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all models: removed options 'src_scale_mod', and 'sort samples by yaw as target'
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.
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Colombo
parent
8866dce22e
commit
50f892d57d
26 changed files with 577 additions and 433 deletions
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@ -1,204 +0,0 @@
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import operator
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import pickle
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import traceback
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from enum import IntEnum
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from pathlib import Path
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import cv2
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import numpy as np
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from facelib import FaceType, LandmarksProcessor
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from interact import interact as io
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from utils import Path_utils
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from utils.DFLJPG import DFLJPG
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from utils.DFLPNG import DFLPNG
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from .Sample import Sample, SampleType
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class SampleLoader:
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cache = dict()
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@staticmethod
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def get_person_id_max_count(samples_path):
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return len ( Path_utils.get_all_dir_names(samples_path) )
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@staticmethod
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def load(sample_type, samples_path, target_samples_path=None, person_id_mode=False, use_caching=False):
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cache = SampleLoader.cache
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if str(samples_path) not in cache.keys():
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cache[str(samples_path)] = [None]*SampleType.QTY
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datas = cache[str(samples_path)]
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if sample_type == SampleType.IMAGE:
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if datas[sample_type] is None:
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datas[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( Path_utils.get_image_paths(samples_path), "Loading") ]
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elif sample_type == SampleType.FACE:
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if datas[sample_type] is None:
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if not use_caching:
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datas[sample_type] = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
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else:
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samples_dat = samples_path / 'samples.dat'
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if samples_dat.exists():
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io.log_info (f"Using saved samples info from '{samples_dat}' ")
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all_samples = pickle.loads(samples_dat.read_bytes())
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if person_id_mode:
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for samples in all_samples:
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for sample in samples:
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sample.filename = str( samples_path / Path(sample.filename) )
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else:
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for sample in all_samples:
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sample.filename = str( samples_path / Path(sample.filename) )
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datas[sample_type] = all_samples
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else:
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if person_id_mode:
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dir_names = Path_utils.get_all_dir_names(samples_path)
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all_samples = []
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for i, dir_name in io.progress_bar_generator( [*enumerate(dir_names)] , "Loading"):
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all_samples += [ SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename, person_id=i) for filename in Path_utils.get_image_paths( samples_path / dir_name ) ], silent=True ) ]
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datas[sample_type] = all_samples
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else:
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datas[sample_type] = all_samples = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
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if person_id_mode:
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for samples in all_samples:
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for sample in samples:
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sample.filename = str(Path(sample.filename).relative_to(samples_path))
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else:
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for sample in all_samples:
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sample.filename = str(Path(sample.filename).relative_to(samples_path))
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samples_dat.write_bytes (pickle.dumps(all_samples))
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if person_id_mode:
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for samples in all_samples:
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for sample in samples:
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sample.filename = str( samples_path / Path(sample.filename) )
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else:
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for sample in all_samples:
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sample.filename = str( samples_path / Path(sample.filename) )
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elif sample_type == SampleType.FACE_TEMPORAL_SORTED:
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if datas[sample_type] is None:
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datas[sample_type] = SampleLoader.upgradeToFaceTemporalSortedSamples( SampleLoader.load(SampleType.FACE, samples_path) )
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elif sample_type == SampleType.FACE_YAW_SORTED:
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if datas[sample_type] is None:
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datas[sample_type] = SampleLoader.upgradeToFaceYawSortedSamples( SampleLoader.load(SampleType.FACE, samples_path) )
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elif sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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if datas[sample_type] is None:
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if target_samples_path is None:
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raise Exception('target_samples_path is None for FACE_YAW_SORTED_AS_TARGET')
