train_images, train_segs = load_training_data()
val_images, val_segs = load_validation_data()
train_labels = dataset_construction.create_all_area_masks(
train_images, train_segs)
val_labels = dataset_construction.create_all_area_masks(val_images, val_segs)
NUM_CLASSES = train_segs.shape[1] + 1
train_labels = to_categorical(train_labels, NUM_CLASSES)
val_labels = to_categorical(val_labels, NUM_CLASSES)
train_imdb = imdb.ImageDatabase(images=train_images,
labels=train_labels,
name=DATASET_NAME,
filename=DATASET_NAME,
mode_type='fullsize',
num_classes=NUM_CLASSES)
val_imdb = imdb.ImageDatabase(images=val_images,
labels=val_labels,
name=DATASET_NAME,
filename=DATASET_NAME,
mode_type='fullsize',
num_classes=NUM_CLASSES)
# models from the "Automatic choroidal segmentation in OCT images using supervised deep learning methods" paper (currently excluding RNN bottleneck and Combined)
model_residual = sem_models.resnet(8,
4,
2,
1, (3, 3), (2, 2),
input_channels=INPUT_CHANNELS,
return val_images, val_segs
train_images, train_segs = load_training_data()
val_images, val_segs = load_validation_data()
# if you would like to generate patches differently, comment out these lines and write a replacement function
train_patches, train_patch_labels = dataset_construction.sample_all_training_patches(train_images, train_segs, range(0, train_images.shape[1]), PATCH_SIZE)
val_patches, val_patch_labels = dataset_construction.sample_all_training_patches(val_images, val_segs, range(0, val_images.shape[1]), PATCH_SIZE)
NUM_CLASSES = train_segs.shape[1] + 1
train_patch_labels = keras.utils.to_categorical(train_patch_labels, num_classes=NUM_CLASSES)
val_patch_labels = keras.utils.to_categorical(val_patch_labels, num_classes=NUM_CLASSES)
train_patch_imdb = imdb.ImageDatabase(images=train_patches, labels=train_patch_labels, name=DATASET_NAME, filename=DATASET_NAME, mode_type='patch', num_classes=NUM_CLASSES)
val_patch_imdb = imdb.ImageDatabase(images=val_patches, labels=val_patch_labels, name=DATASET_NAME, filename=DATASET_NAME, mode_type='patch', num_classes=NUM_CLASSES)
# patch-based models from the "Automatic choroidal segmentation in OCT images using supervised deep learning methods" paper
model_cifar = patch_models.cifar_cnn(NUM_CLASSES, train_patches.shape[1], train_patches.shape[2])
model_complex = patch_models.complex_cnn(NUM_CLASSES, train_patches.shape[1], train_patches.shape[2])
model_rnn = patch_models.rnn_stack(4, ('ver', 'hor', 'ver', 'hor'), (True, True, True, True),
(CuDNNGRU, CuDNNGRU, CuDNNGRU, CuDNNGRU), (0.25, 0.25, 0.25, 0.25), (1, 1, 2, 2), (1, 1, 2, 2),
(16, 16, 16, 16), False, 0, INPUT_CHANNELS, train_patches.shape[1], train_patches.shape[2],
NUM_CLASSES)
opt_con = keras.optimizers.Adam
opt_params = {} # default params
loss = keras.losses.categorical_crossentropy
metric = keras.metrics.categorical_accuracy
epochs = 100
def evaluate_patch_based_network(eval_params, imdb):
# patches need to be constructed and passed to the generator for one image at a time
if eval_params.save_params.output_var is True:
eval_outputs = []
else:
eval_outputs = None
for ind in imdb.image_range:
if eval_params.save_params.output_var is True:
eval_output = eoutput.EvaluationOutput()
else:
eval_output = None
cur_full_image = imdb.get_image(ind)
cur_patch_labels = imdb.get_patch_label(ind)
cur_image_name = imdb.get_image_name(ind)
cur_seg = imdb.get_seg(ind)
if eval_params.save_params.output_var is True:
eval_output.raw_image = cur_full_image
eval_output.raw_label = cur_patch_labels
eval_output.image_name = cur_image_name
eval_output.raw_seg = cur_seg
if eval_params.verbosity >= 2:
print("Evaluating image number: " + str(ind + 1) + " (" +
cur_image_name + ")...")
