def func(gam, prevgamma, prevdiff, sampling, rhatnorm):
while(gam < 10):
r_gam = tgamma(2/gam) * tgamma(2/gam) / (tgamma(1/gam) * tgamma(3/gam))
diff = abs(r_gam - rhatnorm)
if(diff > prevdiff): break
prevdiff = diff
prevgamma = gam
gam += sampling
gamma_best = prevgamma
return gamma_best
def func(gam, prevgamma, prevdiff, sampling, rhatnorm):
while(gam < 10):
r_gam = tgamma(2/gam) * tgamma(2/gam) / (tgamma(1/gam) * tgamma(3/gam))
diff = abs(r_gam - rhatnorm)
if(diff > prevdiff): break
prevdiff = diff
prevgamma = gam
gam += sampling
gamma_best = prevgamma
return gamma_best
def compute_features(img):
scalenum = 2
feat = []
# make a copy of the image
im_original = img.copy()
# scale the images twice
for itr_scale in range(scalenum):
im = im_original.copy()
# normalize the image
im = im / 255.0
# calculating MSCN coefficients
mu = cv2.GaussianBlur(im, (7, 7), 1.166)
mu_sq = mu * mu
sigma = cv2.GaussianBlur(im*im, (7, 7), 1.166)
sigma = (sigma - mu_sq)**0.5
# structdis is the MSCN image
structdis = im - mu
structdis /= (sigma + 1.0/255)
# calculate best fitted parameters from MSCN image
best_fit_params = AGGDfit(structdis)
# unwrap the best fit parameters
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
# append the best fit parameters for MSCN image
feat.append(gamma_best)
feat.append((lsigma_best*lsigma_best + rsigma_best*rsigma_best)/2)
# shifting indices for creating pair-wise products
shifts = [[0,1], [1,0], [1,1], [-1,1]] # H V D1 D2
for itr_shift in range(1, len(shifts) + 1):
OrigArr = structdis
reqshift = shifts[itr_shift-1] # shifting index
# create transformation matrix for warpAffine function
M = np.float32([[1, 0, reqshift[1]], [0, 1, reqshift[0]]])
ShiftArr = cv2.warpAffine(OrigArr, M, (structdis.shape[1], structdis.shape[0]))
Shifted_new_structdis = ShiftArr
Shifted_new_structdis = Shifted_new_structdis * structdis
# shifted_new_structdis is the pairwise product
# best fit the pairwise product
best_fit_params = AGGDfit(Shifted_new_structdis)
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
constant = m.pow(tgamma(1/gamma_best), 0.5)/m.pow(tgamma(3/gamma_best), 0.5)
meanparam = (rsigma_best - lsigma_best) * (tgamma(2/gamma_best)/tgamma(1/gamma_best)) * constant
# append the best fit calculated parameters
feat.append(gamma_best) # gamma best
feat.append(meanparam) # mean shape
feat.append(m.pow(lsigma_best, 2)) # left variance square
feat.append(m.pow(rsigma_best, 2)) # right variance square
# resize the image on next iteration
im_original = cv2.resize(im_original, (0,0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)
return feat
def gamma(cls, arg):
assert arg > 0.0
return tgamma(arg)
def compute_features(img):
scalenum = 2
feat = []
# make a copy of the image
im_original = img.copy()
# scale the images twice
for itr_scale in range(scalenum):
im = im_original.copy()
# normalize the image
im = im / 255.0
# calculating MSCN coefficients
mu = cv2.GaussianBlur(im, (7, 7), 1.166)
mu_sq = mu * mu
sigma = cv2.GaussianBlur(im*im, (7, 7), 1.166)
sigma = (sigma - mu_sq)**0.5
# structdis is the MSCN image
structdis = im - mu
structdis /= (sigma + 1.0/255)
# calculate best fitted parameters from MSCN image
best_fit_params = AGGDfit(structdis)
# unwrap the best fit parameters
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
# append the best fit parameters for MSCN image
feat.append(gamma_best)
feat.append((lsigma_best*lsigma_best + rsigma_best*rsigma_best)/2)
# shifting indices for creating pair-wise products
shifts = [[0,1], [1,0], [1,1], [-1,1]] # H V D1 D2
for itr_shift in range(1, len(shifts) + 1):
OrigArr = structdis
reqshift = shifts[itr_shift-1] # shifting index
# create transformation matrix for warpAffine function
M = np.float32([[1, 0, reqshift[1]], [0, 1, reqshift[0]]])
ShiftArr = cv2.warpAffine(OrigArr, M, (structdis.shape[1], structdis.shape[0]))
Shifted_new_structdis = ShiftArr
Shifted_new_structdis = Shifted_new_structdis * structdis
# shifted_new_structdis is the pairwise product
# best fit the pairwise product
best_fit_params = AGGDfit(Shifted_new_structdis)
lsigma_best = best_fit_params[0]
rsigma_best = best_fit_params[1]
gamma_best = best_fit_params[2]
constant = m.pow(tgamma(1/gamma_best), 0.5)/m.pow(tgamma(3/gamma_best), 0.5)
meanparam = (rsigma_best - lsigma_best) * (tgamma(2/gamma_best)/tgamma(1/gamma_best)) * constant
# append the best fit calculated parameters
feat.append(gamma_best) # gamma best
feat.append(meanparam) # mean shape
feat.append(m.pow(lsigma_best, 2)) # left variance square
feat.append(m.pow(rsigma_best, 2)) # right variance square
# resize the image on next iteration
im_original = cv2.resize(im_original, (0,0), fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)
return feat
