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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
from gradio_imageslider import ImageSlider
current_output = None
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
model_name = 'fbcnn_gray.pth'
else:
n_channels = 3
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)
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'] = []
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)
img = np.expand_dims(open_cv_image, axis=2)
elif n_channels == 3:
open_cv_image = np.array(input_img)
if open_cv_image.ndim == 2:
open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_GRAY2RGB)
else:
open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB)
print("#util.uint2tensor4(open_cv_image)")
img_L = util.uint2tensor4(open_cv_image)
print("#img_L.to(device)")
img_L = img_L.to(device)
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]]
global current_output
current_output = img_E.copy()
print("--inference finished")
(in_img, out_img) = zoom_image(zoom, x_shift, y_shift, input_img, img_E)
print("--generating preview finished")
return img_E, (in_img, out_img)
def zoom_image(zoom, x_shift, y_shift, input_img, output_img = None):
global current_output
if output_img is None:
if current_output is None:
return None
output_img = current_output
img = Image.fromarray(input_img)
out_img = Image.fromarray(output_img)
img_w, img_h = img.size
zoom_factor = (100 - zoom) / 100
x_shift /= 100
y_shift /= 100
zoom_w, zoom_h = int(img_w * zoom_factor), int(img_h * zoom_factor)
x_offset = int((img_w - zoom_w) * x_shift)
y_offset = int((img_h - zoom_h) * y_shift)
crop_box = (x_offset, y_offset, x_offset + zoom_w, y_offset + zoom_h)
img = img.crop(crop_box).resize((img_w, img_h), Image.BILINEAR)
out_img = out_img.crop(crop_box).resize((img_w, img_h), Image.BILINEAR)
return (img, out_img)
with gr.Blocks() as demo:
gr.Markdown("# JPEG Artifacts Removal [FBCNN]")
with gr.Row():
input_img = gr.Image(label="Input Image")
output_img = gr.Image(label="Result")
is_gray = gr.Checkbox(label="Grayscale (Check this if your image is grayscale)")
input_quality = gr.Slider(1, 100, step=1, label="Intensity (Higher = stronger JPEG artifact removal)")
zoom = gr.Slider(10, 100, step=1, value=50, label="Zoom Percentage (0 = original size)")
x_shift = gr.Slider(0, 100, step=1, label="Horizontal shift Percentage (Before/After)")
y_shift = gr.Slider(0, 100, step=1, label="Vertical shift Percentage (Before/After)")
run = gr.Button("Run")
with gr.Row():
before_after = ImageSlider(label="Before/After", type="pil", value=None)
run.click(
inference,
inputs=[input_img, is_gray, input_quality, zoom, x_shift, y_shift],
outputs=[output_img, before_after]
)
gr.Examples([
["doraemon.jpg", False, 60, 58, 50, 50],
["tomandjerry.jpg", False, 60, 60, 57, 44],
["somepanda.jpg", True, 100, 70, 8, 24],
["cemetry.jpg", False, 70, 80, 76, 62],
["michelangelo_david.jpg", True, 30, 88, 53, 27],
["elon_musk.jpg", False, 45, 75, 33, 30],
["text.jpg", True, 70, 50, 11, 29]
], inputs=[input_img, is_gray, input_quality, zoom, x_shift, y_shift])
zoom.release(zoom_image, inputs=[zoom, x_shift, y_shift, input_img], outputs=[before_after])
x_shift.release(zoom_image, inputs=[zoom, x_shift, y_shift, input_img], outputs=[before_after])
y_shift.release(zoom_image, inputs=[zoom, x_shift, y_shift, input_img], outputs=[before_after])
gr.Markdown("""
JPEG Artifacts are noticeable distortions of images caused by JPEG lossy compression.
Note that this is not an AI Upscaler, but just a JPEG Compression Artifact Remover.
[Original Demo](https://huggingface.co/spaces/danielsapit/JPEG_Artifacts_Removal)
[FBCNN GitHub Repo](https://github.com/jiaxi-jiang/FBCNN)
[Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)](https://arxiv.org/abs/2109.14573)
[Jiaxi Jiang](https://jiaxi-jiang.github.io/),
[Kai Zhang](https://cszn.github.io/),
[Radu Timofte](http://people.ee.ethz.ch/~timofter/)
""")
demo.launch() |