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calculate_psnr_ssim_NH_GTA5.py
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import os
import cv2
import torch
import numpy as np
# This script is adapted from the following repository: https://github.com/JingyunLiang/SwinIR
def calculate_psnr(img1, img2, test_y_channel=False):
"""Calculate PSNR (Peak Signal-to-Noise Ratio).
Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Args:
img1 (ndarray): Images with range [0, 255].
img2 (ndarray): Images with range [0, 255].
test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
Returns:
float: psnr result.
"""
assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
assert img1.shape[2] == 3
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
return 20. * np.log10(255. / np.sqrt(mse))
def _ssim(img1, img2):
"""Calculate SSIM (structural similarity) for one channel images.
It is called by func:`calculate_ssim`.
Args:
img1 (ndarray): Images with range [0, 255] with order 'HWC'.
img2 (ndarray): Images with range [0, 255] with order 'HWC'.
Returns:
float: ssim result.
"""
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def calculate_ssim(img1, img2, test_y_channel=False):
"""Calculate SSIM (structural similarity).
Ref:
Image quality assessment: From error visibility to structural similarity
The results are the same as that of the official released MATLAB code in
https://ece.uwaterloo.ca/~z70wang/research/ssim/.
For three-channel images, SSIM is calculated for each channel and then
averaged.
Args:
img1 (ndarray): Images with range [0, 255].
img2 (ndarray): Images with range [0, 255].
test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
Returns:
float: ssim result.
"""
assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
assert img1.shape[2] == 3
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
ssims = []
for i in range(img1.shape[2]):
ssims.append(_ssim(img1[..., i], img2[..., i]))
return np.array(ssims).mean()
def to_y_channel(img):
"""Change to Y channel of YCbCr.
Args:
img (ndarray): Images with range [0, 255].
Returns:
(ndarray): Images with range [0, 255] (float type) without round.
"""
img = img.astype(np.float32) / 255.
if img.ndim == 3 and img.shape[2] == 3:
img = bgr2ycbcr(img, y_only=True)
img = img[..., None]
return img * 255.
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
convertion functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
(ndarray): The converted image with type of np.float32 and range of
[0, 1].
"""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type.
It converts the image to desired type and range. If `dst_type` is np.uint8,
images will be converted to np.uint8 type with range [0, 255]. If
`dst_type` is np.float32, it converts the image to np.float32 type with
range [0, 1].
It is mainly used for post-processing images in colorspace convertion
functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The image to be converted with np.float32 type and
range [0, 255].
dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
converts the image to np.uint8 type with range [0, 255]. If
dst_type is np.float32, it converts the image to np.float32 type
with range [0, 1].
Returns:
(ndarray): The converted image with desired type and range.
"""
if dst_type not in (np.uint8, np.float32):
raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
def bgr2ycbcr(img, y_only=False):
"""Convert a BGR image to YCbCr image.
The bgr version of rgb2ycbcr.
It implements the ITU-R BT.601 conversion for standard-definition
television. See more details in
https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
In OpenCV, it implements a JPEG conversion. See more details in
https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
y_only (bool): Whether to only return Y channel. Default: False.
Returns:
ndarray: The converted YCbCr image. The output image has the same type
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img)
if y_only:
out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
else:
out_img = np.matmul(
img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
out_img = _convert_output_type_range(out_img, img_type)
return out_img
base_path = '/Users/yeyingjin/Dropbox/nighttime/ACMMM23/0_ACMMM23_RESULTS/'
# dataset_name = 'NHM'
# dataset_name = 'NHR'
# dataset_name = 'NHC'
dataset_name = 'GTA5'
gt_path = base_path + dataset_name + '/img_1/'
results_path = base_path + dataset_name + '/img_2/'
imgslist = []
dir_1 = sorted(os.listdir(results_path))
for j in range(len(dir_1)):
imgslist.append(results_path +dir_1[j])
print('-len(imgslist)-',len(imgslist))
gtslist = []
for i in range(len(imgslist)):
gts = imgslist[i].replace(results_path,gt_path)
gtslist.append(gts)
print(gtslist[0])
print(gtslist[-1])
print(imgslist[0])
print(imgslist[-1])
print('-len(imgslist)-',len(imgslist),len(gtslist))
cumulative_psnr, cumulative_ssim = 0, 0
count= 0
for i in range(len(imgslist)):
res = cv2.imread(imgslist[i], cv2.IMREAD_COLOR)
gt = cv2.imread(gtslist[i], cv2.IMREAD_COLOR)
if i%100==0:
print('current i:',i)
if res.shape == gt.shape:
count = count + 1
cur_psnr = calculate_psnr(res, gt, test_y_channel=True)
cur_ssim = calculate_ssim(res, gt, test_y_channel=True)
#cur_psnr = calculate_psnr(res, gt, test_y_channel=False)
#cur_ssim = calculate_ssim(res, gt, test_y_channel=False)
cumulative_psnr += cur_psnr
cumulative_ssim += cur_ssim
if count%100==0:
print('Testing set,'+str(i)+' PSNR is %.4f, SSIM is %.4f ' % (cumulative_psnr / (count), cumulative_ssim / (count)))
print('Testing set, PSNR is %.4f, SSIM is %.4f ' % (cumulative_psnr / count, cumulative_ssim / count), count)
print(results_path)
#NHR, PSNR is 27.3292, SSIM is 0.9328 897
#NHM, PSNR is 34.7331, SSIM is 0.9420 350
#NHC, PSNR is 39.2277, SSIM is 0.9765 550
#GTA5, PSNR is 30.5298, SSIM is 0.9060 77