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model.py
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model.py
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import matplotlib
#matplotlib.use('cairo')
import numpy as np
np.set_printoptions(suppress=True)
np.set_printoptions(precision=6)
#import tensorflow as tf
import cv2
import itertools
from glob import glob
import time
from datetime import timedelta
from IPython.display import display
from IPython.display import SVG
import matplotlib.pyplot as plt
import sys
from base64 import b64encode
#import seaborn as sns
# scale font size
#sns.set(font_scale=1.8)
hastie_orange = (0.906,0.624,0)
hastie_blue = (0.337,0.706,0.914)
hastie_red = (0.678,0.137,0.137)
hastie_green = (0.114,0.412,0.078)
import os
# os.environ['OMP_NUM_THREADS'] = '8'
# os.environ['CC'] = "/usr/local/bin/clang-omp"
# os.environ['CXX'] = "/usr/local/bin/clang-omp++"
import theano
theano.config.exception_verbosity='high'
#theano.config.profile = 'True'
# theano.config.openmp = 'True'
# theano.config.cc = '/usr/local/bin/clang-omp'
# theano.config.cxx = '/usr/local/bin/clang-omp++'
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D, AtrousConv2D, MaxPooling2D
from keras.utils.visualize_util import model_to_dot
from keras.layers.advanced_activations import ELU
from keras.optimizers import Adam, SGD
from keras.constraints import maxnorm, unitnorm
from keras import backend as K
import theano.tensor as T
from keras.optimizers import Optimizer
import keras.optimizers
from keras.layers.core import Layer
from keras import initializations
from keras.utils.visualize_util import plot
import scipy.ndimage as nd
from keras.callbacks import Callback, TensorBoard
from keras.regularizers import l1l2, activity_l1l2
EPOCH = 21
IMG0_NAME = "1_1"
#IMG0 = cv2.imread("cropped_positives/"+IMG0_NAME+".tif",0)
IMG0 = cv2.imread("cropped_positives_refinement/"+IMG0_NAME+".tif",0)
MEAN= 110.307
STD= 57.117
def save_model(model, model_name):
directory = os.path.dirname(model_name)
if len(directory) > 0 and not os.path.exists(directory):
os.makedirs(directory)
json_string = model.to_json()
open(model_name+'_architecture.json', 'w').write(json_string)
model.save_weights(model_name+'_weights.h5', overwrite=True)
plot(model, to_file=model_name+'.png',show_shapes=True, show_layer_names=True)
class MyCallbackRegression(Callback):
def on_batch_end(self, batch, logs={}):
global IMG0
global IMG0_NAME
global EPOCH
if (batch % 20) == 0:
if not os.path.exists("callback_img0_regression"):
os.makedirs("callback_img0_regression")
EPOCH += 1
test_regression(IMG0, self.model,
name="callback_img0_regression/batch_%s_%05d"%(IMG0_NAME,EPOCH))
def on_epoch_end(self, epoch, logs={}):
global IMG0
global IMG0_NAME
global EPOCH
if not os.path.exists("callback_img0"):
os.makedirs("callback_img0")
EPOCH += 1
test_regression(IMG0, self.model,
name="callback_img0_regression/epoch_%s_%05d"%(IMG0_NAME,EPOCH))
if (epoch % 5) == 0:
save_model(self.model,"callback_model_regression/model_epoch%05d"%epoch)
class MyCallbackClassification(Callback):
def on_batch_end(self, batch, logs={}):
global IMG0
global IMG0_NAME
global EPOCH
if (batch % 20) == 0:
if not os.path.exists("callback_img0"):
os.makedirs("callback_img0")
EPOCH += 1
test_classification(IMG0, self.model,name="callback_img0/batch_%s_%05d"%(IMG0_NAME,EPOCH))
def on_epoch_end(self, epoch, logs={}):
global IMG0
global IMG0_NAME
global EPOCH
if not os.path.exists("callback_img0"):
os.makedirs("callback_img0")
EPOCH += 1
test_classification(IMG0, self.model, name="callback_img0/epoch_%s_%05d"%(IMG0_NAME,EPOCH))
if (epoch % 5) == 0:
save_model(self.model,"callback_model/model_epoch%05d"%epoch)
def test_regression(img, model, name):
predictions = np.squeeze(model.predict(img[np.newaxis,...,np.newaxis]))
shape = predictions.shape[:2]
yx = np.rollaxis(np.mgrid[0:shape[0],0:shape[1]],0,3)
predictions = yx + predictions
predictions = np.round(predictions).astype('int')
predictions = (predictions[:,:,0].flatten(),
predictions[:,:,1].flatten())
flat_indices = np.ravel_multi_index(predictions, shape, mode='clip')
bins = np.bincount(flat_indices, minlength=np.prod(shape))
res = bins.reshape(shape)[...,np.newaxis].astype('float32')
# artefacts of clipping
res[0] = 0
res[-1] = 0
res[:,0] = 0
res[:,-1] = 0
print("max regression:",res.max())
if res.max()>0:
res *= 255/res.max()
cv2.imwrite(name+"_regression.png",res.astype('uint8'))
def test_classification(img, model, name):
seg = np.squeeze(model.predict(img[np.newaxis,...,np.newaxis]))
