def _read_inputfiles(self, queue, resize_mode, input_size, augmentors):
im_path = queue[0]
label_path = queue[1]
old_label_path = queue[2]
img = load_image_tensorflow(im_path, jpg=True)
old_label = load_png_mask_tensorflow(old_label_path)
def my_damage_mask(mask):
return damage_mask(mask, self.old_mask_scale_factor,
self.old_mask_shift_absolute,
self.old_mask_shift_factor)
old_label, = tf.py_func(my_damage_mask, [old_label], [tf.float32])
label = load_png_mask_tensorflow(label_path)
flow_past = flow_future = None
if self.flow_into_past:
flow_past_path = queue[3]
flow_past = load_flow_from_flo_tensorflow(flow_past_path,
self.flow_as_angle)
if self.flow_into_future:
flow_future_path = queue[4] if self.flow_into_past else queue[3]
flow_future = load_flow_from_flo_tensorflow(
flow_future_path, self.flow_as_angle)
tensors = create_tensor_dict(unnormalized_img=img,
label=label,
tag=im_path,
raw_label=label,
old_label=old_label,
flow_past=flow_past,
flow_future=flow_future)
resize_mode, input_size = self._get_resize_params(
self.subset, self.image_size)
tensors = resize(tensors, resize_mode, input_size)
for augmentor in augmentors:
tensors = augmentor.apply(tensors)
tensors = assemble_input_tensors(tensors, DAVIS_IMAGE_SIZE)
label.set_shape(list(DAVIS_IMAGE_SIZE) + [1])
summaries = []
return tensors, summaries
def create_input_tensors_dict(self, batch_size):
use_index_img = self.subset != "train"
tensors = create_tensor_dict(
unnormalized_img=self.img_placeholder,
label=self.label_placeholder,
tag=self.tag_placeholder,
raw_label=self.label_placeholder,
old_label=self.old_label_placeholder,
flow_past=self.flow_into_past_placeholder,
flow_future=self.flow_into_future_placeholder,
use_index_img=use_index_img,
u0=self.u0_placeholder,
u1=self.u1_placeholder)
# TODO: need to set shape here?
resize_mode, input_size = self._get_resize_params(
self.subset, self.image_size)
tensors = resize(tensors, resize_mode, input_size)
if len(input_size) == 3:
input_size = input_size[1:]
tensors = self._prepare_augmented_batch(tensors,
batch_size,
image_size=input_size)
if self.use_summaries:
inputs = tensors["inputs"]
summ0 = tf.summary.image("inputs",
unnormalize(inputs[:, :, :, :3]))
summ1 = tf.summary.image(
"labels", tensors["labels"] *
255) # will only work well for binary segmentation
self.summaries.append(summ0)
self.summaries.append(summ1)
# Old label would be present either as a single channel, or along with 2 other distance transform channels.
if inputs.get_shape()[-1] == 4 or inputs.get_shape == 6:
summ2 = tf.summary.image("old_labels",
inputs[:, :, :, 3:4] * 255)
self.summaries.append(summ2)
return tensors
def create_input_tensors_dict(self, batch_size):
use_index_img = self.subset != "train"
tensors = create_tensor_dict(
unnormalized_img=self.img_placeholder,
label=self.label_placeholder,
tag=self.tag_placeholder,
raw_label=self.label_placeholder,
old_label=self.old_label_placeholder,
flow_past=self.flow_into_past_placeholder,
flow_future=self.flow_into_future_placeholder,
use_index_img=use_index_img,
u0=self.u0_placeholder,
u1=self.u1_placeholder,
flow=self.flow_placeholder)
# TODO: need to set shape here?
resize_mode, input_size = self._get_resize_params(
self.subset, self.image_size)
tensors = resize(tensors, resize_mode, input_size)
if len(input_size) == 3:
input_size = input_size[1:]
tensors = self._prepare_augmented_batch(tensors,
batch_size,
image_size=input_size)
if self.use_summaries:
inputs = tensors["inputs"]
summ0 = tf.summary.image("inputs",
unnormalize(inputs[:, :, :, :3]))
summ1 = tf.summary.image(
"labels", tensors["labels"] *
255) # will only work well for binary segmentation
self.summaries.append(summ0)
self.summaries.append(summ1)
return tensors
def read_images_from_disk(
input_queue,
input_size,
resize_mode,
label_postproc_fn=lambda x: x,
augmentors=(),
label_load_fn=load_label_default,
img_load_fn=load_img_default,
distance_transform_fn=Util.create_distance_transform):
im_path = input_queue[0]
label_path = input_queue[1]
if len(input_queue) > 2:
img_id = input_queue[2]
else:
img_id = None
img = img_load_fn(img_path=im_path)
#TODO: clean up all this stuff!
