mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 13:32:09 -07:00
added XSeg model.
with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
This commit is contained in:
Colombo
parent
2be940092b
commit
45582d129d
27 changed files with 577 additions and 711 deletions
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@ -178,6 +178,7 @@ class DFLJPG(object):
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def embed_data(filename, face_type=None,
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landmarks=None,
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ie_polys=None,
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seg_ie_polys=None,
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source_filename=None,
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source_rect=None,
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source_landmarks=None,
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@ -203,9 +204,14 @@ class DFLJPG(object):
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if not isinstance(ie_polys, list):
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ie_polys = ie_polys.dump()
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if seg_ie_polys is not None:
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if not isinstance(seg_ie_polys, list):
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seg_ie_polys = seg_ie_polys.dump()
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DFLJPG.embed_dfldict (filename, {'face_type': face_type,
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'landmarks': landmarks,
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'ie_polys' : ie_polys,
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'seg_ie_polys' : seg_ie_polys,
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'source_filename': source_filename,
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'source_rect': source_rect,
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'source_landmarks': source_landmarks,
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@ -218,6 +224,7 @@ class DFLJPG(object):
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def embed_and_set(self, filename, face_type=None,
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landmarks=None,
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ie_polys=None,
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seg_ie_polys=None,
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source_filename=None,
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source_rect=None,
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source_landmarks=None,
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@ -230,6 +237,7 @@ class DFLJPG(object):
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if face_type is None: face_type = self.get_face_type()
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if landmarks is None: landmarks = self.get_landmarks()
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if ie_polys is None: ie_polys = self.get_ie_polys()
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if seg_ie_polys is None: seg_ie_polys = self.get_seg_ie_polys()
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if source_filename is None: source_filename = self.get_source_filename()
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if source_rect is None: source_rect = self.get_source_rect()
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if source_landmarks is None: source_landmarks = self.get_source_landmarks()
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@ -240,6 +248,7 @@ class DFLJPG(object):
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DFLJPG.embed_data (filename, face_type=face_type,
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landmarks=landmarks,
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ie_polys=ie_polys,
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seg_ie_polys=seg_ie_polys,
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source_filename=source_filename,
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source_rect=source_rect,
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source_landmarks=source_landmarks,
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@ -251,6 +260,9 @@ class DFLJPG(object):
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def remove_ie_polys(self):
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self.dfl_dict['ie_polys'] = None
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def remove_seg_ie_polys(self):
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self.dfl_dict['seg_ie_polys'] = None
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def remove_fanseg_mask(self):
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self.dfl_dict['fanseg_mask'] = None
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@ -308,6 +320,7 @@ class DFLJPG(object):
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def get_face_type(self): return self.dfl_dict['face_type']
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def get_landmarks(self): return np.array ( self.dfl_dict['landmarks'] )
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def get_ie_polys(self): return self.dfl_dict.get('ie_polys',None)
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def get_seg_ie_polys(self): return self.dfl_dict.get('seg_ie_polys',None)
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def get_source_filename(self): return self.dfl_dict['source_filename']
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def get_source_rect(self): return self.dfl_dict['source_rect']
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def get_source_landmarks(self): return np.array ( self.dfl_dict['source_landmarks'] )
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@ -89,6 +89,9 @@ class IEPolys:
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if poly.n > 0:
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cv2.fillPoly(mask, [poly.points_to_n()], white if poly.type == 1 else black )
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def get_total_points(self):
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return sum([self.list[n].n for n in range(self.n)])
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def dump(self):
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result = []
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for n in range(self.n):
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@ -19,4 +19,8 @@ from .IEPolys import IEPolys
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from .blursharpen import LinearMotionBlur, blursharpen
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from .filters import apply_random_hsv_shift, apply_random_motion_blur, apply_random_gaussian_blur, apply_random_bilinear_resize
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from .filters import apply_random_rgb_levels, \
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apply_random_hsv_shift, \
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apply_random_motion_blur, \
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apply_random_gaussian_blur, \
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apply_random_bilinear_resize
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@ -2,6 +2,27 @@ import numpy as np
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from .blursharpen import LinearMotionBlur
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import cv2
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def apply_random_rgb_levels(img, mask=None, rnd_state=None):
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if rnd_state is None:
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rnd_state = np.random
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np_rnd = rnd_state.rand
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inBlack = np.array([np_rnd()*0.25 , np_rnd()*0.25 , np_rnd()*0.25], dtype=np.float32)
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inWhite = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
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inGamma = np.array([0.5+np_rnd(), 0.5+np_rnd(), 0.5+np_rnd()], dtype=np.float32)
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outBlack = np.array([np_rnd()*0.25 , np_rnd()*0.25 , np_rnd()*0.25], dtype=np.float32)
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outWhite = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
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result = np.clip( (img - inBlack) / (inWhite - inBlack), 0, 1 )
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result = ( result ** (1/inGamma) ) * (outWhite - outBlack) + outBlack