if eval_params.save_params.disable is False:
if eval_helper.check_exists(eval_params.save_foldername,
cur_image_name):
# if the file for this image exists then we have already begun this at some point
print("File already exists")
else:
eval_helper.save_initial_attributes(eval_params,
cur_image_name)
status = eval_helper.get_complete_status(
eval_params.save_foldername,
cur_image_name,
boundaries=eval_params.boundaries)
else:
status = 'none'
if status == 'none' and (eval_params.eval_mode == 'both'
or eval_params.eval_mode == 'network'):
# PERFORM STEP 1: evaluate/predict patches with network
if eval_params.verbosity >= 2:
print("Augmenting data using augmentation: " +
eval_params.aug_desc + "...")
aug_fn = eval_params.aug_fn_arg[0]
aug_arg = eval_params.aug_fn_arg[1]
# augment raw full sized image and label
augment_image, augment_patch_labels, augment_seg, _, augment_time = \
aug_fn(cur_full_image, cur_patch_labels, cur_seg, aug_arg)
if eval_params.save_params.output_var is True:
eval_output.aug_image = augment_image
eval_output.aug_label = augment_patch_labels
eval_output.aug_seg = augment_seg
if eval_params.verbosity >= 2:
print("Constructing patches...")
# construct patches
input_patches, input_labels, patch_time = \
datacon.construct_patches_whole_image(augment_image, augment_patch_labels,
eval_params.patch_size)
patch_imdb = image_db.ImageDatabase(images=input_patches,
labels=input_labels)
if eval_params.verbosity >= 2:
print("Running network predictions...")
# use a generator to supply data to model (predict_generator)
# we have already previously augmented to image so need to augment the individual patches
start_predict_time = time.time()
import keras
class CustomCallback(keras.callbacks.Callback):
def __init__(self, gen):
keras.callbacks.Callback.__init__(self)
self.gen = gen
def on_predict_begin(self, logs=None):
self.gen.batch_gen.batch_counter = 0
self.gen.batch_gen.full_counter = 0
self.gen.batch_gen.aug_counter = 0
if not eval_params.ensemble:
start_gen_time = time.time()
gen = data_generator.DataGenerator(
patch_imdb,
eval_params.batch_size,
aug_fn_args=[],
aug_mode='none',
aug_probs=[],
aug_fly=False,
shuffle=False,
normalise=eval_params.normalise_input,
transpose=eval_params.transpose)
end_gen_time = time.time()
gen_time = end_gen_time - start_gen_time
cust_callback = CustomCallback(gen)
predicted_labels = eval_params.loaded_model.predict_generator(
gen,
verbose=eval_params.predict_verbosity,
callbacks=[cust_callback])
print(predicted_labels.shape)
else:
predicted_labels = []
for i in range(len(eval_params.loaded_models)):
start_gen_time = time.time()
gen = data_generator.DataGenerator(
patch_imdb,
eval_params.batch_size,
aug_fn_args=[],
aug_mode='none',
aug_probs=[],
aug_fly=False,
shuffle=False,
normalise=eval_params.normalise_input,
transpose=eval_params.transpose)
end_gen_time = time.time()
gen_time = end_gen_time - start_gen_time
predicted_labels.append(
eval_params.loaded_models[i].predict_generator(
gen, verbose=eval_params.predict_verbosity))
end_predict_time = time.time()
predict_time = end_predict_time - start_predict_time
if eval_params.verbosity >= 2:
print("Converting predictions to boundary maps...")
