def predict( flag ):
flag.network = "unet"
flag.data_augmentation = 0
flag.primitive_augmentation = "rotation_3"
flag.epochs = 500
flag.learning_rate = 1e-5
flag.batch_size = 32
flag.loss_metric = "categorical_crossentropy"
img_rows = flag.image_size
img_cols = flag.image_size
print('-'*30)
print('Loading testing data...')
print('-'*30)
imgs_train, imgs_test, imgs_mask_train_all, imgs_mask_test_all, imgs_id_test, imgs_id_train = loadDataParams( flag )
images = imgs_test
images = preprocess(images, img_rows, img_cols )
regionList = flag.region_list
nClasses = len(regionList)+1
masks = toMultiLabelMask( imgs_mask_test_all, nClasses, regionList, img_rows, img_cols )
metrics = getMetricFunctions()
model = getModel( flag.network, nClasses, flag.optimizer, flag.activation, flag.loss_metric, metrics, flag.learning_rate, flag.image_size )
model.load_weights( flag.load_weights_file )
#model = load_model( flag.load_model_weights_file )
pred = model.predict( images, batch_size=flag.batch_size, verbose=0, steps=None)
return images, pred, masks
def toMultiLabelMask( masks_all, nClasses, regionList, img_rows, img_cols ):
s = masks_all[regionList[0]].shape
nImages = s[0]
masks = np.zeros( (nImages, img_rows, img_cols, nClasses) )
masks[:,:,:,0] = np.ones( (nImages, img_rows, img_cols) )
for regionIndex in range(len(regionList)):
# put the mask layer of the region index to 1
masks_region = masks_all[regionList[regionIndex]]
masks_region = preprocess(masks_region, img_rows, img_cols )
temp = masks_region[:,:,:,0]
masks[:,:,:,regionIndex+1] = temp
# and the background layer to 0, for every region pixel
masks[:,:,:,0] = (1 - temp) * masks[:,:,:,0]
return masks
def train( flag ):
K.clear_session()
img_rows = flag.image_size
img_cols = flag.image_size
n_epochs = flag.epochs
print('-'*30)
print('Loading train data...')
print('-'*30)
imgs_train, imgs_test, imgs_mask_train_all, imgs_mask_test_all, imgs_id_test, imgs_id_train = loadDataParams( flag )
images = imgs_train#np.expand_dims( imgs_train, axis = 3 )
#images = np.expand_dims( images, axis = 3 )
images = preprocess(images, img_rows, img_cols )
masks_all = imgs_mask_train_all
#loadDataParams( flag )
print('-'*30)
print('Fuse masks to single multi-label image')
print('-'*30)
regionList = flag.region_list
regionIdList = range(1, len(regionList)+1)
nClasses = len(regionList)+1
#masks = masks_all[regionList[0]]
s = masks_all[regionList[0]].shape
nImages = s[0]
masks = np.zeros( (nImages, img_rows, img_cols, nClasses) )
masks_reshape = np.zeros( (nImages, img_rows * img_cols, nClasses) )
#one_temp = np.ones( (s[0], img_rows, img_cols) )
masks[:,:,:,0] = np.ones( (nImages, img_rows, img_cols) )
for regionIndex in range(len(regionList)):
# put the mask layer of the region index to 1
masks_region = masks_all[regionList[regionIndex]]
masks_region = preprocess(masks_region, img_rows, img_cols )
temp = masks_region[:,:,:,0]
masks[:,:,:,regionIndex+1] = temp
# and the background layer to 0, for every region pixel
masks[:,:,:,0] = (1 - temp) * masks[:,:,:,0]
# Reshape (we don't need/use this???)
temp = temp.reshape((nImages,img_rows * img_cols))
masks_reshape[:,:,regionIndex+1] = temp
if len(flag.primitive_augmentation) > 0:
print('-'*30)
print('Primitive data augmentation for testing: ' + flag.primitive_augmentation)
print('-'*30)
images, masks = dataAugmentationPrimitive( flag, images, masks )
s = masks.shape
nImages = s[0]
else:
print('-'*30)
print('No primitive data augmentation used')
print('-'*30)
print('-'*30)
print('Save masks as images')
print('-'*30)
for imageIndex in range( nImages ):
fileName = 'mask_sample_%d.tif' % ( imageIndex )
filePath = os.path.join( flag.output_masks_feed_dir, fileName )
maskData = np.transpose( masks[imageIndex, ...], (2, 0, 1) )
maskData = np.expand_dims( maskData, axis = 3 )
writeData( filePath, maskData )
print('-'*30)
print('Save (preprocessed) images as images')
print('-'*30)
for imageIndex in range( nImages ):
fileName = 'image_sample_%d.tif' % ( imageIndex )
filePath = os.path.join( flag.output_images_feed_dir, fileName )
imageData = np.transpose( images[imageIndex, ...], (2, 0, 1) )
imageData = np.expand_dims( imageData, axis = 3 )
writeData( filePath, imageData )
#masks = np.expand_dims( masks, axis = 3 )
print('-'*30)
print('Load the model')
print('-'*30)
metrics = getMetricFunctions()
if ( flag.load_model_weights_file != "" ):
model = load_model( flag.load_model_weights_file )
elif ( flag.load_model_file != "" ):
json_string = open( flag.load_model_file ).read()
model = model_from_json( json_string )
else:
model = getModel( flag.network, nClasses, flag.optimizer, flag.activation, flag.loss_metric, metrics, flag.learning_rate, flag.image_size )
if ( flag.load_weights_file != "" ):
model.load_weights( flag.load_weights_file )
model_checkpoint = ModelCheckpoint( os.path.join( flag.output_run_dir, 'weights.{epoch:03d}.h5'), period=n_epochs//10)
show_pred_masks = trainCheck(flag)
if flag.data_augmentation:
#steps_per_epoch = len(train_generator) / flag.batch_size
steps_per_epoch = 20
images, imagesValidation = splitDataSet( images, 0.8 )
masks, masksValidation = splitDataSet( masks, 0.8)
train_generator = dataAugmentation( flag, images, masks )
history = model.fit_generator( train_generator,
validation_data = (imagesValidation, masksValidation),
steps_per_epoch = steps_per_epoch,
epochs=n_epochs, verbose=1, shuffle=True,
callbacks=[model_checkpoint,show_pred_masks])
# for e in range(flag.epochs):
# print('Epoch', e)
# batches = 0
# for x_batch, y_batch in train_generator:
# history = model.fit( x_batch[0], y_batch[0], validation_data = (imagesValidation, masksValidation), callbacks=[show_pred_masks])
