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vif_crcopy.py
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from skimage.util.shape import view_as_blocks
import csv
from skimage import filters
import matplotlib.pyplot as plt
from utils.vif.vif_utils import vif
from joblib import dump,Parallel,delayed
from scipy.stats import gmean
import time
from scipy.ndimage import gaussian_filter
from utils.hdr_utils import hdr_yuv_read
from utils.csf_utils import csf_barten_frequency,csf_filter_block,blockwise_csf,windows_csf
import numpy as np
import glob
import pandas as pd
import os
from os.path import join
import scipy
import socket
import sys
if socket.gethostname().find('tacc')>0:
csv_file_vidinfo = 'fall2021_yuv_rw_info.csv'
vid_pth = '/scratch/06776/kmd1995/video/HDR_2021_fall_yuv_upscaled/fall2021_hdr_upscaled_yuv'
out_root = '/scratch/06776/kmd1995/feats/feats/hdrvif_no_nonlinear_cr/vif_cr'
out_pth = out_root
else:
csv_file_vidinfo = '/home/zaixi/code/HDRproject/hdr_vmaf/python_vmaf/fall2021_yuv_rw_info.csv'
vid_pth = '/mnt/7e60dcd9-907d-428e-970c-b7acf5c8636a/fall2021_hdr_upscaled_yuv/'
out_root = '/media/zaixi/zaixi_nas/HDRproject/feats/hdrvif/vif'
df_vidinfo = pd.read_csv(csv_file_vidinfo)
files = df_vidinfo["yuv"]
ref_files = glob.glob(join(vid_pth,'4k_ref_*'))
fps = df_vidinfo["fps"]
framenos_list = df_vidinfo["framenos"] //2
framenos_list = framenos_list.astype(np.int32)
ws =df_vidinfo["w"]
hs = df_vidinfo["h"]
upscaled_yuv_names = [x[:-4]+'_upscaled.yuv' for x in df_vidinfo['yuv']]
def global_exp(image,par):
if np.max(image) > 1.1:
image = image/1023
assert len(np.shape(image)) == 2
avg = np.average(image)
y = np.exp(par*(image-avg))
return y
def gen_gauss_window(lw, sigma):
sd = np.float32(sigma)
lw = int(lw)
weights = [0.0] * (2 * lw + 1)
weights[lw] = 1.0
sum = 1.0
sd *= sd
for ii in range(1, lw + 1):
tmp = np.exp(-0.5 * np.float32(ii * ii) / sd)
weights[lw + ii] = tmp
weights[lw - ii] = tmp
sum += 2.0 * tmp
for ii in range(2 * lw + 1):
weights[ii] /= sum
return weights
def local_exp(image,par,patch_size):
assert len(np.shape(image)) == 2
h, w = np.shape(image)
if np.max(image) > 1.1:
image = image/1023
avg_window = gen_gauss_window(patch_size//2, 7.0/6.0)
mu_image = np.zeros((h, w), dtype=np.float32)
image = np.array(image).astype('float32')
scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode='constant')
scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode='constant')
y = np.exp(par*(image - mu_image))
return y
def logit(Y,delta):
Y = -0.99+(Y-np.amin(Y))* 1.98/(np.amax(Y)-np.amin(Y))
Y_transform = np.log((1+(Y)**delta)/(1-(Y)**delta))
return Y_transform
def vif_refall_wrapper(ind,files):
ref_f = files[ind]
content = os.path.basename(ref_f).split('_')[2]
print(content)
dis_filenames = [x for x in glob.glob(join(vid_pth,"*")) if content in x]
print(dis_filenames)
Parallel(n_jobs=31,verbose=1)(delayed(vif_video_wrapper)(ref_f,dis_f) for dis_f in dis_filenames)