print("range = [%s; %s]"%(seg.min(),seg.max()))
seg[seg<0] = 0
seg[seg>1] = 1
print(name+"_classification.png")
cv2.imwrite(name+"_classification_hard.png",(seg>0.5).astype('uint8')*255)
cv2.imwrite(name+"_classification.png",(seg*255).astype('uint8'))
def predict( img, model_classification, model_regression, filename ):
seg = np.squeeze(model_classification.predict(img[np.newaxis,...,np.newaxis]))
predictions = np.squeeze(model_regression.predict(img[np.newaxis,...,np.newaxis]))
norm = np.linalg.norm(predictions,axis=-1)
shape = predictions.shape[:2]
yx = np.rollaxis(np.mgrid[0:shape[0],0:shape[1]],0,3)
predictions = yx + predictions
predictions = np.round(predictions).astype('int')
predictions = (predictions[:,:,0].flatten(),
predictions[:,:,1].flatten())
# threshold seg at 0.5
seg[seg<0.5] = 0
flat_indices = np.ravel_multi_index(predictions, shape, mode='clip')
bins = np.bincount(flat_indices, minlength=np.prod(shape), weights=seg.flatten()*(1/(1+norm[...,np.newaxis])).flatten())
res = bins.reshape(shape)[...,np.newaxis].astype('float32')
# artefacts of clipping
res[0] = 0
res[-1] = 0
res[:,0] = 0
res[:,-1] = 0
#res *= 1/(1+norm[...,np.newaxis])
res = nd.gaussian_filter(res,0.5)
_max = res.max()
print("max:",_max)
if _max>5:
p = np.unravel_index(np.argmax(res),res.shape)
mask = np.ones(res.shape)
mask[p[0],p[1]] = 0
distance = nd.distance_transform_edt(mask)
res[distance>3]=0
res = res.flat[flat_indices].reshape(shape)
# take the intersection of the Hough back-projection
# and the initial segmentation
res = np.logical_and(res>0,seg>0)
#res = nd.binary_closing(res,morphology.disk(3))
res = nd.binary_fill_holes(res)
# uncrop
crop=16
tmp_res = np.zeros((res.shape[0]+2*crop,res.shape[1]+2*crop),dtype='float32')
tmp_res[crop:-crop,crop:-crop] = res
# resize
res = cv2.resize(tmp_res,(580,420),interpolation=cv2.INTER_CUBIC) > 0.5
# if res.sum()<1000:
# res = np.zeros((420,580),dtype='uint8')
#
else:
res = np.zeros((420,580),dtype='uint8')
cv2.imwrite(filename+".png",res.astype('uint8')*255)
return res
def dice_error(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (1.- (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon()))
def dice_error_metric(y_true,y_pred):
return dice_error(y_true, y_pred)
def regression_error(y_true, y_pred):
inside = K.cast(K.not_equal(y_true, 1e8), K.floatx())
return K.sum(inside*K.square(y_pred - y_true),axis=[1,2,3])/K.sum(inside,axis=[1,2,3])
def regression_error_metric(y_true, y_pred):
return K.mean(regression_error(y_true, y_pred))
def binary_crossentropy(y_true, y_pred):
p1 = 0.175913638795
p0=1-p1
w = p1*K.cast(K.equal(y_true, 0), K.floatx()) + p0*K.cast(K.equal(y_true, 1), K.floatx())
return K.sum(w*K.binary_crossentropy(y_pred, y_true),axis=[1,2,3])/K.sum(w,axis=[1,2,3])
def binary_crossentropy_metric(y_true, y_pred):
return K.mean(binary_crossentropy(y_true, y_pred))
def get_model_regression(input_shape, filename=None,l1_value=1e-12, l2_value=1e-10):
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3,
border_mode='valid',input_shape=(input_shape[0],input_shape[1],1), dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=64, nb_row=3, nb_col=3, atrous_rate=(2, 2), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=128, nb_row=3, nb_col=3, atrous_rate=(4, 4), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=256, nb_row=3, nb_col=3, atrous_rate=(8, 8), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(nb_filter=256, nb_row=1, nb_col=1, border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(nb_filter=2, nb_row=1, nb_col=1, border_mode='valid', dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)
))
model.add(Activation('linear'))
print("compiling model")
# load previously trained model
if filename is not None:
print("Loading weights from", filename)
model.load_weights(filename)
model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),
metrics=[regression_error_metric],
loss=regression_error)
print("Model padding:",
(input_shape[0] - model.output_shape[1])/2,
(input_shape[1] - model.output_shape[2])/2)
return model
def get_model_classification(input_shape, filename=None,l1_value=10e-12, l2_value=10e-14):
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3,
