def update(self, changed, thread):
wavelet = self.wavelet.get()
if wavelets.TriangeWaveletFamily().is_from(wavelet) or wavelets.BSplineWaveletFamily().is_from(wavelet):
dec = wtutils.uiwt
rec = wtutils.uiwt_inv
else:
dec = wtutils.uwt
rec = wtutils.uwt_inv
denoise = Denoise(wavelet, self.scale.get(), self.boundary, dec, rec, mode=self.mode.get(), thread=thread)
img = self.img.get()
if changed in [self.img, self.noise_level] or self.noisy is None:
self.noisy = img.data + nputils.gaussian_noise(img.data.shape, 0, self.noise_level.get())
estimated_noise_sigma = nputils.k_sigma_noise_estimation(self.noisy)
print "Estimated noise:", estimated_noise_sigma
denoised = denoise.do(self.noisy, estimated_noise_sigma, threashold_factor=self.threashold_factor.get())
# denoised = nputils.smooth(self.noisy, 9, mode="same", boundary="symm")
if not thread.is_alive() or denoised == None:
return False
return (img, imgutils.Image.from_image(img, self.noisy), imgutils.Image.from_image(img, denoised))
def get_noise_factor(self, frame, noise_sigma, noise):
if not self._noise_res:
if noise is None:
noise = nputils.gaussian_noise([300, 300], 0, noise_sigma)
res = self.decompose(noise)
if res is None:
return None
self._noise_res = [k.std() for k in itertools.chain(*res)]
return self._noise_res[frame]
def get_noise_factor(self, frame, noise_sigma, noise):
if not self._noise_res:
if noise is None:
noise = nputils.gaussian_noise([300, 300], 0, noise_sigma)
res = self.decompose(noise)
if res is None:
return None
self._noise_res = [k.std() for k in itertools.chain(*res)]
return self._noise_res[frame]
def update(self, changed, thread):
wavelet = self.wavelet.get()
if wavelets.TriangeWaveletFamily().is_from(
wavelet) or wavelets.BSplineWaveletFamily().is_from(wavelet):
dec = wtutils.uiwt
rec = wtutils.uiwt_inv
else:
dec = wtutils.uwt
rec = wtutils.uwt_inv
denoise = Denoise(wavelet,
self.scale.get(),
self.boundary,
dec,
rec,
mode=self.mode.get(),
thread=thread)
img = self.img.get()
if changed in [self.img, self.noise_level] or self.noisy is None:
self.noisy = img.data + nputils.gaussian_noise(
img.data.shape, 0, self.noise_level.get())
estimated_noise_sigma = nputils.k_sigma_noise_estimation(self.noisy)
print "Estimated noise:", estimated_noise_sigma
denoised = denoise.do(self.noisy,
estimated_noise_sigma,
threashold_factor=self.threashold_factor.get())
# denoised = nputils.smooth(self.noisy, 9, mode="same", boundary="symm")
if not thread.is_alive() or denoised is None:
return False
return (img, imgutils.Image.from_image(img, self.noisy),
imgutils.Image.from_image(img, denoised))
