(512 * unet_narrow), + '32': int(512 * unet_narrow), + '64': int(256 * channel_multiplier * unet_narrow), + '128': int(128 * channel_multiplier * unet_narrow), + '256': int(64 * channel_multiplier * unet_narrow), + '512': int(32 * channel_multiplier * unet_narrow), + '1024': int(16 * channel_multiplier * unet_narrow) + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2**(int(math.log(out_size, 2))) + + self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) + + # downsample + in_channels = channels[f'{first_out_size}'] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down')) + in_channels = out_channels + + self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1) + + # upsample + in_channels = channels['4'] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.conv_body_up.append(ResBlock(in_channels, out_channels, mode='up')) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(nn.Conv2d(channels[f'{2**i}'], 3, 1)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorCSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + sft_half=sft_half) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema']) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) + self.condition_shift.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1))) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANv1Clean. + + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder([style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise) + + return image, out_rgbs diff --git a/gfpgan/archs/stylegan2_clean_arch.py b/gfpgan/archs/stylegan2_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2ee94e50401b95e4c9997adef5581d521d725f --- /dev/null +++ b/gfpgan/archs/stylegan2_clean_arch.py @@ -0,0 +1,368 @@ +import math +import random +import torch +from basicsr.archs.arch_util import default_init_weights +from basicsr.utils.registry import ARCH_REGISTRY +from torch import nn +from torch.nn import functional as F + + +class NormStyleCode(nn.Module): + + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + eps (float): A value added to the denominator for numerical stability. Default: 1e-8. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + # modulation inside each modulated conv + self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) + # initialization + default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear') + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) / + math.sqrt(in_channels * kernel_size**2)) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) + + # upsample or downsample if necessary + if self.sample_mode == 'upsample': + x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) + elif self.sample_mode == 'downsample': + x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})') + + +class StyleConv(nn.Module): + """Style conv used in StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + """ + + def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) + self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) * 2**0.5 # for conversion + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # add bias + out = out + self.bias + # activation + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB (image space) from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True): + super(ToRGB, self).__init__() + self.upsample = upsample + self.modulated_conv = ModulatedConv2d( + in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +@ARCH_REGISTRY.register() +class StyleGAN2GeneratorClean(nn.Module): + """Clean version of StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1): + super(StyleGAN2GeneratorClean, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.extend( + [nn.Linear(num_style_feat, num_style_feat, bias=True), + nn.LeakyReLU(negative_slope=0.2, inplace=True)]) + self.style_mlp = nn.Sequential(*style_mlp_layers) + # initialization + default_init_weights(self.style_mlp, scale=1, bias_fill=0, a=0.2, mode='fan_in', nonlinearity='leaky_relu') + + # channel list + channels = { + '4': int(512 * narrow), + '8': int(512 * narrow), + '16': int(512 * narrow), + '32': int(512 * narrow), + '64': int(256 * channel_multiplier * narrow), + '128': int(128 * channel_multiplier * narrow), + '256': int(64 * channel_multiplier * narrow), + '512': int(32 * channel_multiplier * narrow), + '1024': int(16 * channel_multiplier * narrow) + } + self.channels = channels + + self.constant_input = ConstantInput(channels['4'], size=4) + self.style_conv1 = StyleConv( + channels['4'], + channels['4'], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None) + self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels['4'] + # noise + for layer_idx in range(self.num_layers): + resolution = 2**((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode='upsample')) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None)) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward(self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2GeneratorClean. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None diff --git a/gfpgan/data/__init__.py b/gfpgan/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..69fd9f9026407c4d185f86b122000485b06fd986 --- /dev/null +++ b/gfpgan/data/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import dataset modules for registry +# scan all the files that end with '_dataset.py' under the data folder +data_folder = osp.dirname(osp.abspath(__file__)) +dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] +# import all the dataset modules +_dataset_modules = [importlib.import_module(f'gfpgan.data.{file_name}') for file_name in dataset_filenames] diff --git a/gfpgan/data/__pycache__/__init__.cpython-38.pyc b/gfpgan/data/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5508e26494c8570100030a9d5e70c907c4a49eb4 Binary files /dev/null and b/gfpgan/data/__pycache__/__init__.cpython-38.pyc differ diff --git a/gfpgan/data/__pycache__/ffhq_degradation_dataset.cpython-38.pyc b/gfpgan/data/__pycache__/ffhq_degradation_dataset.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6c629fe3ca369fbc107f493012bad1819b9f8977 Binary files /dev/null and b/gfpgan/data/__pycache__/ffhq_degradation_dataset.cpython-38.pyc differ diff --git a/gfpgan/data/ffhq_degradation_dataset.py b/gfpgan/data/ffhq_degradation_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..64e5755e1211f171cb2a883d47e8d253061f90aa --- /dev/null +++ b/gfpgan/data/ffhq_degradation_dataset.py @@ -0,0 +1,230 @@ +import cv2 +import math +import numpy as np +import os.path as osp +import torch +import torch.utils.data as data +from basicsr.data import degradations as degradations +from basicsr.data.data_util import paths_from_folder +from basicsr.data.transforms import augment +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY +from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, + normalize) + + +@DATASET_REGISTRY.register() +class FFHQDegradationDataset(data.Dataset): + """FFHQ dataset for GFPGAN. + + It reads high resolution images, and then generate low-quality (LQ) images on-the-fly. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + io_backend (dict): IO backend type and other kwarg. + mean (list | tuple): Image mean. + std (list | tuple): Image std. + use_hflip (bool): Whether to horizontally flip. + Please see more options in the codes. + """ + + def __init__(self, opt): + super(FFHQDegradationDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + + self.gt_folder = opt['dataroot_gt'] + self.mean = opt['mean'] + self.std = opt['std'] + self.out_size = opt['out_size'] + + self.crop_components = opt.get('crop_components', False) # facial components + self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1) # whether enlarge eye regions + + if self.crop_components: + # load component list from a pre-process pth files + self.components_list = torch.load(opt.get('component_path')) + + # file client (lmdb io backend) + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = self.gt_folder + if not self.gt_folder.endswith('.lmdb'): + raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") + with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: + self.paths = [line.split('.')[0] for line in fin] + else: + # disk backend: scan file list from a folder + self.paths = paths_from_folder(self.gt_folder) + + # degradation configurations + self.blur_kernel_size = opt['blur_kernel_size'] + self.kernel_list = opt['kernel_list'] + self.kernel_prob = opt['kernel_prob'] + self.blur_sigma = opt['blur_sigma'] + self.downsample_range = opt['downsample_range'] + self.noise_range = opt['noise_range'] + self.jpeg_range = opt['jpeg_range'] + + # color jitter + self.color_jitter_prob = opt.get('color_jitter_prob') + self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob') + self.color_jitter_shift = opt.get('color_jitter_shift', 20) + # to gray + self.gray_prob = opt.get('gray_prob') + + logger = get_root_logger() + logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') + logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') + logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') + logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') + + if self.color_jitter_prob is not None: + logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') + if self.gray_prob is not None: + logger.info(f'Use random gray. Prob: {self.gray_prob}') + self.color_jitter_shift /= 255. + + @staticmethod + def color_jitter(img, shift): + """jitter color: randomly jitter the RGB values, in numpy formats""" + jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) + img = img + jitter_val + img = np.clip(img, 0, 1) + return img + + @staticmethod + def color_jitter_pt(img, brightness, contrast, saturation, hue): + """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" + fn_idx = torch.randperm(4) + for fn_id in fn_idx: + if fn_id == 0 and brightness is not None: + brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() + img = adjust_brightness(img, brightness_factor) + + if