def PNN_test(I_MS_LR, I_PAN, inputImg, param, net, path, mode, epochs=0):
test_dir_out = path['test_dir_out']
FTnetwork_dir_out = path['ftnetwork_dir_out']
param['L'] = inputImg['L']
param['ratio'] = inputImg['ratio']
if 'inputType' not in param.keys():
param['inputType'] = 'MS_PAN'
#fine tuning
if epochs != 0:
fine_tuning(I_MS_LR, I_PAN, param, epochs, FTnetwork_dir_out)
ft_model_path = FTnetwork_dir_out + '/PNN_model.mat'
FT_model = sio.loadmat(ft_model_path, squeeze_me=True)
from PNN_testing_model import Network, ConvLayer
layer = []
for j in range(0, len(FT_model['layers']), 2):
layer.append(
ConvLayer(FT_model['layers'][j], FT_model['layers'][j + 1]))
net = Network(layer)
if mode != 'full':
I_MS_LR, I_PAN = downgrade_images(I_MS_LR, I_PAN, param['ratio'],
param['sensor'])
I_PAN = np.expand_dims(I_PAN, axis=0)
NDxI_LR = []
mav_value = 2**(np.float32(param['L']))
# compute radiometric indexes
if param['inputType'] == 'MS_PAN_NDxI':
if I_MS_LR.shape[0] == 8:
NDxI_LR = np.stack(((I_MS_LR[4, :, :] - I_MS_LR[7, :, :]) /
(I_MS_LR[4, :, :] + I_MS_LR[7, :, :]),
(I_MS_LR[0, :, :] - I_MS_LR[7, :, :]) /
(I_MS_LR[0, :, :] + I_MS_LR[7, :, :]),
(I_MS_LR[2, :, :] - I_MS_LR[3, :, :]) /
(I_MS_LR[2, :, :] + I_MS_LR[3, :, :]),
(I_MS_LR[5, :, :] - I_MS_LR[0, :, :]) /
(I_MS_LR[5, :, :] + I_MS_LR[0, :, :])),
axis=0)
else:
NDxI_LR = np.stack(((I_MS_LR[3, :, :] - I_MS_LR[2, :, :]) /
(I_MS_LR[3, :, :] + I_MS_LR[2, :, :]),
(I_MS_LR[1, :, :] - I_MS_LR[3, :, :]) /
(I_MS_LR[1, :, :] + I_MS_LR[3, :, :])),
axis=0)
#input preparation
if param['typeInterp'] == 'interp23tap':
I_MS = interp23(I_MS_LR, param['ratio'])
if len(NDxI_LR) != 0:
NDxI = interp23(NDxI_LR, param['ratio'])
else:
sys.exit('interpolation not supported')
if param['inputType'] == 'MS':
I_in = I_MS.astype('single') / mav_value
elif param['inputType'] == 'MS_PAN':
I_in = np.vstack((I_MS, I_PAN)).astype('single') / mav_value
elif param['inputType'] == 'MS_PAN_NDxI':
I_in = np.vstack((I_MS, I_PAN)).astype('single') / mav_value
I_in = np.vstack((I_in, NDxI)).astype('single')
else:
sys.exit('Configuration not supported')
print I_in.shape
I_in_residual = np.expand_dims(I_in, axis=0)
I_in_residual = I_in_residual[:, :I_MS.shape[0], :, :]
I_in = np.pad(I_in, ((0, 0), (param['padSize'] / 2, param['padSize'] / 2),
(param['padSize'] / 2, param['padSize'] / 2)),
mode='edge')
I_in = np.expand_dims(I_in, axis=0)
#Pansharpening
if param['residual']:
I_out = net.build(I_in) + I_in_residual[:, :I_MS.shape[0], :, :]
else:
I_out = net.build(I_in)
I_out = I_out * mav_value
return np.squeeze(I_out)
def PNN_test_HSMS_ratio_3(
I_MS_LR, I_PAN, inputImg, param, net, path, mode,
epochs): #iniz è uguale a 0 QUI ERA IL PROBLEMA FACEVA UN OVERRIDE
test_dir_out = path['test_dir_out']
FTnetwork_dir_out = path['ftnetwork_dir_out']
# import pdb; pdb.set_trace()
# san paolo hs/ms
param['L'] = 15 # modifica del 23/04
# param['ratio']=3
param['ratio'] = 3 # modificato il 04/04
param[
'lr'] = 0.0001 # prec 10e-4 #modificato il 25/04
param['patchSize'] = 33 #preced = 33
# param['padSize'] = 8
# per il debug
# import pdb; pdb.set_trace()
if 'inputType' not in param.keys():
param['inputType'] = 'MS_PAN'
#fine tuning
if epochs != 0:
# pdb.set_trace()
fine_tuning(I_MS_LR, I_PAN, param, epochs, FTnetwork_dir_out)
ft_model_path = FTnetwork_dir_out + '/PNN_model.mat'
FT_model = sio.loadmat(ft_model_path, squeeze_me=True) #provo25/04
