/
utils.py
173 lines (127 loc) · 4.6 KB
/
utils.py
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import numpy as np
import os
from scipy.stats import multivariate_normal
from matplotlib import pyplot as plt
from PIL import Image
from skimage import io as skio
from skimage import color as skco
from skimage import transform
import warnings
import ipdb
##################################################################################
#
# image read/write operation
#
##################################################################################
def img_route(img_id = "001", method = "LR", scale = 2):
# dataset_route = "/home/e/Eulring/GitProject/Mixture-Function-Sparse-Representation/dataset/Set14"
# dataset_route = "/Users/eulring/Efile/Dataset/Set14"
dataset_route = "./Set14"
img_route = os.path.join(dataset_route, "image_SRF_"+str(scale))
img_name = '/img_' + img_id + '_SRF_' + str(scale) + '_' + method + '.png'
print(img_route + img_name)
return img_route + img_name
#=================================================================================
# turn the img_route to the normalized numpy array of y channel directly
def numpy_from_img_route(img_route, imagecut = False, batch_size = (6, 6)):
img_rgb = Image.open(img_route)
img_ycbcr = rgb2ycbcr(img_rgb)
img1 = np.array(img_ycbcr)
y = img1[:, :, 0]
if imagecut == True:
y = imgcut(y, batch_size = batch_size)
if np.max(y) > 1: y = y / 255.0
return y
##################################################################################
#
# Evalution
#
##################################################################################
#=================================================================================
# RMSE
def RMSE(img1, img2):
if len(img1.shape) == 3:
img1_y = skco.rgb2ycbcr(img1)[:, :, 0]
else:
img1_y = img1
if len(img2.shape) == 3:
img2_y = skco.rgb2ycbcr(img2)[:, :, 0]
else:
img2_y = img2
img_dif = img1_y - img2_y
rmse = np.sqrt(np.mean(img_dif**2))
return rmse
def PSNR(img1, img2):
rmse = RMSE(img1, img2)
return 20 * np.log10(255.0/rmse)
def quick_eval(img_O, img_id = '001', scale = 2):
# ipdb.set_trace()
img_B = skio.imread(img_route(img_id = img_id, method='bicubic', scale = scale))
img_H = skio.imread(img_route(img_id = img_id, method='HR', scale = scale))
img_H, img_O = equal_size(img_H, img_O)
img_H, img_B = equal_size(img_H, img_B)
# ipdb.set_trace()
PSNR_1 = PSNR(img_H, img_O)
PSNR_2 = PSNR(img_H, img_B)
SSIM_1 = SSIM(img_H, img_O)
SSIM_2 = SSIM(img_H, img_B)
print('PSNR/SSIM for Bicubic Interpolation: %f dB', PSNR_2, SSIM_2);
print('PSNR/SSIM for Sparse Representation Recovery: %f dB', PSNR_1, SSIM_1);
#=================================================================================
# PSNR
# refer : https://github.com/aizvorski/video-quality/blob/master/psnr.py
def PSNR1(img1, img2):
import math
mse = np.mean( (img1 - img2) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def PSNR0(img1, img2):
from skimage.measure import compare_psnr
return compare_psnr(img1, img2, 255)
def PSNR2(img1, img2):
diff = img1 - img2
mse = np.mean(np.square(diff))
psnr = 10.0 * np.log10(255.0 * 255 / mse)
return psnr
#=================================================================================
# SSIM
# refer : https://github.com/aizvorski/video-quality/blob/master/ssim.py
def SSIM(img1, img2, data_range = 1.0, multichannel=False):
img1_ = img1.copy()
img2_ = img2.copy()
if np.max(img1_) < 2.0 : img1_*=255.0
if np.max(img2_) < 2.0 : img2_*=255.0
data_range = 255.0
if len(img1_.shape) == 3 : multichannel=True
import skimage
return skimage.measure.compare_ssim(img1_, img2_, data_range=data_range, multichannel=True)
'''
return compare_ssim(
img1, img2,
win_size=11,
gaussian_weights=True,
multichannel=True,
data_range=1.0,
K1=0.01,
K2=0.03,
sigma=1.5)
'''
##################################################################################
#
# Other
#
##################################################################################
def equal_size(img1, img2):
w1, h1 = img1.shape[0], img1.shape[1]
w2, h2 = img2.shape[0], img2.shape[1]
w = min(w1, w2)
h = min(h1, h2)
if len(img1.shape) == 3:
img1 = img1[0 : w, 0 : h, :]
img2 = img2[0 : w, 0 : h, :]
else:
img1 = img1[0 : w, 0 : h]
img2 = img2[0 : w, 0 : h]
return img1, img2