fn_id == 1 and contrast is not None: + contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() + img = adjust_contrast(img, contrast_factor) + + if fn_id == 2 and saturation is not None: + saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() + img = adjust_saturation(img, saturation_factor) + + if fn_id == 3 and hue is not None: + hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() + img = adjust_hue(img, hue_factor) + return img + + def get_component_coordinates(self, index, status): + """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file""" + components_bbox = self.components_list[f'{index:08d}'] + if status[0]: # hflip + # exchange right and left eye + tmp = components_bbox['left_eye'] + components_bbox['left_eye'] = components_bbox['right_eye'] + components_bbox['right_eye'] = tmp + # modify the width coordinate + components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0] + components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0] + components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0] + + # get coordinates + locations = [] + for part in ['left_eye', 'right_eye', 'mouth']: + mean = components_bbox[part][0:2] + half_len = components_bbox[part][2] + if 'eye' in part: + half_len *= self.eye_enlarge_ratio + loc = np.hstack((mean - half_len + 1, mean + half_len)) + loc = torch.from_numpy(loc).float() + locations.append(loc) + return locations + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # load gt image + # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. + gt_path = self.paths[index] + img_bytes = self.file_client.get(gt_path) + img_gt = imfrombytes(img_bytes, float32=True) + + # random horizontal flip + img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) + h, w, _ = img_gt.shape + + # get facial component coordinates + if self.crop_components: + locations = self.get_component_coordinates(index, status) + loc_left_eye, loc_right_eye, loc_mouth = locations + + # ------------------------ generate lq image ------------------------ # + # blur + kernel = degradations.random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + self.blur_kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + noise_range=None) + img_lq = cv2.filter2D(img_gt, -1, kernel) + # downsample + scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) + img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) + # noise + if self.noise_range is not None: + img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) + # jpeg compression + if self.jpeg_range is not None: + img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) + + # resize to original size + img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR) + + # random color jitter (only for lq) + if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): + img_lq = self.color_jitter(img_lq, self.color_jitter_shift) + # random to gray (only for lq) + if self.gray_prob and np.random.uniform() < self.gray_prob: + img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY) + img_lq = np.tile(img_lq[:, :, None], [1, 1, 3]) + if self.opt.get('gt_gray'): # whether convert GT to gray images + img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) + img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) # repeat the color channels + + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) + + # random color jitter (pytorch version) (only for lq) + if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): + brightness = self.opt.get('brightness', (0.5, 1.5)) + contrast = self.opt.get('contrast', (0.5, 1.5)) + saturation = self.opt.get('saturation', (0, 1.5)) + hue = self.opt.get('hue', (-0.1, 0.1)) + img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue) + + # round and clip + img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255. + + # normalize + normalize(img_gt, self.mean, self.std, inplace=True) + normalize(img_lq, self.mean, self.std, inplace=True) + + if self.crop_components: + return_dict = { + 'lq': img_lq, + 'gt': img_gt, + 'gt_path': gt_path, + 'loc_left_eye': loc_left_eye, + 'loc_right_eye': loc_right_eye, + 'loc_mouth': loc_mouth + } + return return_dict + else: + return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path} + + def __len__(self): + return len(self.paths) diff --git a/gfpgan/models/__init__.py b/gfpgan/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6afad57a3794b867dabbdb617a16355a24d6a8b3 --- /dev/null +++ b/gfpgan/models/__init__.py @@ -0,0 +1,10 @@ +import importlib +from basicsr.utils import scandir +from os import path as osp + +# automatically scan and import model modules for registry +# scan all the files that end with '_model.py' under the model folder +model_folder = osp.dirname(osp.abspath(__file__)) +model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] +# import all the model modules +_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames] diff --git a/gfpgan/models/__pycache__/__init__.cpython-38.pyc b/gfpgan/models/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..609115f8ee55d55a48e31f49359b15f6d46b736f Binary files /dev/null and b/gfpgan/models/__pycache__/__init__.cpython-38.pyc differ diff --git a/gfpgan/models/__pycache__/gfpgan_model.cpython-38.pyc b/gfpgan/models/__pycache__/gfpgan_model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6c4dbf538be8d8f5291344c550bfc5f6bbdde80 Binary files /dev/null and b/gfpgan/models/__pycache__/gfpgan_model.cpython-38.pyc differ diff --git a/gfpgan/models/gfpgan_model.py b/gfpgan/models/gfpgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d51b0b80f775c48d8c1701727b871b1f99e02c --- /dev/null +++ b/gfpgan/models/gfpgan_model.py @@ -0,0 +1,580 @@ +import math +import os.path as osp +import torch +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.losses.losses import r1_penalty +from basicsr.metrics import calculate_metric +from basicsr.models.base_model import BaseModel +from basicsr.utils import get_root_logger, imwrite, tensor2img +from basicsr.utils.registry import MODEL_REGISTRY +from collections import OrderedDict +from torch.nn import functional as F +from torchvision.ops import roi_align +from tqdm import tqdm + + +@MODEL_REGISTRY.register() +class GFPGANModel(BaseModel): + """The GFPGAN model for Towards real-world blind face restoratin with generative facial prior""" + + def __init__(self, opt): + super(GFPGANModel, self).__init__(opt) + self.idx = 0 # it is used for saving data for check + + # define network + self.net_g = build_network(opt['network_g']) + self.net_g = self.model_to_device(self.net_g) + self.print_network(self.net_g) + + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_g', 'params') + self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) + + self.log_size = int(math.log(self.opt['network_g']['out_size'], 2)) + + if self.is_train: + self.init_training_settings() + + def init_training_settings(self): + train_opt = self.opt['train'] + + # ----------- define net_d ----------- # + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_d', None) + if load_path is not None: + self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True)) + + # ----------- define net_g with Exponential Moving Average (EMA) ----------- # + # net_g_ema only used for testing on one GPU and saving. There is no need to wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + + self.net_g.train() + self.net_d.train() + self.net_g_ema.eval() + + # ----------- facial component networks ----------- # + if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt): + self.use_facial_disc = True + else: + self.use_facial_disc = False + + if self.use_facial_disc: + # left eye + self.net_d_left_eye = build_network(self.opt['network_d_left_eye']) + self.net_d_left_eye = self.model_to_device(self.net_d_left_eye) + self.print_network(self.net_d_left_eye) + load_path = self.opt['path'].get('pretrain_network_d_left_eye') + if load_path is not None: + self.load_network(self.net_d_left_eye, load_path, True, 'params') + # right eye + self.net_d_right_eye = build_network(self.opt['network_d_right_eye']) + self.net_d_right_eye = self.model_to_device(self.net_d_right_eye) + self.print_network(self.net_d_right_eye) + load_path = self.opt['path'].get('pretrain_network_d_right_eye') + if load_path is not None: + self.load_network(self.net_d_right_eye, load_path, True, 'params') + # mouth + self.net_d_mouth = build_network(self.opt['network_d_mouth']) + self.net_d_mouth = self.model_to_device(self.net_d_mouth) + self.print_network(self.net_d_mouth) + load_path = self.opt['path'].get('pretrain_network_d_mouth') + if load_path is not None: + self.load_network(self.net_d_mouth, load_path, True, 'params') + + self.net_d_left_eye.train() + self.net_d_right_eye.train() + self.net_d_mouth.train() + + # ----------- define facial component gan loss ----------- # + self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device) + + # ----------- define losses ----------- # + # pixel loss + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + # perceptual loss + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + # L1 loss is used in pyramid loss, component style loss and identity loss + self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device) + + # gan loss (wgan) + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + + # ----------- define identity loss ----------- # + if 'network_identity' in self.opt: + self.use_identity = True + else: + self.use_identity = False + + if self.use_identity: + # define identity network + self.network_identity = build_network(self.opt['network_identity']) + self.network_identity = self.model_to_device(self.network_identity) + self.print_network(self.network_identity) + load_path = self.opt['path'].get('pretrain_network_identity') + if load_path is not None: + self.load_network(self.network_identity, load_path, True, None) + self.network_identity.eval() + for param in