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def boolean_flag(parser, name, default=False, help=None):
"""Add a boolean flag to argparse parser.
Parameters
----------
parser: argparse.Parser
parser to add the flag to
name: str
--<name> will enable the flag, while --no-<name> will disable it
default: bool or None
de... |
def get_wrapper_by_name(env, classname):
"""Given an a gym environment possibly wrapped multiple times, returns a wrapper
of class named classname or raises ValueError if no such wrapper was applied
Parameters
----------
env: gym.Env of gym.Wrapper
gym environment
classname: str
... |
def relatively_safe_pickle_dump(obj, path, compression=False):
"""This is just like regular pickle dump, except from the fact that failure cases are
different:
- It's never possible that we end up with a pickle in corrupted state.
- If a there was a different file at the path, that file will re... |
def pickle_load(path, compression=False):
"""Unpickle a possible compressed pickle.
Parameters
----------
path: str
path to the output file
compression: bool
if true assumes that pickle was compressed when created and attempts decompression.
Returns
-------
obj: object
... |
def update(self, new_val):
"""Update the estimate.
Parameters
----------
new_val: float
new observated value of estimated quantity.
"""
if self._value is None:
self._value = new_val
else:
self._value = self._gamma * self._value... |
def store_args(method):
"""Stores provided method args as instance attributes.
"""
argspec = inspect.getfullargspec(method)
defaults = {}
if argspec.defaults is not None:
defaults = dict(
zip(argspec.args[-len(argspec.defaults):], argspec.defaults))
if argspec.kwonlydefaults ... |
def import_function(spec):
"""Import a function identified by a string like "pkg.module:fn_name".
"""
mod_name, fn_name = spec.split(':')
module = importlib.import_module(mod_name)
fn = getattr(module, fn_name)
return fn |
def flatten_grads(var_list, grads):
"""Flattens a variables and their gradients.
"""
return tf.concat([tf.reshape(grad, [U.numel(v)])
for (v, grad) in zip(var_list, grads)], 0) |
def nn(input, layers_sizes, reuse=None, flatten=False, name=""):
"""Creates a simple neural network
"""
for i, size in enumerate(layers_sizes):
activation = tf.nn.relu if i < len(layers_sizes) - 1 else None
input = tf.layers.dense(inputs=input,
units=size,
... |
def mpi_fork(n, extra_mpi_args=[]):
"""Re-launches the current script with workers
Returns "parent" for original parent, "child" for MPI children
"""
if n <= 1:
return "child"
if os.getenv("IN_MPI") is None:
env = os.environ.copy()
env.update(
MKL_NUM_THREADS="1",... |
def convert_episode_to_batch_major(episode):
"""Converts an episode to have the batch dimension in the major (first)
dimension.
"""
episode_batch = {}
for key in episode.keys():
val = np.array(episode[key]).copy()
# make inputs batch-major instead of time-major
episode_batch[... |
def reshape_for_broadcasting(source, target):
"""Reshapes a tensor (source) to have the correct shape and dtype of the target
before broadcasting it with MPI.
"""
dim = len(target.get_shape())
shape = ([1] * (dim - 1)) + [-1]
return tf.reshape(tf.cast(source, target.dtype), shape) |
def add_vtarg_and_adv(seg, gamma, lam):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1
vpred = np.append(seg["vpred"], seg["nextvpred"])
T ... |
def switch(condition, then_expression, else_expression):
"""Switches between two operations depending on a scalar value (int or bool).
Note that both `then_expression` and `else_expression`
should be symbolic tensors of the *same shape*.
