def start_thread(self):
""" Put the training process in a thread so we can keep control """
logger.debug("Launching Trainer thread")
thread = MultiThread(target=self.training)
thread.start()
logger.debug("Launched Trainer thread")
return thread
def detect_faces(self, *args, **kwargs):
""" Detect faces in Multiple Threads """
super().detect_faces(*args, **kwargs)
workers = MultiThread(target=self.detect_thread, thread_count=self.batch_size)
workers.start()
workers.join()
sentinel = self.queues["in"].get()
self.queues["out"].put(sentinel)
logger.debug("Detecting Faces complete")
def start_thread(self, task):
""" Start the DiskIO thread """
logger.debug("Starting thread: '%s'", task)
args = self.completion_event if task == "save" else None
func = getattr(self, task)
io_thread = MultiThread(func, args, thread_count=1)
io_thread.start()
setattr(self, "{}_thread".format(task), io_thread)
logger.debug("Started thread: '%s'", task)
def threaded_io(self, task, io_args=None):
""" Perform I/O task in a background thread """
logger.debug("Threading task: (Task: '%s')", task)
io_args = tuple() if io_args is None else (io_args, )
if task == "load":
func = self.load_images
elif task == "save":
func = self.save_faces
elif task == "reload":
func = self.reload_images
io_thread = MultiThread(func, *io_args, thread_count=1)
io_thread.start()
return io_thread
class Predict():
""" Predict faces from incoming queue """
def __init__(self, in_queue, queue_size, arguments):
logger.debug("Initializing %s: (args: %s, queue_size: %s, in_queue: %s)",
self.__class__.__name__, arguments, queue_size, in_queue)
self.batchsize = min(queue_size, 16)
self.args = arguments
self.in_queue = in_queue
self.out_queue = queue_manager.get_queue("patch")
self.serializer = Serializer.get_serializer("json")
self.faces_count = 0
self.verify_output = False
self.model = self.load_model()
self.predictor = self.model.converter(self.args.swap_model)
self.queues = dict()
self.thread = MultiThread(self.predict_faces, thread_count=1)
self.thread.start()
logger.debug("Initialized %s: (out_queue: %s)", self.__class__.__name__, self.out_queue)
@property
def coverage_ratio(self):
""" Return coverage ratio from training options """
return self.model.training_opts["coverage_ratio"]
@property
def input_size(self):
""" Return the model input size """
return self.model.input_shape[0]
@property
def output_size(self):
""" Return the model output size """
return self.model.output_shape[0]
@property
def input_mask(self):
""" Return the input mask """
mask = np.zeros(self.model.state.mask_shapes[0], dtype="float32")
retval = np.expand_dims(mask, 0)
return retval
@property
def has_predicted_mask(self):
""" Return whether this model has a predicted mask """
return bool(self.model.state.mask_shapes)
def load_model(self):
""" Load the model requested for conversion """
logger.debug("Loading Model")
model_dir = get_folder(self.args.model_dir, make_folder=False)
if not model_dir:
logger.error("%s does not exist.", self.args.model_dir)
exit(1)
trainer = self.get_trainer(model_dir)
model = PluginLoader.get_model(trainer)(model_dir, self.args.gpus, predict=True)
logger.debug("Loaded Model")
return model
def get_trainer(self, model_dir):
""" Return the trainer name if provided, or read from state file """
if self.args.trainer:
logger.debug("Trainer name provided: '%s'", self.args.trainer)
return self.args.trainer
statefile = [fname for fname in os.listdir(str(model_dir))
if fname.endswith("_state.json")]
if len(statefile) != 1:
logger.error("There should be 1 state file in your model folder. %s were found. "
"Specify a trainer with the '-t', '--trainer' option.")
exit(1)
statefile = os.path.join(str(model_dir), statefile[0])
with open(statefile, "rb") as inp:
state = self.serializer.unmarshal(inp.read().decode("utf-8"))
trainer = state.get("name", None)
if not trainer:
logger.error("Trainer name could not be read from state file. "
"Specify a trainer with the '-t', '--trainer' option.")