self.network_identity.parameters(): + param.requires_grad = False + + # regularization weights + self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator + self.net_d_iters = train_opt.get('net_d_iters', 1) + self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) + self.net_d_reg_every = train_opt['net_d_reg_every'] + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + + # ----------- optimizer g ----------- # + net_g_reg_ratio = 1 + normal_params = [] + for _, param in self.net_g.named_parameters(): + normal_params.append(param) + optim_params_g = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }] + optim_type = train_opt['optim_g'].pop('type') + lr = train_opt['optim_g']['lr'] * net_g_reg_ratio + betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) + self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) + self.optimizers.append(self.optimizer_g) + + # ----------- optimizer d ----------- # + net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) + normal_params = [] + for _, param in self.net_d.named_parameters(): + normal_params.append(param) + optim_params_d = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_d']['lr'] + }] + optim_type = train_opt['optim_d'].pop('type') + lr = train_opt['optim_d']['lr'] * net_d_reg_ratio + betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) + self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) + self.optimizers.append(self.optimizer_d) + + # ----------- optimizers for facial component networks ----------- # + if self.use_facial_disc: + # setup optimizers for facial component discriminators + optim_type = train_opt['optim_component'].pop('type') + lr = train_opt['optim_component']['lr'] + # left eye + self.optimizer_d_left_eye = self.get_optimizer( + optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_left_eye) + # right eye + self.optimizer_d_right_eye = self.get_optimizer( + optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_right_eye) + # mouth + self.optimizer_d_mouth = self.get_optimizer( + optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99)) + self.optimizers.append(self.optimizer_d_mouth) + + def feed_data(self, data): + self.lq = data['lq'].to(self.device) + if 'gt' in data: + self.gt = data['gt'].to(self.device) + + if 'loc_left_eye' in data: + # get facial component locations, shape (batch, 4) + self.loc_left_eyes = data['loc_left_eye'] + self.loc_right_eyes = data['loc_right_eye'] + self.loc_mouths = data['loc_mouth'] + + # uncomment to check data + # import torchvision + # if self.opt['rank'] == 0: + # import os + # os.makedirs('tmp/gt', exist_ok=True) + # os.makedirs('tmp/lq', exist_ok=True) + # print(self.idx) + # torchvision.utils.save_image( + # self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) + # torchvision.utils.save_image( + # self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) + # self.idx = self.idx + 1 + + def construct_img_pyramid(self): + """Construct image pyramid for intermediate restoration loss""" + pyramid_gt = [self.gt] + down_img = self.gt + for _ in range(0, self.log_size - 3): + down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False) + pyramid_gt.insert(0, down_img) + return pyramid_gt + + def get_roi_regions(self, eye_out_size=80, mouth_out_size=120): + face_ratio = int(self.opt['network_g']['out_size'] / 512) + eye_out_size *= face_ratio + mouth_out_size *= face_ratio + + rois_eyes = [] + rois_mouths = [] + for b in range(self.loc_left_eyes.size(0)): # loop for batch size + # left eye and right eye + img_inds = self.loc_left_eyes.new_full((2, 1), b) + bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4) + rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5) + rois_eyes.append(rois) + # mouse + img_inds = self.loc_left_eyes.new_full((1, 1), b) + rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5) + rois_mouths.append(rois) + + rois_eyes = torch.cat(rois_eyes, 0).to(self.device) + rois_mouths = torch.cat(rois_mouths, 0).to(self.device) + + # real images + all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio + self.left_eyes_gt = all_eyes[0::2, :, :, :] + self.right_eyes_gt = all_eyes[1::2, :, :, :] + self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio + # output + all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio + self.left_eyes = all_eyes[0::2, :, :, :] + self.right_eyes = all_eyes[1::2, :, :, :] + self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio + + def _gram_mat(self, x): + """Calculate Gram matrix. + + Args: + x (torch.Tensor): Tensor with shape of (n, c, h, w). + + Returns: + torch.Tensor: Gram matrix. + """ + n, c, h, w = x.size() + features = x.view(n, c, w * h) + features_t = features.transpose(1, 2) + gram = features.bmm(features_t) / (c * h * w) + return gram + + def gray_resize_for_identity(self, out, size=128): + out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :]) + out_gray = out_gray.unsqueeze(1) + out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False) + return out_gray + + def optimize_parameters(self, current_iter): + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + self.optimizer_g.zero_grad() + + # do not update facial component net_d + if self.use_facial_disc: + for p in self.net_d_left_eye.parameters(): + p.requires_grad = False + for p in self.net_d_right_eye.parameters(): + p.requires_grad = False + for p in self.net_d_mouth.parameters(): + p.requires_grad = False + + # image pyramid loss weight + if current_iter < self.opt['train'].get('remove_pyramid_loss', float('inf')): + pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 1) + else: + pyramid_loss_weight = 1e-12 # very small loss + if pyramid_loss_weight > 0: + self.output, out_rgbs = self.net_g(self.lq, return_rgb=True) + pyramid_gt = self.construct_img_pyramid() + else: + self.output, out_rgbs = self.net_g(self.lq, return_rgb=False) + + # get roi-align regions + if self.use_facial_disc: + self.get_roi_regions(eye_out_size=80, mouth_out_size=120) + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + + # image pyramid loss + if pyramid_loss_weight > 0: + for i in range(0, self.log_size - 2): + l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight + l_g_total += l_pyramid + loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid + + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + + # gan loss + fake_g_pred = self.net_d(self.output) + l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + # facial component loss + if self.use_facial_disc: + # left eye + fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True) + l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_left_eye'] = l_g_gan + # right eye + fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True) + l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_right_eye'] = l_g_gan + # mouth + fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True) + l_g_gan = self.cri_component(fake_mouth, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan_mouth'] = l_g_gan + + if self.opt['train'].get('comp_style_weight', 0) > 0: + # get gt feat + _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True) + _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True) + _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True) + + def _comp_style(feat, feat_gt, criterion): + return criterion(self._gram_mat(feat[0]), self._gram_mat( + feat_gt[0].detach())) * 0.5 + criterion( + self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach())) + + # facial component style loss + comp_style_loss = 0 + comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1) + comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1) + comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1) + comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight'] + l_g_total += comp_style_loss + loss_dict['l_g_comp_style_loss'] = comp_style_loss + + # identity loss + if self.use_identity: + identity_weight = self.opt['train']['identity_weight'] + # get gray images and resize + out_gray = self.gray_resize_for_identity(self.output) + gt_gray = self.gray_resize_for_identity(self.gt) + + identity_gt = self.network_identity(gt_gray).detach() + identity_out = self.network_identity(out_gray) + l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight + l_g_total += l_identity + loss_dict['l_identity'] = l_identity + + l_g_total.backward() + self.optimizer_g.step() + + # EMA + self.model_ema(decay=0.5**(32 / (10 * 1000))) + + # ----------- optimize net_d ----------- # + for p in self.net_d.parameters(): + p.requires_grad = True + self.optimizer_d.zero_grad() + if self.use_facial_disc: + for p in self.net_d_left_eye.parameters(): + p.requires_grad = True + for p in self.net_d_right_eye.parameters(): + p.requires_grad = True + for p in self.net_d_mouth.parameters(): + p.requires_grad = True + self.optimizer_d_left_eye.zero_grad() + self.optimizer_d_right_eye.zero_grad() + self.optimizer_d_mouth.zero_grad() + + fake_d_pred = self.net_d(self.output.detach()) + real_d_pred = self.net_d(self.gt) + l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True) + loss_dict['l_d'] = l_d + # In WGAN, real_score should be positive and fake_score should be negative + loss_dict['real_score'] = real_d_pred.detach().mean() + loss_dict['fake_score'] = fake_d_pred.detach().mean() + l_d.backward() + + # regularization loss + if current_iter % self.net_d_reg_every == 0: + self.gt.requires_grad = True + real_pred = self.net_d(self.gt) + l_d_r1 = r1_penalty(real_pred, self.gt) + l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) + loss_dict['l_d_r1'] = l_d_r1.detach().mean() + l_d_r1.backward() + + self.optimizer_d.step() + + # optimize facial component discriminators + if self.use_facial_disc: + # left eye + fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach()) + real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt) + l_d_left_eye = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_left_eye'] = l_d_left_eye + l_d_left_eye.backward() + # right eye + fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach()) + real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt) + l_d_right_eye = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_right_eye'] = l_d_right_eye + l_d_right_eye.backward() + # mouth + fake_d_pred, _ = self.net_d_mouth(self.mouths.detach()) + real_d_pred, _ = self.net_d_mouth(self.mouths_gt) + l_d_mouth = self.cri_component( + real_d_pred, True, is_disc=True) + self.cri_gan( + fake_d_pred, False, is_disc=True) + loss_dict['l_d_mouth'] = l_d_mouth + l_d_mouth.backward() + + self.optimizer_d_left_eye.step() + self.optimizer_d_right_eye.step() + self.optimizer_d_mouth.