def denoise(data, num=None, snr=None):
"""
Denoises nd array with the format n x p x b
Parameters:
-----------
data : nd array
3-d numpy array with b = band
num : int
number of bands used
snr : int
threshold
Returns
-------
denoised array with same shape as data
"""
signal = spectral.calc_stats(data)
noise = spectral.noise_from_diffs(data)
mnfr = spectral.mnf(signal, noise)
if num:
denoised, trans = mnfr.denoise(data, num=num)
print(50*'_')
print(trans.shape)
elif snr:
denoised = mnfr.denoise(data, snr=snr)
print("--------------")
print(mnfr.num_with_snr(snr=snr))
else:
raise ValueError('"snr" or "num" must be given!')
return denoised, trans
def test_mnf_all_equals_data(self):
'''Test that MNF transform with all components equals original data.'''
data = self.data
signal = spy.calc_stats(data)
noise = spy.noise_from_diffs(data[117:137, 85:122, :])
mnfr = spy.mnf(signal, noise)
denoised = mnfr.denoise(data, num=data.shape[-1])
assert_allclose(denoised, data)
def test_mnf_all_equals_data(self):
'''Test that MNF transform with all components equals original data.'''
data = self.data
signal = spy.calc_stats(data)
noise = spy.noise_from_diffs(data[117: 137, 85: 122, :])
mnfr = spy.mnf(signal, noise)
denoised = mnfr.denoise(data, num=data.shape[-1])
assert(np.allclose(denoised, data))
outstack = r"/home/jb/Downloads/stack_denoised5.tif"
with rasterio.open(instack) as intif:
stack = intif.read()
land = stack[7, :, :] > 500
np.count_nonzero(land)
out_meta = intif.meta.copy()
(stack[:, land]) = 0
t_stack = np.transpose(stack, (1, 2, 0))
help(spectral.calc_stats)
ss = stack.shape[0]
print(t_stack.shape)
# help(spectral.calc_stats)
#view =imshow(t_stack,(8,3,2))
signal = spectral.calc_stats(t_stack)
noise = spectral.noise_from_diffs(t_stack)
mnfr = spectral.mnf(signal, noise)
t_denoised = mnfr.denoise(t_stack, num=5)
t_reduced = mnfr.reduce(t_stack, num=5)
t_reduced.shape
t_denoised.shape
tt_denoised = np.transpose(t_denoised, (2, 0, 1))
tt_reduced = np.transpose(t_reduced, (2, 0, 1))
tt_stack = np.transpose(t_stack, (2, 0, 1))
tt_reduced.shape
out_meta['count'] = 13
tt_denoised.shape
tt_denoised.shape
np.max(tt_denoised[2:, ])
np.min(tt_denoised[1:, ])
view = spectral.imshow(t_denoised, (1, 3, 2))