# convert predictions to usable probability maps
start_convert_time = time.time()
if eval_params.boundaries is True and eval_params.save_params.boundary_maps is True:
if not eval_params.ensemble:
prob_maps = convert_predictions_to_maps_patch_based(
predicted_labels, imdb.image_width, imdb.image_height)
else:
prob_maps = []
for i in range(len(predicted_labels)):
prob_maps.append(
np.expand_dims(
convert_predictions_to_maps_patch_based(
predicted_labels[i], imdb.image_width,
imdb.image_height),
axis=0))
prob_maps = eval_helper.perform_ensemble_patch(prob_maps)
else:
prob_maps = None
if eval_params.save_params.output_var is True:
eval_output.boundary_maps = prob_maps
end_convert_time = time.time()
convert_time = end_convert_time - start_convert_time
# save data to file
if eval_params.save_params.disable is False:
eval_helper.intermediate_save_patch_based(
eval_params, imdb, cur_image_name, prob_maps, predict_time,
augment_time, gen_time, convert_time, patch_time,
augment_image, augment_patch_labels, augment_seg,
cur_full_image, cur_patch_labels, cur_seg)
if eval_params.save_params.disable is False:
status = eval_helper.get_complete_status(
eval_params.save_foldername,
cur_image_name,
boundaries=eval_params.boundaries)
else:
status = 'predict'
if status == 'predict' and eval_params.boundaries is True and \
(eval_params.eval_mode == 'both' or eval_params.eval_mode == 'gs'):
aug_fn = eval_params.aug_fn_arg[0]
aug_arg = eval_params.aug_fn_arg[1]
# augment raw full sized image and label
augment_image, augment_patch_labels, augment_seg, _, augment_time = \
aug_fn(cur_full_image, cur_patch_labels, cur_seg, aug_arg)
# load probability maps from previous step
if eval_params.save_params.disable is False and eval_params.save_params.boundary_maps is True:
prob_maps = eval_helper.load_dataset_extra(
eval_params, cur_image_name, "boundary_maps")
# PERFORM STEP 2: segment probability maps using graph search
boundary_maps = get_boundary_maps_only(imdb, prob_maps)
eval_helper.eval_second_step(eval_params,
boundary_maps,
augment_seg,
cur_image_name,
augment_image,
augment_patch_labels,
imdb,
dices=None,
eval_output=eval_output)
elif eval_params.boundaries is False:
if eval_params.save_params.disable is False and eval_params.save_params.attributes is True:
eval_helper.save_final_attributes(eval_params,
cur_image_name,
graph_time=None)
if eval_params.save_params.disable is False and eval_params.save_params.temp_extra is True:
eval_helper.delete_loadsaveextra_file(eval_params, cur_image_name)
if eval_params.verbosity >= 2:
print("DONE image number: " + str(ind + 1) + " (" +
cur_image_name + ")...")
