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calculate_metrics.py
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calculate_metrics.py
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import matplotlib.pyplot as plt
import cv2
from PIL import Image
import numpy as np
import math
import os
import tifffile as tiff
from skimage import color
def ProPhotoRGB2XYZ(pp_rgb,reverse=False):
if not reverse:
M = [[0.7976749, 0.1351917, 0.0313534], \
[0.2880402, 0.7118741, 0.0000857], \
[0.0000000, 0.0000000, 0.8252100]]
else:
M = [[ 1.34594337, -0.25560752, -0.05111183],\
[-0.54459882, 1.5081673, 0.02053511],\
[ 0, 0, 1.21181275]]
M = np.array(M)
sp = pp_rgb.shape
xyz = np.transpose(np.dot(M, np.transpose(pp_rgb.reshape((sp[0] * sp[1], sp[2])))))
return xyz.reshape((sp[0], sp[1], 3))
def linearize_ProPhotoRGB(pp_rgb, reverse=False):
if not reverse:
gamma = 1.8
else:
gamma = 1.0/1.8
pp_rgb = np.power(pp_rgb, gamma)
return pp_rgb
def XYZ_chromatic_adapt(xyz, src_white='D65', dest_white='D50'):
if src_white == 'D65' and dest_white == 'D50':
M = [[1.0478112, 0.0228866, -0.0501270], \
[0.0295424, 0.9904844, -0.0170491], \
[-0.0092345, 0.0150436, 0.7521316]]
elif src_white == 'D50' and dest_white == 'D65':
M = [[0.9555766, -0.0230393, 0.0631636], \
[-0.0282895, 1.0099416, 0.0210077], \
[0.0122982, -0.0204830, 1.3299098]]
else:
raise UtilCnnImageEnhanceError('invalid pair of source and destination white reference %s,%s')\
% (src_white, dest_white)
M = np.array(M)
sp = xyz.shape
assert sp[2] == 3
xyz = np.transpose(np.dot(M, np.transpose(xyz.reshape((sp[0] * sp[1], 3)))))
return xyz.reshape((sp[0], sp[1], 3))
def read_tiff_16bit_img_into_XYZ(tiff_fn, exposure=0):
pp_rgb = tiff.imread(tiff_fn)
pp_rgb = np.float64(pp_rgb) / (2 ** 16 - 1.0)
if not pp_rgb.shape[2] == 3:
print('pp_rgb shape',pp_rgb.shape)
raise UtilImageError('image channel number is not 3')
pp_rgb = linearize_ProPhotoRGB(pp_rgb)
pp_rgb *= np.power(2, exposure)
xyz = ProPhotoRGB2XYZ(pp_rgb)
xyz = XYZ_chromatic_adapt(xyz, src_white='D50', dest_white='D65')
return xyz
def read_tiff_16bit_img_into_LAB(tiff_fn, exposure=0, normalize_Lab=False):
xyz = read_tiff_16bit_img_into_XYZ(tiff_fn, exposure)
lab = color.xyz2lab(xyz)
if normalize_Lab:
normalize_Lab_image(lab)
return lab
def calculate_Lab_RMSE(img1, img2):
# img1 and img2 have range [0, 255]
#img1 = img1.astype(np.float64)#/255
#img2 = img2.astype(np.float64)#/255
num_pix = img1.shape[0]*img1.shape[1]
Lab_RMSE = np.mean(np.sqrt(np.sum((img1 - img2)**2, axis=2))) # correct 1
#Lab_RMSE = np.sum(np.sqrt(np.sum((img1 - img2) ** 2, axis=2))) / num_pix # correct 2 same with correct 1
#Lab_RMSE = np.sqrt(np.sum(((img1 - img2) ** 2)) / num_pix) # a liiter different
return Lab_RMSE
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_my(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_my(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim_my(img1, img2))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim_my(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.')
# ##########################################################
# Please specify the paths for input dir and ground truth dir.
input_path=""
GT_path=""
input_fname_list = os.listdir(input_path)
input_fname_list.sort()
input_path_list = [os.path.join(input_path, fname) for fname in input_fname_list]
GT_fname_list = os.listdir(GT_path)
GT_fname_list.sort()
GT_path_list = [os.path.join(GT_path, fname) for fname in GT_fname_list]
assert len(input_path_list) == len(GT_path_list)
print(len(input_path_list))
psnr_list = []
ssim_list = []
Lab_RMSE_list = []
for i in range(len(input_path_list)):
assert input_fname_list[i].split('.')[0] == GT_fname_list[i].split('.')[0]
img1 = cv2.imread(input_path_list[i], cv2.IMREAD_COLOR)
img2 = cv2.imread(GT_path_list[i], cv2.IMREAD_COLOR)
img1_rgb = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
img2_rgb = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
img1_lab = cv2.cvtColor(img1, cv2.COLOR_BGR2Lab)
img2_lab = cv2.cvtColor(img2, cv2.COLOR_BGR2Lab)
psnr = calculate_psnr(img1_rgb, img2_rgb)
ssim = calculate_ssim(img1_rgb, img2_rgb)
Lab_RMSE = calculate_Lab_RMSE(img1_lab, img2_lab)
print('img: {} PSNR: {} SSIM: {} Lab_RMSE: {}'.format(input_fname_list[i].split('.')[0], psnr, ssim, Lab_RMSE))
psnr_list.append(psnr)
ssim_list.append(ssim)
Lab_RMSE_list.append(Lab_RMSE)
print('Average PSNR: {} SSIM: {} Lab_RMSE: {} Total image: {}'.format(np.mean(psnr_list), np.mean(ssim_list), np.mean(Lab_RMSE_list), len(psnr_list)))