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
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,
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()
def __init__(self, arguments):
logger.debug("Initializing %s: (args: %s)", self.__class__.__name__, arguments)
self._args = arguments
self.batch = list()
self._serializer = get_serializer("json")
self._pre_process = PostProcess(arguments)
self._writer = self._get_writer()
self._extractor = self._load_extractor()
self._batchsize = self._get_batchsize(self._queue_size)
self._model = self._load_model()
self._output_indices = {"face": self._model.largest_face_index,
"mask": self._model.largest_mask_index}
self._predictor = self._model.converter(False)
configfile = self._args.configfile if hasattr(self._args, "configfile") else None
self._converter = Converter(self.output_size,
self.coverage_ratio,
self.draw_transparent,
self.pre_encode,
arguments,
configfile=configfile)
logger.debug("Initialized %s", self.__class__.__name__)
def __init__(self, arguments):
logger.debug("Initializing %s: (args: %s)", self.__class__.__name__,
arguments)
self.args = arguments
Utils.set_verbosity(self.args.loglevel)
self.patch_threads = None
self.images = Images(self.args)
self.validate()
self.alignments = Alignments(self.args, False, self.images.is_video)
self.opts = OptionalActions(self.args, self.images.input_images,
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)
configfile = self.args.configfile if hasattr(self.args,
"configfile") else None
self.converter = Converter(get_folder(self.args.output_dir),
self.predictor.output_size,
self.predictor.has_predicted_mask,
self.disk_io.draw_transparent,
self.disk_io.pre_encode,
arguments,
configfile=configfile)
logger.debug("Initialized %s", self.__class__.__name__)
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)
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._disk_io.draw_transparent,
self._disk_io.pre_encode,
arguments,
configfile=configfile)
logger.debug("Initialized %s", self.__class__.__name__)
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__)
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
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,
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 Live(): # 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.batch = list()
self._serializer = get_serializer("json")
self._pre_process = PostProcess(arguments)
self._writer = self._get_writer()
self._extractor = self._load_extractor()
self._batchsize = self._get_batchsize(self._queue_size)
self._model = self._load_model()
self._output_indices = {"face": self._model.largest_face_index,
"mask": self._model.largest_mask_index}
self._predictor = self._model.converter(False)
configfile = self._args.configfile if hasattr(self._args, "configfile") else None
self._converter = Converter(self.output_size,
self.coverage_ratio,
self.draw_transparent,
self.pre_encode,
arguments,
configfile=configfile)
logger.debug("Initialized %s", self.__class__.__name__)
@property
def draw_transparent(self):
""" bool: ``True`` if the selected writer's Draw_transparent configuration item is set
otherwise ``False`` """
return self._writer.config.get("draw_transparent", False)
@property
def pre_encode(self):
""" python function: Selected writer's pre-encode function, if it has one,
otherwise ``None`` """
dummy = np.zeros((20, 20, 3), dtype="uint8")
test = self._writer.pre_encode(dummy)
retval = None if test is None else self._writer.pre_encode
logger.debug("Writer pre_encode function: %s", retval)
return retval
@property
def coverage_ratio(self):
""" float: The coverage ratio that the model was trained at. """
return self._model.training_opts["coverage_ratio"]
@property
def output_size(self):
""" int: The size in pixels of the Faceswap model output. """
return self._model.output_shape[0]
@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. """
retval = 1
return retval
@staticmethod
def _get_batchsize(queue_size):
""" Get the batch size for feeding the model.
Sets the batch size to 1 if inference is being run on CPU, otherwise the minimum of the
:attr:`self._queue_size` and 16.
Returns
-------
int
The batch size that the model is to be fed at.
"""
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 _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.ScriptExecutor`
"""
logger.debug("Starting Conversion")
# queue_manager.debug_monitor(5)
try:
self._convert_images()
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")
video_capture = cv2.VideoCapture(0)
time.sleep(1)
width = video_capture.get(3) # float
height = video_capture.get(4) # float
print("webcam dimensions = {} x {}".format(width, height))
while True:
ret, frame = video_capture.read()
#frame = cv2.resize(frame, (640, 480))
