main.py

import lpips


class util_of_lpips():
    def __init__(self, net, use_gpu=False):
        '''
        Parameters
        ----------
        net: str
            抽取特征的网络，['alex', 'vgg']
        use_gpu: bool
            是否使用GPU，默认不使用
        Returns
        -------
        References
        -------
        https://github.com/richzhang/PerceptualSimilarity/blob/master/lpips_2imgs.py

        '''
        ## Initializing the model
        self.loss_fn = lpips.LPIPS(net=net)
        self.use_gpu = use_gpu
        if use_gpu:
            self.loss_fn.cuda()

    def calc_lpips(self, img1_path, img2_path):
        '''
        Parameters
        ----------
        img1_path : str
            图像1的路径.
        img2_path : str
            图像2的路径.
        Returns
        -------
        dist01 : torch.Tensor
            学习的感知图像块相似度(Learned Perceptual Image Patch Similarity, LPIPS).

        References
        -------
        https://github.com/richzhang/PerceptualSimilarity/blob/master/lpips_2imgs.py

        '''
        # Load images
        img0 = lpips.im2tensor(lpips.load_image(img1_path))  # RGB image from [-1,1]
        img1 = lpips.im2tensor(lpips.load_image(img2_path))

        if self.use_gpu:
            img0 = img0.cuda()
            img1 = img1.cuda()
        dist01 = self.loss_fn.forward(img0, img1)
        return dist01
a = util_of_lpips('vgg')
sum1 = float(0)
for i in range(60):
    file1 = "D:/Project/ImageEncipher/thumbnail/body/"+str(i+1)+".png"
    file2 = "D:/Project/ImageEncipher/result2/body/"+str(i+1)+".png"
    d = a.calc_lpips(file1,file2)
    sum1 += float(d)
print(sum1/60)