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save_stats.py
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save_stats.py
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import numpy as np
import cv2
import glob
import os
import skvideo.io
from scipy.special import gamma
import skvideo.utils
import pandas as pd
import math
from joblib import dump
import scipy
from joblib import load
from scipy.fftpack import fft, dct
import scipy.io as sio
from scipy.stats import norm,lognorm,skew,kurtosis
from skvideo.measure import videobliinds,videobliinds_features,niqe
from skimage.util.shape import view_as_windows
win = np.array(skvideo.utils.gen_gauss_window(3, 7.0/6.0))
gamma_range = np.arange(0.2, 10, 0.001)
a = scipy.special.gamma(2.0/gamma_range)
a *= a
b = scipy.special.gamma(1.0/gamma_range)
c = scipy.special.gamma(3.0/gamma_range)
prec_gammas = a/(b*c)
def generate_ggd(x,alphaparam,sigma):
betaparam = sigma*np.sqrt(gamma(1.0/alphaparam)/gamma(3.0/alphaparam))
y = alphaparam/(2*betaparam*gamma(1.0/alphaparam))*np.exp(-(np.abs(x)/betaparam)**alphaparam)
return y
def stat_feats(chroma_mscn):
alpha,sigma = estimateggdparam(chroma_mscn)
skewness = skew(chroma_mscn.flatten())
kurt =kurtosis(chroma_mscn.flatten())
return alpha,sigma,skewness,kurt
def extract_secondord_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2]) # (D2)
def extract_subband_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
alpha_m, sigma = estimateggdparam(mscncoefs.copy())
pps1, pps2, pps3, pps4 = paired_product(mscncoefs)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
alpha_m, sigma,
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2, # (D2)
])
def estimateggdparam(vec):
gam = np.asarray([x / 1000.0 for x in range(200, 10000, 1)])
r_gam = (gamma(1.0/gam)*gamma(3.0/gam))/((gamma(2.0/gam))**2)
# print(np.mean(vec))
sigma_sq = np.mean(vec**2) #-(np.mean(vec))**2
sigma = np.sqrt(sigma_sq)
E = np.mean(np.abs(vec))
rho = sigma_sq/(E**2+1e-6)
array_position =(np.abs(rho - r_gam)).argmin()
alphaparam = gam[array_position]
return alphaparam,sigma
def all_aggd(y):
falpha1,fN1,fbl1,fbr1,flsq1,frsq1 = aggd_features(y.copy())
pps1, pps2, pps3, pps4 = paired_product(y)
alpha1, N1, bl1, br1, lsq1, rsq1 = aggd_features(pps1)
alpha2, N2, bl2, br2, lsq2, rsq2 = aggd_features(pps2)
alpha3, N3, bl3, br3, lsq3, rsq3 = aggd_features(pps3)
alpha4, N4, bl4, br4, lsq4, rsq4 = aggd_features(pps4)
return np.array([
falpha1, fN1, flsq1**2,frsq1**2,
alpha1, N1, lsq1**2, rsq1**2, # (V)
alpha2, N2, lsq2**2, rsq2**2, # (H)
alpha3, N3, lsq3**2, rsq3**2, # (D1)
alpha4, N4, lsq4**2, rsq4**2, # (D2)
])
def brisque_halfscale(Y,C):
# half_scale = cv2.resize(y, dsize=(0,0),fx=0.5,fy=0.5, interpolation=cv2.INTER_LANCZOS4)
y_mscn = compute_image_mscn_transform(Y,C)
feats_full = extract_subband_feats(y_mscn)
# feats_half = _extract_subband_feats(half_scale)
return feats_full#np.concatenate((feats_full,feats_half))
def aggd_features(imdata):
#flatten imdata
imdata = np.reshape(imdata, (len(imdata.flat),))
imdata2 = imdata*imdata
left_data = imdata2[imdata<0]
right_data = imdata2[imdata>=0]
left_mean_sqrt = 0
right_mean_sqrt = 0
if len(left_data) > 0:
left_mean_sqrt = np.sqrt(np.average(left_data))
if len(right_data) > 0:
right_mean_sqrt = np.sqrt(np.average(right_data))
if right_mean_sqrt != 0:
gamma_hat = left_mean_sqrt/right_mean_sqrt
else:
gamma_hat = np.inf
#solve r-hat norm
imdata2_mean = np.mean(imdata2)
if imdata2_mean != 0:
r_hat = (np.average(np.abs(imdata))**2) / (np.average(imdata2))
else:
r_hat = np.inf
rhat_norm = r_hat * (((math.pow(gamma_hat, 3) + 1)*(gamma_hat + 1)) / math.pow(math.pow(gamma_hat, 2) + 1, 2))
#solve alpha by guessing values that minimize ro
pos = np.argmin((prec_gammas - rhat_norm)**2);
