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import gradio as gr
import os.path
import numpy as np
from collections import OrderedDict
import torch
import cv2
from PIL import Image, ImageOps
import utils_image as util
from network_fbcnn import FBCNN as net
import requests
import datetime
for model_path in ['fbcnn_gray.pth','fbcnn_color.pth']:
if os.path.exists(model_path):
print(f'{model_path} exists.')
else:
print("downloading model")
url = 'https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/{}'.format(os.path.basename(model_path))
r = requests.get(url, allow_redirects=True)
open(model_path, 'wb').write(r.content)
def inference(input_img, is_gray, input_quality, zoom, x_shift, y_shift):
print("datetime:",datetime.datetime.utcnow())
input_img_width, input_img_height = Image.fromarray(input_img).size
print("img size:",(input_img_width,input_img_height))
if (input_img_width > 1080) or (input_img_height > 1080):
resize_ratio = min(1080/input_img_width, 1080/input_img_height)
resized_input = Image.fromarray(input_img).resize((int(input_img_width*resize_ratio)+(input_img_width*resize_ratio < 1),
int(input_img_height*resize_ratio)+(input_img_height*resize_ratio < 1)),
resample=Image.BICUBIC)
input_img = np.array(resized_input)
print("input image resized to:", resized_input.size)
if is_gray:
n_channels = 1 # set 1 for grayscale image, set 3 for color image
model_name = 'fbcnn_gray.pth'
else:
n_channels = 3 # set 1 for grayscale image, set 3 for color image
model_name = 'fbcnn_color.pth'
nc = [64,128,256,512]
nb = 4
input_quality = 100 - input_quality
model_path = model_name
if os.path.exists(model_path):
print(f'{model_path} already exists.')
else:
print("downloading model")
os.makedirs(os.path.dirname(model_path), exist_ok=True)
url = 'https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/{}'.format(os.path.basename(model_path))
r = requests.get(url, allow_redirects=True)
open(model_path, 'wb').write(r.content)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("device:",device)
# ----------------------------------------
# load model
# ----------------------------------------
print(f'loading model from {model_path}')
model = net(in_nc=n_channels, out_nc=n_channels, nc=nc, nb=nb, act_mode='R')
print("#model.load_state_dict(torch.load(model_path), strict=True)")
model.load_state_dict(torch.load(model_path), strict=True)
print("#model.eval()")
model.eval()
print("#for k, v in model.named_parameters()")
for k, v in model.named_parameters():
v.requires_grad = False
print("#model.to(device)")
model = model.to(device)
print("Model loaded.")
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnrb'] = []
# ------------------------------------
# (1) img_L
# ------------------------------------
print("#if n_channels")
if n_channels == 1:
open_cv_image = Image.fromarray(input_img)
open_cv_image = ImageOps.grayscale(open_cv_image)
open_cv_image = np.array(open_cv_image) # PIL to open cv image
img = np.expand_dims(open_cv_image, axis=2) # HxWx1
elif n_channels == 3:
open_cv_image = np.array(input_img) # PIL to open cv image
if open_cv_image.ndim == 2:
open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_GRAY2RGB) # GGG
else:
open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB) # RGB
print("#util.uint2tensor4(open_cv_image)")
img_L = util.uint2tensor4(open_cv_image)
print("#img_L.to(device)")
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
print("#model(img_L)")
img_E,QF = model(img_L)
print("#util.tensor2single(img_E)")
img_E = util.tensor2single(img_E)
print("#util.single2uint(img_E)")
