app.py

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 reconstruction of noisy scanned, photocopied documents\n
using <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
<a href='https://medium.com/towards-data-science/effective-data-augmentation-for-ocr-8013080aa9fa'>[See my article for more details]</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). 
"""

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)