labels = label_load_fn(im_path, label_path)
if 'label' in list(labels.keys()):
label = labels['label']
label = label_postproc_fn(label)
label.set_shape(img.get_shape().as_list()[:-1] + [1])
else:
label = None
if 'old_label' in list(labels.keys()):
old_label = labels['old_label']
old_label.set_shape(img.get_shape().as_list()[:-1] + [1])
else:
old_label = None
if Constants.BBOXES in list(labels.keys()):
bboxes = labels[Constants.BBOXES]
else:
bboxes = None
if Constants.IDS in list(labels.keys()):
ids = labels[Constants.IDS]
else:
ids = None
if Constants.CLASSES in list(labels.keys()):
classes = labels[Constants.CLASSES]
else:
classes = None
if Constants.IGNORE_REGIONS in list(labels.keys()):
ignore_regions = labels[Constants.IGNORE_REGIONS]
else:
ignore_regions = None
if Constants.SCENE_INFOS in list(labels.keys()):
scene_infos = labels[Constants.SCENE_INFOS]
else:
scene_infos = None
if Constants.OLD_LABEL_AS_DT in list(labels.keys()):
old_label_as_dt = labels[Constants.OLD_LABEL_AS_DT]
else:
old_label_as_dt = None
u0 = None
u1 = None
tensors = create_tensor_dict(unnormalized_img=img,
label=label,
old_label=old_label,
u0=u0,
u1=u1,
tag=im_path,
raw_label=label,
bboxes=bboxes,
ids=ids,
classes=classes,
img_id=img_id,
ignore_regions=ignore_regions,
scene_infos=scene_infos,
old_label_as_dt=old_label_as_dt)
tensors = resize(tensors, resize_mode, input_size)
# Create distance transform after image resize to speed up the computation.
if Constants.USE_CLICKS in list(labels.keys()):
assert Constants.STRATEGY in labels and Constants.IGNORE_CLASSES in labels
tensors = add_distance_transform(tensors, labels,
distance_transform_fn)
elif Constants.OLD_LABEL_AS_DT in list(labels.keys()):
tensors["old_label"] = tf.py_func(distance_transform_fn,
[tensors["label"]], [tf.float32])[0]
tensors["old_label"].set_shape(tensors["label"].get_shape())
tensors = apply_augmentors(tensors, augmentors)
tensors = assemble_input_tensors(tensors)
summaries = []
return tensors, summaries
def read_images_from_disk(input_queue,
input_size,
resize_mode,
label_postproc_fn=lambda x: x,
augmentors=(),
label_load_fn=load_label_default,
img_load_fn=load_img_default):
im_path = input_queue[0]
label_path = input_queue[1]
img = img_load_fn(img_path=im_path)
labels = label_load_fn(im_path, label_path)
label = labels['label']
label = label_postproc_fn(label)
label.set_shape(img.get_shape().as_list()[:-1] + [1])
old_label = u0 = u1 = None
if len(input_queue) == 3:
flow_path = input_queue[2]
flow = img_load_fn(img_path=flow_path)
else:
flow = None
if 'old_label' in labels.keys():
old_label = labels['old_label']
old_label.set_shape(img.get_shape().as_list()[:-1] + [1])
if Constants.DT_NEG in labels.keys() and Constants.DT_POS in labels.keys():
u0 = labels[Constants.DT_NEG]
u0.set_shape(img.get_shape().as_list()[:-1] + [1])
# Create a negative click map, where the click points are denoted as 1 and the rest of it as 0.
# This would majorly be used to show the clicks in summaries.
[neg_clicks] = tf.py_func(create_clicks_map,
[labels['neg_clicks'], u0], [tf.float32],
name="create_click_map")
neg_clicks.set_shape(img.get_shape().as_list()[:-1] + [1])
u0 = tf.concat([u0, neg_clicks], axis=2)
u1 = labels[Constants.DT_POS]
u1.set_shape(img.get_shape().as_list()[:-1] + [1])
[pos_clicks] = tf.py_func(create_clicks_map,
[labels['pos_clicks'], u1], [tf.float32],
name="create_click_map")
pos_clicks.set_shape(img.get_shape().as_list()[:-1] + [1])
u1 = tf.concat([u1, pos_clicks], axis=2)
shape = im_path.get_shape()
im_path = tf.string_join(
[im_path, tf.as_string(labels['num_clicks'])],
separator=":",
name="JoinPath")
im_path.set_shape(shape)
tensors = create_tensor_dict(unnormalized_img=img,
label=label,
old_label=old_label,
u0=u0,
u1=u1,
tag=im_path,
raw_label=label,
flow=flow)
tensors = resize(tensors, resize_mode, input_size)
tensors = apply_augmentors(tensors, augmentors)
tensors = assemble_input_tensors(tensors)
summaries = []
return tensors, summaries