img_E = util.single2uint(img_E)
print("#torch.tensor([[1-input_quality/100]]).cuda() || torch.tensor([[1-input_quality/100]])")
qf_input = torch.tensor([[1-input_quality/100]]).cuda() if device == torch.device('cuda') else torch.tensor([[1-input_quality/100]])
print("#util.single2uint(img_E)")
img_E,QF = model(img_L, qf_input)
print("#util.tensor2single(img_E)")
img_E = util.tensor2single(img_E)
print("#util.single2uint(img_E)")
img_E = util.single2uint(img_E)
if img_E.ndim == 3:
img_E = img_E[:, :, [2, 1, 0]]
print("--inference finished")
out_img = Image.fromarray(img_E)
out_img_w, out_img_h = out_img.size # output image size
zoom = zoom/100
x_shift = x_shift/100
y_shift = y_shift/100
zoom_w, zoom_h = out_img_w*zoom, out_img_h*zoom
zoom_left, zoom_right = int((out_img_w - zoom_w)*x_shift), int(zoom_w + (out_img_w - zoom_w)*x_shift)
zoom_top, zoom_bottom = int((out_img_h - zoom_h)*y_shift), int(zoom_h + (out_img_h - zoom_h)*y_shift)
in_img = Image.fromarray(input_img)
in_img = in_img.crop((zoom_left, zoom_top, zoom_right, zoom_bottom))
in_img = in_img.resize((int(zoom_w/zoom), int(zoom_h/zoom)), Image.NEAREST)
out_img = out_img.crop((zoom_left, zoom_top, zoom_right, zoom_bottom))
out_img = out_img.resize((int(zoom_w/zoom), int(zoom_h/zoom)), Image.NEAREST)
print("--generating preview finished")
return img_E, in_img, out_img
gr.Interface(
fn = inference,
inputs = [gr.inputs.Image(label="Input Image"),
gr.inputs.Checkbox(label="Grayscale (Check this if your image is grayscale)"),
gr.inputs.Slider(minimum=1, maximum=100, step=1, label="Intensity (Higher = stronger JPEG artifact removal)"),
gr.inputs.Slider(minimum=10, maximum=100, step=1, default=50, label="Zoom Image "
"(Use this to see a copy of the output image up close. "
"100 = original size)"),
gr.inputs.Slider(minimum=0, maximum=100, step=1, label="Zoom horizontal shift "
"(Increase to shift to the right)"),
gr.inputs.Slider(minimum=0, maximum=100, step=1, label="Zoom vertical shift "
"(Increase to shift downwards)")
],
outputs = [gr.outputs.Image(label="Result"),
gr.outputs.Image(label="Before:"),
gr.outputs.Image(label="After:")],
examples = [["doraemon.jpg",False,60,42,50,50],
["tomandjerry.jpg",False,60,40,57,44],
["somepanda.jpg",True,100,30,8,24],
["cemetry.jpg",False,70,20,76,62],
["michelangelo_david.jpg",True,30,12,53,27],
["elon_musk.jpg",False,45,15,33,30],
["text.jpg",True,70,50,11,29]],
title = "JPEG Artifacts Removal [FBCNN]",
description = "Gradio Demo for JPEG Artifacts Removal. To use it, simply upload your image, "
"or click one of the examples to load them. Check out the paper and the original GitHub repo at the links below. "
"JPEG artifacts are noticeable distortions of images caused by JPEG lossy compression. "
"This is not a super-resolution AI but a JPEG compression artifact remover. "
"Written below the examples are the limitations of the input image. ",
article = "<p style='text-align: left;'>Uploaded images with a length longer than 1080 pixels will be downscaled to a smaller size "
"with a length of 1080 pixels. Uploaded images with transparency will be incorrectly reconstructed at the output.</p>"
"<p style='text-align: center;'><a href='https://github.com/jiaxi-jiang/FBCNN'>FBCNN GitHub Repo</a><br>"
"<a href='https://arxiv.org/abs/2109.14573'>Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)</a><br>"
"<a href='https://jiaxi-jiang.github.io/'>Jiaxi Jiang, </a>"
"<a href='https://cszn.github.io/'>Kai Zhang, </a>"
"<a href='http://people.ee.ethz.ch/~timofter/'>Radu Timofte</a></p>",
allow_flagging="never"
).launch(enable_queue=True)