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result = np.clip(result, 0, 1)
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if mask is not None:
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result = img*(1-mask) + result*mask
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return result
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def apply_random_hsv_shift(img, mask=None, rnd_state=None):
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if rnd_state is None:
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rnd_state = np.random
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@ -22,7 +22,11 @@ def circle_faded( hw, center, fade_dists ):
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pts_dists = np.abs ( npla.norm(pts-center, axis=-1) )
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if fade_dists[1] == 0:
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fade_dists[1] = 1
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pts_dists = ( pts_dists - fade_dists[0] ) / fade_dists[1]
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pts_dists = np.clip( 1-pts_dists, 0, 1)
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return pts_dists.reshape ( (h,w,1) ).astype(np.float32)
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@ -1,151 +0,0 @@
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from core.leras import nn
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tf = nn.tf
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class DFLSegnetArchi(nn.ArchiBase):
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def __init__(self):
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super().__init__()
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class ConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2D (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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class UpConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2DTranspose (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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class Encoder(nn.ModelBase):
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def on_build(self, in_ch, base_ch):
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self.conv01 = ConvBlock(in_ch, base_ch)
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self.conv02 = ConvBlock(base_ch, base_ch)
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self.bp0 = nn.BlurPool (filt_size=3)
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self.conv11 = ConvBlock(base_ch, base_ch*2)
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self.conv12 = ConvBlock(base_ch*2, base_ch*2)
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self.bp1 = nn.BlurPool (filt_size=3)
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self.conv21 = ConvBlock(base_ch*2, base_ch*4)
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self.conv22 = ConvBlock(base_ch*4, base_ch*4)
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self.conv23 = ConvBlock(base_ch*4, base_ch*4)
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self.bp2 = nn.BlurPool (filt_size=3)
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self.conv31 = ConvBlock(base_ch*4, base_ch*8)
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self.conv32 = ConvBlock(base_ch*8, base_ch*8)
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self.conv33 = ConvBlock(base_ch*8, base_ch*8)
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self.bp3 = nn.BlurPool (filt_size=3)
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self.conv41 = ConvBlock(base_ch*8, base_ch*8)
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self.conv42 = ConvBlock(base_ch*8, base_ch*8)
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self.conv43 = ConvBlock(base_ch*8, base_ch*8)
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self.bp4 = nn.BlurPool (filt_size=3)
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self.conv_center = ConvBlock(base_ch*8, base_ch*8)
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def forward(self, inp):
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x = inp
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x = self.conv01(x)
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x = x0 = self.conv02(x)
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x = self.bp0(x)
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x = self.conv11(x)
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x = x1 = self.conv12(x)
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x = self.bp1(x)
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x = self.conv21(x)
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x = self.conv22(x)
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x = x2 = self.conv23(x)
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x = self.bp2(x)
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x = self.conv31(x)
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x = self.conv32(x)
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x = x3 = self.conv33(x)
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x = self.bp3(x)
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x = self.conv41(x)
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x = self.conv42(x)
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x = x4 = self.conv43(x)
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x = self.bp4(x)
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x = self.conv_center(x)
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return x0,x1,x2,x3,x4, x
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class Decoder(nn.ModelBase):
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def on_build(self, base_ch, out_ch):
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self.up4 = UpConvBlock (base_ch*8, base_ch*4)
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self.conv43 = ConvBlock(base_ch*12, base_ch*8)
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self.conv42 = ConvBlock(base_ch*8, base_ch*8)
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self.conv41 = ConvBlock(base_ch*8, base_ch*8)
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self.up3 = UpConvBlock (base_ch*8, base_ch*4)
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self.conv33 = ConvBlock(base_ch*12, base_ch*8)
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self.conv32 = ConvBlock(base_ch*8, base_ch*8)
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self.conv31 = ConvBlock(base_ch*8, base_ch*8)
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self.up2 = UpConvBlock (base_ch*8, base_ch*4)
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self.conv23 = ConvBlock(base_ch*8, base_ch*4)
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self.conv22 = ConvBlock(base_ch*4, base_ch*4)
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self.conv21 = ConvBlock(base_ch*4, base_ch*4)
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self.up1 = UpConvBlock (base_ch*4, base_ch*2)
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self.conv12 = ConvBlock(base_ch*4, base_ch*2)
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self.conv11 = ConvBlock(base_ch*2, base_ch*2)
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self.up0 = UpConvBlock (base_ch*2, base_ch)
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self.conv02 = ConvBlock(base_ch*2, base_ch)
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self.conv01 = ConvBlock(base_ch, base_ch)
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self.out_conv = nn.Conv2D (base_ch, out_ch, kernel_size=3, padding='SAME')
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def forward(self, inp):
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x0,x1,x2,x3,x4,x = inp
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x = self.up4(x)
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x = self.conv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
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x = self.conv42(x)
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x = self.conv41(x)
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x = self.up3(x)
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x = self.conv33(tf.concat([x,x3],axis=nn.conv2d_ch_axis))
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x = self.conv32(x)
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x = self.conv31(x)
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x = self.up2(x)
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x = self.conv23(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
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x = self.conv22(x)