alpha = gamma_range[pos]
gam1 = scipy.special.gamma(1.0/alpha)
gam2 = scipy.special.gamma(2.0/alpha)
gam3 = scipy.special.gamma(3.0/alpha)
aggdratio = np.sqrt(gam1) / np.sqrt(gam3)
bl = aggdratio * left_mean_sqrt
br = aggdratio * right_mean_sqrt
#mean parameter
N = (br - bl)*(gam2 / gam1)#*aggdratio
return (alpha, N, bl, br, left_mean_sqrt, right_mean_sqrt)
# def ggd_features(imdata):
# nr_gam = 1/prec_gammas
# sigma_sq = np.var(imdata)
# E = np.mean(np.abs(imdata))
# rho = sigma_sq/E**2
# pos = np.argmin(np.abs(nr_gam - rho));
# return gamma_range[pos], sigma_sq
def sigma_map(image):
im = image.astype(np.float32)
mu = cv2.GaussianBlur(im,(7,7),7.0/6.0,7.0/6.0)
mu_sq = mu*mu
sigma = np.sqrt(np.abs(cv2.GaussianBlur(im**2,(7,7),7.0/6.0,7.0/6.0)-mu_sq))
return sigma
def dog(image):
image = image.astype(np.float32)
gauss1 = cv2.GaussianBlur(image,(7,7),7.0/6.0,7.0/6.0)
gauss2 = cv2.GaussianBlur(image,(7,7),7.0*1.5/6.0,7.0*1.5/6.0)
dog = gauss1-gauss2
return dog
def paired_product(new_im):
shift1 = np.roll(new_im.copy(), 1, axis=1)
shift2 = np.roll(new_im.copy(), 1, axis=0)
shift3 = np.roll(np.roll(new_im.copy(), 1, axis=0), 1, axis=1)
shift4 = np.roll(np.roll(new_im.copy(), 1, axis=0), -1, axis=1)
H_img = shift1 * new_im
V_img = shift2 * new_im
D1_img = shift3 * new_im
D2_img = shift4 * new_im
return (H_img, V_img, D1_img, D2_img)
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 compute_image_ms_transform(image, C=1, avg_window=None, extend_mode='constant'):
if avg_window is None:
avg_window = gen_gauss_window(3, 7.0/6.0)
assert len(np.shape(image)) == 2
h, w = np.shape(image)
mu_image = np.zeros((h, w), dtype=np.float32)
scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode=extend_mode)
scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode=extend_mode)
return (image-mu_image)
def compute_image_mscn_transform(image, C=1, avg_window=None, extend_mode='constant'):
if avg_window is None:
avg_window = gen_gauss_window(3, 7.0/6.0)
assert len(np.shape(image)) == 2
h, w = np.shape(image)
mu_image = np.zeros((h, w), dtype=np.float32)
var_image = np.zeros((h, w), dtype=np.float32)
image = np.array(image).astype('float32')
scipy.ndimage.correlate1d(image, avg_window, 0, mu_image, mode=extend_mode)
scipy.ndimage.correlate1d(mu_image, avg_window, 1, mu_image, mode=extend_mode)
scipy.ndimage.correlate1d(image**2, avg_window, 0, var_image, mode=extend_mode)
scipy.ndimage.correlate1d(var_image, avg_window, 1, var_image, mode=extend_mode)
var_image = np.sqrt(np.abs(var_image - mu_image**2))
return (image - mu_image)/(var_image + C), var_image, mu_image
def generate_aggd(x1,x2,alpha,sigma_l,sigma_r):
beta_l = sigma_l*np.sqrt(gamma(1/alpha)/gamma(3/alpha))
beta_r= sigma_r*np.sqrt(gamma(1/alpha)/gamma(3/alpha))
f1 = alpha/((beta_l+beta_r)*gamma(1/alpha))*np.exp(-(-x1/beta_l)**alpha)
f2 = alpha/((beta_l+beta_r)*gamma(1/alpha))*np.exp(-(x2/beta_r)**alpha)
f = np.concatenate((f1,f2),axis=0)
return f
def chroma_feats(lab):
# lab = cv2.cvtColor(bgr,cv2.COLOR_BGR2Lab)
a = lab[:,:,1]
b = lab[:,:,2]
chroma = np.sqrt(a**2+b**2)
chroma_mscn,sigma_map,_ = compute_image_mscn_transform(chroma)
sigma_mscn,_,_ =compute_image_mscn_transform(sigma_map)
alpha,sigma,skewness,kurt= stat_feats(chroma_mscn)
salpha,ssigma,sskewness,skurt= stat_feats(sigma_mscn)
half_scale = cv2.resize(chroma.astype(np.uint8), dsize=(0,0),fx=0.5,fy=0.5, interpolation=cv2.INTER_CUBIC)
half_chroma_mscn,half_sigma_map,_ = compute_image_mscn_transform(half_scale)
half_sigma_mscn,_,_ = compute_image_mscn_transform(half_sigma_map)
halpha,hsigma,hskewness,hkurt= stat_feats(half_chroma_mscn)
hsalpha,hssigma,hsskewness,hskurt= stat_feats(half_sigma_mscn)
first_order_feats = np.asarray([alpha,sigma,skewness,kurt,halpha,hsigma,hskewness,hkurt,salpha,ssigma,sskewness,skurt,hsalpha,hssigma,hsskewness,hskurt])