print("______________________________")
return eval_outputs
NUM_CLASSES = test_segs.shape[1] + 1 # update for required number of classes
# boundary names should be a list of strings with length = NUM_CLASSES - 1
# class names should be a list of strings with length = NUM_CLASSES
AREA_NAMES = ["area_" + str(i) for i in range(NUM_CLASSES)]
BOUNDARY_NAMES = ["boundary_" + str(i) for i in range(NUM_CLASSES - 1)]
PATCH_CLASS_NAMES = ["BG"]
for i in range(len(BOUNDARY_NAMES)):
PATCH_CLASS_NAMES.append(BOUNDARY_NAMES[i])
GSGRAD = 1
CUSTOM_OBJECTS = dict(list(custom_losses.custom_loss_objects.items()) +
list(custom_metrics.custom_metric_objects.items()))
eval_imdb = imdb.ImageDatabase(images=test_images, labels=None, patch_labels=test_patch_labels, segs=test_segs, image_names=test_image_names,
boundary_names=BOUNDARY_NAMES, area_names=AREA_NAMES,
fullsize_class_names=AREA_NAMES, patch_class_names=PATCH_CLASS_NAMES, num_classes=NUM_CLASSES, name=TEST_DATA_NAME, filename=TEST_DATA_NAME, mode_type='fullsize')
batch_size = 992 # CURRENTLY THIS NEEDS TO BE CHOSEN AS A VALUE WHICH IS A FACTOR OF THE AREA (IN PIXELS) OF THE FULL IMAGE (i.e. 992 is a factor of a 761856 (1536x496) pixel image [992 x 768 = 761856])
network_folder = parameters.RESULTS_LOCATION + "\\2020-03-10 14_25_08 Cifar CNN 32x32 stargardt_girard_patches_fold1\\" # name of network folder for which to evaluate model
model_name = "model_epoch04.hdf5" # name of model file inside network folder to evaluate
loaded_model = load_model(network_folder + "/" + model_name, custom_objects=CUSTOM_OBJECTS)
aug_fn_arg = (aug.no_aug, {})
eval_helper.evaluate_network(eval_imdb, model_name, network_folder,
batch_size, save_parameters.SaveParameters(pngimages=True, raw_image=True, temp_extra=True, boundary_maps=True, area_maps=True, comb_area_maps=True, seg_plot=True),
gsgrad=GSGRAD, aug_fn_arg=aug_fn_arg, eval_mode='both', boundaries=True, boundary_errors=True, dice_errors=False, col_error_range=None, normalise_input=True, transpose=False)
def evaluate_single_images(eval_params, imdb):
# pass images to network one at a time
eval_outputs = []
for ind in imdb.image_range:
eval_output = eoutput.EvaluationOutput()
cur_raw_image = imdb.get_image(ind)
cur_label = imdb.get_label(ind)
cur_image_name = imdb.get_image_name(ind)
cur_seg = imdb.get_seg(ind)
eval_output.raw_image = cur_raw_image
eval_output.raw_label = cur_label
eval_output.image_name = cur_image_name
eval_output.raw_seg = cur_seg
if eval_params.verbosity >= 2:
print("Evaluating image number: " + str(ind + 1) + " (" +
cur_image_name + ")...")
if eval_params.save_params.disable is False:
if eval_helper.check_exists(eval_params.save_foldername,
cur_image_name):
# if the file for this image exists then we have already begun this at some point
print("File already exists")
else:
eval_helper.save_initial_attributes(eval_params,
cur_image_name)
status = eval_helper.get_complete_status(
eval_params.save_foldername, cur_image_name,
eval_params.boundaries)
else:
status = 'none'
if status == 'none' and (eval_params.eval_mode == 'both'
or eval_params.eval_mode == 'network'):
# PERFORM STEP 1: evaluate/predict patches with network
if eval_params.verbosity >= 2:
print("Augmenting data using augmentation: " +
eval_params.aug_desc + "...")
aug_fn = eval_params.aug_fn_arg[0]
aug_arg = eval_params.aug_fn_arg[1]
# augment raw full sized image and label
augment_image, augment_label, augment_seg, _, augment_time = \
aug_fn(cur_raw_image, cur_label, cur_seg, aug_arg, sample_ind=ind, set=imdb.set)
eval_output.aug_image = augment_image
eval_output.aug_label = augment_label
eval_output.aug_seg = augment_seg
if eval_params.verbosity >= 2:
print("Running network predictions...")
