Dehazing.py

import os
import torch
import numpy as np
from torchvision import transforms
from PIL import Image
import time
import torchvision
import cv2
import torchvision.utils as tvu
import torch.functional as F
import argparse

def inference_img(haze_path,Net):
    
    haze_image = Image.open(haze_path).convert('RGB')
    enhance_transforms = transforms.Compose([
    transforms.Resize((400,400)),
    transforms.ToTensor()
    ])

    print(haze_image.size)
    with torch.no_grad():
        haze_image = enhance_transforms(haze_image)
        #print(haze_image)
        haze_image = haze_image.unsqueeze(0)
        start = time.time()
        restored2 = Net(haze_image)
        end = time.time()


    return restored2,end-start

if __name__ == '__main__': 
    parser=argparse.ArgumentParser()    
    parser.add_argument('--test_path',type=str,required=True,help='Path to test')
    parser.add_argument('--save_path',type=str,required=True,help='Path to save')
    parser.add_argument('--pk_path',type=str,default='model_zoo/Haze4k.tjm',help='Path of the checkpoint')
    opt = parser.parse_args()
    if not os.path.isdir(opt.save_path):
        os.mkdir(opt.save_path)
    Net=torch.jit.load(opt.pk_path,map_location=torch.device('cpu')).eval()
    image = opt.test_path
    print(image)
    restored2,time_num = inference_img(image,Net)
    torchvision.utils.save_image(restored2,opt.save_path+'output.png')