exit(1)
logger.debug("Trainer from state file: '%s'", trainer)
return trainer
def predict_faces(self):
""" Get detected faces from images """
faces_seen = 0
batch = list()
while True:
item = self.in_queue.get()
if item != "EOF":
logger.trace("Got from queue: '%s'", item["filename"])
faces_count = len(item["detected_faces"])
self.faces_count += faces_count
if faces_count > 1:
self.verify_output = True
logger.verbose("Found more than one face in an image! '%s'",
os.path.basename(item["filename"]))
self.load_aligned(item)
faces_seen += faces_count
batch.append(item)
if faces_seen < self.batchsize and item != "EOF":
logger.trace("Continuing. Current batchsize: %s", faces_seen)
continue
if batch:
logger.trace("Batching to predictor. Frames: %s, Faces: %s",
len(batch), faces_seen)
detected_batch = [detected_face for item in batch
for detected_face in item["detected_faces"]]
if faces_seen != 0:
feed_faces = self.compile_feed_faces(detected_batch)
predicted = self.predict(feed_faces)
else:
predicted = list()
self.queue_out_frames(batch, predicted)
faces_seen = 0
batch = list()
if item == "EOF":
logger.debug("Load queue complete")
break
self.out_queue.put("EOF")
def load_aligned(self, item):
""" Load the feed faces and reference output faces """
logger.trace("Loading aligned faces: '%s'", item["filename"])
for detected_face in item["detected_faces"]:
detected_face.load_feed_face(item["image"],
size=self.input_size,
coverage_ratio=self.coverage_ratio,
dtype="float32")
if self.input_size == self.output_size:
detected_face.reference = detected_face.feed
else:
detected_face.load_reference_face(item["image"],
size=self.output_size,
coverage_ratio=self.coverage_ratio,
dtype="float32")
logger.trace("Loaded aligned faces: '%s'", item["filename"])
@staticmethod
def compile_feed_faces(detected_faces):
""" Compile the faces for feeding into the predictor """
logger.trace("Compiling feed face. Batchsize: %s", len(detected_faces))
feed_faces = np.stack([detected_face.feed_face for detected_face in detected_faces])
logger.trace("Compiled Feed faces. Shape: %s", feed_faces.shape)
return feed_faces
def predict(self, feed_faces):
""" Perform inference on the feed """
logger.trace("Predicting: Batchsize: %s", len(feed_faces))
feed = [feed_faces]
if self.has_predicted_mask:
feed.append(np.repeat(self.input_mask, feed_faces.shape[0], axis=0))
logger.trace("Input shape(s): %s", [item.shape for item in feed])
predicted = self.predictor(feed)
predicted = predicted if isinstance(predicted, list) else [predicted]
logger.trace("Output shape(s): %s", [predict.shape for predict in predicted])
# Compile masks into alpha channel or keep raw faces
predicted = np.concatenate(predicted, axis=-1) if len(predicted) == 2 else predicted[0]
predicted = predicted.astype("float32")
logger.trace("Final shape: %s", predicted.shape)
return predicted
def queue_out_frames(self, batch, swapped_faces):
""" Compile the batch back to original frames and put to out_queue """
logger.trace("Queueing out batch. Batchsize: %s", len(batch))
pointer = 0
for item in batch:
num_faces = len(item["detected_faces"])
if num_faces == 0:
item["swapped_faces"] = np.array(list())
else:
item["swapped_faces"] = swapped_faces[pointer:pointer + num_faces]
logger.trace("Putting to queue. ('%s', detected_faces: %s, swapped_faces: %s)",
item["filename"], len(item["detected_faces"]),
item["swapped_faces"].shape[0])
self.out_queue.put(item)
pointer += num_faces
logger.trace("Queued out batch. Batchsize: %s", len(batch))
class Convert(): # pylint:disable=too-few-public-methods
""" The Faceswap Face Conversion Process.
The conversion process is responsible for swapping the faces on source frames with the output
from a trained model.
It leverages a series of user selected post-processing plugins, executed from
:class:`lib.convert.Converter`.
The convert process is self contained and should not be referenced by any other scripts, so it
contains no public properties.
