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metrics.py
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import random
import numpy as np
import os
from os.path import join
from torch.utils.data import Dataset
from scipy import misc
import imageio
import cv2
import torch
import math
from tqdm import tqdm
import lpips
import glob
from skimage.metrics import structural_similarity as ssim
from skimage import exposure
import argparse
def calc_psnr_np(sr, hr, range=255.):
# shave = 2
diff = (sr.astype(np.float32) - hr.astype(np.float32)) / range
# diff = diff[shave:-shave, shave:-shave, :]
total_mse = np.power(diff, 2)
total_psnr = -10 * math.log10(total_mse.mean())
return total_psnr
def calc_psnr_corner(sr, hr, range=255.):
# shave = 2
diff = (sr.astype(np.float32) - hr.astype(np.float32)) / range
total_mse = np.power(diff, 2).mean()
return total_mse
def lpips_norm(img):
img = img[:, :, :, np.newaxis].transpose((3, 2, 0, 1))
img = img / (255. / 2.) - 1
return torch.Tensor(img).to(device)
def calc_lpips(x_mask_out, x_canon, loss_fn_alex_1, loss_fn_alex_0=None):
lpips_mask_out = lpips_norm(x_mask_out)
lpips_canon = lpips_norm(x_canon)
# LPIPS_0 = loss_fn_alex_0(lpips_mask_out, lpips_canon)
LPIPS_1 = loss_fn_alex_1(lpips_mask_out, lpips_canon)
return LPIPS_1.detach().cpu() #, LPIPS_1.detach().cpu()
def crop_part(out, ref, s):
H, W, _ = out.shape
c_H, c_W = (H - H//s)//2, (W - W//s)//2
top = [out[:, 0:c_W,...], ref[:, 0:c_W,...]]
left = [out[:c_H, c_W:c_W+W//s,...], ref[:c_H, c_W:c_W+W//s,...]]
right = [out[c_H+H//s:, c_W:c_W+W//s, ...], ref[c_H+H//s:, c_W:c_W+W//s, ...]]
bottom = [out[:, c_W+W//s:,...], ref[:, c_W+W//s:,...]]
center = [out[c_H:c_H+H//s, c_W:c_W+W//s,...], ref[c_H:c_H+H//s, c_W:c_W+W//s,...]]
return top, left, right, bottom, center
def calc_metrics(out, ref, s):
parts = crop_part(out, ref, s)
psnr_corner = 0.0
ssim_corner = 0.0
lpips_corner = 0.0
areas = 0.0
for part in parts[:-1]:
total_psnr = calc_psnr_corner(part[0], part[1])
SSIM = ssim(part[0], part[1], win_size=11, data_range=255, multichannel=True, gaussian_weights=True)
LPIPS_1 = calc_lpips(part[0], part[1], loss_fn_alex_1)
area = part[0].shape[0] * part[0].shape[1]
psnr_corner += total_psnr * area
ssim_corner += SSIM
lpips_corner += LPIPS_1
areas += area
psnr_corner, ssim_corner, lpips_corner = - 10 * math.log10(psnr_corner / areas), ssim_corner / 4, lpips_corner / 4
center_psnr = calc_psnr_np(parts[-1][0], parts[-1][1])
center_SSIM = ssim(parts[-1][0], parts[-1][1], win_size=11, data_range=255, multichannel=True, gaussian_weights=True)
center_lpips = calc_lpips(parts[-1][0], parts[-1][1], loss_fn_alex_1)
total_psnr = calc_psnr_np(out, ref)
total_ssim = ssim(out, ref, win_size=11, data_range=255, multichannel=True, gaussian_weights=True)
total_lpips = calc_lpips(out, ref, loss_fn_alex_1)
return [psnr_corner, center_psnr, total_psnr, ssim_corner, center_SSIM, total_ssim, lpips_corner, center_lpips, total_lpips]
def str2bool(v):
return v.lower() in ('yes', 'y', 'true', 't', '1')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Test for argparse')
parser.add_argument('--name', '-n', help='name')
parser.add_argument('--device', default="0")
parser.add_argument('--load_iter', default="401")
parser.add_argument('--full_res', type=str2bool, default=True)
parser.add_argument('--cam', type=str2bool, default=False)
parser.add_argument('--dataroot', type=str, default='')
args = parser.parse_args()
print(args)
args.device = "cuda:" + args.device
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
loss_fn_alex_1 = lpips.LPIPS(net='alex', version='0.1').to(device)
files = [
'./ckpt/' + args.name + '/'
]
if args.full_res:
if args.cam:
s = 2
ori_target = args.dataroot + '/CameraFusion/test_HR/'
camera_names = {'iphone': ['IMG']}
else:
s = 4
ori_target = args.dataroot + '/Nikon/test_HR/'
# camera_names = {'canon': ['IMG', 'Canon', '0510'],
# 'sony': ['sony'],
# 'nikon': ['DSC'],
# 'oly': ['P11'],
# 'pan': ['pan'] }
camera_names = {'nikon': ['DSC']}
else:
print('Error, please set --full_res \'True\'.')