# Arguments
condition: scalar tensor.
then_expres... |
def huber_loss(x, delta=1.0):
"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
return tf.where(
tf.abs(x) < delta,
tf.square(x) * 0.5,
delta * (tf.abs(x) - 0.5 * delta)
) |
def get_session(config=None):
"""Get default session or create one with a given config"""
sess = tf.get_default_session()
if sess is None:
sess = make_session(config=config, make_default=True)
return sess |
def make_session(config=None, num_cpu=None, make_default=False, graph=None):
"""Returns a session that will use <num_cpu> CPU's only"""
if num_cpu is None:
num_cpu = int(os.getenv('RCALL_NUM_CPU', multiprocessing.cpu_count()))
if config is None:
config = tf.ConfigProto(
allow_sof... |
def initialize():
"""Initialize all the uninitialized variables in the global scope."""
new_variables = set(tf.global_variables()) - ALREADY_INITIALIZED
get_session().run(tf.variables_initializer(new_variables))
ALREADY_INITIALIZED.update(new_variables) |
def function(inputs, outputs, updates=None, givens=None):
"""Just like Theano function. Take a bunch of tensorflow placeholders and expressions
computed based on those placeholders and produces f(inputs) -> outputs. Function f takes
values to be fed to the input's placeholders and produces the values of the... |
def adjust_shape(placeholder, data):
'''
adjust shape of the data to the shape of the placeholder if possible.
If shape is incompatible, AssertionError is thrown
Parameters:
placeholder tensorflow input placeholder
data input data to be (potentially) reshaped to be fed i... |
def _check_shape(placeholder_shape, data_shape):
''' check if two shapes are compatible (i.e. differ only by dimensions of size 1, or by the batch dimension)'''
return True
squeezed_placeholder_shape = _squeeze_shape(placeholder_shape)
squeezed_data_shape = _squeeze_shape(data_shape)
for i, s_data... |
def profile(n):
"""
Usage:
@profile("my_func")
def my_func(): code
"""
def decorator_with_name(func):
def func_wrapper(*args, **kwargs):
with profile_kv(n):
return func(*args, **kwargs)
return func_wrapper
return decorator_with_name |
def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
"""Configure environment for DeepMind-style Atari.
"""
if episode_life:
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(e... |
def reset(self, **kwargs):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset(**kwargs)
... |
def sync_from_root(sess, variables, comm=None):
"""
Send the root node's parameters to every worker.
Arguments:
sess: the TensorFlow session.
variables: all parameter variables including optimizer's
"""
if comm is None: comm = MPI.COMM_WORLD
import tensorflow as tf
values = comm.... |
def gpu_count():
"""
Count the GPUs on this machine.
"""
if shutil.which('nvidia-smi') is None:
return 0
output = subprocess.check_output(['nvidia-smi', '--query-gpu=gpu_name', '--format=csv'])
return max(0, len(output.split(b'\n')) - 2) |
def setup_mpi_gpus():
"""
Set CUDA_VISIBLE_DEVICES to MPI rank if not already set
"""
if 'CUDA_VISIBLE_DEVICES' not in os.environ:
if sys.platform == 'darwin': # This Assumes if you're on OSX you're just
ids = [] # doing a smoke test and don't want GPUs
else:
... |
def get_local_rank_size(comm):
"""
Returns the rank of each process on its machine
The processes on a given machine will be assigned ranks
0, 1, 2, ..., N-1,
where N is the number of processes on this machine.
Useful if you want to assign one gpu per machine
"""
this_node = platform... |
def share_file(comm, path):
"""
Copies the file from rank 0 to all other ranks
Puts it in the same place on all machines
"""
localrank, _ = get_local_rank_size(comm)
if comm.Get_rank() == 0:
with open(path, 'rb') as fh:
data = fh.read()
comm.bcast(data)
else:
... |
def dict_gather(comm, d, op='mean', assert_all_have_data=True):
"""
Perform a reduction operation over dicts
"""
if comm is None: return d
alldicts = comm.allgather(d)
size = comm.size
k2li = defaultdict(list)
for d in alldicts:
for (k,v) in d.items():
k2li[k].append(... |
def mpi_weighted_mean(comm, local_name2valcount):
"""
Perform a weighted average over dicts that are each on a different node
Input: local_name2valcount: dict mapping key -> (value, count)
Returns: key -> mean
"""
all_name2valcount = comm.gather(local_name2valcount)
if comm.rank == 0:
... |
def learn(*,
network,
env,
total_timesteps,
timesteps_per_batch=1024, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
... |
def discount(x, gamma):
"""
computes discounted sums along 0th dimension of x.
inputs
------
x: ndarray
gamma: float
outputs
-------
y: ndarray with same shape as x, satisfying
y[t] = x[t] + gamma*x[t+1] + gamma^2*x[t+2] + ... + gamma^k x[t+k],
where k = le... |
def explained_variance(ypred,y):
"""
Computes fraction of variance that ypred explains about y.
