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import torch
import torch.nn.functional as F
import os
from skimage import img_as_ubyte
import cv2
import argparse
import shutil
import gradio as gr
from PIL import Image
from runpy import run_path
import numpy as np
examples = [['sample1.png'],
['sample2.png'],
['Sample3.png'],
['Sample4.png'],
['Sample5.png'],
['Sample6.png']]
title = "Restormer"
description = """
Gradio demo for <b>Restormer: Efficient Transformer for High-Resolution Image Restoration</b>, CVPR 2022--ORAL. <a href='https://arxiv.org/abs/2111.09881'>[Paper]</a><a href='https://github.com/swz30/Restormer'>[Github Code]</a>\n
<b> Note:</b> Since this demo uses CPU, by default it will run on the downsampled version of the input image (for speedup). But if you want to perform inference on the original input, then choose the option "Full Resolution Image" from the dropdown menu.
"""
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.09881'>Restormer: Efficient Transformer for High-Resolution Image Restoration </a> | <a href='https://github.com/swz30/Restormer'>Github Repo</a></p>"
def inference(img):
if not os.path.exists('temp'):
os.system('mkdir temp')
# 'Downsampled Image'
#### Resize the longer edge of the input image
max_res = 200
width, height = img.size
if max(width,height) > max_res:
scale = max_res /max(width,height)
width = int(scale*width)
height = int(scale*height)
img = img.resize((width,height))
parameters = {'inp_channels':3, 'out_channels':3, 'dim':48, 'num_blocks':[4,6,6,8], 'num_refinement_blocks':4, 'heads':[1,2,4,8], 'ffn_expansion_factor':2.66, 'bias':False, 'LayerNorm_type':'WithBias', 'dual_pixel_task':False}
load_arch = run_path('restormer_arch.py')
model = load_arch['Restormer'](**parameters)
checkpoint = torch.load('net_g_92000.pth')
model.load_state_dict(checkpoint['params'])
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
img_multiple_of = 8
with torch.inference_mode():
if torch.cuda.is_available():
torch.cuda.ipc_collect()
torch.cuda.empty_cache()
open_cv_image = np.array(img)
img = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB)
input_ = torch.from_numpy(img).float().div(255.).permute(2,0,1).unsqueeze(0).to(device)
# Pad the input if not_multiple_of 8
h,w = input_.shape[2], input_.shape[3]
H,W = ((h+img_multiple_of)//img_multiple_of)*img_multiple_of, ((w+img_multiple_of)//img_multiple_of)*img_multiple_of
padh = H-h if h%img_multiple_of!=0 else 0
padw = W-w if w%img_multiple_of!=0 else 0
input_ = F.pad(input_, (0,padw,0,padh), 'reflect')
restored = torch.clamp(model(input_),0,1)
# Unpad the output
restored = img_as_ubyte(restored[:,:,:h,:w].permute(0, 2, 3, 1).cpu().detach().numpy()[0])
#convert to pil when returning
return Image.fromarray(cv2.cvtColor(restored, cv2.COLOR_RGB2BGR))
gr.Interface(
inference,
[
gr.Image(type="pil", label="Input"),
],
gr.Image(type="pil", label="cleaned and restored"),
title=title,
description=description,
article=article,
examples=examples,
).launch(debug=False,enable_queue=True)
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