# flip image, because webcam inverts it and we trained the model the other way!
frame = cv2.flip(frame, 1)
image = self._convert_frame(frame, convert_colors=False)
# flip it back
#image = cv2.flip(image, 1)
#image = cv2.resize(image, (640, 480))
img = cv2.imread(self._writer.output_filename("result"))
img = cv2.resize(img, (1280, 720))
cv2.imshow('Video', img)
# print("writing to screen")
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
video_capture.release()
break
cv2.destroyAllWindows()
exit()
def _convert_frame(self, frame, convert_colors=True):
detected_faces = self._get_detected_faces("camera", frame)
if len(detected_faces) == 0:
return frame
item = dict(filename="camera", image=frame, detected_faces=detected_faces)
self.load_aligned(item)
self.batch.clear()
self.batch.append(item)
detected_batch = [detected_face for item in self.batch
for detected_face in item["detected_faces"]]
feed_faces = self._compile_feed_faces(detected_batch)
predicted = self._predict(feed_faces, 1)
pointer = 0
for item in self.batch:
num_faces = len(item["detected_faces"])
if num_faces == 0:
item["swapped_faces"] = np.array(list())
else:
item["swapped_faces"] = predicted[pointer:pointer + num_faces]
pointer += num_faces
imager = self._converter._patch_image(item)
self._writer.write("result", imager)
return imager
def _convert_images2(self):
""" Start the multi-threaded patching process, monitor all threads for errors and join on
completion. """
for filename, image in self._images.load():
imager = self._process_image(filename, image)
self._writer.write(filename, imager)
def _process_image(self, filename, image):
detected_faces = self._get_detected_faces(filename, image)
item = dict(filename=filename, image=image, detected_faces=detected_faces)
self._pre_process.do_actions(item)
self.load_aligned(item)
self.batch.clear()
self.batch.append(item)
detected_batch = [detected_face for item in self.batch
for detected_face in item["detected_faces"]]
feed_faces = self._compile_feed_faces(detected_batch)
predicted = self._predict(feed_faces, 1)
pointer = 0
for item in self.batch:
num_faces = len(item["detected_faces"])
if num_faces == 0:
item["swapped_faces"] = np.array(list())
else:
item["swapped_faces"] = predicted[pointer:pointer + num_faces]
pointer += num_faces
imager = self._converter._patch_image(item)
return imager
def _get_writer(self):
""" Load the selected writer plugin.
Returns
-------
:mod:`plugins.convert.writer` plugin
The requested writer plugin
"""
args = [os.path.abspath(os.path.dirname(sys.argv[0]))]
logger.debug("Writer args: %s", args)
configfile = self._args.configfile if hasattr(self._args, "configfile") else None
return PluginLoader.get_live("writer", self._args.writer)(*args,
configfile=configfile)
def _load_extractor(self):
""" Load the CV2-DNN Face Extractor Chain.
For On-The-Fly conversion we use a CPU based extractor to avoid stacking the GPU.
Results are poor.
Returns
-------
:class:`plugins.extract.Pipeline.Extractor`
The face extraction chain to be used for on-the-fly conversion
"""
logger.debug("Loading extractor")
logger.warning("On-The-Fly conversion selected. This will use the inferior cv2-dnn for "
"extraction and will produce poor results.")
logger.warning("It is recommended to generate an alignments file for your destination "
"video with Extract first for superior results.")
extractor = Extractor(detector="cv2-dnn",
aligner="cv2-dnn",
masker="none",
multiprocess=True,
rotate_images=None,
min_size=20)
extractor.launch()
logger.debug("Loaded extractor")
return extractor
def _get_detected_faces(self, filename, image):
""" Return the detected faces for the given image.
If we have an alignments file, then the detected faces are created from that file. If
we're running On-The-Fly then they will be extracted from the extractor.
Parameters
----------
filename: str
The filename to return the detected faces for
image: :class:`numpy.ndarray`
The frame that the detected faces exist in
Returns
-------
list
List of :class:`lib.faces_detect.DetectedFace` objects
"""
logger.trace("Getting faces for: '%s'", filename)
if not self._extractor:
detected_faces = self._alignments_faces(os.path.basename(filename), image)
else:
detected_faces = self._detect_faces(filename, image)
logger.trace("Got %s faces for: '%s'", len(detected_faces), filename)
return detected_faces
def _detect_faces(self, filename, image):
""" Extract the face from a frame for On-The-Fly conversion.
Pulls detected faces out of the Extraction pipeline.
Parameters
----------
filename: str
The filename to return the detected faces for
image: :class:`numpy.ndarray`
The frame that the detected faces exist in
Returns
-------
list
List of :class:`lib.faces_detect.DetectedFace` objects
"""
self._extractor.input_queue.put(ExtractMedia(filename, image))
faces = next(self._extractor.detected_faces())
final_faces = [face for face in faces.detected_faces]
return final_faces
def _load_model(self):
""" Load the Faceswap model.
Returns
-------
:mod:`plugins.train.model` plugin
The trained model in the specified model folder
"""
logger.debug("Loading Model")
model_dir = get_folder(self._args.model_dir, make_folder=False)
if not model_dir:
raise FaceswapError("{} does not exist.".format(self._args.model_dir))
trainer = self._get_model_name(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_model_name(self, model_dir):
""" Return the name of the Faceswap model used.
If a "trainer" option has been selected in the command line arguments, use that value,
otherwise retrieve the name of the model from the model's state file.
Parameters
----------
model_dir: str
The folder that contains the trained Faceswap model
Returns
-------
str
The name of the Faceswap model being used.