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x = self.conv21(x)
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x = self.up1(x)
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x = self.conv12(tf.concat([x,x1],axis=nn.conv2d_ch_axis))
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x = self.conv11(x)
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x = self.up0(x)
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x = self.conv02(tf.concat([x,x0],axis=nn.conv2d_ch_axis))
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x = self.conv01(x)
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logits = self.out_conv(x)
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return logits, tf.nn.sigmoid(logits)
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self.Encoder = Encoder
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self.Decoder = Decoder
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nn.DFLSegnetArchi = DFLSegnetArchi
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@ -1,3 +1,2 @@
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from .ArchiBase import *
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from .DeepFakeArchi import *
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from .DFLSegnet import *
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137
core/leras/models/XSeg.py
Normal file
137
core/leras/models/XSeg.py
Normal file
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@ -0,0 +1,137 @@
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from core.leras import nn
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tf = nn.tf
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class XSeg(nn.ModelBase):
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def on_build (self, in_ch, base_ch, out_ch):
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class ConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2D (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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class UpConvBlock(nn.ModelBase):
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def on_build(self, in_ch, out_ch):
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self.conv = nn.Conv2DTranspose (in_ch, out_ch, kernel_size=3, padding='SAME')
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self.frn = nn.FRNorm2D(out_ch)
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self.tlu = nn.TLU(out_ch)
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def forward(self, x):
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x = self.conv(x)
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x = self.frn(x)
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x = self.tlu(x)
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return x
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self.conv01 = ConvBlock(in_ch, base_ch)
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self.conv02 = ConvBlock(base_ch, base_ch)
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self.bp0 = nn.BlurPool (filt_size=3)
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self.conv11 = ConvBlock(base_ch, base_ch*2)
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self.conv12 = ConvBlock(base_ch*2, base_ch*2)
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self.bp1 = nn.BlurPool (filt_size=3)
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self.conv21 = ConvBlock(base_ch*2, base_ch*4)
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self.conv22 = ConvBlock(base_ch*4, base_ch*4)
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self.conv23 = ConvBlock(base_ch*4, base_ch*4)
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self.bp2 = nn.BlurPool (filt_size=3)
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self.conv31 = ConvBlock(base_ch*4, base_ch*8)
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self.conv32 = ConvBlock(base_ch*8, base_ch*8)
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self.conv33 = ConvBlock(base_ch*8, base_ch*8)
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self.bp3 = nn.BlurPool (filt_size=3)
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self.conv41 = ConvBlock(base_ch*8, base_ch*8)
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self.conv42 = ConvBlock(base_ch*8, base_ch*8)
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self.conv43 = ConvBlock(base_ch*8, base_ch*8)
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self.bp4 = nn.BlurPool (filt_size=3)
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self.up4 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv43 = ConvBlock(base_ch*12, base_ch*8)
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self.uconv42 = ConvBlock(base_ch*8, base_ch*8)
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self.uconv41 = ConvBlock(base_ch*8, base_ch*8)
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self.up3 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv33 = ConvBlock(base_ch*12, base_ch*8)
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self.uconv32 = ConvBlock(base_ch*8, base_ch*8)
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self.uconv31 = ConvBlock(base_ch*8, base_ch*8)
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self.up2 = UpConvBlock (base_ch*8, base_ch*4)
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self.uconv23 = ConvBlock(base_ch*8, base_ch*4)
|
||||
self.uconv22 = ConvBlock(base_ch*4, base_ch*4)
|
||||
self.uconv21 = ConvBlock(base_ch*4, base_ch*4)
|
||||
|
||||
self.up1 = UpConvBlock (base_ch*4, base_ch*2)
|
||||
self.uconv12 = ConvBlock(base_ch*4, base_ch*2)
|
||||
self.uconv11 = ConvBlock(base_ch*2, base_ch*2)
|
||||
|
||||
self.up0 = UpConvBlock (base_ch*2, base_ch)
|
||||
self.uconv02 = ConvBlock(base_ch*2, base_ch)
|
||||
self.uconv01 = ConvBlock(base_ch, base_ch)
|
||||
self.out_conv = nn.Conv2D (base_ch, out_ch, kernel_size=3, padding='SAME')
|
||||
|
||||
self.conv_center = ConvBlock(base_ch*8, base_ch*8)
|
||||
|
||||
def forward(self, inp):
|
||||
x = inp
|
||||
|
||||
x = self.conv01(x)
|
||||
x = x0 = self.conv02(x)
|
||||
x = self.bp0(x)
|
||||
|
||||
x = self.conv11(x)
|
||||
x = x1 = self.conv12(x)
|
||||
x = self.bp1(x)
|
||||
|
||||
x = self.conv21(x)
|
||||
x = self.conv22(x)
|
||||
x = x2 = self.conv23(x)
|
||||
x = self.bp2(x)
|
||||
|
||||
x = self.conv31(x)
|
||||
x = self.conv32(x)
|
||||
x = x3 = self.conv33(x)
|
||||
x = self.bp3(x)
|
||||
|
||||
x = self.conv41(x)
|
||||
x = self.conv42(x)
|
||||
x = x4 = self.conv43(x)
|
||||
x = self.bp4(x)
|
||||
|
||||
x = self.conv_center(x)
|
||||
|
||||
x = self.up4(x)
|
||||
x = self.uconv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
|
||||
x = self.uconv42(x)
|
||||
x = self.uconv41(x)
|
||||
|
||||
x = self.up3(x)
|
||||
x = self.uconv33(tf.concat([x,x3],axis=nn.conv2d_ch_axis))
|
||||
x = self.uconv32(x)
|
||||
x = self.uconv31(x)
|
||||
|
||||
x = self.up2(x)
|
||||
x = self.uconv23(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
|
||||
x = self.uconv22(x)
|
||||
x = self.uconv21(x)
|
||||
|
||||
x = self.up1(x)
|
||||
x = self.uconv12(tf.concat([x,x1],axis=nn.conv2d_ch_axis))
|
||||
x = self.uconv11(x)
|
||||
|
||||
x = self.up0(x)
|
||||
x = self.uconv02(tf.concat([x,x0],axis=nn.conv2d_ch_axis))
|
||||
x = self.uconv01(x)
|
||||
|
||||
logits = self.out_conv(x)
|
||||
return logits, tf.nn.sigmoid(logits)
|
||||
|
||||
nn.XSeg = XSeg
|
||||
|
|
@ -2,3 +2,4 @@ from .ModelBase import *
|
|||
from .PatchDiscriminator import *
|
||||
from .CodeDiscriminator import *
|
||||
from .Ternaus import *
|
||||
from .XSeg import *
|
||||
|
|
@ -10,7 +10,7 @@ from core.interact import interact as io
|
|||
from core.leras import nn
|
||||
|
||||
|
||||
class DFLSegNet(object):
|
||||
class XSegNet(object):
|
||||
VERSION = 1
|
||||
|
||||
def __init__ (self, name,
|
||||
|
|
@ -34,28 +34,24 @@ class DFLSegNet(object):
|
|||
self.target_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,1) )
|
||||
|
||||
# Initializing model classes
|
||||
archi = nn.DFLSegnetArchi()
|
||||
with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
|
||||
self.enc = archi.Encoder(3, 64, name='Encoder')
|
||||
self.dec = archi.Decoder(64, 1, name='Decoder')
|
||||
self.enc_dec_weights = self.enc.get_weights()+self.dec.get_weights()
|
||||
self.model = nn.XSeg(3, 32, 1, name=name)
|
||||
self.model_weights = self.model.get_weights()
|
||||
|
||||
model_name = f'{name}_{resolution}'
|
||||
|
||||
self.model_filename_list = [ [self.enc, f'{model_name}_enc.npy'],
|
||||
[self.dec, f'{model_name}_dec.npy'],
|
||||
]
|
||||
self.model_filename_list = [ [self.model, f'{model_name}.npy'] ]
|
||||
|
||||
if training:
|
||||
if optimizer is None:
|
||||
raise ValueError("Optimizer should be provided for training mode.")
