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calculate_PSNR_SSIM.py
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calculate_PSNR_SSIM.py
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'''
calculate the PSNR and SSIM.
same as MATLAB's results
'''
import math
import os
import sys
import cv2
import numpy as np
sys.path.append('../')
from matplotlib import pyplot as plt
def main(folder_GT,folder_Gen):
# Configurations
# GT - Ground-truth;
# Gen: Generated / Restored / Recovered images
# folder_GT = '/mnt/cv/code/IMDN/Test_Datasets/Set5'
# folder_Gen = '/mnt/cv/code/IMDN/results/Set5/x2'
test_Y =False
PSNR_all = []
SSIM_all = []
img_list = os.listdir(folder_GT)
imgreal_list = os.listdir(folder_Gen)
file_num = len(img_list)
for i in range(file_num):
path_GT=os.path.join(folder_GT,img_list[i])
im_GT = cv2.imread(path_GT,0) / 255.
path_Gen=os.path.join(folder_Gen,imgreal_list[i])
im_Gen = cv2.imread(path_Gen,0) / 255.
# calculate PSNR and SSIM
PSNR = calculate_psnr(im_GT,im_Gen)
SSIM = calculate_ssim(im_GT*255,im_Gen*255)
print('the {:3d}th - {:25}. \tPSNR: {:.6f} dB, \tSSIM: {:.6f}'.format(
i + 1, img_list[i], PSNR, SSIM))
PSNR_all.append(PSNR)
SSIM_all.append(SSIM)
print('Average: PSNR: {:.6f} dB, SSIM: {:.6f}'.format(
sum(PSNR_all) / len(PSNR_all),
sum(SSIM_all) / len(SSIM_all)))
#return [int(folder_Gen.split('/')[-1].split('_')[-1][2:]), sum(PSNR_all) / len(PSNR_all), sum(SSIM_all) / len(SSIM_all)]
'''
def calculate_psnr(img1, img2):
# img1 and img2 have range [0, 255]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
'''
def calculate_psnr(img1, img2):
mse = np.mean((img1 - img2) ** 2)
if np.max(img1) > 128.:
PIXEL_MAX = 255.
else:
PIXEL_MAX = 1.
if mse < 1.0e-10:
return 100
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def ssim(img1, img2):
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] # valid
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):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1, img2))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
def bgr2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type)
def draw_nf_rf_vs_psnr_ssim(NF_X, RF_X, NF_Y_PSNR, NF_Y_SSIM, RF_Y_PSNR, RF_Y_SSIM):
plt.subplot(2, 2, 1)
plt.title('PSNR VS channel', fontsize=15) # 标题，并设定字号大小
plt.xlabel("channel", fontsize=14) # 设置x轴，并设定字号大小
plt.ylabel("PSNR", fontsize=14) # 设置y轴，并设定字号大小
plt.grid()
plt.plot(NF_X[:-4], NF_Y_PSNR[:-4], color="deeppink", linewidth=2, marker='^')
plt.plot(NF_X[-4:], NF_Y_PSNR[-4:], color="g", linewidth=2, marker='^',linestyle=':')
plt.subplot(2, 2, 2)
plt.title('SSIM VS channel', fontsize=15) # 标题，并设定字号大小
plt.xlabel("channel", fontsize=14) # 设置x轴，并设定字号大小
plt.ylabel("SSIM", fontsize=14) # 设置y轴，并设定字号大小
plt.grid()
plt.plot(NF_X[:-4], NF_Y_SSIM[:-4], color="deeppink", linewidth=2, marker='^')
plt.plot(NF_X[-4:], NF_Y_SSIM[-4:], color="g", linewidth=2, marker='^',linestyle=':')
plt.subplot(2, 2, 3)
plt.title('PSNR VS RF', fontsize=15) # 标题，并设定字号大小
plt.xlabel("channel", fontsize=14) # 设置x轴，并设定字号大小
plt.ylabel("PSNR", fontsize=14) # 设置y轴，并设定字号大小
plt.grid()
plt.plot(RF_X, RF_Y_PSNR, color="deeppink", linewidth=2, marker='^')
plt.subplot(2, 2, 4)
plt.title('SSIM VS RF', fontsize=15) # 标题，并设定字号大小
plt.xlabel("channel", fontsize=14) # 设置x轴，并设定字号大小
plt.ylabel("SSIM", fontsize=14) # 设置y轴，并设定字号大小
plt.grid()
plt.plot(RF_X, RF_Y_SSIM, color="deeppink", linewidth=2, marker='^')
plt.show()
if __name__ == '__main__':
folder_GT='./results/Derain/test_220/images'
folder_Gen='./results/Derain/test_220/real'
# folder_GT='../../dataset/RAIN800_DID_MDN_dataset/Rain12'
# folder_Gen='../rgb_in_RAIN1400_FSPANet_detal_15/Rain12'
# folder_GT='../../dataset/RAIN800_DID_MDN_dataset/SPANet_test'
# folder_Gen='../rgb_in_RAIN1400_FSPANet_detal_15/SPANet_test'
# folder_GT='../../dataset/RAIN800_DID_MDN_dataset/rain100L_test'
# folder_Gen='../rgb_in_RAIN1400_FSPANet_detal_15/rain100L_test'
# folder_GT='../../dataset/RAIN800_DID_MDN_dataset/rain100H_test'
# folder_Gen='../rgb_in_RAIN1400_FSPANet_detal_15/rain100H_test'
# folder_GT='../../dataset/RescanDataset/test'
# folder_Gen='../rgb_in_RAIN1400_FSPANet_detal_15/test'
# DDN RescanNet PReNet SPANet FSPANet_detail FSPANet_X DSC
main(folder_GT,folder_Gen)