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https://github.com/iperov/DeepFaceLab.git
synced 2025-08-20 21:43:21 -07:00
update == 04.20.2019 == (#242)
* superb improved fanseg * _ * _ * added FANseg extractor for src and dst faces to use it in training * - * _ * _ * update to 'partial' func * _ * trained FANSeg_256_full_face.h5, new experimental models: AVATAR, RecycleGAN * _ * _ * _ * fix for TCC mode cards(tesla), was conflict with plaidML initialization. * _ * update manuals * _
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iperov
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32 changed files with 1152 additions and 329 deletions
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@ -1,7 +1,13 @@
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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 utils.cv2_utils import *
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from utils.DFLJPG import DFLJPG
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from utils.DFLPNG import DFLPNG
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class SampleType(IntEnum):
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IMAGE = 0 #raw image
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@ -10,13 +16,12 @@ class SampleType(IntEnum):
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FACE = 1 #aligned face unsorted
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FACE_YAW_SORTED = 2 #sorted by yaw
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FACE_YAW_SORTED_AS_TARGET = 3 #sorted by yaw and included only yaws which exist in TARGET also automatic mirrored
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FACE_WITH_CLOSE_TO_SELF = 4
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FACE_END = 4
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FACE_END = 3
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QTY = 5
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QTY = 4
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class Sample(object):
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def __init__(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch=None, yaw=None, mirror=None, close_target_list=None):
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def __init__(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch=None, yaw=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask_exist=False):
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self.sample_type = sample_type if sample_type is not None else SampleType.IMAGE
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self.filename = filename
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self.face_type = face_type
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@ -25,10 +30,12 @@ class Sample(object):
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self.ie_polys = ie_polys
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self.pitch = pitch
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self.yaw = yaw
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self.source_filename = source_filename
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self.mirror = mirror
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self.close_target_list = close_target_list
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self.fanseg_mask_exist = fanseg_mask_exist
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def copy_and_set(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch=None, yaw=None, mirror=None, close_target_list=None):
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def copy_and_set(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch=None, yaw=None, source_filename=None, mirror=None, close_target_list=None, fanseg_mask=None, fanseg_mask_exist=None):
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return Sample(
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sample_type=sample_type if sample_type is not None else self.sample_type,
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filename=filename if filename is not None else self.filename,
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@ -38,8 +45,10 @@ class Sample(object):
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ie_polys=ie_polys if ie_polys is not None else self.ie_polys,
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pitch=pitch if pitch is not None else self.pitch,
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yaw=yaw if yaw is not None else self.yaw,
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source_filename=source_filename if source_filename is not None else self.source_filename,
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mirror=mirror if mirror is not None else self.mirror,
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close_target_list=close_target_list if close_target_list is not None else self.close_target_list)
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close_target_list=close_target_list if close_target_list is not None else self.close_target_list,
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fanseg_mask_exist=fanseg_mask_exist if fanseg_mask_exist is not None else self.fanseg_mask_exist)
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def load_bgr(self):
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img = cv2_imread (self.filename).astype(np.float32) / 255.0
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@ -47,6 +56,19 @@ class Sample(object):
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img = img[:,::-1].copy()
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return img
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def load_fanseg_mask(self):
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if self.fanseg_mask_exist:
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filepath = Path(self.filename)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load ( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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return dflimg.get_fanseg_mask()
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return None
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def get_random_close_target_sample(self):
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if self.close_target_list is None:
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return None
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@ -15,7 +15,7 @@ output_sample_types = [
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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, batch_size, sort_by_yaw=False, sort_by_yaw_target_samples_path=None, with_close_to_self=False, sample_process_options=SampleProcessor.Options(), output_sample_types=[], add_sample_idx=False, add_pitch=False, add_yaw=False, generators_count=2, generators_random_seed=None, **kwargs):
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def __init__ (self, samples_path, debug, batch_size, sort_by_yaw=False, sort_by_yaw_target_samples_path=None, sample_process_options=SampleProcessor.Options(), output_sample_types=[], add_sample_idx=False, add_pitch=False, add_yaw=False, generators_count=2, generators_random_seed=None, **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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@ -27,8 +27,6 @@ class SampleGeneratorFace(SampleGeneratorBase):
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self.sample_type = SampleType.FACE_YAW_SORTED_AS_TARGET
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elif sort_by_yaw:
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self.sample_type = SampleType.FACE_YAW_SORTED
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elif with_close_to_self:
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self.sample_type = SampleType.FACE_WITH_CLOSE_TO_SELF
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else:
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self.sample_type = SampleType.FACE
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@ -82,7 +80,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
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if all ( [ samples[idx] == None for idx in samples_idxs] ):
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raise ValueError('Not enough training data. Gather more faces!')