Parameters
----------
arguments: :class:`argparse.Namespace`
The arguments to be passed to the convert process as generated from Faceswap's command
line arguments
"""
def __init__(self, arguments):
logger.debug("Initializing %s: (args: %s)", self.__class__.__name__,
arguments)
self._args = arguments
self._patch_threads = None
self._images = ImagesLoader(self._args.input_dir, fast_count=True)
self._alignments = Alignments(self._args, False, self._images.is_video)
if self._alignments.version == 1.0:
logger.error(
"The alignments file format has been updated since the given alignments "
"file was generated. You need to update the file to proceed.")
logger.error(
"To do this run the 'Alignments Tool' > 'Extract' Job.")
sys.exit(1)
self._opts = OptionalActions(self._args, self._images.file_list,
self._alignments)
self._add_queues()
self._disk_io = DiskIO(self._alignments, self._images, arguments)
self._predictor = Predict(self._disk_io.load_queue, self._queue_size,
arguments)
self._validate()
get_folder(self._args.output_dir)
configfile = self._args.configfile if hasattr(self._args,
"configfile") else None
self._converter = Converter(self._predictor.output_size,
self._predictor.coverage_ratio,
self._predictor.centering,
self._disk_io.draw_transparent,
self._disk_io.pre_encode,
arguments,
configfile=configfile)
logger.debug("Initialized %s", self.__class__.__name__)
@property
def _queue_size(self):
""" int: Size of the converter queues. 16 for single process otherwise 32 """
if self._args.singleprocess:
retval = 16
else:
retval = 32
logger.debug(retval)
return retval
@property
def _pool_processes(self):
""" int: The number of threads to run in parallel. Based on user options and number of
available processors. """
if self._args.singleprocess:
retval = 1
elif self._args.jobs > 0:
retval = min(self._args.jobs, total_cpus(), self._images.count)
else:
retval = min(total_cpus(), self._images.count)
retval = 1 if retval == 0 else retval
logger.debug(retval)
return retval
def _validate(self):
""" Validate the Command Line Options.
Ensure that certain cli selections are valid and won't result in an error. Checks:
* If frames have been passed in with video output, ensure user supplies reference
video.
* If a mask-type is selected, ensure it exists in the alignments file.
* If a predicted mask-type is selected, ensure model has been trained with a mask
otherwise attempt to select first available masks, otherwise raise error.
Raises
------
FaceswapError
If an invalid selection has been found.
"""
if (self._args.writer == "ffmpeg" and not self._images.is_video
and self._args.reference_video is None):
raise FaceswapError(
"Output as video selected, but using frames as input. You must "
"provide a reference video ('-ref', '--reference-video').")
if (self._args.mask_type not in ("none", "predicted")
and not self._alignments.mask_is_valid(self._args.mask_type)):
msg = (
"You have selected the Mask Type `{}` but at least one face does not have this "
"mask stored in the Alignments File.\nYou should generate the required masks "
"with the Mask Tool or set the Mask Type option to an existing Mask Type.\nA "
"summary of existing masks is as follows:\nTotal faces: {}, Masks: "
"{}".format(self._args.mask_type, self._alignments.faces_count,
self._alignments.mask_summary))
raise FaceswapError(msg)
if self._args.mask_type == "predicted" and not self._predictor.has_predicted_mask:
available_masks = [
k for k, v in self._alignments.mask_summary.items()
if k != "none" and v == self._alignments.faces_count
]
if not available_masks:
msg = (
"Predicted Mask selected, but the model was not trained with a mask and no "
"masks are stored in the Alignments File.\nYou should generate the "
"required masks with the Mask Tool or set the Mask Type to `none`."
)
raise FaceswapError(msg)
mask_type = available_masks[0]
logger.warning(
"Predicted Mask selected, but the model was not trained with a "
"mask. Selecting first available mask: '%s'", mask_type)
self._args.mask_type = mask_type
def _add_queues(self):
""" Add the queues for in, patch and out. """
logger.debug("Adding queues. Queue size: %s", self._queue_size)
for qname in ("convert_in", "convert_out", "patch"):
queue_manager.add_queue(qname, self._queue_size)
def process(self):
""" The entry point for triggering the Conversion Process.
Should only be called from :class:`lib.cli.launcher.ScriptExecutor`
"""
logger.debug("Starting Conversion")
# queue_manager.debug_monitor(5)
try:
self._convert_images()
self._disk_io.save_thread.join()
queue_manager.terminate_queues()
finalize(self._images.count, self._predictor.faces_count,
self._predictor.verify_output)
logger.debug("Completed Conversion")
except MemoryError as err:
msg = (
"Faceswap ran out of RAM running convert. Conversion is very system RAM "
"heavy, so this can happen in certain circumstances when you have a lot of "
"cpus but not enough RAM to support them all."