|
||||
|
||||
self.opt = optimizer
|
||||
self.opt.initialize_variables (self.enc_dec_weights, vars_on_cpu=place_model_on_cpu)
|
||||
self.opt.initialize_variables (self.model_weights, vars_on_cpu=place_model_on_cpu)
|
||||
self.model_filename_list += [ [self.opt, f'{model_name}_opt.npy' ] ]
|
||||
else:
|
||||
with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
|
||||
_, pred = self.dec(self.enc(self.input_t))
|
||||
_, pred = self.model(self.input_t)
|
||||
|
||||
def net_run(input_np):
|
||||
return nn.tf_sess.run ( [pred], feed_dict={self.input_t :input_np})[0]
|
||||
|
|
@ -72,10 +68,10 @@ class DFLSegNet(object):
|
|||
model.init_weights()
|
||||
|
||||
def flow(self, x):
|
||||
return self.dec(self.enc(x))
|
||||
return self.model(x)
|
||||
|
||||
def get_weights(self):
|
||||
return self.enc_dec_weights
|
||||
return self.model_weights
|
||||
|
||||
def save_weights(self):
|
||||
for model, filename in io.progress_bar_generator(self.model_filename_list, "Saving", leave=False):
|
||||
|
|
@ -3,4 +3,4 @@ from .S3FDExtractor import S3FDExtractor
|
|||
from .FANExtractor import FANExtractor
|
||||
from .FaceEnhancer import FaceEnhancer
|
||||
from .TernausNet import TernausNet
|
||||
from .DFLSegNet import DFLSegNet
|
||||
from .XSegNet import XSegNet
|
||||
121
main.py
121
main.py
|
|
@ -55,86 +55,6 @@ if __name__ == "__main__":
|
|||
|
||||
p.set_defaults (func=process_extract)
|
||||
|
||||
def process_dev_extract_vggface2_dataset(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.extract_vggface2_dataset( arguments.input_dir,
|
||||
device_args={'cpu_only' : arguments.cpu_only,
|
||||
'multi_gpu' : arguments.multi_gpu,
|
||||
}
|
||||
)
|
||||
|
||||
p = subparsers.add_parser( "dev_extract_vggface2_dataset", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
|
||||
p.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="Enables multi GPU.")
|
||||
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU.")
|
||||
p.set_defaults (func=process_dev_extract_vggface2_dataset)
|
||||
|
||||
def process_dev_extract_umd_csv(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.extract_umd_csv( arguments.input_csv_file,
|
||||
device_args={'cpu_only' : arguments.cpu_only,
|
||||
'multi_gpu' : arguments.multi_gpu,
|
||||
}
|
||||
)
|
||||
|
||||
p = subparsers.add_parser( "dev_extract_umd_csv", help="")
|
||||
p.add_argument('--input-csv-file', required=True, action=fixPathAction, dest="input_csv_file", help="input_csv_file")
|
||||
p.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="Enables multi GPU.")
|
||||
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU.")
|
||||
p.set_defaults (func=process_dev_extract_umd_csv)
|
||||
|
||||
|
||||
def process_dev_apply_celebamaskhq(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.apply_celebamaskhq( arguments.input_dir )
|
||||
|
||||
p = subparsers.add_parser( "dev_apply_celebamaskhq", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
p.set_defaults (func=process_dev_apply_celebamaskhq)
|
||||
|
||||
def process_dev_test(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.dev_test( arguments.input_dir )
|
||||
|
||||
p = subparsers.add_parser( "dev_test", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
p.set_defaults (func=process_dev_test)
|
||||
|
||||
def process_dev_segmented_extract(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.dev_segmented_extract(arguments.input_dir, arguments.output_dir)
|
||||
|
||||
p = subparsers.add_parser( "dev_segmented_extract", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir")
|
||||
|
||||
p.set_defaults (func=process_dev_segmented_extract)
|
||||
|
||||
def process_dev_segmented_trash(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.dev_segmented_trash(arguments.input_dir)
|
||||
|
||||
p = subparsers.add_parser( "dev_segmented_trash", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
|
||||
p.set_defaults (func=process_dev_segmented_trash)
|
||||
|
||||
def process_dev_resave_pngs(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.dev_resave_pngs(arguments.input_dir)
|
||||
|
||||
p = subparsers.add_parser( "dev_resave_pngs", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
|
||||
p.set_defaults (func=process_dev_resave_pngs)
|
||||
|
||||
def process_sort(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import Sorter
|
||||
|
|
@ -342,27 +262,36 @@ if __name__ == "__main__":
|
|||
|
||||
p.set_defaults(func=process_faceset_enhancer)
|
||||
|
||||
"""
|
||||
def process_relight_faceset(arguments):
|
||||
def process_dev_test(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import FacesetRelighter
|
||||
FacesetRelighter.relight (arguments.input_dir, arguments.lighten, arguments.random_one)
|
||||
from mainscripts import dev_misc
|
||||
dev_misc.dev_test( arguments.input_dir )
|
||||
|
||||
def process_delete_relighted(arguments):
|
||||
p = subparsers.add_parser( "dev_test", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
p.set_defaults (func=process_dev_test)
|
||||
|
||||
# ========== XSeg util
|
||||
xseg_parser = subparsers.add_parser( "xseg", help="XSeg utils.").add_subparsers()
|
||||
|
||||
def process_xseg_merge(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import FacesetRelighter
|
||||
FacesetRelighter.delete_relighted (arguments.input_dir)
|
||||
from mainscripts import XSegUtil
|
||||
XSegUtil.merge(arguments.input_dir)
|
||||
p = xseg_parser.add_parser( "merge", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
|
||||
p = facesettool_parser.add_parser ("relight", help="Synthesize new faces from existing ones by relighting them. With the relighted faces neural network will better reproduce face shadows.")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
|
||||
p.add_argument('--lighten', action="store_true", dest="lighten", default=None, help="Lighten the faces.")
