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evaluatuion.py
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evaluatuion.py
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import os
import math
import argparse
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from PIL import Image
import numpy as np
from torch.autograd import Variable
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from data.dataloader import devdata
from scipy import signal, ndimage
import gauss
parser = argparse.ArgumentParser()
parser.add_argument('--target_path', type=str, default='',
help='results')
parser.add_argument('--gt_path', type=str, default='',
help='labels')
args = parser.parse_args()
sum_psnr = 0
sum_ssim = 0
sum_AGE = 0
sum_pCEPS = 0
sum_pEPS = 0
sum_mse = 0
count = 0
sum_time = 0.0
l1_loss = 0
img_path = args.target_path
gt_path = args.gt_path
def ssim(img1, img2, cs_map=False):
"""Return the Structural Similarity Map corresponding to input images img1
and img2 (images are assumed to be uint8)
This function attempts to mimic precisely the functionality of ssim.m a
MATLAB provided by the author's of SSIM
https://ece.uwaterloo.ca/~z70wang/research/ssim/ssim_index.m
"""
img1 = img1.astype(float)
img2 = img2.astype(float)
size = min(img1.shape[0], 11)
sigma = 1.5
window = gauss.fspecial_gauss(size, sigma)
K1 = 0.01
K2 = 0.03
L = 255 #bitdepth of image
C1 = (K1 * L) ** 2
C2 = (K2 * L) ** 2
# import pdb;pdb.set_trace()
mu1 = signal.fftconvolve(img1, window, mode = 'valid')
mu2 = signal.fftconvolve(img2, window, mode = 'valid')
mu1_sq = mu1 * mu1
mu2_sq = mu2 * mu2
mu1_mu2 = mu1 * mu2
sigma1_sq = signal.fftconvolve(img1 * img1, window, mode = 'valid') - mu1_sq
sigma2_sq = signal.fftconvolve(img2 * img2, window, mode = 'valid') - mu2_sq
sigma12 = signal.fftconvolve(img1 * img2, window, mode = 'valid') - mu1_mu2
if cs_map:
return (((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)),
(2.0 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2))
else:
return ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
def msssim(img1, img2):
"""This function implements Multi-Scale Structural Similarity (MSSSIM) Image
Quality Assessment according to Z. Wang's "Multi-scale structural similarity
for image quality assessment" Invited Paper, IEEE Asilomar Conference on
Signals, Systems and Computers, Nov. 2003
Author's MATLAB implementation:-
http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
"""
level = 5
weight = np.array([0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
downsample_filter = np.ones((2, 2)) / 4.0
# im1 = img1.astype(np.float64)
# im2 = img2.astype(np.float64)
mssim = np.array([])
mcs = np.array([])
for l in range(level):
ssim_map, cs_map = ssim(img1, img2, cs_map = True)
mssim = np.append(mssim, ssim_map.mean())
mcs = np.append(mcs, cs_map.mean())
filtered_im1 = ndimage.filters.convolve(img1, downsample_filter,
mode = 'reflect')
filtered_im2 = ndimage.filters.convolve(img2, downsample_filter,
mode = 'reflect')
im1 = filtered_im1[: : 2, : : 2]
im2 = filtered_im2[: : 2, : : 2]
# Note: Remove the negative and add it later to avoid NaN in exponential.
sign_mcs = np.sign(mcs[0 : level - 1])
sign_mssim = np.sign(mssim[level - 1])
mcs_power = np.power(np.abs(mcs[0 : level - 1]), weight[0 : level - 1])
mssim_power = np.power(np.abs(mssim[level - 1]), weight[level - 1])
return np.prod(sign_mcs * mcs_power) * sign_mssim * mssim_power
def ImageTransform(loadSize, cropSize):
return Compose([
Resize(size=loadSize, interpolation=Image.BICUBIC),
# RandomCrop(size=cropSize),
#RandomHorizontalFlip(p=0.5),
ToTensor(),
])
def visual(image):
im =(image).transpose(1,2).transpose(2,3).detach().cpu().numpy()
Image.fromarray(im[0].astype(np.uint8)).show()
imgData = devdata(dataRoot=img_path, gtRoot=gt_path)
data_loader = DataLoader(imgData, batch_size=1, shuffle=True, num_workers=0, drop_last=False)
for k, (img,lbl,path) in enumerate(data_loader):
##import pdb;pdb.set_trace()
mse = ((lbl - img)**2).mean()
sum_mse += mse
print(path,count, 'mse: ', mse)
if mse == 0:
continue
count += 1
psnr = 10 * math.log10(1/mse)
sum_psnr += psnr
print(path,count, ' psnr: ', psnr)
#l1_loss += nn.L1Loss()(img, lbl)
R = lbl[0,0,:, :]
G = lbl[0,1,:, :]
B = lbl[0,2,:, :]
YGT = .299 * R + .587 * G + .114 * B
R = img[0,0,:, :]
G = img[0,1,:, :]
B = img[0,2,:, :]
YBC = .299 * R + .587 * G + .114 * B
Diff = abs(np.array(YBC*255) - np.array(YGT*255)).round().astype(np.uint8)
AGE = np.mean(Diff)
print(' AGE: ', AGE)
mssim = msssim(np.array(YGT*255), np.array(YBC*255))
sum_ssim += mssim
print(count, ' ssim:', mssim)
threshold = 20
Errors = Diff > threshold
EPs = sum(sum(Errors)).astype(float)
pEPs = EPs / float(512*512)
print(' pEPS: ' , pEPs)
sum_pEPS += pEPs
########################## CEPs and pCEPs ################################
structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
sum_AGE+=AGE
erodedErrors = ndimage.binary_erosion(Errors, structure).astype(Errors.dtype)
CEPs = sum(sum(erodedErrors))
pCEPs = CEPs / float(512*512)
print(' pCEPS: ' , pCEPs)
sum_pCEPS += pCEPs
print(sum_psnr)
print('avg mse:', sum_mse / count)
print('average psnr:', sum_psnr / count)
print('average ssim:', sum_ssim / count)
print('average AGE:', sum_AGE / count)
print('average pEPS:', sum_pEPS / count)
print('average pCEPS:', sum_pCEPS / count)