images = np.expand_dims(augment_image, axis=0)
labels = np.expand_dims(augment_label, axis=0)
single_image_imdb = image_db.ImageDatabase(images=images,
labels=labels)
# use a generator to supply data to model (predict_generator)
start_gen_time = time.time()
gen = data_generator.DataGenerator(
single_image_imdb,
eval_params.batch_size,
aug_fn_args=[],
aug_mode='none',
aug_probs=[],
aug_fly=False,
shuffle=False,
transpose=eval_params.transpose,
normalise=eval_params.normalise_input)
end_gen_time = time.time()
gen_time = end_gen_time - start_gen_time
start_predict_time = time.time()
if not eval_params.ensemble:
predicted_labels = eval_params.loaded_model.predict_generator(
gen, verbose=eval_params.predict_verbosity)
else:
predicted_labels = []
for i in range(len(eval_params.loaded_models)):
predicted_labels.append(
eval_params.loaded_models[i].predict_generator(
gen, verbose=eval_params.predict_verbosity))
end_predict_time = time.time()
predict_time = end_predict_time - start_predict_time
if eval_params.save_params.activations is True:
if not eval_params.ensemble:
if eval_params.save_params.act_layers is None:
layer_outputs = [
layer.output for layer in eval_params.loaded_model.
layers[1:len(eval_params.loaded_model.layers)]
]
else:
layer_outputs = [
layer.output
for layer in eval_params.save_params.act_layers
]
activation_model = Model(
inputs=eval_params.loaded_model.input,
outputs=layer_outputs)
# Creates a model that will return these outputs, given the model input
if eval_params.normalise_input:
images_norm = images / 255
else:
images_norm = images
activations = activation_model.predict(images_norm)
else:
layer_outputs = []
activations = []
# TODO: implement write handling for ensemble activations
for i in range(len(eval_params.loaded_models)):
layer_outputs.append([
layer.output
for layer in eval_params.loaded_models[i].
layers[1:len(eval_params.loaded_models[i].layers)]
])
activation_model = Model(
inputs=eval_params.loaded_models[i].input,
outputs=layer_outputs[i])
# Creates a model that will return these outputs, given the model input
if eval_params.normalise_input:
images_norm = images / 255
else:
images_norm = images
activations.append(
activation_model.predict(images_norm))
else:
activations = None
layer_outputs = None
if eval_params.verbosity >= 2:
print("Converting predictions to boundary maps...")
if not eval_params.ensemble:
if eval_params.transpose is True:
predicted_labels = np.transpose(predicted_labels,
axes=(0, 2, 1, 3))
# convert predictions to usable boundary probability maps
start_convert_time = time.time()
[comb_area_map, area_maps
] = eval_helper.perform_argmax(predicted_labels,
ensemble=False,
bin=eval_params.binarize)
else:
if eval_params.transpose is True:
for i in range(len(eval_params.loaded_models)):
predicted_labels[i] = np.transpose(predicted_labels[i],
axes=(0, 2, 1, 3))
# convert predictions to usable boundary probability maps
start_convert_time = time.time()
[comb_area_map_sep, area_maps_sep
] = eval_helper.perform_argmax(predicted_labels,
ensemble=True,
bin=eval_params.binarize)
# ensemble using majority voting scheme
[comb_area_map,
area_maps] = eval_helper.perform_ensemble(area_maps_sep)
print(area_maps.shape)
if eval_params.binarize_after is True:
num_maps = area_maps.shape[1]
if eval_params.use_thresh:
area_maps[:,
1][area_maps[:, 1] >= eval_params.thresh] = 1
area_maps[:,
1][area_maps[:, 1] < eval_params.thresh] = 0
area_maps[:,
0][area_maps[:, 0] < eval_params.thresh] = 1
area_maps[:,
0][area_maps[:, 0] >= eval_params.thresh] = 0
area_maps = np.argmax(area_maps, axis=1)
else:
area_maps = np.argmax(area_maps, axis=1)