|
||||
p.add_argument('--random-one', action="store_true", dest="random_one", default=None, help="Relight the faces only with one random direction, otherwise relight with all directions.")
|
||||
p.set_defaults(func=process_relight_faceset)
|
||||
p.set_defaults (func=process_xseg_merge)
|
||||
|
||||
p = facesettool_parser.add_parser ("delete_relighted", help="Delete relighted faces.")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
|
||||
p.set_defaults(func=process_delete_relighted)
|
||||
"""
|
||||
def process_xseg_split(arguments):
|
||||
osex.set_process_lowest_prio()
|
||||
from mainscripts import XSegUtil
|
||||
XSegUtil.split(arguments.input_dir)
|
||||
|
||||
p = xseg_parser.add_parser( "split", help="")
|
||||
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
|
||||
|
||||
p.set_defaults (func=process_xseg_split)
|
||||
|
||||
def bad_args(arguments):
|
||||
parser.print_help()
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from core.interact import interact as io
|
|||
from core.joblib import MPClassFuncOnDemand, MPFunc
|
||||
from core.leras import nn
|
||||
from DFLIMG import DFLIMG
|
||||
from facelib import FaceEnhancer, FaceType, LandmarksProcessor, TernausNet, DFLSegNet
|
||||
from facelib import FaceEnhancer, FaceType, LandmarksProcessor, TernausNet, XSegNet
|
||||
from merger import FrameInfo, MergerConfig, InteractiveMergerSubprocessor
|
||||
|
||||
def main (model_class_name=None,
|
||||
|
|
@ -61,9 +61,10 @@ def main (model_class_name=None,
|
|||
place_model_on_cpu=True,
|
||||
run_on_cpu=run_on_cpu)
|
||||
|
||||
skinseg_256_extract_func = MPClassFuncOnDemand(DFLSegNet, 'extract',
|
||||
name='SkinSeg',
|
||||
xseg_256_extract_func = MPClassFuncOnDemand(XSegNet, 'extract',
|
||||
name='XSeg',
|
||||
resolution=256,
|
||||
weights_file_root=saved_models_path,
|
||||
place_model_on_cpu=True,
|
||||
run_on_cpu=run_on_cpu)
|
||||
|
||||
|
|
@ -199,7 +200,7 @@ def main (model_class_name=None,
|
|||
predictor_input_shape = predictor_input_shape,
|
||||
face_enhancer_func = face_enhancer_func,
|
||||
fanseg_full_face_256_extract_func = fanseg_full_face_256_extract_func,
|
||||
skinseg_256_extract_func = skinseg_256_extract_func,
|
||||
xseg_256_extract_func = xseg_256_extract_func,
|
||||
merger_config = cfg,
|
||||
frames = frames,
|
||||
frames_root_path = input_path,
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
|
|||
self.predictor_input_shape = client_dict['predictor_input_shape']
|
||||
self.face_enhancer_func = client_dict['face_enhancer_func']
|
||||
self.fanseg_full_face_256_extract_func = client_dict['fanseg_full_face_256_extract_func']
|
||||
self.skinseg_256_extract_func = client_dict['skinseg_256_extract_func']
|
||||
self.xseg_256_extract_func = client_dict['xseg_256_extract_func']
|
||||
|
||||
|
||||
#transfer and set stdin in order to work code.interact in debug subprocess
|
||||
|
|
@ -104,7 +104,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
|
|||
final_img = MergeMasked (self.predictor_func, self.predictor_input_shape,
|
||||
face_enhancer_func=self.face_enhancer_func,
|
||||
fanseg_full_face_256_extract_func=self.fanseg_full_face_256_extract_func,
|
||||
skinseg_256_extract_func=self.skinseg_256_extract_func,
|
||||
xseg_256_extract_func=self.xseg_256_extract_func,
|
||||
cfg=cfg,
|
||||
frame_info=frame_info)
|
||||
except Exception as e:
|
||||
|
|
@ -137,7 +137,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
|
|||
|
||||
|
||||
#override
|
||||
def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, face_enhancer_func, fanseg_full_face_256_extract_func, skinseg_256_extract_func, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter):
|
||||
def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, face_enhancer_func, fanseg_full_face_256_extract_func, xseg_256_extract_func, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter):
|
||||
if len (frames) == 0:
|
||||
raise ValueError ("len (frames) == 0")
|
||||
|
||||
|
|
@ -152,7 +152,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
|
|||
|
||||
self.face_enhancer_func = face_enhancer_func
|
||||
self.fanseg_full_face_256_extract_func = fanseg_full_face_256_extract_func
|
||||
self.skinseg_256_extract_func = skinseg_256_extract_func
|
||||
self.xseg_256_extract_func = xseg_256_extract_func
|
||||
|
||||
self.frames_root_path = frames_root_path
|
||||
self.output_path = output_path
|
||||
|
|
@ -274,7 +274,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
|
|||
'predictor_input_shape' : self.predictor_input_shape,
|
||||
'face_enhancer_func': self.face_enhancer_func,
|
||||
