def _read_dicom_image(self, filename):
d = dicom.read_file(filename)
img = d.pixel_array.astype('float')
img = crop_resize(img, newsize=(64, 64)) # PH Added preprocessing
img = np.true_divide(img, 255)
return img
def _read_dicom_image(self, filename):
d = dicom.read_file(filename)
img = d.pixel_array.astype('float')
img = crop_resize(img, newsize=(64,64)) # PH Added preprocessing
img = np.true_divide(img,255)
return img
def predict(inputimages, nkerns = 100, batch_size = 260, fine_tuned_params_path = None):
######################
# INITIALIZATIONS #
######################
dim = len(inputimages)
new_images = numpy.zeros((dim,64,64))
for i in xrange(dim):
new_images[i, :, :] = crop_resize(inputimages[i], newsize=(64, 64))
new_images[i, :, :] = numpy.true_divide(new_images[i], 255)
if fine_tuned_params_path is None:
b_CNN_input = None
W_CNN_input = None
W_logistic = None
b_logistic = None
else:
with open(fine_tuned_params_path) as f:
params = pickle.load(f)
# load pre-trained parameters
W_logistic, b_logistic, W_CNN_input, b_CNN_input = params[0]
W_logistic = numpy.asarray(W_logistic,dtype='float32')
b_logistic = numpy.asarray(b_logistic,dtype='float32')
W_CNN_input = numpy.asarray(W_CNN_input,dtype='float32')
b_CNN_input= numpy.asarray(b_CNN_input,dtype='float32')
W_logistic = theano.shared(W_logistic.astype(fx), borrow=True)
b_logistic = theano.shared(b_logistic.astype(fx), borrow=True)
W_CNN_input = theano.shared(W_CNN_input.astype(fx), borrow=True)
b_CNN_input= theano.shared(b_CNN_input.astype(fx), borrow=True)
rng = numpy.random.RandomState(23455)
# manipulate data
train_set_x = numpy.asarray(new_images, dtype='float32')
dim = train_set_x.shape
train_set_x = numpy.reshape(train_set_x, (dim[0], (dim[1]*dim[2])))
train_set_x = theano.shared(train_set_x.astype(fx), borrow=True) # convert to 260 x 4096
n_batches = train_set_x.get_value(borrow=True).shape[0]
n_batches /= batch_size
###############
# BUILD MODEL #
###############
# build model
#print('... building the model')
index = T.lscalar()
x = T.matrix('x', dtype=fx)
# Convolution + Pooling Layer
layer0_input = x.reshape((batch_size, 1, 64, 64))
layer0 = LeNetConvPoolLayer(
rng = rng,
input = layer0_input,
filter_shape = (nkerns, 1, 11, 11),
image_shape = (batch_size, 1, 64, 64),
poolsize = (6, 6),
W = W_CNN_input,
b = b_CNN_input
)
layer0_output = layer0.output.flatten(2)
# Logistic Regression Layer
layer3 = LogisticRegression(
input = layer0_output,
n_in = 8100,
n_out = 1024,
W = W_logistic,
b = b_logistic
)
predict_model = theano.function(
inputs = [index],
outputs=layer3.thresh,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size]
}
)
preds = [predict_model(minibatch_index) for minibatch_index in xrange(n_batches)]
images = [numpy.reshape(preds[i],(32,32)) for i in xrange(n_batches)]
'''
with open('/Users/Peadar/Documents/KagglePythonProjects/AML/DataScienceBowl/data/CNN_output.pickle', 'wb') as f:
pickle.dump(images, f)
'''
return images