"\nYou should lower the number of processes in use by either setting the "
"'singleprocess' flag (-sp) or lowering the number of parallel jobs (-j)."
)
raise FaceswapError(msg) from err
def _convert_images(self):
""" Start the multi-threaded patching process, monitor all threads for errors and join on
completion. """
logger.debug("Converting images")
save_queue = queue_manager.get_queue("convert_out")
patch_queue = queue_manager.get_queue("patch")
self._patch_threads = MultiThread(self._converter.process,
patch_queue,
save_queue,
thread_count=self._pool_processes,
name="patch")
self._patch_threads.start()
while True:
self._check_thread_error()
if self._disk_io.completion_event.is_set():
logger.debug("DiskIO completion event set. Joining Pool")
break
if self._patch_threads.completed():
logger.debug("All patch threads completed")
break
sleep(1)
self._patch_threads.join()
logger.debug("Putting EOF")
save_queue.put("EOF")
logger.debug("Converted images")
def _check_thread_error(self):
""" Monitor all running threads for errors, and raise accordingly. """
for thread in (self._predictor.thread, self._disk_io.load_thread,
self._disk_io.save_thread, self._patch_threads):
thread.check_and_raise_error()
def in_thread(self, action):
""" Perform selected action inside a thread """
logger.debug("Performing help action: %s", action)
thread = MultiThread(getattr(self, action), thread_count=1)
thread.start()
logger.debug("Performed help action: %s", action)
class Patch():
""" The patch pipeline
To be run within it's own thread """
def __init__(self, arguments, samples, display, lock, trigger,
config_tools, tk_vars):
logger.debug(
"Initializing %s: (arguments: '%s', samples: %s: display: %s, lock: %s,"
" trigger: %s, config_tools: %s, tk_vars %s)",
self.__class__.__name__, arguments, samples, display, lock,
trigger, config_tools, tk_vars)
self.samples = samples
self.queue_patch_in = queue_manager.get_queue("preview_patch_in")
self.display = display
self.lock = lock
self.trigger = trigger
self.current_config = config_tools.config
self.converter_arguments = None # Updated converter arguments dict
configfile = arguments.configfile if hasattr(arguments,
"configfile") else None
self.converter = Converter(
output_dir=None,
output_size=self.samples.predictor.output_size,
output_has_mask=self.samples.predictor.has_predicted_mask,
draw_transparent=False,
pre_encode=None,
configfile=configfile,
arguments=self.generate_converter_arguments(arguments))
self.shutdown = Event()
self.thread = MultiThread(self.process,
self.trigger,
self.shutdown,
self.queue_patch_in,
self.samples,
tk_vars,
thread_count=1,
name="patch_thread")
self.thread.start()
@staticmethod
def generate_converter_arguments(arguments):
""" Get the default converter arguments """
converter_arguments = ConvertArgs(None,
"convert").get_optional_arguments()
for item in converter_arguments:
value = item.get("default", None)
# Skip options without a default value
if value is None:
continue
option = item.get("dest", item["opts"][1].replace("--", ""))
# Skip options already in arguments
if hasattr(arguments, option):
continue
# Add option to arguments
setattr(arguments, option, value)
logger.debug(arguments)
return arguments
def process(self, trigger_event, shutdown_event, patch_queue_in, samples,
tk_vars):
""" Wait for event trigger and run when process when set """
patch_queue_out = queue_manager.get_queue("preview_patch_out")
while True:
trigger = trigger_event.wait(1)
if shutdown_event.is_set():
logger.debug("Shutdown received")
break
if not trigger:
continue
# Clear trigger so calling process can set it during this run
trigger_event.clear()
tk_vars["busy"].set(True)
queue_manager.flush_queue("preview_patch_in")
self.feed_swapped_faces(patch_queue_in, samples)
with self.lock:
self.update_converter_arguments()
self.converter.reinitialize(config=self.current_config)
swapped = self.patch_faces(patch_queue_in, patch_queue_out,
samples.sample_size)
with self.lock:
self.display.destination = swapped
tk_vars["refresh"].set(True)
tk_vars["busy"].set(False)
def update_converter_arguments(self):
""" Update the converter arguments """