Returns 1 - Var[y-ypred] / Var[y]
interpretation:
ev=0 => might as well have predicted zero
ev=1 => perfect prediction
ev<0 => worse than just predicting zero
"""
asser... |
def discount_with_boundaries(X, New, gamma):
"""
X: 2d array of floats, time x features
New: 2d array of bools, indicating when a new episode has started
"""
Y = np.zeros_like(X)
T = X.shape[0]
Y[T-1] = X[T-1]
for t in range(T-2, -1, -1):
Y[t] = X[t] + gamma * Y[t+1] * (1 - New[t... |
def sample(self, batch_size):
"""Sample a batch of experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch ... |
def add(self, *args, **kwargs):
"""See ReplayBuffer.store_effect"""
idx = self._next_idx
super().add(*args, **kwargs)
self._it_sum[idx] = self._max_priority ** self._alpha
self._it_min[idx] = self._max_priority ** self._alpha |
def sample(self, batch_size, beta):
"""Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
... |
def update_priorities(self, idxes, priorities):
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities:... |
def wrap_deepmind_retro(env, scale=True, frame_stack=4):
"""
Configure environment for retro games, using config similar to DeepMind-style Atari in wrap_deepmind
"""
env = WarpFrame(env)
env = ClipRewardEnv(env)
if frame_stack > 1:
env = FrameStack(env, frame_stack)
if scale:
... |
def scope_vars(scope, trainable_only=False):
"""
Get variables inside a scope
The scope can be specified as a string
Parameters
----------
scope: str or VariableScope
scope in which the variables reside.
trainable_only: bool
whether or not to return only the variables that we... |
def build_act(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None):
"""Creates the act function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that take a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) ->... |
def build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None, param_noise_filter_func=None):
"""Creates the act function with support for parameter space noise exploration (https://arxiv.org/abs/1706.01905):
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInp... |
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0,
double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None):
"""Creates the train function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a f... |
def profile_tf_runningmeanstd():
import time
from baselines.common import tf_util
tf_util.get_session( config=tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
allow_soft_placement=True
))
x = np.random.random((376,))
n_trials = 10000
... |
def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
"""Creates a sample function that can be used for HER experience replay.
Args:
replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
regular DDPG experience replay is used
replay_k ... |
def model(inpt, num_actions, scope, reuse=False):
"""This model takes as input an observation and returns values of all actions."""
with tf.variable_scope(scope, reuse=reuse):
out = inpt
out = layers.fully_connected(out, num_outputs=64, activation_fn=tf.nn.tanh)
out = layers.fully_connec... |
def sample(self, batch_size):
"""Returns a dict {key: array(batch_size x shapes[key])}
"""
buffers = {}
with self.lock:
assert self.current_size > 0
for key in self.buffers.keys():
buffers[key] = self.buffers[key][:self.current_size]
buff... |
def store_episode(self, episode_batch):
"""episode_batch: array(batch_size x (T or T+1) x dim_key)
"""
batch_sizes = [len(episode_batch[key]) for key in episode_batch.keys()]
assert np.all(np.array(batch_sizes) == batch_sizes[0])
batch_size = batch_sizes[0]
with self.loc... |
def store_episode(self, episode_batch, update_stats=True):
"""
episode_batch: array of batch_size x (T or T+1) x dim_key
'o' is of size T+1, others are of size T
"""
self.buffer.store_episode(episode_batch)
if update_stats:
# add transitions t... |
def parse_cmdline_kwargs(args):
'''
convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible
'''
def parse(v):
assert isinstance(v, str)
try:
return eval(v)
except (NameError, SyntaxError):
return v
... |
def cached_make_env(make_env):
"""
Only creates a new environment from the provided function if one has not yet already been
created. This is useful here because we need to infer certain properties of the env, e.g.
its observation and action spaces, without any intend of actually using it.