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if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
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if self.sample_type == SampleType.FACE:
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shuffle_idxs = []
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elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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shuffle_idxs = []
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@ -102,12 +100,12 @@ class SampleGeneratorFace(SampleGeneratorBase):
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if len(repeat_samples_idxs) > 0:
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idx = repeat_samples_idxs.pop()
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if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
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if self.sample_type == SampleType.FACE:
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sample = samples[idx]
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elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
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sample = samples[(idx >> 16) & 0xFFFF][idx & 0xFFFF]
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else:
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if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
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if self.sample_type == SampleType.FACE:
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if len(shuffle_idxs) == 0:
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shuffle_idxs = samples_idxs.copy()
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np.random.shuffle(shuffle_idxs)
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@ -1,19 +1,19 @@
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import operator
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import traceback
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from enum import IntEnum
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import cv2
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import numpy as np
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from pathlib import Path
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from utils import Path_utils
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from utils.DFLPNG import DFLPNG
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from utils.DFLJPG import DFLJPG
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import cv2
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import numpy as np
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from .Sample import Sample
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from .Sample import SampleType
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from facelib import FaceType
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from facelib import LandmarksProcessor
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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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@ -35,6 +35,10 @@ class SampleLoader:
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if datas[sample_type] is None:
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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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# 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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@ -44,10 +48,6 @@ class SampleLoader:
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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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elif sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
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if datas[sample_type] is None:
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datas[sample_type] = SampleLoader.upgradeToFaceCloseToSelfSamples( SampleLoader.load(SampleType.FACE, samples_path) )
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return datas[sample_type]
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@ -77,44 +77,20 @@ class SampleLoader:
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landmarks=dflimg.get_landmarks(),
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ie_polys=dflimg.get_ie_polys(),
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pitch=pitch,
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yaw=yaw) )
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yaw=yaw,
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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 upgradeToFaceCloseToSelfSamples (samples):
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yaw_samples = SampleLoader.upgradeToFaceYawSortedSamples(samples)
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yaw_samples_len = len(yaw_samples)
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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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sample_list = []
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for i in io.progress_bar_generator( range(yaw_samples_len), "Sorting"):
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if yaw_samples[i] is not None:
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for s in yaw_samples[i]:
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s_t = []
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for n in range(2000):
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yaw_idx = np.clip ( i-10 +np.random.randint(20), 0, yaw_samples_len-1 )
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if yaw_samples[yaw_idx] is None:
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continue
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yaw_idx_samples_len = len(yaw_samples[yaw_idx])
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yaw_idx_sample = yaw_samples[yaw_idx][ np.random.randint(yaw_idx_samples_len) ]
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if s.filename == yaw_idx_sample.filename:
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continue
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s_t.append ( yaw_idx_sample )
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if len(s_t) >= 50:
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break
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if len(s_t) == 0:
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s_t = [s]
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sample_list.append( s.copy_and_set(close_target_list = s_t) )
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return sample_list
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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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@ -13,18 +13,18 @@ class SampleProcessor(object):
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WARPED_TRANSFORMED = 0x00000004,
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TRANSFORMED = 0x00000008,
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LANDMARKS_ARRAY = 0x00000010, #currently unused
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RANDOM_CLOSE = 0x00000020, #currently unused
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MORPH_TO_RANDOM_CLOSE = 0x00000040, #currently unused
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FACE_TYPE_HALF = 0x00000100,
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FACE_TYPE_FULL = 0x00000200,
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FACE_TYPE_HEAD = 0x00000400, #currently unused
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FACE_TYPE_AVATAR = 0x00000800, #currently unused
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FACE_MASK_FULL = 0x00001000,
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FACE_MASK_EYES = 0x00002000, #currently unused
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MODE_BGR = 0x00010000, #BGR
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MODE_G = 0x00020000, #Grayscale
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MODE_GGG = 0x00040000, #3xGrayscale
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@ -35,7 +35,7 @@ class SampleProcessor(object):
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class Options(object):
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#motion_blur = [chance_int, range] - chance 0..100 to apply to face (not mask), and range [1..3] where 3 is highest power of motion blur
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def __init__(self, random_flip = True, normalize_tanh = False, rotation_range=[-10,10], scale_range=[-0.05, 0.05], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], motion_blur=None ):
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self.random_flip = random_flip
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self.normalize_tanh = normalize_tanh
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@ -49,11 +49,11 @@ class SampleProcessor(object):
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chance = np.clip(chance, 0, 100)
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range = [3,5,7,9][ : np.clip(range, 0, 3)+1 ]
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self.motion_blur = (chance, range)
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@staticmethod
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def process (sample, sample_process_options, output_sample_types, debug):
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SPTF = SampleProcessor.TypeFlags
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sample_bgr = sample.load_bgr()
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h,w,c = sample_bgr.shape
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@ -113,7 +113,7 @@ class SampleProcessor(object):
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target_face_type = FaceType.HEAD
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elif f & SPTF.FACE_TYPE_AVATAR != 0:
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target_face_type = FaceType.AVATAR
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apply_motion_blur = f & SPTF.OPT_APPLY_MOTION_BLUR != 0
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if img_type == 4:
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@ -170,9 +170,16 @@ class SampleProcessor(object):
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if np.random.randint(100) < chance :
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dim = mb_range[ np.random.randint(len(mb_range) ) ]
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img = imagelib.LinearMotionBlur (img, dim, np.random.randint(180) )
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if face_mask_type == 1:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks, cur_sample.ie_polys)
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mask = cur_sample.load_fanseg_mask() #using fanseg_mask if exist
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if mask is None:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, cur_sample.landmarks)
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if cur_sample.ie_polys is not None:
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cur_sample.ie_polys.overlay_mask(mask)
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img = np.concatenate( (img, mask ), -1 )
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elif face_mask_type == 2:
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mask = LandmarksProcessor.get_image_eye_mask (img.shape, cur_sample.landmarks)
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@ -202,7 +209,7 @@ class SampleProcessor(object):
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img_mask = img[...,3:4]
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if f & SPTF.MODE_BGR != 0:
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img = img
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img = img_bgr
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elif f & SPTF.MODE_BGR_SHUFFLE != 0:
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img_bgr = np.take (img_bgr, np.random.permutation(img_bgr.shape[-1]), axis=-1)
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img = np.concatenate ( (img_bgr,img_mask) , -1 )
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