'fanseg_full_face_256_extract_func' : self.fanseg_full_face_256_extract_func,
|
||||
'skinseg_256_extract_func' : self.skinseg_256_extract_func,
|
||||
'xseg_256_extract_func' : self.xseg_256_extract_func,
|
||||
'stdin_fd': sys.stdin.fileno() if MERGER_DEBUG else None
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@ from core.interact import interact as io
|
|||
from core.cv2ex import *
|
||||
|
||||
fanseg_input_size = 256
|
||||
skinseg_input_size = 256
|
||||
xseg_input_size = 256
|
||||
|
||||
def MergeMaskedFace (predictor_func, predictor_input_shape,
|
||||
face_enhancer_func,
|
||||
fanseg_full_face_256_extract_func,
|
||||
skinseg_256_extract_func,
|
||||
xseg_256_extract_func,
|
||||
cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
|
||||
img_size = img_bgr.shape[1], img_bgr.shape[0]
|
||||
img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
|
||||
|
|
@ -111,23 +111,23 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
|
|||
|
||||
elif cfg.mask_mode >= 8 and cfg.mask_mode <= 11:
|
||||
if cfg.mask_mode == 8 or cfg.mask_mode == 10 or cfg.mask_mode == 11:
|
||||
prd_face_skinseg_bgr = cv2.resize (prd_face_bgr, (skinseg_input_size,)*2 )
|
||||
prd_face_skinseg_mask = skinseg_256_extract_func(prd_face_skinseg_bgr)
|
||||
X_prd_face_mask_a_0 = cv2.resize ( prd_face_skinseg_mask, (output_size, output_size), cv2.INTER_CUBIC)
|
||||
prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, cv2.INTER_CUBIC)
|
||||
prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr)
|
||||
X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), cv2.INTER_CUBIC)
|
||||
|
||||
if cfg.mask_mode >= 9 and cfg.mask_mode <= 11:
|
||||
whole_face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, skinseg_input_size, face_type=FaceType.WHOLE_FACE)
|
||||
dst_face_skinseg_bgr = cv2.warpAffine(img_bgr, whole_face_mat, (skinseg_input_size,)*2, flags=cv2.INTER_CUBIC )
|
||||
dst_face_skinseg_mask = skinseg_256_extract_func(dst_face_skinseg_bgr)
|
||||
X_dst_face_mask_a_0 = cv2.resize (dst_face_skinseg_mask, (output_size,output_size), cv2.INTER_CUBIC)
|
||||
whole_face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=FaceType.WHOLE_FACE)
|
||||
dst_face_xseg_bgr = cv2.warpAffine(img_bgr, whole_face_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
|
||||
dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr)
|
||||
X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), cv2.INTER_CUBIC)
|
||||
|
||||
if cfg.mask_mode == 8: #'SkinSeg-prd',
|
||||
if cfg.mask_mode == 8: #'XSeg-prd',
|
||||
prd_face_mask_a_0 = X_prd_face_mask_a_0
|
||||
elif cfg.mask_mode == 9: #'SkinSeg-dst',
|
||||
elif cfg.mask_mode == 9: #'XSeg-dst',
|
||||
prd_face_mask_a_0 = X_dst_face_mask_a_0
|
||||
elif cfg.mask_mode == 10: #'SkinSeg-prd*SkinSeg-dst',
|
||||
elif cfg.mask_mode == 10: #'XSeg-prd*XSeg-dst',
|
||||
prd_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0
|
||||
elif cfg.mask_mode == 11: #learned*SkinSeg-prd*SkinSeg-dst'
|
||||
elif cfg.mask_mode == 11: #learned*XSeg-prd*XSeg-dst'
|
||||
prd_face_mask_a_0 = prd_face_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0
|
||||
|
||||
prd_face_mask_a_0[ prd_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
|
||||
|
|
@ -347,7 +347,7 @@ def MergeMasked (predictor_func,
|
|||
predictor_input_shape,
|
||||
face_enhancer_func,
|
||||
fanseg_full_face_256_extract_func,
|
||||
skinseg_256_extract_func,
|
||||
xseg_256_extract_func,
|
||||
cfg,
|
||||
frame_info):
|
||||
img_bgr_uint8 = cv2_imread(frame_info.filepath)
|
||||
|
|
@ -356,7 +356,7 @@ def MergeMasked (predictor_func,
|
|||
|
||||
outs = []
|
||||
for face_num, img_landmarks in enumerate( frame_info.landmarks_list ):
|
||||
out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, fanseg_full_face_256_extract_func, skinseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
|
||||
out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, fanseg_full_face_256_extract_func, xseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
|
||||
outs += [ (out_img, out_img_merging_mask) ]
|
||||
|
||||
#Combining multiple face outputs
|
||||
|
|
|
|||
|
|
@ -83,6 +83,7 @@ mode_str_dict = {}
|
|||
for key in mode_dict.keys():
|
||||
mode_str_dict[ mode_dict[key] ] = key
|
||||
|
||||
"""
|
||||
whole_face_mask_mode_dict = {1:'learned',
|
||||
2:'dst',
|
||||
3:'FAN-prd',
|
||||
|
|
@ -91,11 +92,14 @@ whole_face_mask_mode_dict = {1:'learned',
|
|||
6:'learned*FAN-prd*FAN-dst'
|
||||
}
|
||||
"""
|
||||
8:'SkinSeg-prd',
|
||||
9:'SkinSeg-dst',
|
||||
10:'SkinSeg-prd*SkinSeg-dst',
|
||||
11:'learned*SkinSeg-prd*SkinSeg-dst'
|
||||
"""
|
||||
whole_face_mask_mode_dict = {1:'learned',
|
||||
2:'dst',