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + def test(self): + with torch.no_grad(): + if hasattr(self, 'net_g_ema'): + self.net_g_ema.eval() + self.output, _ = self.net_g_ema(self.lq) + else: + logger = get_root_logger() + logger.warning('Do not have self.net_g_ema, use self.net_g.') + self.net_g.eval() + self.output, _ = self.net_g(self.lq) + self.net_g.train() + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + if self.opt['rank'] == 0: + self.nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + dataset_name = dataloader.dataset.opt['name'] + with_metrics = self.opt['val'].get('metrics') is not None + use_pbar = self.opt['val'].get('pbar', False) + + if with_metrics: + if not hasattr(self, 'metric_results'): # only execute in the first run + self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} + # initialize the best metric results for each dataset_name (supporting multiple validation datasets) + self._initialize_best_metric_results(dataset_name) + # zero self.metric_results + self.metric_results = {metric: 0 for metric in self.metric_results} + + metric_data = dict() + if use_pbar: + pbar = tqdm(total=len(dataloader), unit='image') + + for idx, val_data in enumerate(dataloader): + img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] + self.feed_data(val_data) + self.test() + + sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1)) + metric_data['img'] = sr_img + if hasattr(self, 'gt'): + gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1)) + metric_data['img2'] = gt_img + del self.gt + + # tentative for out of GPU memory + del self.lq + del self.output + torch.cuda.empty_cache() + + if save_img: + if self.opt['is_train']: + save_img_path = osp.join(self.opt['path']['visualization'], img_name, + f'{img_name}_{current_iter}.png') + else: + if self.opt['val']['suffix']: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["val"]["suffix"]}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["name"]}.png') + imwrite(sr_img, save_img_path) + + if with_metrics: + # calculate metrics + for name, opt_ in self.opt['val']['metrics'].items(): + self.metric_results[name] += calculate_metric(metric_data, opt_) + if use_pbar: + pbar.update(1) + pbar.set_description(f'Test {img_name}') + if use_pbar: + pbar.close() + + if with_metrics: + for metric in self.metric_results.keys(): + self.metric_results[metric] /= (idx + 1) + # update the best metric result + self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) + + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): + log_str = f'Validation {dataset_name}\n' + for metric, value in self.metric_results.items(): + log_str += f'\t # {metric}: {value:.4f}' + if hasattr(self, 'best_metric_results'): + log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' + f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') + log_str += '\n' + + logger = get_root_logger() + logger.info(log_str) + if tb_logger: + for metric, value in self.metric_results.items(): + tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) + + def save(self, epoch, current_iter): + # save net_g and net_d + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + self.save_network(self.net_d, 'net_d', current_iter) + # save component discriminators + if self.use_facial_disc: + self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter) + self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter) + self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter) + # save training state + self.save_training_state(epoch, current_iter) diff --git a/gfpgan/train.py b/gfpgan/train.py new file mode 100644 index 0000000000000000000000000000000000000000..fe5f1f909ae15a8d830ef65dcb43436d4f4ee7ae --- /dev/null +++ b/gfpgan/train.py @@ -0,0 +1,11 @@ +# flake8: noqa +import os.path as osp +from basicsr.train import train_pipeline + +import gfpgan.archs +import gfpgan.data +import gfpgan.models + +if __name__ == '__main__': + root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) + train_pipeline(root_path) diff --git a/gfpgan/utils.py b/gfpgan/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3e163e9e21a2e56d7dce404cfd2b21bcc61402f --- /dev/null +++ b/gfpgan/utils.py @@ -0,0 +1,130 @@ +import cv2 +import os +import torch +from basicsr.utils import img2tensor, tensor2img +from basicsr.utils.download_util import load_file_from_url +from facexlib.utils.face_restoration_helper import FaceRestoreHelper +from torchvision.transforms.functional import normalize + +from gfpgan.archs.gfpganv1_arch import GFPGANv1 +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean + +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +class GFPGANer(): + """Helper for restoration with GFPGAN. + + It will detect and crop faces, and then resize the faces to 512x512. + GFPGAN is used to restored the resized faces. + The background is upsampled with the bg_upsampler. + Finally, the faces will be pasted back to the upsample background image. + + Args: + model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). + upscale (float): The upscale of the final output. Default: 2. + arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + bg_upsampler (nn.Module): The upsampler for the background. Default: None. + """ + + def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None): + self.upscale = upscale + self.bg_upsampler = bg_upsampler + + # initialize model + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + # initialize the GFP-GAN + if arch == 'clean': + self.gfpgan = GFPGANv1Clean( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + else: + self.gfpgan = GFPGANv1( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=True, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + # initialize face helper + self.face_helper = FaceRestoreHelper( + upscale, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + device=self.device) + + if model_path.startswith('https://'): + model_path = load_file_from_url( + url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None) + loadnet = torch.load(model_path) + if 'params_ema' in loadnet: + keyname = 'params_ema' + else: + keyname = 'params' + self.gfpgan.load_state_dict(loadnet[keyname], strict=True) + self.gfpgan.eval() + self.gfpgan = self.gfpgan.to(self.device) + + @torch.no_grad() + def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True): + self.face_helper.clean_all() + + if has_aligned: # the inputs are already aligned + img = cv2.resize(img, (512, 512)) + self.face_helper.cropped_faces = [img] + else: + self.face_helper.read_image(img) + # get face landmarks for each face + self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) + # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels + # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations. + # align and warp each face + self.face_helper.align_warp_face() + + # face restoration + for cropped_face in self.face_helper.cropped_faces: + # prepare data + cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device) + + try: + output = self.gfpgan(cropped_face_t, return_rgb=False)[0] + # convert to image + restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) + except RuntimeError as error: + print(f'\tFailed inference for GFPGAN: {error}.') + restored_face = cropped_face + + restored_face = restored_face.astype('uint8') + self.face_helper.add_restored_face(restored_face) + + if not has_aligned and paste_back: + # upsample the background + if self.bg_upsampler is not None: + # Now only support RealESRGAN for upsampling background + bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] + else: + bg_img = None + + self.face_helper.get_inverse_affine(None) + # paste each restored face to the input image + restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) + return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img + else: + return self.face_helper.cropped_faces, self.face_helper.restored_faces, None diff --git a/gfpgan/weights/README.md b/gfpgan/weights/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4d7b7e642591ef88575d9e6c360a4d29e0cc1a4f --- /dev/null +++ b/gfpgan/weights/README.md @@ -0,0 +1,3 @@ +# Weights + +Put the downloaded weights to this folder. diff --git a/gradio_cached_examples/Output/0.png b/gradio_cached_examples/Output/0.png new file mode 100644 index 0000000000000000000000000000000000000000..4a9718b1e1128126ec924cf8664bdae9d2f7462f Binary files /dev/null and b/gradio_cached_examples/Output/0.png differ diff --git a/gradio_cached_examples/Output/1.png b/gradio_cached_examples/Output/1.png new file mode 100644 index 0000000000000000000000000000000000000000..c1b76b9acdf93199dd55359f0d17aa0037085c99 Binary files /dev/null and b/gradio_cached_examples/Output/1.png differ diff --git a/gradio_cached_examples/Output/2.png b/gradio_cached_examples/Output/2.png new file mode 100644 index 0000000000000000000000000000000000000000..4190a644a9d995617fc55b524e2c12f132a711b0 Binary files /dev/null and b/gradio_cached_examples/Output/2.png differ diff --git a/gradio_cached_examples/Output/3.png b/gradio_cached_examples/Output/3.png new file mode 100644 index 0000000000000000000000000000000000000000..0b15fd5ee28a77061fe407e6dc11f6d93f4680d5 Binary files /dev/null and b/gradio_cached_examples/Output/3.png differ diff --git