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datas[sample_type] = SampleLoader.upgradeToFaceYawSortedAsTargetSamples( SampleLoader.load(SampleType.FACE_YAW_SORTED, samples_path), SampleLoader.load(SampleType.FACE_YAW_SORTED, target_samples_path) )
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return datas[sample_type]
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@staticmethod
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def upgradeToFaceSamples ( samples, silent=False ):
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sample_list = []
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for s in (samples if silent else io.progress_bar_generator(samples, "Loading")):
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s_filename_path = Path(s.filename)
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try:
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if s_filename_path.suffix == '.png':
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dflimg = DFLPNG.load ( str(s_filename_path) )
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elif s_filename_path.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(s_filename_path) )
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else:
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dflimg = None
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if dflimg is None:
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print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
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continue
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landmarks = dflimg.get_landmarks()
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pitch_yaw_roll = dflimg.get_pitch_yaw_roll()
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eyebrows_expand_mod = dflimg.get_eyebrows_expand_mod()
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if pitch_yaw_roll is None:
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pitch_yaw_roll = LandmarksProcessor.estimate_pitch_yaw_roll(landmarks)
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sample_list.append( s.copy_and_set(sample_type=SampleType.FACE,
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face_type=FaceType.fromString (dflimg.get_face_type()),
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shape=dflimg.get_shape(),
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landmarks=landmarks,
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ie_polys=dflimg.get_ie_polys(),
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pitch_yaw_roll=pitch_yaw_roll,
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eyebrows_expand_mod=eyebrows_expand_mod,
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source_filename=dflimg.get_source_filename(),
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fanseg_mask_exist=dflimg.get_fanseg_mask() is not None, ) )
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except:
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print ("Unable to load %s , error: %s" % (str(s_filename_path), traceback.format_exc() ) )
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return sample_list
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@staticmethod
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def upgradeToFaceTemporalSortedSamples( samples ):
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new_s = [ (s, s.source_filename) for s in samples]
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new_s = sorted(new_s, key=operator.itemgetter(1))
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return [ s[0] for s in new_s]
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@staticmethod
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def upgradeToFaceYawSortedSamples( samples ):
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lowest_yaw, highest_yaw = -1.0, 1.0
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gradations = 64
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diff_rot_per_grad = abs(highest_yaw-lowest_yaw) / gradations
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yaws_sample_list = [None]*gradations
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for i in io.progress_bar_generator(range(gradations), "Sorting"):
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yaw = lowest_yaw + i*diff_rot_per_grad
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next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad
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yaw_samples = []
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for s in samples:
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s_yaw = s.pitch_yaw_roll[1]
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if (i == 0 and s_yaw < next_yaw) or \
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(i < gradations-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
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(i == gradations-1 and s_yaw >= yaw):
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yaw_samples.append ( s.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED) )
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if len(yaw_samples) > 0:
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yaws_sample_list[i] = yaw_samples
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return yaws_sample_list
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@staticmethod
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def upgradeToFaceYawSortedAsTargetSamples (s, t):
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l = len(s)
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if l != len(t):
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raise Exception('upgradeToFaceYawSortedAsTargetSamples() s_len != t_len')
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b = l // 2
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s_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in s] ) == 1 )[:,0]
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t_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in t] ) == 1 )[:,0]
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new_s = [None]*l
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for t_idx in t_idxs:
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search_idxs = []
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for i in range(0,l):
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search_idxs += [t_idx - i, (l-t_idx-1) - i, t_idx + i, (l-t_idx-1) + i]
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for search_idx in search_idxs:
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if search_idx in s_idxs:
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mirrored = ( t_idx != search_idx and ((t_idx < b and search_idx >= b) or (search_idx < b and t_idx >= b)) )
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new_s[t_idx] = [ sample.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED_AS_TARGET,
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mirror=True,
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pitch_yaw_roll=(sample.pitch_yaw_roll[0],-sample.pitch_yaw_roll[1],sample.pitch_yaw_roll[2]),
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landmarks=LandmarksProcessor.mirror_landmarks (sample.landmarks, sample.shape[1] ))
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for sample in s[search_idx]
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] if mirrored else s[search_idx]
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break
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return new_s
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