from PNN_testing_model import Network, ConvLayer
layer = []
for j in range(0, len(FT_model['layers']), 2):
layer.append(
ConvLayer(FT_model['layers'][j], FT_model['layers'][j + 1]))
net = Network(layer)
# layer=[]
# for j in range(0,len(param['layers']),2):
# layer.append(ConvLayer(param['layers'][j], param['layers'][j+1]))
# net=Network(layer)
# pdb.set_trace()
if mode != 'full':
I_MS_LR, I_PAN = downgrade_images(I_MS_LR, I_PAN, param['ratio'],
param['sensor'])
I_PAN = np.expand_dims(I_PAN, axis=0)
NDxI_LR = []
mav_value = 2**(np.float32(param['L']))
# compute radiometric indexes
if param['inputType'] == 'MS_PAN_NDxI':
if I_MS_LR.shape[0] == 8:
NDxI_LR = np.stack(((I_MS_LR[4, :, :] - I_MS_LR[7, :, :]) /
(I_MS_LR[4, :, :] + I_MS_LR[7, :, :]),
(I_MS_LR[0, :, :] - I_MS_LR[7, :, :]) /
(I_MS_LR[0, :, :] + I_MS_LR[7, :, :]),
(I_MS_LR[2, :, :] - I_MS_LR[3, :, :]) /
(I_MS_LR[2, :, :] + I_MS_LR[3, :, :]),
(I_MS_LR[5, :, :] - I_MS_LR[0, :, :]) /
(I_MS_LR[5, :, :] + I_MS_LR[0, :, :])),
axis=0)
else:
NDxI_LR = np.stack(((I_MS_LR[3, :, :] - I_MS_LR[2, :, :]) /
(I_MS_LR[3, :, :] + I_MS_LR[2, :, :]),
(I_MS_LR[1, :, :] - I_MS_LR[3, :, :]) /
(I_MS_LR[1, :, :] + I_MS_LR[3, :, :])),
axis=0)
# %input preparation
if param['typeInterp'] == 'interp23tap':
# import pdb; pdb.set_trace()
I_MS = interp23(I_MS_LR, param['ratio'])
# if len(NDxI_LR)!=0:
# NDxI = interp23(NDxI_LR, param['ratio'])
# else:
# sys.exit('interpolation not supported')
if param['inputType'] == 'MS':
I_in = I_MS.astype('single') / mav_value
elif param['inputType'] == 'MS_PAN':
# import pdb; pdb.set_trace()
I_in = np.vstack((I_MS, I_PAN)).astype('single') / mav_value
elif param['inputType'] == 'MS_PAN_NDxI':
I_in = np.vstack((I_MS, I_PAN)).astype('single') / mav_value
I_in = np.vstack((I_in, NDxI)).astype('single')
else:
sys.exit('Configuration not supported')
print(I_in.shape)
# import pdb; pdb.set_trace()
I_in_residual = np.expand_dims(I_in, axis=0)
I_in_residual = I_in_residual[:, :I_MS.shape[0], :, :]
import pdb
pdb.set_trace()
I_in = np.pad(I_in, ((0, 0), (param['padSize'] / 2, param['padSize'] / 2),
(param['padSize'] / 2, param['padSize'] / 2)),
mode='edge')
I_in = np.expand_dims(I_in, axis=0)
#Pansharpening MODIFICA 17/04
# param['residual'] = 'false'
# I_MS_residual = np.expand_dims(I_MS,axis=0)
if param['residual'] == 'true' or param['residual'] == 1:
I_out = net.build(I_in) + I_in_residual[:, :I_MS.shape[0], :, :]
# I_out = net.build(I_in) + I_MS_residual
else:
I_out = net.build(I_in)
I_out = I_out * mav_value
return np.squeeze(I_out)
from PNN_testing_model import Network, ConvLayer
from PNN_test import PNN_test
from others import parser_xml, export2
model = parser_xml('config_testing_' + sensor + '.xml')
"""
in case of testing/finetuning on more images all the following
lines have to be included in the loop, in order to avoid the overwriting
of the pre-trained model
"""
execfile('copy_xml_fields_testing.py')
layer = []
for i in xrange(0, len(PNN_model['layers']), 2):
layer.append(ConvLayer(PNN_model['layers'][i], PNN_model['layers'][i + 1]))
net = Network(layer)
#%% Pansharpening
#load images
inputImg = sio.loadmat(testset_path)
I_MS_LR = np.array(inputImg['I_MS'], dtype='double').transpose(2, 0, 1)
I_PAN = np.array(inputImg['I_PAN'], dtype='double')
#Testing
I_MS_HR = PNN_test(I_MS_LR, I_PAN, inputImg, PNN_model, net, path, mode,
epochs)
#%% save data
export2(I_MS_HR, test_dir_out)