"""
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:
raise FaceswapError("There should be 1 state file in your model folder. {} were "
"found. Specify a trainer with the '-t', '--trainer' "
"option.".format(len(statefile)))
statefile = os.path.join(str(model_dir), statefile[0])
state = self._serializer.load(statefile)
trainer = state.get("name", None)
if not trainer:
raise FaceswapError("Trainer name could not be read from state file. "
"Specify a trainer with the '-t', '--trainer' option.")
logger.debug("Trainer from state file: '%s'", trainer)
return trainer
def _alignments_faces(self, frame_name, image):
""" Return detected faces from an alignments file.
Parameters
----------
frame_name: str
The name of the frame to return the detected faces for
image: :class:`numpy.ndarray`
The frame that the detected faces exist in
Returns
-------
list
List of :class:`lib.faces_detect.DetectedFace` objects
"""
if not self._check_alignments(frame_name):
return list()
faces = self._alignments.get_faces_in_frame(frame_name)
detected_faces = list()
for rawface in faces:
face = DetectedFace()
face.from_alignment(rawface, image=image)
detected_faces.append(face)
return detected_faces
def _check_alignments(self, frame_name):
""" Ensure that we have alignments for the current frame.
If we have no alignments for this image, skip it and output a message.
Parameters
----------
frame_name: str
The name of the frame to check that we have alignments for
Returns
-------
bool
``True`` if we have alignments for this face, otherwise ``False``
"""
have_alignments = self._alignments.frame_exists(frame_name)
if not have_alignments:
tqdm.write("No alignment found for {}, "
"skipping".format(frame_name))
return have_alignments
@property
def _input_size(self):
""" int: The size in pixels of the Faceswap model input. """
return self._model.input_shape[0]
@property
def coverage_ratio(self):
""" float: The coverage ratio that the model was trained at. """
return self._model.training_opts["coverage_ratio"]
@property
def output_size(self):
""" int: The size in pixels of the Faceswap model output. """
return self._model.output_shape[0]
def load_aligned(self, item):
""" Load the model's feed faces and the reference output faces.
For each detected face in the incoming item, load the feed face and reference face
images, correctly sized for input and output respectively.
Parameters
----------
item: dict
The incoming image and list of :class:`~lib.faces_detect.DetectedFace` objects
"""
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 a batch of faces for feeding into the Predictor.
Parameters
----------
detected_faces: list
List of `~lib.faces_detect.DetectedFace` objects
Returns
-------
:class:`numpy.ndarray`
A batch of faces ready for feeding into the Faceswap model.
"""
logger.trace("Compiling feed face. Batchsize: %s", len(detected_faces))
feed_faces = np.stack([detected_face.feed_face[..., :3]
for detected_face in detected_faces]) / 255.0
logger.trace("Compiled Feed faces. Shape: %s", feed_faces.shape)
return feed_faces
def _predict(self, feed_faces, batch_size=None):
""" Run the Faceswap models' prediction function.
Parameters
----------
feed_faces: :class:`numpy.ndarray`
The batch to be fed into the model
batch_size: int, optional
Used for plaidml only. Indicates to the model what batch size is being processed.
Default: ``None``
Returns
-------
:class:`numpy.ndarray`
The swapped faces for the given batch
"""
logger.trace("Predicting: Batchsize: %s", len(feed_faces))
feed = [feed_faces]
if self._model.feed_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, batch_size=batch_size)
predicted = predicted if isinstance(predicted, list) else [predicted]
logger.trace("Output shape(s): %s", [predict.shape for predict in predicted])
predicted = self._filter_multi_out(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 _filter_multi_out(self, predicted):
""" Filter the model output to just the required image.
Some models have multi-scale outputs, so just make sure we take the largest
output.
Parameters
----------
predicted: :class:`numpy.ndarray`
The predictions retrieved from the Faceswap model.
Returns
-------
:class:`numpy.ndarray`
The predictions with any superfluous outputs removed.
"""
if not predicted:
return predicted
face = predicted[self._output_indices["face"]]
mask_idx = self._output_indices["mask"]
mask = predicted[mask_idx] if mask_idx is not None else None
predicted = [face, mask] if mask is not None else [face]
logger.trace("Filtered output shape(s): %s", [predict.shape for predict in predicted])
return predicted
def __init__(self, arguments):
logger.debug("Initializing %s: (args: %s)", self.__class__.__name__, arguments)
self._args = arguments
# load faces
faces_alignments = AlignmentsBase(self._args.faces_align_dir)
print()
print(f'Faces alignments: {len(faces_alignments._data.keys())}')
print(faces_alignments._data.keys())
self._faces = {}
faces_loader = ImagesLoader(self._args.faces_dir)
for filename, image in faces_loader.load():
face_name = os.path.basename(filename)
faces = faces_alignments.get_faces_in_frame(face_name)
detected_faces = list()
for rawface in faces:
face = DetectedFace()
face.from_alignment(rawface, image=image)
feed_face = AlignedFace(face.landmarks_xy,
image=image,
centering='face',
size=image.shape[0],
coverage_ratio=1.0,
dtype="float32")
detected_faces.append(feed_face)
self._faces[face_name] = (filename, image, detected_faces)
print('Faces:', len(self._faces))
print(self._faces.keys())
print()
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, self._faces, 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__)