图像质量评价指标： PSNR 和 SSIM 和 LPIPS

简单介绍：

1、PSNR：峰值信噪比

图像与影像压缩中典型的峰值讯噪比值在30dB 到50dB 之间，愈高愈好。

• PSNR接近50dB ，代表压缩后的图像仅有些许非常小的误差。
• PSNR大于30dB ，人眼很难察觉压缩后和原始影像的差异。
• PSNR介于20dB 到30dB 之间，人眼就可以察觉出图像的差异。
• PSNR介于10dB 到20dB 之间，人眼还是可以用肉眼看出这个图像原始的结构，且直观上会判断两张图像不存在很大的差异。
• PSNR低于10dB，人类很难用肉眼去判断两个图像是否为相同，一个图像是否为另一个图像的压缩结果。

    

2、SSIM：结构相似性

一般取值范围：0-1.值越大，质量越好。

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直接上代码（这个直接就可以用）：

直接修改下面的两个文件夹的路径就可以了：

parser.add_argument('--input_images_path', default='./input_images')
parser.add_argument('--image2smiles2image_save_path', default='./output_images')

from tqdm import tqdm
import torch
import os
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
import cv2
import lpips
from skimage.metrics import peak_signal_noise_ratio as psnr_loss
from skimage.metrics import structural_similarity as ssim_loss
import argparse

parser = argparse.ArgumentParser(description='PSNR SSIM script', add_help=False)
parser.add_argument('--input_images_path', default='./input_images')
parser.add_argument('--image2smiles2image_save_path', default='./output_images')
parser.add_argument('-v','--version', type=str, default='0.1')
args = parser.parse_args()

def is_png_file(filename):
    return any(filename.endswith(extension) for extension in [".jpg", ".png", ".jpeg"])

def load_img(filepath):
    img = cv2.cvtColor(cv2.imread(filepath), cv2.COLOR_BGR2RGB)
    img = img.astype(np.float32)
    img = img / 255.
    return img

class DataLoaderVal(Dataset):
    def __init__(self, target_transform=None):
        super(DataLoaderVal, self).__init__()

        self.target_transform = target_transform

        gt_dir = args.input_images_path
        input_dir = args.image2smiles2image_save_path

        clean_files = sorted(os.listdir(os.path.join(gt_dir)))
        noisy_files = sorted(os.listdir(os.path.join(input_dir)))

        self.clean_filenames = [os.path.join(gt_dir, x) for x in clean_files if is_png_file(x)]
        self.noisy_filenames = [os.path.join(input_dir, x) for x in noisy_files if is_png_file(x)]

        self.tar_size = len(self.clean_filenames)

    def __len__(self):
        return self.tar_size

    def __getitem__(self, index):
        tar_index = index % self.tar_size

        clean = torch.from_numpy(np.float32(load_img(self.clean_filenames[tar_index])))
        noisy = torch.from_numpy(np.float32(load_img(self.noisy_filenames[tar_index])))

        clean_filename = os.path.split(self.clean_filenames[tar_index])[-1]
        noisy_filename = os.path.split(self.noisy_filenames[tar_index])[-1]

        clean = clean.permute(2, 0, 1)
        noisy = noisy.permute(2, 0, 1)

        return clean, noisy, clean_filename, noisy_filename

def get_validation_data():
    return DataLoaderVal(None)

test_dataset = get_validation_data()
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, num_workers=0, drop_last=False)

## Initializing the model
loss_fn = lpips.LPIPS(net='alex', version=args.version)




if __name__ == '__main__':
    
    # ---------------------- PSNR + SSIM ----------------------
    psnr_val_rgb = []
    ssim_val_rgb = []
    for ii, data_test in enumerate(tqdm(test_loader), 0):

        rgb_groundtruth = data_test[0].numpy().squeeze().transpose((1, 2, 0))
        rgb_restored = data_test[1].cuda()

        rgb_restored = torch.clamp(rgb_restored, 0, 1).cpu().numpy().squeeze().transpose((1, 2, 0))
        psnr_val_rgb.append(psnr_loss(rgb_restored, rgb_groundtruth))
        ssim_val_rgb.append(ssim_loss(rgb_restored, rgb_groundtruth, multichannel=True))

    psnr_val_rgb = sum(psnr_val_rgb) / len(test_dataset)
    ssim_val_rgb = sum(ssim_val_rgb) / len(test_dataset)



    # ---------------------- LPIPS ----------------------
    files = os.listdir(args.input_images_path)
    i = 0
    total_lpips_distance = 0
    average_lpips_distance = 0
    for file in files:

        try:
            # Load images
            img0 = lpips.im2tensor(lpips.load_image(os.path.join(args.input_images_path, file)))
            img1 = lpips.im2tensor(lpips.load_image(os.path.join(args.image2smiles2image_save_path, file)))

            if (os.path.exists(os.path.join(args.input_images_path, file)), os.path.exists(os.path.join(args.image2smiles2image_save_path, file))):
                i = i + 1

            # Compute distance
            current_lpips_distance = loss_fn.forward(img0, img1)
            total_lpips_distance = total_lpips_distance + current_lpips_distance

        except Exception as e:
            print(e)

    average_lpips_distance = float(total_lpips_distance) / i

    print("The processed iamges is ", i)
    print("PSNR: %f, SSIM: %f, LPIPS: %f " % (psnr_val_rgb, ssim_val_rgb, average_lpips_distance))

文件夹格式：

需要注意的是：input与output 的图片个数要一致