# for dis_f in dis_filenames:
# vif_video_wrapper(ref_f,dis_f)
# dis_f = dis_filenames[0]
# vif_video_wrapper(ref_f,dis_f)
def vif_video_wrapper(ref_f,dis_f):
basename = os.path.basename(dis_f)
print(basename)
if(ref_f==dis_f):
print('Videos are the same')
return
dis_index = upscaled_yuv_names.index(basename)
h = 2160 #hs[dis_index]
w = 3840 #ws[dis_index]
framenos = framenos_list[dis_index]
vif_image_wrapper(ref_f,dis_f,framenos,h,w,use_adaptive_csf=False)
def vif_image_wrapper(ref_f,dis_f,framenos,h,w,nonlinear = None,use_adaptive_csf=True,adaptation='bilateral',use_non_overlapping_blocks=True,use_views=False,par = None):
ref_file_object = open(ref_f)
dis_file_object = open(dis_f)
randlist = np.random.randint(0,framenos,10)
score_df = pd.DataFrame([])
dis_name = os.path.splitext(os.path.basename(dis_f))[0]
output_csv = os.path.join(out_pth,dis_name+'.csv')
print('name to be is ',output_csv)
if((os.path.exists(output_csv)==True) and (os.path.getsize(output_csv) >1000)):
print(output_csv,' is found')
return
with open(output_csv,'a') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow(['framenum','vif','nums','denoms'])
for framenum in randlist:
try:
_,_,ref_cr_pq = hdr_yuv_read(ref_file_object,framenum,h,w)
_,_,dis_cr_pq = hdr_yuv_read(dis_file_object,framenum,h,w)
except Exception as e:
print(e)
break
if(use_adaptive_csf==True):
# apply CSF here
if(use_non_overlapping_blocks==True): # apply CSF on non-overlapping blocks of the image
csf_filtered_ref_cr_pq = blockwise_csf(ref_cr_pq,adaptation=adaptation)
csf_filtered_dis_cr_pq = blockwise_csf(dis_cr_pq,adaptation=adaptation)
else: # sliding window; returns filtered value at center of each sliding window
csf_filtered_ref_cr_pq = windows_csf(ref_cr_pq,use_views=use_views)
csf_filtered_dis_cr_pq = windows_csf(dis_cr_pq,use_views=use_views)
# standard VIF but without CSF
vif_val = vif(csf_filtered_ref_cr_pq,csf_filtered_dis_cr_pq)
elif(nonlinear == 'logit'):
logit_ref_cr_pq = logit(ref_cr_pq,1)
logit_dis_cr_pq = logit(dis_cr_pq,1)
vif_val = vif(logit_ref_cr_pq,logit_dis_cr_pq)
elif(nonlinear == 'local_exp'):
logit_ref_cr_pq = local_exp(ref_cr_pq,par,31)
logit_dis_cr_pq = local_exp(dis_cr_pq,par,31)
vif_val1 = vif(logit_ref_cr_pq,logit_dis_cr_pq)
logit_ref_cr_pq = local_exp(ref_cr_pq,-par,31)
logit_dis_cr_pq = local_exp(dis_cr_pq,-par,31)
vif_val2 = vif(logit_ref_cr_pq,logit_dis_cr_pq)
elif(nonlinear == 'global_exp'):
logit_ref_cr_pq = global_exp(ref_cr_pq,par)
logit_dis_cr_pq = global_exp(dis_cr_pq,par)
vif_val1 = vif(logit_ref_cr_pq,logit_dis_cr_pq)
logit_ref_cr_pq = global_exp(ref_cr_pq,-par)
logit_dis_cr_pq = global_exp(dis_cr_pq,-par)
vif_val2 = vif(logit_ref_cr_pq,logit_dis_cr_pq)
else:
# standard VIF
vif_val = vif(ref_cr_pq,dis_cr_pq)
# standard VIF
if 'vif_val1' in locals():
row = [framenum,vif_val1[0],vif_val1[1],vif_val1[2],vif_val2[0],vif_val2[1],vif_val2[2]]
else:
row = [framenum,vif_val[0],vif_val[1],vif_val[2]]
writer.writerow(row)
if not os.path.exists(out_pth):
os.makedirs(out_pth)
Parallel(n_jobs=3,verbose=1)(delayed(vif_refall_wrapper)(i,ref_files) for i in range(len(ref_files)))