"""
i... |
def compute_geometric_median(X, eps=1e-5):
"""
Estimate the geometric median of points in 2D.
Code from https://stackoverflow.com/a/30305181
Parameters
----------
X : (N,2) ndarray
Points in 2D. Second axis must be given in xy-form.
eps : float, optional
Distance threshold... |
def project(self, from_shape, to_shape):
"""
Project the keypoint onto a new position on a new image.
E.g. if the keypoint is on its original image at x=(10 of 100 pixels)
and y=(20 of 100 pixels) and is projected onto a new image with
size (width=200, height=200), its new posit... |
def shift(self, x=0, y=0):
"""
Move the keypoint around on an image.
Parameters
----------
x : number, optional
Move by this value on the x axis.
y : number, optional
Move by this value on the y axis.
Returns
-------
imga... |
def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=3,
copy=True, raise_if_out_of_image=False):
"""
Draw the keypoint onto a given image.
The keypoint is drawn as a square.
Parameters
----------
image : (H,W,3) ndarray
The... |
def generate_similar_points_manhattan(self, nb_steps, step_size, return_array=False):
"""
Generate nearby points to this keypoint based on manhattan distance.
To generate the first neighbouring points, a distance of S (step size) is moved from the
center point (this keypoint) to the top... |
def copy(self, x=None, y=None):
"""
Create a shallow copy of the Keypoint object.
Parameters
----------
x : None or number, optional
Coordinate of the keypoint on the x axis.
If ``None``, the instance's value will be copied.
y : None or number, o... |
def deepcopy(self, x=None, y=None):
"""
Create a deep copy of the Keypoint object.
Parameters
----------
x : None or number, optional
Coordinate of the keypoint on the x axis.
If ``None``, the instance's value will be copied.
y : None or number, ... |
def on(self, image):
"""
Project keypoints from one image to a new one.
Parameters
----------
image : ndarray or tuple of int
New image onto which the keypoints are to be projected.
May also simply be that new image's shape tuple.
Returns
... |
def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=3,
copy=True, raise_if_out_of_image=False):
"""
Draw all keypoints onto a given image.
Each keypoint is marked by a square of a chosen color and size.
Parameters
----------
image : (... |
def shift(self, x=0, y=0):
"""
Move the keypoints around on an image.
Parameters
----------
x : number, optional
Move each keypoint by this value on the x axis.
y : number, optional
Move each keypoint by this value on the y axis.
Returns... |
def to_xy_array(self):
"""
Convert keypoint coordinates to ``(N,2)`` array.
Returns
-------
(N, 2) ndarray
Array containing the coordinates of all keypoints.
Shape is ``(N,2)`` with coordinates in xy-form.
"""
result = np.zeros((len(self.... |
def from_xy_array(cls, xy, shape):
"""
Convert an array (N,2) with a given image shape to a KeypointsOnImage object.
Parameters
----------
xy : (N, 2) ndarray
Coordinates of ``N`` keypoints on the original image, given
as ``(N,2)`` array of xy-coordinates... |
def to_keypoint_image(self, size=1):
"""
Draws a new black image of shape ``(H,W,N)`` in which all keypoint coordinates are set to 255.
(H=shape height, W=shape width, N=number of keypoints)
This function can be used as a helper when augmenting keypoints with a method that only supports... |
def from_keypoint_image(image, if_not_found_coords={"x": -1, "y": -1}, threshold=1, nb_channels=None): # pylint: disable=locally-disabled, dangerous-default-value, line-too-long
"""
Converts an image generated by ``to_keypoint_image()`` back to a KeypointsOnImage object.
Parameters
----... |
def to_distance_maps(self, inverted=False):
"""
Generates a ``(H,W,K)`` output containing ``K`` distance maps for ``K`` keypoints.
The k-th distance map contains at every location ``(y, x)`` the euclidean distance to the k-th keypoint.
This function can be used as a helper when augment... |
def from_distance_maps(distance_maps, inverted=False, if_not_found_coords={"x": -1, "y": -1}, threshold=None, # pylint: disable=locally-disabled, dangerous-default-value, line-too-long
nb_channels=None):
"""
Converts maps generated by ``to_distance_maps()`` back to a Keypoints... |
def copy(self, keypoints=None, shape=None):
"""
Create a shallow copy of the KeypointsOnImage object.
