'''获得清晰图像'''
from DEA_Net.code.model.backbone import Backbone
def img2tensor(path):
import torchvision.transforms as transforms
import cv2 as cv
img = cv.imread(path)
transf = transforms.ToTensor()
img_tensor = transf(img)
# print('opencv', img)
# print('torch', img_tensor)
return img_tensor
def tensor2img(img,name):
from torchvision import utils as vutils
vutils.save_image(img, name, normalize=True)
model = Backbone()
model.to('cuda')
ckpt = torch.load('/home8T/swx/yolov3/DEA_Net/trained_models/Hazy4K/PSNR3426_SSIM9885.pth',
map_location='cuda')
import torch.nn.functional as F
def pad_img(x, patch_size):
_, _, h, w = x.size()
mod_pad_h = (patch_size - h % patch_size) % patch_size
mod_pad_w = (patch_size - w % patch_size) % patch_size
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
return x
# if isinstance(ckpt, torch.nn.DataParallel):
# ckpt = ckpt.module
# network.load_state_dict(ckpt.state_dict())
'''用的话直接把下面的解除注释'''
model.load_state_dict(ckpt)
img = img2tensor('/home8T/swx/yolov3/DEA_Net/dataset/Hazy4K/train/hazy/1001_0.89_1.66.png').unsqueeze(0).to('cuda')
# print(img)
H, W = img.shape[2:]
img = pad_img(img, 4)
output = model(img)
tensor2img(output,"./test.jpg")
获取DEA清晰图
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转载自blog.csdn.net/swx595182208/article/details/130061147
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