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import multiprocessing
import operator
import pickle
import traceback
from pathlib import Path
import samplelib.PackedFaceset
from core import pathex
from core.mplib import MPSharedList
from core.interact import interact as io
from core.joblib import Subprocessor
from DFLIMG import *
from facelib import FaceType, LandmarksProcessor
from .Sample import Sample, SampleType
class SampleLoader:
samples_cache = dict()
@staticmethod
def get_person_id_max_count(samples_path):
samples = None
try:
samples = samplelib.PackedFaceset.load(samples_path)
except:
io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_path)}, {traceback.format_exc()}")
if samples is None:
raise ValueError("packed faceset not found.")
persons_name_idxs = {}
for sample in samples:
persons_name_idxs[sample.person_name] = 0
return len(list(persons_name_idxs.keys()))
@staticmethod
def load(sample_type, samples_path, subdirs=False):
"""
Return MPSharedList of samples
"""
samples_cache = SampleLoader.samples_cache
if str(samples_path) not in samples_cache.keys():
samples_cache[str(samples_path)] = [None]*SampleType.QTY
samples = samples_cache[str(samples_path)]
if sample_type == SampleType.IMAGE:
if samples[sample_type] is None:
samples[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( pathex.get_image_paths(samples_path, subdirs=subdirs), "Loading") ]
elif sample_type == SampleType.FACE:
if samples[sample_type] is None:
try:
result = samplelib.PackedFaceset.load(samples_path)
except:
io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_dat_path)}, {traceback.format_exc()}")
if result is not None:
io.log_info (f"Loaded {len(result)} packed faces from {samples_path}")
if result is None:
result = SampleLoader.load_face_samples( pathex.get_image_paths(samples_path, subdirs=subdirs) )
samples[sample_type] = MPSharedList(result)
elif sample_type == SampleType.FACE_TEMPORAL_SORTED:
result = SampleLoader.load (SampleType.FACE, samples_path)
result = SampleLoader.upgradeToFaceTemporalSortedSamples(result)
samples[sample_type] = MPSharedList(result)
return samples[sample_type]
@staticmethod
def load_face_samples ( image_paths):
result = FaceSamplesLoaderSubprocessor(image_paths).run()
sample_list = []
for filename, data in result:
if data is None:
continue
( face_type,
shape,
landmarks,
seg_ie_polys,
xseg_mask_compressed,
eyebrows_expand_mod,
source_filename ) = data
sample_list.append( Sample(filename=filename,
sample_type=SampleType.FACE,
face_type=FaceType.fromString (face_type),
shape=shape,
landmarks=landmarks,
seg_ie_polys=seg_ie_polys,
xseg_mask_compressed=xseg_mask_compressed,
eyebrows_expand_mod=eyebrows_expand_mod,
source_filename=source_filename,
))
return sample_list
@staticmethod
def upgradeToFaceTemporalSortedSamples( samples ):
new_s = [ (s, s.source_filename) for s in samples]
new_s = sorted(new_s, key=operator.itemgetter(1))
return [ s[0] for s in new_s]
class FaceSamplesLoaderSubprocessor(Subprocessor):
#override
def __init__(self, image_paths ):
self.image_paths = image_paths
self.image_paths_len = len(image_paths)
self.idxs = [*range(self.image_paths_len)]
self.result = [None]*self.image_paths_len
super().__init__('FaceSamplesLoader', FaceSamplesLoaderSubprocessor.Cli, 60)
#override
def on_clients_initialized(self):
io.progress_bar ("Loading samples", len (self.image_paths))
#override
def on_clients_finalized(self):
io.progress_bar_close()
#override
def process_info_generator(self):
for i in range(min(multiprocessing.cpu_count(), 8) ):
yield 'CPU%d' % (i), {}, {}
#override
def get_data(self, host_dict):
if len (self.idxs) > 0:
idx = self.idxs.pop(0)
return idx, self.image_paths[idx]
return None
#override
def on_data_return (self, host_dict, data):
self.idxs.insert(0, data[0])
#override
def on_result (self, host_dict, data, result):
idx, dflimg = result
self.result[idx] = (self.image_paths[idx], dflimg)
io.progress_bar_inc(1)
#override
def get_result(self):
return self.result
class Cli(Subprocessor.Cli):
#override
def process_data(self, data):
idx, filename = data
dflimg = DFLIMG.load (Path(filename))
if dflimg is None or not dflimg.has_data():
self.log_err (f"FaceSamplesLoader: {filename} is not a dfl image file.")
data = None
else:
data = (dflimg.get_face_type(),
dflimg.get_shape(),
dflimg.get_landmarks(),
dflimg.get_seg_ie_polys(),
dflimg.get_xseg_mask_compressed(),
dflimg.get_eyebrows_expand_mod(),
dflimg.get_source_filename() )
return idx, data
#override
def get_data_name (self, data):
#return string identificator of your data
return data[1]
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