Parameters
----------
keypoints : None or list of imgaug.Keypoint, optional
List of keypoints on the image. If ``None``, the instance's
keypoints will be... |
def deepcopy(self, keypoints=None, shape=None):
"""
Create a deep copy of the KeypointsOnImage object.
Parameters
----------
keypoints : None or list of imgaug.Keypoint, optional
List of keypoints on the image. If ``None``, the instance's
keypoints will b... |
def contains(self, other):
"""
Estimate whether the bounding box contains a point.
Parameters
----------
other : tuple of number or imgaug.Keypoint
Point to check for.
Returns
-------
bool
True if the point is contained in the bou... |
def project(self, from_shape, to_shape):
"""
Project the bounding box onto a differently shaped image.
E.g. if the bounding box is on its original image at
x1=(10 of 100 pixels) and y1=(20 of 100 pixels) and is projected onto
a new image with size (width=200, height=200), its ne... |
def extend(self, all_sides=0, top=0, right=0, bottom=0, left=0):
"""
Extend the size of the bounding box along its sides.
Parameters
----------
all_sides : number, optional
Value by which to extend the bounding box size along all sides.
top : number, optiona... |
def intersection(self, other, default=None):
"""
Compute the intersection bounding box of this bounding box and another one.
Note that in extreme cases, the intersection can be a single point, meaning that the intersection bounding box
will exist, but then also has a height and width of... |
def union(self, other):
"""
Compute the union bounding box of this bounding box and another one.
This is equivalent to drawing a bounding box around all corners points of both
bounding boxes.
Parameters
----------
other : imgaug.BoundingBox
Other bou... |
def iou(self, other):
"""
Compute the IoU of this bounding box with another one.
IoU is the intersection over union, defined as::
``area(intersection(A, B)) / area(union(A, B))``
``= area(intersection(A, B)) / (area(A) + area(B) - area(intersection(A, B)))``
Pa... |
def is_fully_within_image(self, image):
"""
Estimate whether the bounding box is fully inside the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use.
If an ndarray, its shape will be used.
If a ... |
def is_partly_within_image(self, image):
"""
Estimate whether the bounding box is at least partially inside the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use.
If an ndarray, its shape will be used.
... |
def is_out_of_image(self, image, fully=True, partly=False):
"""
Estimate whether the bounding box is partially or fully outside of the image area.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use. If an ndarray, its shape will b... |
def clip_out_of_image(self, image):
"""
Clip off all parts of the bounding box that are outside of the image.
Parameters
----------
image : (H,W,...) ndarray or tuple of int
Image dimensions to use for the clipping of the bounding box.
If an ndarray, its ... |
def shift(self, top=None, right=None, bottom=None, left=None):
"""
Shift the bounding box from one or more image sides, i.e. move it on the x/y-axis.
Parameters
----------
top : None or int, optional
Amount of pixels by which to shift the bounding box from the top.
... |
def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=1,
copy=True, raise_if_out_of_image=False, thickness=None):
"""
Draw the bounding box on an image.
Parameters
----------
image : (H,W,C) ndarray(uint8)
The image onto which to dra... |
def extract_from_image(self, image, pad=True, pad_max=None,
prevent_zero_size=True):
"""
Extract the image pixels within the bounding box.
This function will zero-pad the image if the bounding box is partially/fully outside of
the image.
Parameters
... |
def to_keypoints(self):
"""
Convert the corners of the bounding box to keypoints (clockwise, starting at top left).
Returns
-------
list of imgaug.Keypoint
Corners of the bounding box as keypoints.
"""
# TODO get rid of this deferred import
f... |
def copy(self, x1=None, y1=None, x2=None, y2=None, label=None):
"""
Create a shallow copy of the BoundingBox object.
Parameters
----------
x1 : None or number
If not None, then the x1 coordinate of the copied object will be set to this value.
y1 : None or nu... |
def deepcopy(self, x1=None, y1=None, x2=None, y2=None, label=None):
"""
Create a deep copy of the BoundingBox object.