|
||||
8:'XSeg-prd',
|
||||
9:'XSeg-dst',
|
||||
10:'XSeg-prd*XSeg-dst',
|
||||
11:'learned*XSeg-prd*XSeg-dst'
|
||||
}
|
||||
|
||||
full_face_mask_mode_dict = {1:'learned',
|
||||
2:'dst',
|
||||
3:'FAN-prd',
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ class ModelBase(object):
|
|||
force_gpu_idxs=None,
|
||||
cpu_only=False,
|
||||
debug=False,
|
||||
force_model_class_name=None,
|
||||
**kwargs):
|
||||
self.is_training = is_training
|
||||
self.saved_models_path = saved_models_path
|
||||
|
|
@ -44,80 +45,84 @@ class ModelBase(object):
|
|||
|
||||
self.model_class_name = model_class_name = Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
|
||||
|
||||
if force_model_name is not None:
|
||||
self.model_name = force_model_name
|
||||
else:
|
||||
while True:
|
||||
# gather all model dat files
|
||||
saved_models_names = []
|
||||
for filepath in pathex.get_file_paths(saved_models_path):
|
||||
filepath_name = filepath.name
|
||||
if filepath_name.endswith(f'{model_class_name}_data.dat'):
|
||||
saved_models_names += [ (filepath_name.split('_')[0], os.path.getmtime(filepath)) ]
|
||||
if force_model_class_name is None:
|
||||
if force_model_name is not None:
|
||||
self.model_name = force_model_name
|
||||
else:
|
||||
while True:
|
||||
# gather all model dat files
|
||||
saved_models_names = []
|
||||
for filepath in pathex.get_file_paths(saved_models_path):
|
||||
filepath_name = filepath.name
|
||||
if filepath_name.endswith(f'{model_class_name}_data.dat'):
|
||||
saved_models_names += [ (filepath_name.split('_')[0], os.path.getmtime(filepath)) ]
|
||||
|
||||
# sort by modified datetime
|
||||
saved_models_names = sorted(saved_models_names, key=operator.itemgetter(1), reverse=True )
|
||||
saved_models_names = [ x[0] for x in saved_models_names ]
|
||||
# sort by modified datetime
|
||||
saved_models_names = sorted(saved_models_names, key=operator.itemgetter(1), reverse=True )
|
||||
saved_models_names = [ x[0] for x in saved_models_names ]
|
||||
|
||||
if len(saved_models_names) != 0:
|
||||
io.log_info ("Choose one of saved models, or enter a name to create a new model.")
|
||||
io.log_info ("[r] : rename")
|
||||
io.log_info ("[d] : delete")
|
||||
io.log_info ("")
|
||||
for i, model_name in enumerate(saved_models_names):
|
||||
s = f"[{i}] : {model_name} "
|
||||
if i == 0:
|
||||
s += "- latest"
|
||||
io.log_info (s)
|
||||
if len(saved_models_names) != 0:
|
||||
io.log_info ("Choose one of saved models, or enter a name to create a new model.")
|
||||
io.log_info ("[r] : rename")
|
||||
io.log_info ("[d] : delete")
|
||||
io.log_info ("")
|
||||
for i, model_name in enumerate(saved_models_names):
|
||||
s = f"[{i}] : {model_name} "
|
||||
if i == 0:
|
||||
s += "- latest"
|
||||
io.log_info (s)
|
||||
|
||||
inp = io.input_str(f"", "0", show_default_value=False )
|
||||
model_idx = -1
|
||||
try:
|
||||
model_idx = np.clip ( int(inp), 0, len(saved_models_names)-1 )
|
||||
except:
|
||||
pass
|
||||
inp = io.input_str(f"", "0", show_default_value=False )
|
||||
model_idx = -1
|
||||
try:
|
||||
model_idx = np.clip ( int(inp), 0, len(saved_models_names)-1 )
|
||||
except:
|
||||
pass
|
||||
|
||||
if model_idx == -1:
|
||||
if len(inp) == 1:
|
||||
is_rename = inp[0] == 'r'
|
||||
is_delete = inp[0] == 'd'
|
||||
if model_idx == -1:
|
||||
if len(inp) == 1:
|
||||
is_rename = inp[0] == 'r'
|
||||
is_delete = inp[0] == 'd'
|
||||
|
||||
if is_rename or is_delete:
|
||||
if len(saved_models_names) != 0:
|
||||
|
||||
if is_rename:
|
||||
name = io.input_str(f"Enter the name of the model you want to rename")
|
||||
elif is_delete:
|
||||
name = io.input_str(f"Enter the name of the model you want to delete")
|
||||
|
||||
if name in saved_models_names:
|
||||
if is_rename or is_delete:
|
||||
if len(saved_models_names) != 0:
|
||||
|
||||
if is_rename:
|
||||
new_model_name = io.input_str(f"Enter new name of the model")
|
||||
name = io.input_str(f"Enter the name of the model you want to rename")
|
||||
elif is_delete:
|
||||
name = io.input_str(f"Enter the name of the model you want to delete")
|
||||
|
||||
for filepath in pathex.get_paths(saved_models_path):
|
||||
filepath_name = filepath.name
|
||||
if name in saved_models_names:
|
||||
|
||||
model_filename, remain_filename = filepath_name.split('_', 1)
|
||||
if model_filename == name:
|
||||
if is_rename:
|
||||
new_model_name = io.input_str(f"Enter new name of the model")
|
||||
|
||||
if is_rename:
|
||||
new_filepath = filepath.parent / ( new_model_name + '_' + remain_filename )
|
||||
filepath.rename (new_filepath)
|
||||
elif is_delete:
|
||||
filepath.unlink()
