def __init__(self, batch_loader, augseq, queue_size=50, nb_workers="auto"):
ia.do_assert(queue_size > 0)
self.augseq = augseq
self.queue_source = (batch_loader if isinstance(
batch_loader, multiprocessing.queues.Queue) else
batch_loader.queue)
self.queue_result = multiprocessing.Queue(queue_size)
if nb_workers == "auto":
try:
nb_workers = multiprocessing.cpu_count()
except (ImportError, NotImplementedError):
nb_workers = 1
# try to reserve at least one core for the main process
nb_workers = max(1, nb_workers - 1)
else:
ia.do_assert(nb_workers >= 1)
self.nb_workers = nb_workers
self.workers = []
self.nb_workers_finished = 0
seeds = iarandom.get_global_rng().generate_seeds_(nb_workers)
for i in range(nb_workers):
worker = multiprocessing.Process(
target=self._augment_images_worker,
args=(augseq, self.queue_source, self.queue_result, seeds[i]))
worker.daemon = True
worker.start()
self.workers.append(worker)
def _load_batches(cls, load_batch_func, queue_internal, join_signal,
seedval):
if seedval is not None:
random.seed(seedval)
np.random.seed(seedval)
iarandom.seed(seedval)
try:
gen = (load_batch_func() if not ia.is_generator(load_batch_func)
else load_batch_func)
for batch in gen:
ia.do_assert(
isinstance(batch, Batch),
"Expected batch returned by load_batch_func to "
"be of class imgaug.Batch, got %s." % (type(batch), ))
batch_pickled = pickle.dumps(batch, protocol=-1)
while not join_signal.is_set():
try:
queue_internal.put(batch_pickled, timeout=0.005)
break
except QueueFull:
pass
if join_signal.is_set():
break
except Exception:
traceback.print_exc()
finally:
queue_internal.put("")
time.sleep(0.01)
def __init__(self, batch_loader, augseq, queue_size=50, nb_workers="auto"):
ia.do_assert(queue_size > 0)
self.augseq = augseq
self.queue_source = batch_loader if isinstance(batch_loader, multiprocessing.queues.Queue) else batch_loader.queue
self.queue_result = multiprocessing.Queue(queue_size)
if nb_workers == "auto":
try:
nb_workers = multiprocessing.cpu_count()
except (ImportError, NotImplementedError):
nb_workers = 1
# try to reserve at least one core for the main process
nb_workers = max(1, nb_workers - 1)
else:
ia.do_assert(nb_workers >= 1)
self.nb_workers = nb_workers
self.workers = []
self.nb_workers_finished = 0
seeds = ia.current_random_state().randint(0, 10**6, size=(nb_workers,))
for i in range(nb_workers):
worker = multiprocessing.Process(
target=self._augment_images_worker,
args=(augseq, self.queue_source, self.queue_result, seeds[i])
)
worker.daemon = True
worker.start()
self.workers.append(worker)
def _load_batches(self, load_batch_func, queue_internal, join_signal, seedval):
if seedval is not None:
random.seed(seedval)
np.random.seed(seedval)
ia.seed(seedval)
try:
gen = load_batch_func() if not ia.is_generator(load_batch_func) else load_batch_func
for batch in gen:
ia.do_assert(isinstance(batch, Batch),
"Expected batch returned by load_batch_func to be of class imgaug.Batch, got %s." % (
type(batch),))
batch_pickled = pickle.dumps(batch, protocol=-1)
while not join_signal.is_set():
try:
queue_internal.put(batch_pickled, timeout=0.005)
break
except QueueFull:
pass
if join_signal.is_set():
break
except Exception:
traceback.print_exc()
finally:
queue_internal.put("")
time.sleep(0.01)
def __init__(self, load_batch_func, queue_size=50, nb_workers=1, threaded=True):
ia.do_assert(queue_size >= 2, "Queue size for BatchLoader must be at least 2, got %d." % (queue_size,))
ia.do_assert(nb_workers >= 1, "Number of workers for BatchLoader must be at least 1, got %d" % (nb_workers,))
self._queue_internal = multiprocessing.Queue(queue_size//2)
self.queue = multiprocessing.Queue(queue_size//2)
self.join_signal = multiprocessing.Event()
self.workers = []
self.threaded = threaded
seeds = ia.current_random_state().randint(0, 10**6, size=(nb_workers,))
for i in range(nb_workers):
if threaded:
worker = threading.Thread(
target=self._load_batches,
args=(load_batch_func, self._queue_internal, self.join_signal, None)
)
else:
worker = multiprocessing.Process(
target=self._load_batches,
args=(load_batch_func, self._queue_internal, self.join_signal, seeds[i])
)
worker.daemon = True
worker.start()
self.workers.append(worker)
self.main_worker_thread = threading.Thread(
target=self._main_worker,
args=()
)
self.main_worker_thread.daemon = True
self.main_worker_thread.start()
def __init__(self,
shear=(-40, 41),
cval=255,
vertical=False,
name=None,
deterministic=False,
random_state=None):
"""Initialize the augmentator
# Arguments
shear [float or tuple of 2 floats]: if it is a single number, then
image will be sheared in that degree. If it is a tuple of 2
numbers, then the shear value will be chosen randomly
cval [int]: fill-in value to new pixels
"""
super(ItalicizeLine, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if isinstance(shear, StochasticParameter):
self.shear = shear
elif ia.is_single_number(shear):
self.shear = Deterministic(shear)
elif ia.is_iterable(shear):
ia.do_assert(
len(shear) == 2,
"Expected rotate tuple/list with 2 entries, got {} entries.".
format((len(shear))))
ia.do_assert(all([ia.is_single_number(val) for val in shear]),
"Expected floats/ints in shear tuple/list.")
self.shear = Uniform(shear[0], shear[1])
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or "
"StochasticParameter. Got {}.".format(type(shear)))
self.cval = cval
self.vertical = vertical
def __init__(self,
angle=(-10, 10),
cval=255,
name=None,
deterministic=False,
random_state=None):
"""Initialize the augmentator
# Arguments
angle [float or tuple of 2 floats]: if it is a single number, then
image will be rotated in that degree. If it is a tuple of 2
numbers, then the angle value will be chosen randomly
cval [int]: fill-in value to new pixels
"""
super(RotateLine, self).__init__(name=name,
deterministic=deterministic,
random_state=random_state)
if isinstance(angle, StochasticParameter):
self.angle = angle
elif ia.is_single_number(angle):
self.angle = Deterministic(angle)
elif ia.is_iterable(angle):
ia.do_assert(
len(angle) == 2,
"Expected rotate tuple/list with 2 entries, got {} entries.".
format((len(angle))))
ia.do_assert(all([ia.is_single_number(val) for val in angle]),
"Expected floats/ints in angle tuple/list.")
self.angle = Uniform(angle[0], angle[1])
else:
raise Exception(
"Expected float, int, tuple/list with 2 entries or "
"StochasticParameter. Got {}.".format(type(angle)))
self.cval = cval