a/gradio_cached_examples/Output/4.png b/gradio_cached_examples/Output/4.png new file mode 100644 index 0000000000000000000000000000000000000000..5c01705653b6f29dbc7692b58e81b5d556407852 Binary files /dev/null and b/gradio_cached_examples/Output/4.png differ diff --git a/gradio_cached_examples/log.csv b/gradio_cached_examples/log.csv new file mode 100644 index 0000000000000000000000000000000000000000..01c1162a45ac26a22881f6c8ae998b3c3a6db5fe --- /dev/null +++ b/gradio_cached_examples/log.csv @@ -0,0 +1,6 @@ +'Output' +'Output/0.png' +'Output/1.png' +'Output/2.png' +'Output/3.png' +'Output/4.png' diff --git a/gradio_queue.db b/gradio_queue.db new file mode 100644 index 0000000000000000000000000000000000000000..57736ee31ba0d198e173e0be8ef6a8b3f2715d5e Binary files /dev/null and b/gradio_queue.db differ diff --git a/inference_gfpgan.py b/inference_gfpgan.py new file mode 100644 index 0000000000000000000000000000000000000000..ac814a50ead170ab69f32c1714bd445c6c5baf17 --- /dev/null +++ b/inference_gfpgan.py @@ -0,0 +1,116 @@ +import argparse +import cv2 +import glob +import numpy as np +import os +import torch +from basicsr.utils import imwrite + +from gfpgan import GFPGANer + + +def main(): + """Inference demo for GFPGAN. + """ + parser = argparse.ArgumentParser() + parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') + parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') + parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') + parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.3.pth') + parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') + parser.add_argument( + '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') + parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') + parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') + parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') + parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') + parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images') + parser.add_argument('--save_root', type=str, default='results', help='Path to save root') + parser.add_argument( + '--ext', + type=str, + default='auto', + help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') + args = parser.parse_args() + + args = parser.parse_args() + if args.test_path.endswith('/'): + args.test_path = args.test_path[:-1] + os.makedirs(args.save_root, exist_ok=True) + + # background upsampler + if args.bg_upsampler == 'realesrgan': + if not torch.cuda.is_available(): # CPU + import warnings + warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' + 'If you really want to use it, please modify the corresponding codes.') + bg_upsampler = None + else: + from basicsr.archs.rrdbnet_arch import RRDBNet + from realesrgan import RealESRGANer + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) + bg_upsampler = RealESRGANer( + scale=2, + model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', + model=model, + tile=args.bg_tile, + tile_pad=10, + pre_pad=0, + half=True) # need to set False in CPU mode + else: + bg_upsampler = None + # set up GFPGAN restorer + restorer = GFPGANer( + model_path=args.model_path, + upscale=args.upscale, + arch=args.arch, + channel_multiplier=args.channel, + bg_upsampler=bg_upsampler) + + img_list = sorted(glob.glob(os.path.join(args.test_path, '*'))) + for img_path in img_list: + # read image + img_name = os.path.basename(img_path) + print(f'Processing {img_name} ...') + basename, ext = os.path.splitext(img_name) + input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) + + # restore faces and background if necessary + cropped_faces, restored_faces, restored_img = restorer.enhance( + input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back) + + # save faces + for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): + # save cropped face + save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png') + imwrite(cropped_face, save_crop_path) + # save restored face + if args.suffix is not None: + save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png' + else: + save_face_name = f'{basename}_{idx:02d}.png' + save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name) + imwrite(restored_face, save_restore_path) + # save comparison image + cmp_img = np.concatenate((cropped_face, restored_face), axis=1) + imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png')) + + # save restored img + if restored_img is not None: + if args.ext == 'auto': + extension = ext[1:] + else: + extension = args.ext + + if args.suffix is not None: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', + f'{basename}_{args.suffix}.{extension}') + else: + save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') + imwrite(restored_img, save_restore_path) + + print(f'Results are in the [{args.save_root}] folder.') + + +if __name__ == '__main__': + main() diff --git a/lincoln.jpg b/lincoln.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7c47c792edee8d994ffc767550319f0e48de24f6 Binary files /dev/null and b/lincoln.jpg differ diff --git a/options/train_gfpgan_v1.yml b/options/train_gfpgan_v1.yml new file mode 100644 index 0000000000000000000000000000000000000000..aa5212a81de362daaef306e203f03cc665186d47 --- /dev/null +++ b/options/train_gfpgan_v1.yml @@ -0,0 +1,216 @@ +# general settings +name: train_GFPGANv1_512 +model_type: GFPGANModel +num_gpu: auto # officially, we use 4 GPUs +manual_seed: 0 + +# dataset and data loader settings +datasets: + train: + name: FFHQ + type: FFHQDegradationDataset + # dataroot_gt: datasets/ffhq/ffhq_512.lmdb + dataroot_gt: datasets/ffhq/ffhq_512 + io_backend: + # type: lmdb + type: disk + + use_hflip: true + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + out_size: 512 + + blur_kernel_size: 41 + kernel_list: ['iso', 'aniso'] + kernel_prob: [0.5, 0.5] + blur_sigma: [0.1, 10] + downsample_range: [0.8, 8] + noise_range: [0, 20] + jpeg_range: [60, 100] + + # color jitter and gray + color_jitter_prob: 0.3 + color_jitter_shift: 20 + color_jitter_pt_prob: 0.3 + gray_prob: 0.01 + + # If you do not want colorization, please set + # color_jitter_prob: ~ + # color_jitter_pt_prob: ~ + # gray_prob: 0.01 + # gt_gray: True + + crop_components: true + component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth + eye_enlarge_ratio: 1.4 + + # data loader + use_shuffle: true + num_worker_per_gpu: 6 + batch_size_per_gpu: 3 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + + val: + # Please modify accordingly to use your own validation + # Or comment the val block if do not need validation during training + name: validation + type: PairedImageDataset + dataroot_lq: datasets/faces/validation/input + dataroot_gt: datasets/faces/validation/reference + io_backend: + type: disk + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + scale: 1 + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 1 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + +network_d_left_eye: + type: FacialComponentDiscriminator + +network_d_right_eye: + type: FacialComponentDiscriminator + +network_d_mouth: + type: FacialComponentDiscriminator + +network_identity: + type: ResNetArcFace + block: IRBlock + layers: [2, 2, 2, 2] + use_se: False + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + pretrain_network_d_left_eye: ~ + pretrain_network_d_right_eye: ~ + pretrain_network_d_mouth: ~ + pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth + # resume + resume_state: ~ + ignore_resume_networks: ['network_identity'] + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + # facial component loss + gan_component_opt: + type: GANLoss + gan_type: vanilla + real_label_val: 1.0 + fake_label_val: 0.0 + loss_weight: !!float 1 + comp_style_weight: 200 + # identity loss + identity_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 16 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: true + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false + +# logging settings +logger: + print_freq: 100 + save_checkpoint_freq: !!float 5e3 + use_tb_logger: true + wandb: + project: ~ + resume_id: ~ + +# dist training settings +dist_params: + backend: nccl + port: 29500 + +find_unused_parameters: true diff --git a/options/train_gfpgan_v1_simple.yml b/options/train_gfpgan_v1_simple.yml new file mode 100644 index 0000000000000000000000000000000000000000..3807575826a5e7ed97335f607c091c8a4039a213 --- /dev/null +++ b/options/train_gfpgan_v1_simple.yml @@ -0,0 +1,182 @@ +# general settings +name: train_GFPGANv1_512_simple +model_type: GFPGANModel +num_gpu: auto # officially, we use 4 GPUs +manual_seed: 0 + +# dataset and data loader settings +datasets: + train: + name: FFHQ + type: FFHQDegradationDataset + # dataroot_gt: datasets/ffhq/ffhq_512.lmdb + dataroot_gt: datasets/ffhq/ffhq_512 + io_backend: + # type: lmdb + type: disk + + use_hflip: true + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + out_size: 512 + + blur_kernel_size: 41 + kernel_list: ['iso', 'aniso'] + kernel_prob: [0.5, 0.5] + blur_sigma: [0.1, 10] + downsample_range: [0.8, 8] + noise_range: [0, 20] + jpeg_range: [60, 100] + + # color jitter and gray + color_jitter_prob: 0.3 + color_jitter_shift: 20 + color_jitter_pt_prob: 0.3 + gray_prob: 0.01 + + # If you do not want colorization, please set + # color_jitter_prob: ~ + # color_jitter_pt_prob: ~ + # gray_prob: 0.01 + # gt_gray: True + + # data loader + use_shuffle: true + num_worker_per_gpu: 6 + batch_size_per_gpu: 3 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + + val: + # Please modify accordingly to use your own validation + # Or comment the val block if do not need validation during training + name: validation + type: PairedImageDataset + dataroot_lq: datasets/faces/validation/input + dataroot_gt: datasets/faces/validation/reference + io_backend: + type: disk + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + scale: 1 + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 1 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + resume_state: ~ + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 16 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: true + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false + +# logging settings +logger: + print_freq: 100 + save_checkpoint_freq: !!float 5e3 + use_tb_logger: true + wandb: + project: ~ + resume_id: ~ + +# dist training settings +dist_params: + backend: nccl + port: 29500 + +find_unused_parameters: true diff --git a/packages.txt b/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..470c382fb8274431c2f659b7ebf2ef5bee43253f --- /dev/null +++ b/packages.txt @@ -0,0 +1,3 @@ +ffmpeg +libsm6 +libxext6 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..04bb32ef4e7473618dede80662d8b4808f2e74eb --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +torch>=1.7 +numpy<1.21 # numba requires numpy<1.21,>=1.17 +opencv-python +torchvision +scipy +tqdm +lmdb +pyyaml +tb-nightly +yapf \ No newline at end of file diff --git a/scripts/parse_landmark.py b/scripts/parse_landmark.py new file mode 100644 index 0000000000000000000000000000000000000000..74e2ff9e130ad4f2395c9666dca3ba78526d7a8a --- /dev/null +++ b/scripts/parse_landmark.py @@ -0,0 +1,85 @@ +import cv2 +import json +import numpy as np +import os +import torch +from basicsr.utils import FileClient, imfrombytes +from collections import OrderedDict + +# ---------------------------- This script is used to parse facial landmarks ------------------------------------- # +# Configurations +save_img = False +scale = 0.5 # 0.5 for official FFHQ (512x512), 1 for others +enlarge_ratio = 1.4 # only for eyes +json_path = 'ffhq-dataset-v2.json' +face_path = 'datasets/ffhq/ffhq_512.lmdb' +save_path = './FFHQ_eye_mouth_landmarks_512.pth' + +print('Load JSON metadata...') +# use the official json file in FFHQ dataset +with open(json_path, 'rb') as f: + json_data = json.load(f, object_pairs_hook=OrderedDict) + +print('Open LMDB file...') +# read ffhq images +file_client = FileClient('lmdb', db_paths=face_path) +with open(os.path.join(face_path, 'meta_info.txt')) as fin: + paths = [line.split('.')[0] for line in fin] + +save_dict = {} + +for item_idx, item in enumerate(json_data.values()): + print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True) + + # parse landmarks + lm = np.array(item['image']['face_landmarks']) + lm = lm * scale + + item_dict = {} + # get image + if save_img: + img_bytes = file_client.get(paths[item_idx]) + img = imfrombytes(img_bytes, float32=True) + + # get landmarks for each component + map_left_eye = list(range(36, 42)) + map_right_eye = list(range(42, 48)) + map_mouth = list(range(48, 68)) + + # eye_left + mean_left_eye = np.mean(lm[map_left_eye], 0) # (x, y) + half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16)) + item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye] + # mean_left_eye[0] = 512 - mean_left_eye[0] # for testing flip + half_len_left_eye *= enlarge_ratio + loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int) + if save_img: + eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255) + + # eye_right + mean_right_eye = np.mean(lm[map_right_eye], 0) + half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16)) + item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye] + # mean_right_eye[0] = 512 - mean_right_eye[0] # # for testing flip + half_len_right_eye *= enlarge_ratio + loc_right_eye = np.hstack( + (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int) + if save_img: + eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255) + + # mouth + mean_mouth = np.mean(lm[map_mouth], 0) + half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16)) + item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth] + # mean_mouth[0] = 512 - mean_mouth[0] # for testing flip + loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int) + if save_img: + mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :] + cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255) + + save_dict[f'{item_idx:08d}'] = item_dict + +print('Save...') +torch.save(save_dict, save_path) diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..3d90d600476f24315855b73c777bd7571f42f954 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,33 @@ +[flake8] +ignore = + # line break before binary operator (W503) + W503, + # line break after binary operator (W504) + W504, +max-line-length=120 + +[yapf] +based_on_style = pep8 +column_limit = 120 +blank_line_before_nested_class_or_def = true +split_before_expression_after_opening_paren = true + +[isort] +line_length = 120 +multi_line_output = 0 +known_standard_library = pkg_resources,setuptools +known_first_party = gfpgan +known_third_party = basicsr,cv2,facexlib,numpy,pytest,torch,torchvision,tqdm,yaml +no_lines_before = STDLIB,LOCALFOLDER +default_section = THIRDPARTY + +[codespell] +skip = .git,./docs/build +count = +quiet-level = 3 + +[aliases] +test=pytest + +[tool:pytest] +addopts=tests/ diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..474e9188aa2dc5c19614921760ce4ad99bd19c13 --- /dev/null +++ b/setup.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python + +from setuptools import find_packages, setup + +import os +import subprocess +import time + +version_file = 'gfpgan/version.py' + + +def readme(): + with open('README.md', encoding='utf-8') as f: + content = f.read() + return content + + +def get_git_hash(): + + def _minimal_ext_cmd(cmd): + # construct minimal environment + env = {} + for k in ['SYSTEMROOT', 'PATH', 'HOME']: + v = os.environ.get(k) + if v is not None: + env[k] = v + # LANGUAGE is used on win32 + env['LANGUAGE'] = 'C' + env['LANG'] = 'C' + env['LC_ALL'] = 'C' + out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0] + return out + + try: + out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) + sha = out.strip().decode('ascii') + except OSError: + sha = 'unknown' + + return sha + + +def get_hash(): + if os.path.exists('.git'): + sha = get_git_hash()[:7] + else: + sha = 'unknown' + + return sha + + +def write_version_py(): + content = """# GENERATED VERSION FILE +# TIME: {} +__version__ = '{}' +__gitsha__ = '{}' +version_info = ({}) +""" + sha = get_hash() + with open('VERSION', 'r') as f: + SHORT_VERSION = f.read().strip() + VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')]) + + version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO) + with open(version_file, 'w') as f: + f.write(version_file_str) + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +def get_requirements(filename='requirements.txt'): + here = os.path.dirname(os.path.realpath(__file__)) + with open(os.path.join(here, filename), 'r') as f: + requires = [line.replace('\n', '') for line in f.readlines()] + return requires + + +if __name__ == '__main__': + write_version_py() + setup( + name='gfpgan', + version=get_version(), + description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration', + long_description=readme(), + long_description_content_type='text/markdown', + author='Xintao Wang', + author_email='xintao.wang@outlook.com', + keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan', + url='https://github.com/TencentARC/GFPGAN', + include_package_data=True, + packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')), + classifiers=[ + 'Development Status :: 4 - Beta', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + ], + license='Apache License Version 2.0', + setup_requires=['cython', 'numpy'], + install_requires=get_requirements(), + zip_safe=False) diff --git a/tests/data/ffhq_gt.lmdb/data.mdb b/tests/data/ffhq_gt.lmdb/data.mdb new file mode 100644 index 0000000000000000000000000000000000000000..823e0a9dae90d0699777770760ff012155974290 Binary files /dev/null and b/tests/data/ffhq_gt.lmdb/data.mdb differ diff --git a/tests/data/ffhq_gt.lmdb/lock.mdb b/tests/data/ffhq_gt.lmdb/lock.mdb new file mode 100644 index 0000000000000000000000000000000000000000..c53d2e56457060392f18d1dc7ab6574b15f42794 Binary files /dev/null and b/tests/data/ffhq_gt.lmdb/lock.mdb differ diff --git a/tests/data/ffhq_gt.lmdb/meta_info.txt b/tests/data/ffhq_gt.lmdb/meta_info.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f18d95c03214990dbfd7e6ab520eb7b337038f2 --- /dev/null +++ b/tests/data/ffhq_gt.lmdb/meta_info.txt @@ -0,0 +1 @@ +00000000.png (512,512,3) 1 diff --git a/tests/data/gt/00000000.png b/tests/data/gt/00000000.png new file mode 100644 index 0000000000000000000000000000000000000000..33425aad207003300a8df43a3fe78dde492c552e Binary files /dev/null and b/tests/data/gt/00000000.png differ diff --git a/tests/data/test_eye_mouth_landmarks.pth b/tests/data/test_eye_mouth_landmarks.pth new file mode 100644 index 0000000000000000000000000000000000000000..a27f35286fecf9bf098033a57698690c0d3e8f8d --- /dev/null +++ b/tests/data/test_eye_mouth_landmarks.