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import multiprocessing
import pickle
import time
import traceback
import cv2
import numpy as np
from core import mplib
from core.joblib import SubprocessGenerator, ThisThreadGenerator
from facelib import LandmarksProcessor
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
SampleType)
class SampleGeneratorFaceTemporal(SampleGeneratorBase):
def __init__ (self, samples_path, debug, batch_size,
temporal_image_count=3,
sample_process_options=SampleProcessor.Options(),
output_sample_types=[],
generators_count=2,
**kwargs):
super().__init__(debug, batch_size)
self.temporal_image_count = temporal_image_count
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 = generators_count
samples = SampleLoader.load (SampleType.FACE_TEMPORAL_SORTED, samples_path)
samples_len = len(samples)
if samples_len == 0:
raise ValueError('No training data provided.')
mult_max = 1
l = samples_len - ( (self.temporal_image_count)*mult_max - (mult_max-1) )
index_host = mplib.IndexHost(l+1)
pickled_samples = pickle.dumps(samples, 4)
if self.debug:
self.generators = [ThisThreadGenerator ( self.batch_func, (pickled_samples, index_host.create_cli(),) )]
else:
self.generators = [SubprocessGenerator ( self.batch_func, (pickled_samples, index_host.create_cli(),) ) for i in range(self.generators_count) ]
self.generator_counter = -1
def __iter__(self):
return self
def __next__(self):
self.generator_counter += 1
generator = self.generators[self.generator_counter % len(self.generators) ]
return next(generator)
def batch_func(self, param):
mult_max = 1
bs = self.batch_size
pickled_samples, index_host = param
samples = pickle.loads(pickled_samples)
while True:
batches = None
indexes = index_host.multi_get(bs)
for n_batch in range(self.batch_size):
idx = indexes[n_batch]
temporal_samples = []
mult = np.random.randint(mult_max)+1
for i in range( self.temporal_image_count ):
sample = samples[ idx+i*mult ]
try:
temporal_samples += SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug)[0]
except:
raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
if batches is None:
batches = [ [] for _ in range(len(temporal_samples)) ]
for i in range(len(temporal_samples)):
batches[i].append ( temporal_samples[i] )
yield [ np.array(batch) for batch in batches]
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