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50f892d57d
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw. Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster. added 4.2.other) data_src/dst util faceset pack.bat Packs /aligned/ samples into one /aligned/samples.pak file. After that, all faces will be deleted. 4.2.other) data_src/dst util faceset unpack.bat unpacks faces from /aligned/samples.pak to /aligned/ dir. After that, samples.pak will be deleted. Packed faceset load and work faster.
102 lines
No EOL
4 KiB
Python
102 lines
No EOL
4 KiB
Python
import multiprocessing
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import traceback
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import cv2
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import numpy as np
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from facelib import LandmarksProcessor
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from samplelib import (SampleGeneratorBase, SampleHost, SampleProcessor,
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SampleType)
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from utils import iter_utils
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from utils import mp_utils
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'''
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arg
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output_sample_types = [
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[SampleProcessor.TypeFlags, size, (optional) {} opts ] ,
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...
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]
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'''
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class SampleGeneratorFace(SampleGeneratorBase):
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def __init__ (self, samples_path, debug=False, batch_size=1,
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random_ct_samples_path=None,
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sample_process_options=SampleProcessor.Options(),
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output_sample_types=[],
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add_sample_idx=False,
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**kwargs):
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super().__init__(samples_path, debug, batch_size)
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self.sample_process_options = sample_process_options
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self.output_sample_types = output_sample_types
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self.add_sample_idx = add_sample_idx
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samples_host = SampleHost.mp_host (SampleType.FACE, self.samples_path)
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self.samples_len = len(samples_host)
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if self.samples_len == 0:
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raise ValueError('No training data provided.')
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index_host = mp_utils.IndexHost(self.samples_len)
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if random_ct_samples_path is not None:
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ct_samples_host = SampleHost.mp_host (SampleType.FACE, random_ct_samples_path)
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ct_index_host = mp_utils.IndexHost( len(ct_samples_host) )
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else:
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ct_samples_host = None
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ct_index_host = None
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if self.debug:
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self.generators_count = 1
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self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None) )]
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else:
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self.generators_count = np.clip(multiprocessing.cpu_count(), 2, 4)
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self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (samples_host.create_cli(), index_host.create_cli(), ct_samples_host.create_cli() if ct_index_host is not None else None, ct_index_host.create_cli() if ct_index_host is not None else None), start_now=True ) for i in range(self.generators_count) ]
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self.generator_counter = -1
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#overridable
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def get_total_sample_count(self):
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return self.samples_len
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def __iter__(self):
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return self
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def __next__(self):
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self.generator_counter += 1
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generator = self.generators[self.generator_counter % len(self.generators) ]
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return next(generator)
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def batch_func(self, param ):
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samples, index_host, ct_samples, ct_index_host = param
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bs = self.batch_size
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while True:
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batches = None
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indexes = index_host.get(bs)
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ct_indexes = ct_index_host.get(bs) if ct_samples is not None else None
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for n_batch in range(bs):
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sample = samples[ indexes[n_batch] ]
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ct_sample = ct_samples[ ct_indexes[n_batch] ] if ct_samples is not None else None
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try:
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x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug, ct_sample=ct_sample)
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except:
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raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
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if batches is None:
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batches = [ [] for _ in range(len(x)) ]
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if self.add_sample_idx:
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batches += [ [] ]
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i_sample_idx = len(batches)-1
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for i in range(len(x)):
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batches[i].append ( x[i] )
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if self.add_sample_idx:
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batches[i_sample_idx].append (idx)
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yield [ np.array(batch) for batch in batches]
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@staticmethod
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def get_person_id_max_count(samples_path):
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return SampleHost.get_person_id_max_count(samples_path) |