border_mode='valid',input_shape=(input_shape[0],input_shape[1],1), dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=64, nb_row=3, nb_col=3, atrous_rate=(2, 2), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=128, nb_row=3, nb_col=3, atrous_rate=(4, 4), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(AtrousConv2D(nb_filter=512, nb_row=3, nb_col=3, atrous_rate=(8, 8), border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(nb_filter=512, nb_row=1, nb_col=1, border_mode='valid', dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(nb_filter=1, nb_row=1, nb_col=1, border_mode='valid', dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)
))
model.add(Activation('sigmoid'))
print("compiling model")
# load previously trained model
if filename is not None:
print("Loading weights from", filename)
model.load_weights(filename)
model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),
metrics=[dice_error_metric,binary_crossentropy_metric,'accuracy'],
loss=binary_crossentropy)
print("Model padding:",
(input_shape[0] - model.output_shape[1])/2,
(input_shape[1] - model.output_shape[2])/2)
return model
def standardise(img):
img -= MEAN
img /+ STD
return img
def get_training_data_regression(folder):
X = []
Y = []
all_imgs = list(sorted(filter(lambda x: 'mask' not in x, glob(folder+"/*.tif"))))
for f in all_imgs:
img = standardise(cv2.imread(f, 0).astype('float32'))
seg = (cv2.imread(f[:-len(".tif")]+"_mask.tif", 0)>0).astype('float32')
center = np.array(nd.center_of_mass(seg),dtype='float32')
yx = np.rollaxis(np.mgrid[0:seg.shape[0],0:seg.shape[1]],0,3).astype('float32')
offsets = center - yx
offsets[seg==0] = 1e8
X.append(img[...,np.newaxis].astype('float32'))
Y.append(offsets.astype('float32'))
return np.array(X, dtype='float32'), np.array(Y, dtype='float32')
def get_training_data_classification(folder):
X = []
Y = []
all_imgs = list(sorted(filter(lambda x: 'mask' not in x, glob(folder+"/*.tif"))))
for f in all_imgs:
img = standardise(cv2.imread(f, 0).astype('float32'))
seg = (cv2.imread(f[:-len(".tif")]+"_mask.tif", 0)>0).astype('float32')
X.append(img[...,np.newaxis].astype('float32'))
Y.append(seg[...,np.newaxis].astype('float32'))
return np.array(X, dtype='float32'), np.array(Y, dtype='float32')
def get_test_data(folder, downsample_factor=6):
X = []
X_names = []
all_imgs = list(sorted(filter(lambda x: 'mask' not in x, glob(folder+"/*.tif"))))
for f in all_imgs:
img = standardise(cv2.imread(f, 0).astype('float32'))
if downsample_factor > 1:
img = cv2.resize(img,(img.shape[1]//downsample_factor, img.shape[0]//downsample_factor),
interpolation=cv2.INTER_AREA)
X.append(img)
X_names.append(os.path.basename(f)[:-len('.tif')])
return np.array(X, dtype='float32'), X_names
def get_model_classification_refinement(input_shape, filename=None,l1_value=10e-12, l2_value=10e-14,mode='valid'):
def binary_crossentropy(y_true, y_pred):
p1 = 0.412244897959
p0=1-p1
w = p1*K.cast(K.equal(y_true, 0), K.floatx()) + p0*K.cast(K.equal(y_true, 1), K.floatx())
return K.sum(w*K.binary_crossentropy(y_pred, y_true),axis=[1,2,3])/K.sum(w,axis=[1,2,3])
def binary_crossentropy_metric(y_true, y_pred):
return K.mean(binary_crossentropy(y_true, y_pred))
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3,
border_mode=mode,input_shape=(input_shape[0],input_shape[1],1), dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3,
border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(MaxPooling2D(dim_ordering='tf'))
model.add(Convolution2D(nb_filter=64, nb_row=3, nb_col=3,
border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filter=64, nb_row=3, nb_col=3,
border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(MaxPooling2D(dim_ordering='tf'))
model.add(Convolution2D(nb_filter=128, nb_row=3, nb_col=3,
border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filter=128, nb_row=3, nb_col=3,
border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(MaxPooling2D(dim_ordering='tf'))
model.add(Convolution2D(nb_filter=512, nb_row=16, nb_col=16, border_mode=mode, dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filter=512, nb_row=1, nb_col=1, border_mode=mode, dim_ordering='tf',W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filter=1, nb_row=1, nb_col=1, border_mode=mode, dim_ordering='tf',
W_regularizer=l1l2(l1=l1_value, l2=l2_value), activity_regularizer=activity_l1l2(l1=l1_value, l2=l2_value)
))
model.add(Activation('sigmoid'))
print("compiling model")
# load previously trained model
if filename is not None:
print("Loading weights from", filename)
model.load_weights(filename)
model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),
metrics=[dice_error_metric,binary_crossentropy_metric,'accuracy'],
loss=binary_crossentropy)
print("Model padding:",
(input_shape[0] - model.output_shape[1])/2,
(input_shape[1] - model.output_shape[2])/2)
return model