from ..utils import *
import numpy as np
import scipy.misc
import scipy.io
from os.path import dirname
from os.path import join
# only used during training
#from skimage.util.shape import view_as_windows
def _niqe_extract_subband_feats(mscncoefs):
# alpha_m, = extract_ggd_features(mscncoefs)
alpha_m, N, bl, br, lsq, rsq = aggd_features(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, (bl+br)/2.0,
alpha1, N1, bl1, br1, # (V)
alpha2, N2, bl2, br2, # (H)
alpha3, N3, bl3, bl3, # (D1)
alpha4, N4, bl4, bl4, # (D2)
])
def get_patches_train_features(img, patch_size, stride=8):
return _get_patches_generic(img, patch_size, 1, stride)
def get_patches_test_features(img, patch_size, stride=8):
return _get_patches_generic(img, patch_size, 0, stride)
def extract_on_patches(img, patch_size):
h, w = img.shape
patch_size = np.int(patch_size)
patches = []
for j in range(0, h-patch_size+1, patch_size):
for i in range(0, w-patch_size+1, patch_size):
patch = img[j:j+patch_size, i:i+patch_size]
patches.append(patch)
patches = np.array(patches)
patch_features = []
for p in patches:
patch_features.append(_niqe_extract_subband_feats(p))
patch_features = np.array(patch_features)
return patch_features
def _get_patches_generic(img, patch_size, is_train, stride):
h, w = np.shape(img)
if h < patch_size or w < patch_size:
print("Input image is too small")
exit(0)
# ensure that the patch divides evenly into img
hoffset = (h % patch_size)
woffset = (w % patch_size)
if hoffset > 0:
img = img[:-hoffset, :]
if woffset > 0:
img = img[:, :-woffset]
img = img.astype(np.float32)
img2 = scipy.misc.imresize(img, 0.5, interp='bicubic', mode='F')
mscn1, var, mu = compute_image_mscn_transform(img)
mscn1 = mscn1.astype(np.float32)
mscn2, _, _ = compute_image_mscn_transform(img2)
mscn2 = mscn2.astype(np.float32)
feats_lvl1 = extract_on_patches(mscn1, patch_size)
feats_lvl2 = extract_on_patches(mscn2, patch_size/2)
feats = np.hstack((feats_lvl1, feats_lvl2))# feats_lvl3))
#if is_train:
# variancefield = view_as_windows(var, (patch_size, patch_size), step=patch_size)
# variancefield = variancefield.reshape(-1, patch_size, patch_size)
# avg_variance = np.mean(np.mean(variancefield, axis=2), axis=1)
# avg_variance /= np.max(avg_variance)
# feats = feats[avg_variance > 0.75]
return feats
[docs]def niqe(inputVideoData):
"""Computes Naturalness Image Quality Evaluator. [#f1]_
Input a video of any quality and get back its distance frame-by-frame
from naturalness.
Parameters
----------
inputVideoData : ndarray
Input video, ndarray of dimension (T, M, N, C), (T, M, N), (M, N, C), or (M, N),
where T is the number of frames, M is the height, N is width,
and C is number of channels. Here C is only allowed to be 1.
Returns
-------
niqe_array : ndarray
The niqe results, ndarray of dimension (T,), where T
is the number of frames
References
----------
.. [#f1] Mittal, Anish, Rajiv Soundararajan, and Alan C. Bovik. "Making a 'completely blind' image quality analyzer." IEEE Signal Processing Letters 20.3 (2013): 209-212.
"""
# cache
patch_size = 96
module_path = dirname(__file__)
# TODO: memoize
params = scipy.io.loadmat(join(module_path, 'data', 'niqe_image_params.mat'))
pop_mu = np.ravel(params["pop_mu"])
pop_cov = params["pop_cov"]
# load the training data
inputVideoData = vshape(inputVideoData)
T, M, N, C = inputVideoData.shape
assert C == 1, "niqe called with videos containing %d channels. Please supply only the luminance channel" % (C,)
assert M > (patch_size*2+1), "niqe called with small frame size, requires > 192x192 resolution video using current training parameters"
assert N > (patch_size*2+1), "niqe called with small frame size, requires > 192x192 resolution video using current training parameters"
niqe_scores = np.zeros(T, dtype=np.float32)
for t in range(T):
feats = get_patches_test_features(inputVideoData[t, :, :, 0], patch_size)
sample_mu = np.mean(feats, axis=0)
sample_cov = np.cov(feats.T)
X = sample_mu - pop_mu
covmat = ((pop_cov+sample_cov)/2.0)
pinvmat = scipy.linalg.pinv(covmat)
niqe_scores[t] = np.sqrt(np.dot(np.dot(X, pinvmat), X))
return niqe_scores