# print('batch ' + str(batches) + ' : do nothing, save something?')
# #model.fit(x_batch, y_batch)
# batches += 1
# if batches >= len(images) / 32:
# # we need to break the loop by hand because
# # the generator loops indefinitely
# break
else:
#categorical_labels = to_categorical(int_labels, num_classes=None)
# if model.loss is "categorical_crossentropy":
# masks_cat = to_categorical(masks, num_classes=None)
history = model.fit( images, masks, batch_size=flag.batch_size, epochs=n_epochs, verbose=1, shuffle=True,
validation_split=0.2, callbacks=[model_checkpoint,show_pred_masks])
print('-'*30)
print('Saving model and training data (weights)')
print('-'*30)
model.save( os.path.join( flag.output_models_weights_dir, flag.run_id + ".h5" ) )
weights_path = os.path.join( flag.output_weights_dir, flag.run_id + ".h5" )
model.save_weights( weights_path )
json_path = os.path.join( flag.output_models_dir, flag.model_id + ".json" )
json_string = model.to_json()
with open( json_path, 'w') as json_file:
json_file.write( json_string )
plotVarList = getMetrics( history )
showTrainingHistory( flag, history, plotVarList )
def showData(flag):
img_rows = flag.image_size
img_cols = flag.image_size
print('-' * 30)
print('Loading train data...')
print('-' * 30)
imgs_train, imgs_test, imgs_mask_train_all, imgs_mask_test_all, imgs_id_test, imgs_id_train = loadDataParams(
flag)
images = imgs_train
images = preprocess(images, img_rows, img_cols)
masks_all = imgs_mask_train_all
print('-' * 30)
print('Fuse masks to single multi-label image')
print('-' * 30)
regionList = flag.region_list
nClasses = len(regionList) + 1
s = masks_all[regionList[0]].shape
nImages = s[0]
masks = np.zeros((nImages, img_rows, img_cols, nClasses))
masks[:, :, :, 0] = np.ones((nImages, img_rows, img_cols))
for regionIndex in range(len(regionList)):
# put the mask layer of the region index to 1
masks_region = masks_all[regionList[regionIndex]]
masks_region = preprocess(masks_region, img_rows, img_cols)
temp = masks_region[:, :, :, 0]
masks[:, :, :, regionIndex + 1] = temp
# and the background layer to 0, for every region pixel
masks[:, :, :, 0] = (1 - temp) * masks[:, :, :, 0]
print('-' * 30)
print('Save masks as images')
print('-' * 30)
scaleIntensity = 1
for imageIndex in range(nImages):
fileName = 'mask_sample_%d.tif' % (imageIndex)
filePath = os.path.join(flag.output_masks_feed_dir, fileName)
maskData = np.transpose(masks[imageIndex, ...], (2, 0, 1))
maskData = np.expand_dims(maskData, axis=3)
writeData(filePath, maskData)
y = masks[imageIndex, ...]
y = mergeLabelImage(y).astype(np.float32)
y /= y.max()
x = images[imageIndex, ...].astype(np.float32)
x = np.fmin(255.0 * np.ones(x.shape, np.float32), scaleIntensity * x)
x = x.astype(np.uint8)
x = np.squeeze(x)
print(x.shape)
#x /= x.max()
#img = (x*255).astype( np.uint8 )
imgColor = cv2.cvtColor(x, cv2.COLOR_GRAY2BGR)
imgMaskOri = (y * 255).astype(np.uint8)
imgMaskOriColor = cv2.applyColorMap(imgMaskOri, cv2.COLORMAP_JET)
imgOverlay = cv2.addWeighted(imgColor, 0.9, imgMaskOriColor, 0.4, 0.0)
output_path_mask_ori = os.path.join(
flag.output_images_dir, 'mask_index-%04d.png' % (imageIndex))
output_path_overlay = os.path.join(
flag.output_plots_dir, 'overlay_index-%04d.png' % (imageIndex))
output_path_image = os.path.join(flag.output_plots_dir,
'image_index-%04d.png' % (imageIndex))
cv2.imwrite(output_path_image, imgColor)
cv2.imwrite(output_path_mask_ori, imgMaskOriColor)
cv2.imwrite(output_path_overlay, imgOverlay)
print('-' * 30)
print('Save (preprocessed) images as images')
print('-' * 30)
for imageIndex in range(nImages):
fileName = 'image_sample_%d.tif' % (imageIndex)
filePath = os.path.join(flag.output_images_feed_dir, fileName)
imageData = scaleIntensity * np.transpose(images[imageIndex, ...],
(2, 0, 1))
imageData = np.expand_dims(imageData, axis=3)
writeData(filePath, imageData)