logger.debug("Updating Converter cli arguments")
if self.converter_arguments is None:
logger.debug("No arguments to update")
return
for key, val in self.converter_arguments.items():
logger.debug("Updating %s to %s", key, val)
setattr(self.converter.args, key, val)
logger.debug("Updated Converter cli arguments")
@staticmethod
def feed_swapped_faces(patch_queue_in, samples):
""" Feed swapped faces to the converter and trigger a run """
logger.trace("feeding swapped faces to converter")
for item in samples.predicted_images:
patch_queue_in.put(item)
logger.trace("fed %s swapped faces to converter",
len(samples.predicted_images))
logger.trace("Putting EOF to converter")
patch_queue_in.put("EOF")
def patch_faces(self, queue_in, queue_out, sample_size):
""" Patch faces """
logger.trace("Patching faces")
self.converter.process(queue_in, queue_out)
swapped = list()
idx = 0
while idx < sample_size:
logger.trace("Patching image %s of %s", idx + 1, sample_size)
item = queue_out.get()
swapped.append(item[1])
logger.trace("Patched image %s of %s", idx + 1, sample_size)
idx += 1
logger.trace("Patched faces")
return swapped
class Predict():
""" Predict faces from incoming queue """
def __init__(self, in_queue, queue_size, arguments):
logger.debug(
"Initializing %s: (args: %s, queue_size: %s, in_queue: %s)",
self.__class__.__name__, arguments, queue_size, in_queue)
self.batchsize = self.get_batchsize(queue_size)
self.args = arguments
self.in_queue = in_queue
self.out_queue = queue_manager.get_queue("patch")
self.serializer = Serializer.get_serializer("json")
self.faces_count = 0
self.verify_output = False
self.model = self.load_model()
self.predictor = self.model.converter(self.args.swap_model)
self.queues = dict()
self.thread = MultiThread(self.predict_faces, thread_count=1)
self.thread.start()
logger.debug("Initialized %s: (out_queue: %s)",
self.__class__.__name__, self.out_queue)
@property
def coverage_ratio(self):
""" Return coverage ratio from training options """
return self.model.training_opts["coverage_ratio"]
@property
def input_size(self):
""" Return the model input size """
return self.model.input_shape[0]
@property
def output_size(self):
""" Return the model output size """
return self.model.output_shape[0]
@property
def input_mask(self):
""" Return the input mask """
mask = np.zeros(self.model.state.mask_shapes[0], dtype="float32")
retval = np.expand_dims(mask, 0)
return retval
@property
def has_predicted_mask(self):
""" Return whether this model has a predicted mask """
return bool(self.model.state.mask_shapes)
@staticmethod
def get_batchsize(queue_size):
""" Get the batchsize """
logger.debug("Getting batchsize")
is_cpu = GPUStats().device_count == 0
batchsize = 1 if is_cpu else 16
batchsize = min(queue_size, batchsize)
logger.debug("Batchsize: %s", batchsize)
logger.debug("Got batchsize: %s", batchsize)
return batchsize
def load_model(self):
""" Load the model requested for conversion """
logger.debug("Loading Model")
model_dir = get_folder(self.args.model_dir, make_folder=False)
if not model_dir:
logger.error("%s does not exist.", self.args.model_dir)
exit(1)
trainer = self.get_trainer(model_dir)
gpus = 1 if not hasattr(self.args, "gpus") else self.args.gpus
model = PluginLoader.get_model(trainer)(model_dir, gpus, predict=True)
logger.debug("Loaded Model")
return model
def get_trainer(self, model_dir):
""" Return the trainer name if provided, or read from state file """
if hasattr(self.args, "trainer") and self.args.trainer:
logger.debug("Trainer name provided: '%s'", self.args.trainer)
return self.args.trainer
statefile = [
fname for fname in os.listdir(str(model_dir))
if fname.endswith("_state.json")
]
if len(statefile) != 1:
logger.error(
"There should be 1 state file in your model folder. %s were found. "
"Specify a trainer with the '-t', '--trainer' option.")
exit(1)
statefile = os.path.join(str(model_dir), statefile[0])
with open(statefile, "rb") as inp:
state = self.serializer.unmarshal(inp.read().decode("utf-8"))
trainer = state.get("name", None)
if not trainer:
logger.error(
"Trainer name could not be read from state file. "
"Specify a trainer with the '-t', '--trainer' option.")
