| import pyiqa |
| import os |
| import cv2 |
| import glob |
| import torch |
| import numpy as np |
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| def ls(filename): |
| return sorted(glob.glob(filename)) |
|
|
| class NTIRE_evaluation(): |
| def __init__(self): |
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| self.iqa_psnr = pyiqa.create_metric('psnr', test_y_channel=False) |
| self.iqa_ssim = pyiqa.create_metric('ssim', test_y_channel=False) |
| self.iqa_lpips = pyiqa.create_metric('lpips') |
| self.iqa_dists = pyiqa.create_metric('dists') |
| self.iqa_niqe = pyiqa.create_metric('niqe') |
|
|
| def img2tensor(self, img, bgr2rgb, float32): |
| ''' |
| Numpy array to tensor. |
| |
| Args: |
| imgs (list[ndarray] | ndarray): Input images. |
| bgr2rgb (bool): Whether to change bgr to rgb. |
| float32 (bool): Whether to change to float32. |
| ''' |
| if img.shape[2] == 3 and bgr2rgb: |
| if img.dtype == 'float64': |
| img = img.astype('float32') |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| img = torch.from_numpy(img.transpose(2, 0, 1)) |
| if float32: |
| img = img.float() |
| return img |
| |
| def single_image_eval(self, in_path, ref_path): |
| lr = cv2.imread(in_path, cv2.IMREAD_COLOR) |
| lr = self.img2tensor(lr, bgr2rgb=True, float32=True).unsqueeze(0).contiguous() |
| hr = cv2.imread(ref_path, cv2.IMREAD_COLOR) |
| hr = self.img2tensor(hr, bgr2rgb=True, float32=True).unsqueeze(0).contiguous() |
| if (lr.shape != hr.shape): raise ValueError("Bad prediction shape. Prediction shape: {}\nSolution shape:{}".format(lr.shape, hr.shape)) |
|
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| hr = hr[..., 4:-4, 4:-4]/255. |
| lr = lr[..., 4:-4, 4:-4]/255. |
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| PSNR = self.iqa_psnr(lr, hr).item() |
| SSIM = self.iqa_ssim(lr, hr).item() |
| LPIPS = self.iqa_lpips(lr, hr).item() |
| DISTS = self.iqa_dists(lr, hr).item() |
| NIQE = self.iqa_niqe(lr).item() |
| |
| return {'psnr':PSNR, 'ssim':SSIM, 'lpips':LPIPS, 'dists':DISTS, 'niqe':NIQE} |
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| def folder_score(self, lr_list, gt_list): |
| psnr_list = [] |
| ssim_list = [] |
| lpips_list = [] |
| dists_list = [] |
| niqe_list = [] |
|
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| for p in list(zip(lr_list, gt_list)): |
| lr_path = p[0] |
| hr_path = p[1] |
| score_dict = self.single_image_eval(lr_path, hr_path) |
| psnr_list.append(score_dict['psnr']) |
| ssim_list.append(score_dict['ssim']) |
| lpips_list.append(score_dict['lpips']) |
| dists_list.append(score_dict['dists']) |
| niqe_list.append(score_dict['niqe']) |
| |
| psnr_mean = np.array(psnr_list).mean() |
| ssim_mean = np.array(ssim_list).mean() |
| lpips_mean = np.array(lpips_list).mean() |
| dists_mean = np.array(dists_list).mean() |
| niqe_mean = np.array(niqe_list).mean() |
|
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| return psnr_mean, ssim_mean, lpips_mean, dists_mean, niqe_mean |
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| |
| default_result_dir = './OpenRR-5k_val/output' |
| default_GT_dir = './OpenRR-5k_val/transmission_layer' |
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| output_dir = "./" |
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| if __name__ == '__main__': |
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| if not os.path.exists(output_dir): |
| os.makedirs(output_dir) |
| score_file = open(os.path.join(output_dir, 'scores.txt'), 'w') |
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| |
| hr_list = sorted(ls(os.path.join(default_GT_dir, '*.jpg'))) |
| sr_list = sorted(ls(os.path.join(default_result_dir, '*.jpg'))) |
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| if (len(sr_list) != len(hr_list)): raise ValueError("Bad number of predictions. # of predictions: {}\n # of solutions:{}".format(len(sr_list), len(hr_list))) |
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| EvalScheme = NTIRE_evaluation() |
| score = EvalScheme.folder_score(sr_list, hr_list) |
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| score_file.write("PSNR: %0.4f\n" % float(score[0])) |
| score_file.write("SSIM: %0.4f\n" % float(score[1])) |
| score_file.write("LPIPS: %0.4f\n" % float(score[2])) |
| score_file.write("DISTS: %0.4f\n" % float(score[3])) |
| score_file.write("NIQE: %0.4f\n" % float(score[4])) |
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| score_file.close() |
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