def __init__ (self, samples_path, debug=False, batch_size=1,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
person_id_mode=1,
**kwargs):
super().__init__(samples_path, debug, batch_size)
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
self.person_id_mode = person_id_mode
raise NotImplementedError("Currently SampleGeneratorFacePerson is not implemented.")
samples_host = SampleHost.mp_host (SampleType.FACE, self.samples_path)
samples = samples_host.get_list()
self.samples_len = len(samples)
if self.samples_len == 0:
raise ValueError('No training data provided.')
unique_person_names = { sample.person_name for sample in samples }
persons_name_idxs = { person_name : [] for person_name in unique_person_names }
for i,sample in enumerate(samples):
persons_name_idxs[sample.person_name].append (i)
indexes2D = [ persons_name_idxs[person_name] for person_name in unique_person_names ]
index2d_host = mplib.Index2DHost(indexes2D)
if self.debug:
self.generators_count = 1
self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (samples_host.create_cli(), index2d_host.create_cli(),) )]
else:
self.generators_count = np.clip(multiprocessing.cpu_count(), 2, 4)
self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (samples_host.create_cli(), index2d_host.create_cli(),), start_now=True ) for i in range(self.generators_count) ]
self.generator_counter = -1
def __init__ (self, samples_path, debug=False, batch_size=1,
random_ct_samples_path=None,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
uniform_yaw_distribution=False,
generators_count=4,
raise_on_no_data=True,
**kwargs):
super().__init__(debug, batch_size)
self.initialized = False
self.sample_process_options = sample_process_options
self.output_sample_types = output_sample_types
if self.debug:
self.generators_count = 1
else:
self.generators_count = max(1, generators_count)
samples = SampleLoader.load (SampleType.FACE, samples_path)
self.samples_len = len(samples)
if self.samples_len == 0:
if raise_on_no_data:
raise ValueError('No training data provided.')
else:
return
if uniform_yaw_distribution:
samples_pyr = [ ( idx, sample.get_pitch_yaw_roll() ) for idx, sample in enumerate(samples) ]
grads = 128
#instead of math.pi / 2, using -1.2,+1.2 because actually maximum yaw for 2DFAN landmarks are -1.2+1.2
grads_space = np.linspace (-1.2, 1.2,grads)
yaws_sample_list = [None]*grads
for g in io.progress_bar_generator ( range(grads), "Sort by yaw"):
yaw = grads_space[g]
next_yaw = grads_space[g+1] if g < grads-1 else yaw
yaw_samples = []
for idx, pyr in samples_pyr:
s_yaw = -pyr[1]
if (g == 0 and s_yaw < next_yaw) or \
(g < grads-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
(g == grads-1 and s_yaw >= yaw):
yaw_samples += [ idx ]
if len(yaw_samples) > 0:
yaws_sample_list[g] = yaw_samples
yaws_sample_list = [ y for y in yaws_sample_list if y is not None ]
index_host = mplib.Index2DHost( yaws_sample_list )
else:
index_host = mplib.IndexHost(self.samples_len)
if random_ct_samples_path is not None:
ct_samples = SampleLoader.load (SampleType.FACE, random_ct_samples_path)
ct_index_host = mplib.IndexHost( len(ct_samples) )
else:
ct_samples = None
ct_index_host = None
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (samples, index_host.create_cli(), ct_samples, ct_index_host.create_cli() if ct_index_host is not None else None) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (samples, index_host.create_cli(), ct_samples, ct_index_host.create_cli() if ct_index_host is not None else None), start_now=False ) \
for i in range(self.generators_count) ]
SubprocessGenerator.start_in_parallel( self.generators )
self.generator_counter = -1
self.initialized = True