exit(1)
logger.debug("Trainer from state file: '%s'", trainer)
return trainer
def predict_faces(self):
""" Get detected faces from images """
faces_seen = 0
consecutive_no_faces = 0
batch = list()
while True:
item = self.in_queue.get()
if item != "EOF":
logger.trace("Got from queue: '%s'", item["filename"])
faces_count = len(item["detected_faces"])
# Safety measure. If a large stream of frames appear that do not have faces,
# these will stack up into RAM. Keep a count of consecutive frames with no faces.
# If self.batchsize number of frames appear, force the current batch through
# to clear RAM.
consecutive_no_faces = consecutive_no_faces + 1 if faces_count == 0 else 0
self.faces_count += faces_count
if faces_count > 1:
self.verify_output = True
logger.verbose(
"Found more than one face in an image! '%s'",
os.path.basename(item["filename"]))
self.load_aligned(item)
faces_seen += faces_count
batch.append(item)
if item != "EOF" and (faces_seen < self.batchsize
and consecutive_no_faces < self.batchsize):
logger.trace(
"Continuing. Current batchsize: %s, consecutive_no_faces: %s",
faces_seen, consecutive_no_faces)
continue
if batch:
logger.trace("Batching to predictor. Frames: %s, Faces: %s",
len(batch), faces_seen)
detected_batch = [
detected_face for item in batch
for detected_face in item["detected_faces"]
]
if faces_seen != 0:
feed_faces = self.compile_feed_faces(detected_batch)
predicted = self.predict(feed_faces)
else:
predicted = list()
self.queue_out_frames(batch, predicted)
consecutive_no_faces = 0
faces_seen = 0
batch = list()
if item == "EOF":
logger.debug("EOF Received")
break
logger.debug("Putting EOF")
self.out_queue.put("EOF")
logger.debug("Load queue complete")
def load_aligned(self, item):
""" Load the feed faces and reference output faces """
logger.trace("Loading aligned faces: '%s'", item["filename"])
for detected_face in item["detected_faces"]:
detected_face.load_feed_face(item["image"],
size=self.input_size,
coverage_ratio=self.coverage_ratio,
dtype="float32")
if self.input_size == self.output_size:
detected_face.reference = detected_face.feed
else:
detected_face.load_reference_face(
item["image"],
size=self.output_size,
coverage_ratio=self.coverage_ratio,
dtype="float32")
logger.trace("Loaded aligned faces: '%s'", item["filename"])
@staticmethod
def compile_feed_faces(detected_faces):
""" Compile the faces for feeding into the predictor """
logger.trace("Compiling feed face. Batchsize: %s", len(detected_faces))
feed_faces = np.stack(
[detected_face.feed_face for detected_face in detected_faces])
logger.trace("Compiled Feed faces. Shape: %s", feed_faces.shape)
return feed_faces
def predict(self, feed_faces):
""" Perform inference on the feed """
logger.trace("Predicting: Batchsize: %s", len(feed_faces))
feed = [feed_faces]
if self.has_predicted_mask:
feed.append(np.repeat(self.input_mask, feed_faces.shape[0],
axis=0))
logger.trace("Input shape(s): %s", [item.shape for item in feed])
predicted = self.predictor(feed)
predicted = predicted if isinstance(predicted, list) else [predicted]
logger.trace("Output shape(s): %s",
[predict.shape for predict in predicted])
# Compile masks into alpha channel or keep raw faces
predicted = np.concatenate(
predicted, axis=-1) if len(predicted) == 2 else predicted[0]
predicted = predicted.astype("float32")
logger.trace("Final shape: %s", predicted.shape)
return predicted
def queue_out_frames(self, batch, swapped_faces):
""" Compile the batch back to original frames and put to out_queue """
logger.trace("Queueing out batch. Batchsize: %s", len(batch))
pointer = 0
for item in batch:
num_faces = len(item["detected_faces"])
if num_faces == 0:
item["swapped_faces"] = np.array(list())
else:
item["swapped_faces"] = swapped_faces[pointer:pointer +
num_faces]
logger.trace(
"Putting to queue. ('%s', detected_faces: %s, swapped_faces: %s)",
item["filename"], len(item["detected_faces"]),
item["swapped_faces"].shape[0])
self.out_queue.put(item)
pointer += num_faces
logger.trace("Queued out batch. Batchsize: %s", len(batch))