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ANN_v0.1.py
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ANN_v0.1.py
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#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import h5py as h5
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Merge
# Configurations
configurations = ('simu', 'expr')
metrics = ('resol', 'contr')
ftypes = ('iq', 'rf')
config = configurations[0]
metric = metrics[0]
ftype = ftypes[0]
# Import Settings
if config == 'simu':
phantom_path = './archive_to_download/database/simulation/resolution_distorsion/'
phantom_name = 'resolution_distorsion_simu_phantom.hdf5'
if metric == 'resol':
data_path = './archive_to_download/database/simulation/resolution_distorsion/'
imag_recon_path = './archive_to_download/reconstructed_image/simulation/resolution_distorsion/'
if ftype == 'iq':
data_name = 'resolution_distorsion_simu_dataset_iq.hdf5' # IQ data
imag_recon_name = 'resolution_distorsion_simu_img_from_iq.hdf5'
elif ftype == 'rf':
data_name = 'resolution_distorsion_simu_dataset_rf.hdf5' # RF data
imag_recon_name = 'resolution_distorsion_simu_img_from_rf.hdf5'
scan_path = './archive_to_download/database/simulation/resolution_distorsion/'
scan_name = 'resolution_distorsion_simu_scan.hdf5'
elif metric == 'contr':
data_path = './archive_to_download/database/simulation/contrast_speckle/'
imag_recon_path = 'archive_to_download/reconstructed_image/simulation/contrast_speckle/'
if ftype == 'iq':
data_name = 'contrast_speckle_simu_dataset_iq.hdf5' # IQ data
imag_recon_name = 'contrast_speckle_simu_img_from_iq.hdf5'
elif ftype == 'rf':
data_name = 'contrast_speckle_simu_dataset_rf.hdf5' # IQ data
imag_recon_name = 'contrast_speckle_simu_img_from_rf.hdf5'
scan_path = './archive_to_download/database/simulation/contrast_speckle/'
scan_name = 'contrast_speckle_simu_scan.hdf5'
elif config == 'expr':
phantom_path = './archive_to_download/database/experiments/resolution_distorsion/'
phantom_name = 'resolution_distorsion_expe_phantom.hdf5'
if metric == 'resol':
data_path = './archive_to_download/database/experiments/resolution_distorsion/'
imag_recon_path = 'archive_to_download/reconstructed_image/experiments/resolution_distorsion/'
if ftype == 'iq':
data_name = 'resolution_distorsion_expe_dataset_iq.hdf5' # IQ data
imag_recon_name = 'resolution_distorsion_expe_img_from_iq.hdf5'
elif ftype == 'rf':
data_name = 'resolution_distorsion_expe_dataset_rf.hdf5' # RF data
imag_recon_name = 'resolution_distorsion_expe_img_from_rf.hdf5'
scan_path = './archive_to_download/database/experiments/resolution_distorsion/'
scan_name = 'resolution_distorsion_expe_scan.hdf5'
elif metric == 'contr':
data_path = './archive_to_download/database/experiments/contrast_speckle/'
imag_recon_path = 'archive_to_download/reconstructed_image/experiments/contrast_speckle/'
if ftype == 'iq':
data_name = 'contrast_speckle_expe_dataset_iq.hdf5' # IQ data
imag_recon_name = 'contrast_speckle_expe_img_from_iq.hdf5'
elif ftype == 'rf':
data_name = 'contrast_speckle_expe_dataset_rf.hdf5' # IQ data
imag_recon_name = 'contrast_speckle_expe_img_from_rf.hdf5'
scan_path = './archive_to_download/database/experiments/contrast_speckle/'
scan_name = 'contrast_speckle_expe_scan.hdf5'
# Data Class
class DataSet:
# data = {} # storing the data
# scan = {}
def __init__(self, name):
self.name = name
self.data = {}
self.scan = {}
def __print_name(self, name):
print(name)
def import_data(self, file_path, file_name):
with h5.File(file_path + file_name, 'r') as hf:
print('This %s dataset contains: ' % file_name)
hf.visit(self.__print_name)
print
hf_group = hf['/US/US_DATASET0000']
# for key in hf_group:
# print(hf_group[key])
# tmp = np.array(hf_group[key])
# print(tmp)
if 'data' in hf_group.keys():
self.data['real'] = np.array(hf_group['data/real'])
self.data['imag'] = np.array(hf_group['data/imag'])
if 'scan' in hf_group.keys():
self.scan['x_axis'] = np.array(hf_group['scan/x_axis'])
self.scan['z_axis'] = np.array(hf_group['scan/z_axis'])
if 'phantom_xPts' in hf_group.keys():
posx = np.array(hf_group['phantom_xPts'])
posz = np.array(hf_group['phantom_zPts'])
pos = (posx, posz)
print(pos)
print
print(pos[0])
print
print(pos[1])
plt.figure()
plt.plot(posx, posz, 's')
plt.title('1')
plt.show()
if 'scatterers_positions' in hf_group.keys():
pos = np.array(hf_group['scatterers_positions'])
print(pos.shape)
plt.figure()
plt.plot(pos[0], pos[2], 's')
plt.title('2')
plt.show()
def show_image(self, prange):
num_slices = self.data['real'].shape[0]
plt.figure()
for i in np.arange(num_slices):
amp = np.sqrt(self.data['real'][i, :, :]**2 + self.data['imag'][i, :, :]**2)
plt.subplot(2, 2, i+1)
plt.imshow(amp, extent=prange)
plt.title(i+1)
plt.show()
def train(in_dim, out_dim, X_train, Y_train, X_test, Y_test):
model = Sequential()
model.add(Dense(100000, input_dim = in_dim, init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(100000, init='uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(out_dim, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='sgd',\
metrics=['accuracy'])
hist = model.fit(X_train, Y_train, nb_epoch=5, batch_size=32,\
validation_split=0.1, shuffle=True)
print(hist.history)
loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
classes = model.predict_classes(X_test, batch_size=32)
proba = model.predict_proba(X_test, batch_size=32)
def test_import():
pht_data = DataSet(config+'_'+metric+'_'+ftype+'_'+'pht_data')
pht_data.import_data(phantom_path, phantom_name)
def test_net():
rcv_data = DataSet(config+'_'+metric+'_'+ftype+'_'+'data')
rcv_data.import_data(data_path, data_name)
# import_data(scan_path, scan_name)
# import_data(phantom_path, phantom_name)
img_data = DataSet('reconstructed_image')
img_data.import_data(imag_recon_path, imag_recon_name)
# img_data.show_image([0, 0.1, 0, 0.1])
input_dim = rcv_data.data['real'].size
print input_dim
X_train = rcv_data.data['real'].reshape(1, -1)
output_dim = img_data.data['real'].size
print output_dim
Y_train = img_data.data['real'].reshape(1, -1)
X_test = rcv_data.data['imag'].reshape(1, -1)
print X_test.shape
Y_test = img_data.data['imag'].reshape(1, -1)
print Y_test.shape
# train(input_dim, output_dim, X_train, Y_train, X_test, Y_test)
if __name__ == '__main__':
test_net()
# test_import();