Parameters
----------
x1 : None or number
If not None, then the x1 coordinate of the copied object will be set to this value.
y1 : None or n... |
def on(self, image):
"""
Project bounding boxes from one image to a new one.
Parameters
----------
image : ndarray or tuple of int
New image onto which the bounding boxes are to be projected.
May also simply be that new image's shape tuple.
Retur... |
def from_xyxy_array(cls, xyxy, shape):
"""
Convert an (N,4) ndarray to a BoundingBoxesOnImage object.
This is the inverse of :func:`imgaug.BoundingBoxesOnImage.to_xyxy_array`.
Parameters
----------
xyxy : (N,4) ndarray
Array containing the corner coordinates... |
def to_xyxy_array(self, dtype=np.float32):
"""
Convert the BoundingBoxesOnImage object to an (N,4) ndarray.
This is the inverse of :func:`imgaug.BoundingBoxesOnImage.from_xyxy_array`.
Parameters
----------
dtype : numpy.dtype, optional
Desired output datatyp... |
def draw_on_image(self, image, color=(0, 255, 0), alpha=1.0, size=1,
copy=True, raise_if_out_of_image=False, thickness=None):
"""
Draw all bounding boxes onto a given image.
Parameters
----------
image : (H,W,3) ndarray
The image onto which to d... |
def remove_out_of_image(self, fully=True, partly=False):
"""
Remove all bounding boxes that are fully or partially outside of the image.
Parameters
----------
fully : bool, optional
Whether to remove bounding boxes that are fully outside of the image.
partly... |
def clip_out_of_image(self):
"""
Clip off all parts from all bounding boxes that are outside of the image.
Returns
-------
imgaug.BoundingBoxesOnImage
Bounding boxes, clipped to fall within the image dimensions.
"""
bbs_cut = [bb.clip_out_of_image(se... |
def shift(self, top=None, right=None, bottom=None, left=None):
"""
Shift all bounding boxes from one or more image sides, i.e. move them on the x/y-axis.
Parameters
----------
top : None or int, optional
Amount of pixels by which to shift all bounding boxes from the ... |
def deepcopy(self):
"""
Create a deep copy of the BoundingBoxesOnImage object.
Returns
-------
imgaug.BoundingBoxesOnImage
Deep copy.
"""
# Manual copy is far faster than deepcopy for BoundingBoxesOnImage,
# so use manual copy here too
... |
def Emboss(alpha=0, strength=1, name=None, deterministic=False, random_state=None):
"""
Augmenter that embosses images and overlays the result with the original
image.
The embossed version pronounces highlights and shadows,
letting the image look as if it was recreated on a metal plate ("embossed")... |
def EdgeDetect(alpha=0, name=None, deterministic=False, random_state=None):
"""
Augmenter that detects all edges in images, marks them in
a black and white image and then overlays the result with the original
image.
dtype support::
See ``imgaug.augmenters.convolutional.Convolve``.
Par... |
def DirectedEdgeDetect(alpha=0, direction=(0.0, 1.0), name=None, deterministic=False, random_state=None):
"""
Augmenter that detects edges that have certain directions and marks them
in a black and white image and then overlays the result with the original
image.
dtype support::
See ``imga... |
def normalize_shape(shape):
"""
Normalize a shape tuple or array to a shape tuple.
Parameters
----------
shape : tuple of int or ndarray
The input to normalize. May optionally be an array.
Returns
-------
tuple of int
Shape tuple.
"""
if isinstance(shape, tuple... |
def project_coords(coords, from_shape, to_shape):
"""
Project coordinates from one image shape to another.
This performs a relative projection, e.g. a point at 60% of the old
image width will be at 60% of the new image width after projection.
Parameters
----------
coords : ndarray or tuple... |
def AdditiveGaussianNoise(loc=0, scale=0, per_channel=False, name=None, deterministic=False, random_state=None):
"""
Add gaussian noise (aka white noise) to images.
dtype support::
See ``imgaug.augmenters.arithmetic.AddElementwise``.
Parameters
----------
loc : number or tuple of numb... |
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