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:131583fca2cc346652f8754eb3c5a0bdeda808686039ff10ead7a26254b72358 +size 943 diff --git a/tests/data/test_ffhq_degradation_dataset.yml b/tests/data/test_ffhq_degradation_dataset.yml new file mode 100644 index 0000000000000000000000000000000000000000..df50c4bc5ca7f019cc8c47e1e39cd5709137fbee --- /dev/null +++ b/tests/data/test_ffhq_degradation_dataset.yml @@ -0,0 +1,24 @@ +name: UnitTest +type: FFHQDegradationDataset +dataroot_gt: tests/data/gt +io_backend: + type: disk + +use_hflip: true +mean: [0.5, 0.5, 0.5] +std: [0.5, 0.5, 0.5] +out_size: 512 + +blur_kernel_size: 41 +kernel_list: ['iso', 'aniso'] +kernel_prob: [0.5, 0.5] +blur_sigma: [0.1, 10] +downsample_range: [0.8, 8] +noise_range: [0, 20] +jpeg_range: [60, 100] + +# color jitter and gray +color_jitter_prob: 1 +color_jitter_shift: 20 +color_jitter_pt_prob: 1 +gray_prob: 1 diff --git a/tests/data/test_gfpgan_model.yml b/tests/data/test_gfpgan_model.yml new file mode 100644 index 0000000000000000000000000000000000000000..bac650ef201a383b3ae8c7f3ca3c76b2dbf9cbf1 --- /dev/null +++ b/tests/data/test_gfpgan_model.yml @@ -0,0 +1,140 @@ +num_gpu: 1 +manual_seed: 0 +is_train: True +dist: False + +# network structures +network_g: + type: GFPGANv1 + out_size: 512 + num_style_feat: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + decoder_load_path: ~ + fix_decoder: true + num_mlp: 8 + lr_mlp: 0.01 + input_is_latent: true + different_w: true + narrow: 0.5 + sft_half: true + +network_d: + type: StyleGAN2Discriminator + out_size: 512 + channel_multiplier: 1 + resample_kernel: [1, 3, 3, 1] + +network_d_left_eye: + type: FacialComponentDiscriminator + +network_d_right_eye: + type: FacialComponentDiscriminator + +network_d_mouth: + type: FacialComponentDiscriminator + +network_identity: + type: ResNetArcFace + block: IRBlock + layers: [2, 2, 2, 2] + use_se: False + +# path +path: + pretrain_network_g: ~ + param_key_g: params_ema + strict_load_g: ~ + pretrain_network_d: ~ + pretrain_network_d_left_eye: ~ + pretrain_network_d_right_eye: ~ + pretrain_network_d_mouth: ~ + pretrain_network_identity: ~ + # resume + resume_state: ~ + ignore_resume_networks: ['network_identity'] + +# training settings +train: + optim_g: + type: Adam + lr: !!float 2e-3 + optim_d: + type: Adam + lr: !!float 2e-3 + optim_component: + type: Adam + lr: !!float 2e-3 + + scheduler: + type: MultiStepLR + milestones: [600000, 700000] + gamma: 0.5 + + total_iter: 800000 + warmup_iter: -1 # no warm up + + # losses + # pixel loss + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-1 + reduction: mean + # L1 loss used in pyramid loss, component style loss and identity loss + L1_opt: + type: L1Loss + loss_weight: 1 + reduction: mean + + # image pyramid loss + pyramid_loss_weight: 1 + remove_pyramid_loss: 50000 + # perceptual loss (content and style losses) + perceptual_opt: + type: PerceptualLoss + layer_weights: + # before relu + 'conv1_2': 0.1 + 'conv2_2': 0.1 + 'conv3_4': 1 + 'conv4_4': 1 + 'conv5_4': 1 + vgg_type: vgg19 + use_input_norm: true + perceptual_weight: !!float 1 + style_weight: 50 + range_norm: true + criterion: l1 + # gan loss + gan_opt: + type: GANLoss + gan_type: wgan_softplus + loss_weight: !!float 1e-1 + # r1 regularization for discriminator + r1_reg_weight: 10 + # facial component loss + gan_component_opt: + type: GANLoss + gan_type: vanilla + real_label_val: 1.0 + fake_label_val: 0.0 + loss_weight: !!float 1 + comp_style_weight: 200 + # identity loss + identity_weight: 10 + + net_d_iters: 1 + net_d_init_iters: 0 + net_d_reg_every: 1 + +# validation settings +val: + val_freq: !!float 5e3 + save_img: True + use_pbar: True + + metrics: + psnr: # metric name + type: calculate_psnr + crop_border: 0 + test_y_channel: false diff --git a/tests/test_arcface_arch.py b/tests/test_arcface_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b4b28d33800ae78a354e078e14373d2ee159dc7b --- /dev/null +++ b/tests/test_arcface_arch.py @@ -0,0 +1,49 @@ +import torch + +from gfpgan.archs.arcface_arch import BasicBlock, Bottleneck, ResNetArcFace + + +def test_resnetarcface(): + """Test arch: ResNetArcFace.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=True).cuda().eval() + img = torch.rand((1, 1, 128, 128), dtype=torch.float32).cuda() + output = net(img) + assert output.shape == (1, 512) + + # -------------------- without SE block ----------------------- # + net = ResNetArcFace(block='IRBlock', layers=(2, 2, 2, 2), use_se=False).cuda().eval() + output = net(img) + assert output.shape == (1, 512) + + +def test_basicblock(): + """Test the BasicBlock in arcface_arch""" + block = BasicBlock(1, 3, stride=1, downsample=None).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 3, 12, 12) + + # ----------------- use the downsmaple module--------------- # + downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda() + block = BasicBlock(1, 3, stride=2, downsample=downsample).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 3, 6, 6) + + +def test_bottleneck(): + """Test the Bottleneck in arcface_arch""" + block = Bottleneck(1, 1, stride=1, downsample=None).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 4, 12, 12) + + # ----------------- use the downsmaple module--------------- # + downsample = torch.nn.UpsamplingNearest2d(scale_factor=0.5).cuda() + block = Bottleneck(1, 1, stride=2, downsample=downsample).cuda() + img = torch.rand((1, 1, 12, 12), dtype=torch.float32).cuda() + output = block(img) + assert output.shape == (1, 4, 6, 6) diff --git a/tests/test_ffhq_degradation_dataset.py b/tests/test_ffhq_degradation_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa56c03fb8e23df26aa6ed8442a86b3c676eec78 --- /dev/null +++ b/tests/test_ffhq_degradation_dataset.py @@ -0,0 +1,96 @@ +import pytest +import yaml + +from gfpgan.data.ffhq_degradation_dataset import FFHQDegradationDataset + + +def test_ffhq_degradation_dataset(): + + with open('tests/data/test_ffhq_degradation_dataset.yml', mode='r') as f: + opt = yaml.load(f, Loader=yaml.FullLoader) + + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'disk' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 1 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == 'tests/data/gt/00000000.png' + + # ------------------ test with probability = 0 -------------------- # + opt['color_jitter_prob'] = 0 + opt['color_jitter_pt_prob'] = 0 + opt['gray_prob'] = 0 + opt['io_backend'] = dict(type='disk') + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'disk' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 0 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == 'tests/data/gt/00000000.png' + + # ------------------ test lmdb backend -------------------- # + opt['dataroot_gt'] = 'tests/data/ffhq_gt.lmdb' + opt['io_backend'] = dict(type='lmdb') + + dataset = FFHQDegradationDataset(opt) + assert dataset.io_backend_opt['type'] == 'lmdb' # io backend + assert len(dataset) == 1 # whether to read correct meta info + assert dataset.kernel_list == ['iso', 'aniso'] # correct initialization the degradation configurations + assert dataset.color_jitter_prob == 0 + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == '00000000' + + # ------------------ test with crop_components -------------------- # + opt['crop_components'] = True + opt['component_path'] = 'tests/data/test_eye_mouth_landmarks.pth' + opt['eye_enlarge_ratio'] = 1.4 + opt['gt_gray'] = True + opt['io_backend'] = dict(type='lmdb') + + dataset = FFHQDegradationDataset(opt) + assert dataset.crop_components is True + + # test __getitem__ + result = dataset.__getitem__(0) + # check returned keys + expected_keys = ['gt', 'lq', 'gt_path', 'loc_left_eye', 'loc_right_eye', 'loc_mouth'] + assert set(expected_keys).issubset(set(result.keys())) + # check shape and contents + assert result['gt'].shape == (3, 512, 512) + assert result['lq'].shape == (3, 512, 512) + assert result['gt_path'] == '00000000' + assert result['loc_left_eye'].shape == (4, ) + assert result['loc_right_eye'].shape == (4, ) + assert result['loc_mouth'].shape == (4, ) + + # ------------------ lmdb backend should have paths ends with lmdb -------------------- # + with pytest.raises(ValueError): + opt['dataroot_gt'] = 'tests/data/gt' + opt['io_backend'] = dict(type='lmdb') + dataset = FFHQDegradationDataset(opt) diff --git a/tests/test_gfpgan_arch.py b/tests/test_gfpgan_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..cef14a435aa824a1b7c4baaf2d1fe0a2f6cc4441 --- /dev/null +++ b/tests/test_gfpgan_arch.py @@ -0,0 +1,203 @@ +import torch + +from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1, StyleGAN2GeneratorSFT +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean, StyleGAN2GeneratorCSFT + + +def test_stylegan2generatorsft(): + """Test arch: StyleGAN2GeneratorSFT.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorSFT( + out_size=32, + num_style_feat=512, + num_mlp=8, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda() + condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda() + condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda() + conditions = [condition1, condition1, condition2, condition2, condition3, condition3] + output = net([style], conditions) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], conditions, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], conditions, randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], conditions, truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + +def test_gfpganv1(): + """Test arch: GFPGANv1.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = GFPGANv1( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + # -------------------- with different_w = True ----------------------- # + net = GFPGANv1( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=True, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + +def test_facialcomponentdiscriminator(): + """Test arch: FacialComponentDiscriminator.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = FacialComponentDiscriminator().cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert len(output) == 2 + assert output[0].shape == (1, 1, 8, 8) + assert output[1] is None + + # -------------------- return intermediate features ----------------------- # + output = net(img, return_feats=True) + assert len(output) == 2 + assert output[0].shape == (1, 1, 8, 8) + assert len(output[1]) == 2 + assert output[1][0].shape == (1, 128, 16, 16) + assert output[1][1].shape == (1, 256, 8, 8) + + +def test_stylegan2generatorcsft(): + """Test arch: StyleGAN2GeneratorCSFT.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorCSFT( + out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=1, sft_half=False).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + condition1 = torch.rand((1, 512, 8, 8), dtype=torch.float32).cuda() + condition2 = torch.rand((1, 512, 16, 16), dtype=torch.float32).cuda() + condition3 = torch.rand((1, 512, 32, 32), dtype=torch.float32).cuda() + conditions = [condition1, condition1, condition2, condition2, condition3, condition3] + output = net([style], conditions) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], conditions, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], conditions, randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], conditions, truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + +def test_gfpganv1clean(): + """Test arch: GFPGANv1Clean.