def _create_scale_space(self):
# create Gaussian kernels with sigma=1 and sigma=sqrt(2)
sig_one_kernel = create_gauss_kernel(l=9, sig=1.)
sig_sqrt_kernel = 1 * create_gauss_kernel(l=9, sig=np.sqrt(2))
# initialize scale space variables
self._scale_space = []
img_scale = scipy.signal.convolve2d(self._top_img, sig_one_kernel, 'same') # initial scale
# compute scale space by Gaussian filtering and down-sampling
while (img_scale.shape[0] or img_scale.shape[1]) >= 3:
# filter scaled image by Gaussian kernel with sigma=1
gauss_img_one = (scipy.signal.convolve2d(img_scale, sig_one_kernel, 'same'))
# append scaled image to pyramid
self._scale_space.append(-(gauss_img_one-img_scale)) # negative for maximum detection
# filter scaled image by Gaussian kernel with sigma=sqrt(2)
gauss_img_two = (scipy.signal.convolve2d(gauss_img_one, sig_sqrt_kernel, 'same'))
# append scaled image to pyramid
self._scale_space.append(-(gauss_img_two-gauss_img_one)) # negative for maximum detection
# down-sample to half the image resolution where Gaussian filters prevent from aliasing
img_scale = gauss_img_two[::2, ::2]
# check interrupt status
if self.sta.interrupt:
return False
return True
def _crop_img(self, CR=1):
# crop image (for computational speed-up and smaller region of interest)
S = self._img.shape[0]//2
self._top_img = self._img[S-S//CR:S+S//CR-1, S-S//CR:S+S//CR-1].copy().astype(np.float64)
# darken image edges to exclude micro images at border
self._top_img *= create_gauss_kernel(l=S//CR*2-1, sig=S//CR//2)
return True
def _estimate_noise_level(self):
''' estimate white image noise level '''
# print status
self.sta.status_msg('Estimate white image noise level',
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
M = np.mean(self.cfg.calibs[self.cfg.ptc_mean])
lp_kernel = misc.create_gauss_kernel(l=M)
bw_img = np.mean(self._wht_img, axis=2)
flt_img = convolve2d(bw_img, lp_kernel, 'same')
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
return np.std(bw_img - flt_img)
def compute_log(self):
''' compute Laplacian of Gaussian (LoG) '''
# print status
self._sta.status_msg('Compute LoG', self._cfg.params[self._cfg.opt_prnt])
# Gaussian sigma
sig = int(self._M/4)/1.18#int(self._M/2)/np.sqrt(2)
# convolutions
laplace_kernel = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
gauss_kernel = create_gauss_kernel(int(sig*6), sig)
mexican_hat = -scipy.signal.convolve2d(gauss_kernel, laplace_kernel)[2:-2, 2:-2] #sig**2 *
self._peak_img = scipy.signal.convolve2d(self._img, mexican_hat, 'same')
# print progress
self._sta.progress(100, self._cfg.params[self._cfg.opt_prnt])
return True
def _estimate_noise_level(self):
""" estimate white image noise level """
# print status
self.sta.status_msg('Estimate white image noise level',
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
M = np.mean(self.cfg.calibs[self.cfg.ptc_mean])
lp_kernel = misc.create_gauss_kernel(length=M)
if len(self._wht_img.shape) == 3:
bw_img = rgb2gry(self._wht_img)[
..., 0] if self._wht_img.shape[2] == 3 else self._wht_img[...,
0]
else:
bw_img = self._wht_img
flt_img = convolve2d(bw_img, lp_kernel, 'same')
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
return np.std(bw_img - flt_img)