area_maps = to_categorical(area_maps, num_maps)
area_maps = np.transpose(area_maps, axes=(0, 3, 1, 2))
print(area_maps.shape)
eval_output.comb_area_map = comb_area_map
eval_output.area_maps = area_maps
if eval_params.boundaries is False or eval_params.save_params.boundary_maps is False:
boundary_maps = None
else:
if eval_params.vertical_graph_search is False:
boundary_maps = convert_predictions_to_maps_semantic(
np.array(area_maps),
bg_ilm=eval_params.bg_ilm,
bg_csi=eval_params.bg_csi)
elif eval_params.vertical_graph_search is True:
boundary_maps = convert_predictions_to_maps_semantic_vertical(
np.array(area_maps),
bg_ilm=eval_params.bg_ilm,
bg_csi=eval_params.bg_csi)
elif eval_params.vertical_graph_search == "ilm_vertical":
ilm_map = np.expand_dims(
convert_predictions_to_maps_semantic_vertical(
np.array(area_maps),
bg_ilm=eval_params.bg_ilm,
bg_csi=eval_params.bg_csi)[0, 0],
axis=0)
other_maps = convert_predictions_to_maps_semantic(
np.array(area_maps),
bg_ilm=eval_params.bg_ilm,
bg_csi=eval_params.bg_csi)[0][1:]
boundary_maps = np.expand_dims(np.concatenate(
[ilm_map, other_maps], axis=0),
axis=0)
eval_output.boundary_maps = boundary_maps
end_convert_time = time.time()
convert_time = end_convert_time - start_convert_time
if eval_params.dice_errors is True:
dices = eval_helper.calc_dice(eval_params, area_maps, labels)
else:
dices = None
area_maps = np.squeeze(area_maps)
comb_area_map = np.squeeze(comb_area_map)
boundary_maps = np.squeeze(boundary_maps)
# save data to files
if eval_params.save_params.disable is False:
eval_helper.intermediate_save_semantic(
eval_params, imdb, cur_image_name, boundary_maps,
predict_time, augment_time, gen_time, augment_image,
augment_label, augment_seg, cur_raw_image, cur_label,
cur_seg, area_maps, comb_area_map, dices, convert_time,
activations, layer_outputs)
if eval_params.save_params.disable is False:
status = eval_helper.get_complete_status(
eval_params.save_foldername, cur_image_name,
eval_params.boundaries)
else:
status = 'predict'
if status == 'predict' and eval_params.boundaries is True and \
(eval_params.eval_mode == 'both' or eval_params.eval_mode == 'gs'):
cur_image_name = imdb.get_image_name(ind)
cur_seg = imdb.get_seg(ind)
cur_raw_image = imdb.get_image(ind)
cur_label = imdb.get_label(ind)
aug_fn = eval_params.aug_fn_arg[0]
aug_arg = eval_params.aug_fn_arg[1]
# augment raw full sized image and label
augment_image, augment_label, augment_seg, _, _ = \
aug_fn(cur_raw_image, cur_label, cur_seg, aug_arg, sample_ind=ind, set=imdb.set)
if eval_params.save_params.disable is False and eval_params.save_params.boundary_maps is True:
boundary_maps = eval_helper.load_dataset_extra(
eval_params, cur_image_name, "boundary_maps")
if eval_params.dice_errors is True:
dices = eval_helper.load_dataset(eval_params,
cur_image_name, "dices")
else:
dices = None
# PERFORM STEP 2: segment probability maps using graph search
eval_output = eval_helper.eval_second_step(
eval_params, boundary_maps, augment_seg, cur_image_name,
augment_image, augment_label, imdb, dices, eval_output)
elif eval_params.boundaries is False:
if eval_params.save_params.disable is False and eval_params.save_params.attributes is True:
eval_helper.save_final_attributes(eval_params,
cur_image_name,
graph_time=None)
if eval_params.save_params.disable is False and eval_params.save_params.temp_extra is True:
eval_helper.delete_loadsaveextra_file(eval_params, cur_image_name)
if eval_params.verbosity >= 2:
print("DONE image number: " + str(ind + 1) + " (" +
cur_image_name + ")...")
print("______________________________")
return eval_outputs