return first_order_feats
def estimate_log_deri_ggd(image):
log_im = np.log(image+0.5)
log_feats = []
shifts= [(0,1),(1,0),(1,1),(1,-1)]
for i in range(len(shifts)):
rolled = np.roll(log_im, shift=shifts[i],axis=(0,1))
log_deri = log_im - rolled
alpha,sigma = estimateggdparam(log_deri)
log_feats.append(np.asarray([alpha,sigma]))
D5 = log_im + np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))-np.roll(log_im,shift=(1,0),axis=(0,1))
D6 = np.roll(log_im,shift=(-1,0),axis=(0,1))+np.roll(log_im,shift=(1,0),axis=(0,1))-np.roll(log_im,shift=(0,-1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))
D7 = np.roll(log_im,shift=(-1,-1),axis=(0,1))+np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(-1,1),axis=(0,1))-np.roll(log_im,shift=(1,-1),axis=(0,1))
alpha,sigma = estimateggdparam(D6)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D7)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D5)
log_feats.append(np.asarray([alpha,sigma]))
log_feats = np.asarray(log_feats)
log_feats = np.reshape(log_feats,(14,))
return log_feats
def estimate_extralogderis(image):
log_im = np.log(image+0.5)
log_feats =[]
D6 = np.roll(log_im,shift=(-1,0),axis=(0,1))+np.roll(log_im,shift=(1,0),axis=(0,1))-np.roll(log_im,shift=(0,-1),axis=(0,1))-np.roll(log_im,shift=(0,1),axis=(0,1))
D7 = np.roll(log_im,shift=(-1,-1),axis=(0,1))+np.roll(log_im,shift=(1,1),axis=(0,1))-np.roll(log_im,shift=(-1,1),axis=(0,1))-np.roll(log_im,shift=(1,-1),axis=(0,1))
alpha,sigma = estimateggdparam(D6)
log_feats.append(np.asarray([alpha,sigma]))
alpha,sigma = estimateggdparam(D7)
log_feats.append(np.asarray([alpha,sigma]))
log_feats = np.asarray(log_feats)
log_feats = np.reshape(log_feats,(4,))
return log_feats
def chroma_gradients(lab):
# lab = cv2.cvtColor(bgr,cv2.COLOR_BGR2Lab)
# a = lab[:,:,1]
# b = lab[:,:,2]
chroma_grad_feats = []
gradient_x = cv2.Sobel(lab,ddepth=-1,dx=1,dy=0)
gradient_y = cv2.Sobel(lab,ddepth=-1,dx=0,dy=1)
gradient_mag = np.sqrt(gradient_x**2+gradient_y**2)
return [gradient_mag[:,:,0],gradient_mag[:,:,1],gradient_mag[:,:,2]]
def chroma_gradient_feats(lab):
gradient_mag = chroma_gradients(lab)
for i in range(3):
gradient_mscn,_,_ = compute_image_mscn_transform(gradient_mag[i])
alpha,sigma = estimateggdparam(gradient_mscn)
# log_ggd_params = estimate_log_ggd(gradient_mag[:,:,i])
grad_sigma = strided_variance(gradient_mag[:,:,i],5)
grad_sigma_mean = np.mean(grad_sigma.flatten())
grad_sigma_var = np.std(grad_sigma.flatten())
dispersion = grad_sigma_var/grad_sigma_mean
log_sigma_params = np.asarray([alpha,sigma,dispersion,grad_sigma_mean])
# chroma_grad_feats.append(np.asarray([sigma_alpha,sigma_var]))
chroma_grad_feats.append(log_sigma_params)
chroma_grad_feats= np.asarray(chroma_grad_feats)
chroma_grad_feats = np.reshape(chroma_grad_feats,(12,))
return chroma_grad_feats
def colorfulness(image):
rg = image[:,:,2]-image[:,:,1]
yb = 0.5*(image[:,:,2]+image[:,:,1])-image[:,:,0]
mu = np.sqrt(np.mean(rg.flatten())**2+np.mean(yb.flatten())**2)
sigma = np.sqrt(np.std(rg.flatten())**2+np.std(yb.flatten())**2)
c = sigma+0.3*mu
return c
def main():
dataset = 'vqc'
if(dataset=='konvid'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/konvid_sts_mscn_down_videos'
results_folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/konvid_sts_mscn_down_features'
csv_file = "/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/konvid/KoNViD_1k_metadata/KoNViD_1k_mos.csv"
elif(dataset=='vqc'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQC/vqc_sts_medianof'
results_folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQC/vqc_sts_medianof_feats'
elif(dataset=='vqa'):
folder = '/mnt/b9f5646b-2c64-4699-8766-c4bba45fb442/VQA/sts_mscn_down'