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import os
import io
import gradio as gr
from PIL import Image
import tempfile
from pathlib import Path
import argparse
import shutil
import cv2
import glob
import torch
from collections import OrderedDict
import numpy as np
from main_test_swinir import define_model, setup, get_image_pair
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth -P model_zoo/swinir')
os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth -P model_zoo/swinir')
def sentence_builder(image, task_type, noise, jpeg):
model_dir = 'model_zoo/swinir'
model_zoo = {
'real_sr': {
4: os.path.join(model_dir, '003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth')
},
'gray_dn': {
15: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth')
},
'color_dn': {
15: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth')
},
'jpeg_car': {
10: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth'),
20: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth'),
30: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth'),
40: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth')
}
}
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='real_sr', help='classical_sr, lightweight_sr, real_sr, '
'gray_dn, color_dn, jpeg_car')
parser.add_argument('--scale', type=int, default=1, help='scale factor: 1, 2, 3, 4, 8') # 1 for dn and jpeg car
parser.add_argument('--noise', type=int, default=15, help='noise level: 15, 25, 50')
parser.add_argument('--jpeg', type=int, default=40, help='scale factor: 10, 20, 30, 40')
parser.add_argument('--training_patch_size', type=int, default=128, help='patch size used in training SwinIR. '
'Just used to differentiate two different settings in Table 2 of the paper. '
'Images are NOT tested patch by patch.')
parser.add_argument('--large_model', action='store_true',
help='use large model, only provided for real image sr')
parser.add_argument('--model_path', type=str,
default=model_zoo['real_sr'][4])
parser.add_argument('--folder_lq', type=str, default=None, help='input low-quality test image folder')
parser.add_argument('--folder_gt', type=str, default=None, help='input ground-truth test image folder')
args = parser.parse_args('')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tasks = {
'Real-World Image Super-Resolution': 'real_sr',
'Grayscale Image Denoising': 'gray_dn',
'Color Image Denoising': 'color_dn',
'JPEG Compression Artifact Reduction': 'jpeg_car'
}
args.task = tasks[task_type]
args.noise = noise
args.jpeg = jpeg
# set model path
if args.task == 'real_sr':
args.scale = 4
args.model_path = model_zoo[args.task][4]
elif args.task in ['gray_dn', 'color_dn']:
args.model_path = model_zoo[args.task][noise]
else:
args.model_path = model_zoo[args.task][jpeg]
try:
# set input folder
input_dir = 'input_cog_temp'
os.makedirs(input_dir, exist_ok=True)
input_path = os.path.join(input_dir, guess_filename(image))
#shutil.copy(str(image), input_path)
image.save(input_path, "JPEG")
if args.task == 'real_sr':
args.folder_lq = input_dir
else:
args.folder_gt = input_dir
model = define_model(args)
model.eval()
model = model.to(device)
# setup folder and path
folder, save_dir, border, window_size = setup(args)
os.makedirs(save_dir, exist_ok=True)
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
test_results['psnr_b'] = []
# psnr, ssim, psnr_y, ssim_y, psnr_b = 0, 0, 0, 0, 0
out_path = Path(tempfile.mkdtemp()) / "out.png"
for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, '*')))):
# read image
imgname, img_lq, img_gt = get_image_pair(args, path) # image to HWC-BGR, float32
img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]],
(2, 0, 1)) # HCW-BGR to CHW-RGB
img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device) # CHW-RGB to NCHW-RGB
# inference
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
output = model(img_lq)
output = output[..., :h_old * args.scale, :w_old * args.scale]
# save image
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
cv2.imwrite(str(out_path), output)
finally:
clean_folder(input_dir)
return out_path
def guess_filename(obj: io.IOBase) -> str:
"""Tries to guess the filename of the given object."""
name = getattr(obj, "name", "input")
return os.path.basename(name)
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
title = "Dmonin-SwinIR"
description = "Gradio for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.10257' target='_blank'>SwinIR: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>"
examples=[['ETH_LR.png']]
gr.Interface(
sentence_builder,
[
gr.inputs.Image(type="pil", label="Input"),
gr.Dropdown(choices=["Real-World Image Super-Resolution", "Grayscale Image Denoising", "Color Image Denoising", "JPEG Compression Artifact Reduction"], default= "Real-World Image Super-Resolution", value= "Real-World Image Super-Resolution"),
gr.Dropdown(choices=["15", "25", "50"], default = "15", value="15"),
gr.Dropdown(choices=["10", "20", "30", "40"], default="40", value="40")
],
gr.outputs.Image(type="filepath", label="Output"),
title=title,
description=description,
article=article,
enable_queue=True,
examples=examples
).launch() |