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = GFPGANv1Clean( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=True).cuda().eval() + + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) + + # -------------------- with different_w = True ----------------------- # + net = GFPGANv1Clean( + out_size=32, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + input_is_latent=False, + different_w=True, + narrow=1, + sft_half=True).cuda().eval() + img = torch.rand((1, 3, 32, 32), dtype=torch.float32).cuda() + output = net(img) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 3 + # check out_rgbs for intermediate loss + assert output[1][0].shape == (1, 3, 8, 8) + assert output[1][1].shape == (1, 3, 16, 16) + assert output[1][2].shape == (1, 3, 32, 32) diff --git a/tests/test_gfpgan_model.py b/tests/test_gfpgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1408ddd7c909c7257fbcea79f8576231a40f9211 --- /dev/null +++ b/tests/test_gfpgan_model.py @@ -0,0 +1,132 @@ +import tempfile +import torch +import yaml +from basicsr.archs.stylegan2_arch import StyleGAN2Discriminator +from basicsr.data.paired_image_dataset import PairedImageDataset +from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss + +from gfpgan.archs.arcface_arch import ResNetArcFace +from gfpgan.archs.gfpganv1_arch import FacialComponentDiscriminator, GFPGANv1 +from gfpgan.models.gfpgan_model import GFPGANModel + + +def test_gfpgan_model(): + with open('tests/data/test_gfpgan_model.yml', mode='r') as f: + opt = yaml.load(f, Loader=yaml.FullLoader) + + # build model + model = GFPGANModel(opt) + # test attributes + assert model.__class__.__name__ == 'GFPGANModel' + assert isinstance(model.net_g, GFPGANv1) # generator + assert isinstance(model.net_d, StyleGAN2Discriminator) # discriminator + # facial component discriminators + assert isinstance(model.net_d_left_eye, FacialComponentDiscriminator) + assert isinstance(model.net_d_right_eye, FacialComponentDiscriminator) + assert isinstance(model.net_d_mouth, FacialComponentDiscriminator) + # identity network + assert isinstance(model.network_identity, ResNetArcFace) + # losses + assert isinstance(model.cri_pix, L1Loss) + assert isinstance(model.cri_perceptual, PerceptualLoss) + assert isinstance(model.cri_gan, GANLoss) + assert isinstance(model.cri_l1, L1Loss) + # optimizer + assert isinstance(model.optimizers[0], torch.optim.Adam) + assert isinstance(model.optimizers[1], torch.optim.Adam) + + # prepare data + gt = torch.rand((1, 3, 512, 512), dtype=torch.float32) + lq = torch.rand((1, 3, 512, 512), dtype=torch.float32) + loc_left_eye = torch.rand((1, 4), dtype=torch.float32) + loc_right_eye = torch.rand((1, 4), dtype=torch.float32) + loc_mouth = torch.rand((1, 4), dtype=torch.float32) + data = dict(gt=gt, lq=lq, loc_left_eye=loc_left_eye, loc_right_eye=loc_right_eye, loc_mouth=loc_mouth) + model.feed_data(data) + # check data shape + assert model.lq.shape == (1, 3, 512, 512) + assert model.gt.shape == (1, 3, 512, 512) + assert model.loc_left_eyes.shape == (1, 4) + assert model.loc_right_eyes.shape == (1, 4) + assert model.loc_mouths.shape == (1, 4) + + # ----------------- test optimize_parameters -------------------- # + model.feed_data(data) + model.optimize_parameters(1) + assert model.output.shape == (1, 3, 512, 512) + assert isinstance(model.log_dict, dict) + # check returned keys + expected_keys = [ + 'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth', + 'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye', + 'l_d_right_eye', 'l_d_mouth' + ] + assert set(expected_keys).issubset(set(model.log_dict.keys())) + + # ----------------- remove pyramid_loss_weight-------------------- # + model.feed_data(data) + model.optimize_parameters(100000) # large than remove_pyramid_loss = 50000 + assert model.output.shape == (1, 3, 512, 512) + assert isinstance(model.log_dict, dict) + # check returned keys + expected_keys = [ + 'l_g_pix', 'l_g_percep', 'l_g_style', 'l_g_gan', 'l_g_gan_left_eye', 'l_g_gan_right_eye', 'l_g_gan_mouth', + 'l_g_comp_style_loss', 'l_identity', 'l_d', 'real_score', 'fake_score', 'l_d_r1', 'l_d_left_eye', + 'l_d_right_eye', 'l_d_mouth' + ] + assert set(expected_keys).issubset(set(model.log_dict.keys())) + + # ----------------- test save -------------------- # + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['path']['models'] = tmpdir + model.opt['path']['training_states'] = tmpdir + model.save(0, 1) + + # ----------------- test the test function -------------------- # + model.test() + assert model.output.shape == (1, 3, 512, 512) + # delete net_g_ema + model.__delattr__('net_g_ema') + model.test() + assert model.output.shape == (1, 3, 512, 512) + assert model.net_g.training is True # should back to training mode after testing + + # ----------------- test nondist_validation -------------------- # + # construct dataloader + dataset_opt = dict( + name='Demo', + dataroot_gt='tests/data/gt', + dataroot_lq='tests/data/gt', + io_backend=dict(type='disk'), + scale=4, + phase='val') + dataset = PairedImageDataset(dataset_opt) + dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) + assert model.is_train is True + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['path']['visualization'] = tmpdir + model.nondist_validation(dataloader, 1, None, save_img=True) + assert model.is_train is True + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) + + # validation + with tempfile.TemporaryDirectory() as tmpdir: + model.opt['is_train'] = False + model.opt['val']['suffix'] = 'test' + model.opt['path']['visualization'] = tmpdir + model.opt['val']['pbar'] = True + model.nondist_validation(dataloader, 1, None, save_img=True) + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) + + # if opt['val']['suffix'] is None + model.opt['val']['suffix'] = None + model.opt['name'] = 'demo' + model.opt['path']['visualization'] = tmpdir + model.nondist_validation(dataloader, 1, None, save_img=True) + # check metric_results + assert 'psnr' in model.metric_results + assert isinstance(model.metric_results['psnr'], float) diff --git a/tests/test_stylegan2_clean_arch.py b/tests/test_stylegan2_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..78bb920e73ce28cfec9ea89a4339cc5b87981b47 --- /dev/null +++ b/tests/test_stylegan2_clean_arch.py @@ -0,0 +1,52 @@ +import torch + +from gfpgan.archs.stylegan2_clean_arch import StyleGAN2GeneratorClean + + +def test_stylegan2generatorclean(): + """Test arch: StyleGAN2GeneratorClean.""" + + # model init and forward (gpu) + if torch.cuda.is_available(): + net = StyleGAN2GeneratorClean( + out_size=32, num_style_feat=512, num_mlp=8, channel_multiplier=1, narrow=0.5).cuda().eval() + style = torch.rand((1, 512), dtype=torch.float32).cuda() + output = net([style], input_is_latent=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with return_latents ----------------------- # + output = net([style], input_is_latent=True, return_latents=True) + assert output[0].shape == (1, 3, 32, 32) + assert len(output[1]) == 1 + # check latent + assert output[1][0].shape == (8, 512) + + # -------------------- with randomize_noise = False ----------------------- # + output = net([style], randomize_noise=False) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # -------------------- with truncation = 0.5 and mixing----------------------- # + output = net([style, style], truncation=0.5, truncation_latent=style) + assert output[0].shape == (1, 3, 32, 32) + assert output[1] is None + + # ------------------ test make_noise ----------------------- # + out = net.make_noise() + assert len(out) == 7 + assert out[0].shape == (1, 1, 4, 4) + assert out[1].shape == (1, 1, 8, 8) + assert out[2].shape == (1, 1, 8, 8) + assert out[3].shape == (1, 1, 16, 16) + assert out[4].shape == (1, 1, 16, 16) + assert out[5].shape == (1, 1, 32, 32) + assert out[6].shape == (1, 1, 32, 32) + + # ------------------ test get_latent ----------------------- # + out = net.get_latent(style) + assert out.shape == (1, 512) + + # ------------------ test mean_latent ----------------------- # + out = net.mean_latent(2) + assert out.shape == (1, 512) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a963b3269dea05f9b7ec6c3db016e9a579c92fc8 --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,43 @@ +import cv2 +from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +from gfpgan.archs.gfpganv1_arch import GFPGANv1 +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean +from gfpgan.utils import GFPGANer + + +def test_gfpganer(): + # initialize with the clean model + restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=None) + # test attribute + assert isinstance(restorer.gfpgan, GFPGANv1Clean) + assert isinstance(restorer.face_helper, FaceRestoreHelper) + + # initialize with the original model + restorer = GFPGANer( + model_path='experiments/pretrained_models/GFPGANv1.pth', + upscale=2, + arch='original', + channel_multiplier=1, + bg_upsampler=None) + # test attribute + assert isinstance(restorer.gfpgan, GFPGANv1) + assert isinstance(restorer.face_helper, FaceRestoreHelper) + + # ------------------ test enhance ---------------- # + img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR) + result = restorer.enhance(img, has_aligned=False, paste_back=True) + assert result[0][0].shape == (512, 512, 3) + assert result[1][0].shape == (512, 512, 3) + assert result[2].shape == (1024, 1024, 3) + + # with has_aligned=True + result = restorer.enhance(img, has_aligned=True, paste_back=False) + assert result[0][0].shape == (512, 512, 3) + assert result[1][0].shape == (512, 512, 3) + assert result[2] is None