for file in files:
if args.full_res:
log_dir = '%s/log_full_%s.txt' % (file, args.load_iter)
else:
log_dir = '%s/log_patch_%s.txt' % (file, args.load_iter)
f = open(log_dir, 'a')
for camera in sorted(camera_names.keys()):
names = []
for file_name in os.listdir(ori_target):
for i in camera_names[camera]:
if file_name.startswith(i):
names.append(file_name)
if names == []:
continue
names = sorted(names)
f.write('\n=============%s=============\n' % (camera))
print('\n=============%s=============\n' % (camera))
ori_metrics = np.zeros([len(names), 9])
i = 0
for name in tqdm(names):
if args.full_res:
pre_out = cv2.imread(file + 'sr_full_' + args.load_iter + '/' + name)[..., ::-1]
else:
pre_out = cv2.imread(file + 'sr_patch_' + args.load_iter + '/' + name)[..., ::-1]
# pre_out = cv2.imread(file + 'sr_full_400/' + name)[..., ::-1]
out = pre_out
pre_ref = cv2.imread(ori_target + name)[..., ::-1]
ref = pre_ref
ori_metrics[i] = calc_metrics(out, ref, s)
f.write('name: %s, \n corner_psnr: %.2f, \t center_psnr: %.2f, \t total_psnr: %.2f, \
\n corner_SSIM: %.4f, \t center_SSIM: %.4f, \t total_SSIM: %.4f, \
\n corner_LPIPS: %.3f, \t center_LPIPS: %.3f, \t total_LPIPS: %.3f \t \n' \
% (name, ori_metrics[i][0], ori_metrics[i][1], ori_metrics[i][2], ori_metrics[i][3], ori_metrics[i][4],
ori_metrics[i][5], ori_metrics[i][6], ori_metrics[i][7], ori_metrics[i][8]))
print('name: %s, \n corner_psnr: %.2f, \t center_psnr: %.2f, \t total_psnr: %.2f, \
\n corner_SSIM: %.4f, \t center_SSIM: %.4f, \t total_SSIM: %.4f, \
\n corner_LPIPS: %.3f, \t center_LPIPS: %.3f, \t total_LPIPS: %.3f \t \n' \
% (name, ori_metrics[i][0], ori_metrics[i][1], ori_metrics[i][2], ori_metrics[i][3], ori_metrics[i][4],
ori_metrics[i][5], ori_metrics[i][6], ori_metrics[i][7], ori_metrics[i][8]))
i = i + 1
metrics_mean = np.mean(ori_metrics, axis=0)
f.write('\n camera: %s ====== \
\n corner_psnr: %.2f, \t center_psnr: %.2f, \t total_psnr: %.2f, \
\n corner_SSIM: %.4f, \t center_SSIM: %.4f, \t total_SSIM: %.4f, \
\n corner_LPIPS: %.3f, \t center_LPIPS: %.3f, \t total_LPIPS: %.3f \t \n' \
% (camera, metrics_mean[0], metrics_mean[1], metrics_mean[2],
metrics_mean[3], metrics_mean[4], metrics_mean[5],
metrics_mean[6], metrics_mean[7], metrics_mean[8]))
print('\n camera: %s ====== \
\n corner_psnr: %.2f, \t center_psnr: %.2f, \t total_psnr: %.2f, \
\n corner_SSIM: %.4f, \t center_SSIM: %.4f, \t total_SSIM: %.4f, \
\n corner_LPIPS: %.3f, \t center_LPIPS: %.3f, \t total_LPIPS: %.3f \t \n' \
% (camera, metrics_mean[0], metrics_mean[1], metrics_mean[2],
metrics_mean[3], metrics_mean[4], metrics_mean[5],
metrics_mean[6], metrics_mean[7], metrics_mean[8]))
f.flush()
f.close()