Spaces:
Runtime error
Runtime error
File size: 4,385 Bytes
f518bf0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
import argparse
import cv2
import os
import numpy as np
from skimage.metrics import mean_squared_error
from skimage.measure import compare_ssim
from skimage.metrics import structural_similarity
from skimage.metrics import peak_signal_noise_ratio
#import lpips
import torch
from tqdm import tqdm
#from niqe.niqe import compute_niqe
#criterion = lpips.LPIPS(net='vgg', lpips=True, pnet_rand=False, pretrained=True).cuda()
def rgb2ycbcr(im, only_y=True):
'''
same as matlab rgb2ycbcr
:parame img: uint8 or float ndarray
'''
in_im_type = im.dtype
im = im.astype(np.float64)
if in_im_type != np.uint8:
im *= 255.
# convert
if only_y:
rlt = np.dot(im, np.array([65.481, 128.553, 24.966])/ 255.0) + 16.0
else:
rlt = np.matmul(im, np.array([[65.481, -37.797, 112.0 ],
[128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]])/255.0) + [16, 128, 128]
if in_im_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_im_type)
def rgb2ycbcrTorch(im, only_y=True):
'''
same as matlab rgb2ycbcr
Input:
im: float [0,1], N x 3 x H x W
only_y: only return Y channel
'''
im_temp = im.permute([0,2,3,1]) * 255.0 # N x H x W x C --> N x H x W x C, [0,255]
# convert
if only_y:
rlt = torch.matmul(im_temp, torch.tensor([65.481, 128.553, 24.966],
device=im.device, dtype=im.dtype).view([3,1])/ 255.0) + 16.0
else:
rlt = torch.matmul(im_temp, torch.tensor([[65.481, -37.797, 112.0 ],
[128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]],
device=im.device, dtype=im.dtype)/255.0) + \
torch.tensor([16, 128, 128]).view([-1, 1, 1, 3])
rlt /= 255.0
rlt.clamp_(0.0, 1.0)
return rlt.permute([0, 3, 1, 2])
def readim(file):
# print(file)
img = cv2.imread(file)
img = img.astype(np.float32)
return img / 255.
def loadfiles(folder):
files = os.listdir(folder)
return natsorted(files)
def resize(im, size, crop=True):
if crop:
return im[:size[0], :size[1]]
else:
return cv2.resize(im, size)
from natsort import natsorted
def np2torch(img):
im = img.astype(np.float32) / 255
im = torch.tensor(im).permute((2,0,1)).unsqueeze(0)
return im.cuda()
def compute_metrics(path1, path2, ycbcr=True):
print(path1)
files1 = loadfiles(path1)
files2 = loadfiles(path2)
print(len(files1), len(files2))
psnr = []
ssim = []
mse = []
lpips = []
niqe = []
crop = False
for file1, file2 in tqdm(zip(files1, files2)):
img1 = readim(os.path.join(path1, file1))
img2 = readim(os.path.join(path2, file2))
if img1.shape != img2.shape:
if not crop:
img1 = resize(img1, img2.shape[:2][::-1], False)
else:
img1 = resize(img1, img2.shape, True)
# print(img1.shape, img2.shape, img1.max())
MSE = mean_squared_error(img1, img2)
if ycbcr:
img1 = rgb2ycbcr(img1, True)
img2 = rgb2ycbcr(img2, True)
diff = (img2 - img1)
# print(diff.mean(), diff.max(), diff.min(), diff.shape)
PSNR = peak_signal_noise_ratio(img1, img2, data_range=1)
SSIM = structural_similarity(img1, img2, win_size=11, multichannel=False if ycbcr else True, data_range=1)
mse.append(MSE)
psnr.append(PSNR)
ssim.append(SSIM)
mean_mse, mean_psnr, mean_ssim = np.mean(mse), np.mean(psnr), np.mean(ssim)
print(mean_mse, mean_psnr, mean_ssim)
return mean_mse, mean_psnr, mean_ssim
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# path setting
parser.add_argument('--path1', type=str,default= "") # modify the experiments name-->modify all save path
parser.add_argument('--path2', type=str,default= "")
args = parser.parse_args()
path1 = ''
path2 = ''
if len(args.path1) != 0:
path1 = args.path1
if len(args.path2) != 0:
path2 = args.path2
compute_metrics(path1, path2, True)
|