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calculate_APD_RMSE.py
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calculate_APD_RMSE.py
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'''
calculate the PSNR and SSIM.
same as MATLAB's results
'''
import os
import math
import numpy as np
import cv2
import glob
from natsort import natsorted
def main():
# Configurations
# GT - Ground-truth;
# Gen: Generated / Restored / Recovered images
folder_GT = '/home/jjp/DeepMIH/image/cover/'
folder_Gen = '/home/jjp/DeepMIH/image/steg_1/'
crop_border = 1
suffix = '_secret_rev' # suffix for Gen images
APD_all = []
RMSE_all = []
img_list = sorted(glob.glob(folder_GT + '/*'))
img_list = natsorted(img_list)
for i, img_path in enumerate(img_list):
base_name = os.path.splitext(os.path.basename(img_path))[0]
# base_name = base_name[:5]
im_GT = cv2.imread(img_path) / 255.
# print(base_name)
# print(img_path)
# print(os.path.join(folder_Gen, base_name + '.png'))
im_Gen = cv2.imread(os.path.join(folder_Gen, base_name + '.png')) / 255.
im_GT_in = im_GT
im_Gen_in = im_Gen
# # crop borders
# if im_GT_in.ndim == 3:
# cropped_GT = im_GT_in[crop_border:-crop_border, crop_border:-crop_border, :]
# cropped_Gen = im_Gen_in[crop_border:-crop_border, crop_border:-crop_border, :]
# elif im_GT_in.ndim == 2:
# cropped_GT = im_GT_in[crop_border:-crop_border, crop_border:-crop_border]
# cropped_Gen = im_Gen_in[crop_border:-crop_border, crop_border:-crop_border]
# else:
# raise ValueError('Wrong image dimension: {}. Should be 2 or 3.'.format(im_GT_in.ndim))
# calculate PSNR and SSIM
APD = calculate_apd(im_GT_in * 255, im_Gen_in * 255)
RMSE = calculate_rmse(im_GT_in * 255, im_Gen_in * 255)
print('{:3d} - {:25}. \tAPD: {:.6f} , \tRMSE: {:.6f}'.format(
i + 1, base_name, APD, RMSE))
APD_all.append(APD)
RMSE_all.append(RMSE)
print('Average: APD: {:.6f} , RMSE: {:.6f}'.format(
sum(APD_all) / len(APD_all),
sum(RMSE_all) / len(RMSE_all)))
def calculate_rmse(img1, img2):
"""
Root Mean Squared Error
Calculated individually for all bands, then averaged
"""
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
rmse = np.sqrt(mse)
return np.mean(rmse)
def calculate_apd(img1, img2):
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
apd = np.mean(np.abs(img1 - img2))
if apd == 0:
return float('inf')
return np.mean(apd)
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 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)
if __name__ == '__main__':
main()