|
||||
continue
|
||||
for filepath in pathex.get_paths(saved_models_path):
|
||||
filepath_name = filepath.name
|
||||
|
||||
model_filename, remain_filename = filepath_name.split('_', 1)
|
||||
if model_filename == name:
|
||||
|
||||
if is_rename:
|
||||
new_filepath = filepath.parent / ( new_model_name + '_' + remain_filename )
|
||||
filepath.rename (new_filepath)
|
||||
elif is_delete:
|
||||
filepath.unlink()
|
||||
continue
|
||||
|
||||
self.model_name = inp
|
||||
else:
|
||||
self.model_name = saved_models_names[model_idx]
|
||||
|
||||
self.model_name = inp
|
||||
else:
|
||||
self.model_name = saved_models_names[model_idx]
|
||||
self.model_name = io.input_str(f"No saved models found. Enter a name of a new model", "new")
|
||||
self.model_name = self.model_name.replace('_', ' ')
|
||||
break
|
||||
|
||||
else:
|
||||
self.model_name = io.input_str(f"No saved models found. Enter a name of a new model", "new")
|
||||
self.model_name = self.model_name.replace('_', ' ')
|
||||
break
|
||||
|
||||
self.model_name = self.model_name + '_' + self.model_class_name
|
||||
self.model_name = self.model_name + '_' + self.model_class_name
|
||||
else:
|
||||
self.model_name = force_model_class_name
|
||||
|
||||
self.iter = 0
|
||||
self.options = {}
|
||||
|
|
|
|||
|
|
@ -13,6 +13,9 @@ from samplelib import *
|
|||
|
||||
class FANSegModel(ModelBase):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, force_model_class_name='FANSeg', **kwargs)
|
||||
|
||||
#override
|
||||
def on_initialize_options(self):
|
||||
device_config = nn.getCurrentDeviceConfig()
|
||||
|
|
@ -48,7 +51,7 @@ class FANSegModel(ModelBase):
|
|||
mask_shape = nn.get4Dshape(resolution,resolution,1)
|
||||
|
||||
# Initializing model classes
|
||||
self.model = TernausNet(f'{self.model_name}_FANSeg_{FaceType.toString(self.face_type)}',
|
||||
self.model = TernausNet(f'FANSeg_{FaceType.toString(self.face_type)}',
|
||||
resolution,
|
||||
load_weights=not self.is_first_run(),
|
||||
weights_file_root=self.get_model_root_path(),
|
||||
|
|
@ -117,14 +120,14 @@ class FANSegModel(ModelBase):
|
|||
|
||||
src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'lct', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'lct', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'random_motion_blur':(25, 5), 'random_gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=src_generators_count )
|
||||
|
||||
dst_generator = SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=dst_generators_count,
|
||||
raise_on_no_data=False )
|
||||
|
|
|
|||
|
|
@ -7,28 +7,18 @@ import numpy as np
|
|||
from core import mathlib
|
||||
from core.interact import interact as io
|
||||
from core.leras import nn
|
||||
from facelib import FaceType, TernausNet, DFLSegNet
|
||||
from facelib import FaceType, TernausNet, XSegNet
|
||||
from models import ModelBase
|
||||
from samplelib import *
|
||||
|
||||
class SkinSegModel(ModelBase):
|
||||
class XSegModel(ModelBase):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, force_model_class_name='XSeg', **kwargs)
|
||||
|
||||
#override
|
||||
def on_initialize_options(self):
|
||||
device_config = nn.getCurrentDeviceConfig()
|
||||
yn_str = {True:'y',False:'n'}
|
||||
|
||||
ask_override = self.ask_override()
|
||||
if self.is_first_run() or ask_override:
|
||||
self.ask_autobackup_hour()
|
||||
self.ask_write_preview_history()
|
||||
self.ask_target_iter()
|
||||
self.ask_batch_size(8)
|
||||
|
||||
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
|
||||
|
||||
if self.is_first_run() or ask_override:
|
||||
self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations.")
|
||||
self.set_batch_size(4)
|
||||
|
||||
#override
|
||||
def on_initialize(self):
|
||||
|
|
@ -43,20 +33,20 @@ class SkinSegModel(ModelBase):
|
|||
self.resolution = resolution = 256
|
||||
self.face_type = FaceType.WHOLE_FACE
|
||||
|
||||
place_model_on_cpu = True #len(devices) == 0
|
||||
place_model_on_cpu = len(devices) == 0
|
||||
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
|
||||
|
||||
bgr_shape = nn.get4Dshape(resolution,resolution,3)
|
||||
mask_shape = nn.get4Dshape(resolution,resolution,1)
|
||||
|
||||
# Initializing model classes
|
||||
self.model = DFLSegNet(name=f'{self.model_name}_SkinSeg',
|
||||
self.model = XSegNet(name=f'XSeg',
|
||||
resolution=resolution,
|
||||
load_weights=not self.is_first_run(),
|
||||
weights_file_root=self.get_model_root_path(),
|
||||
training=True,
|
||||
place_model_on_cpu=place_model_on_cpu,
|
||||
optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3 if self.options['lr_dropout'] else 1.0, name='opt'),
|
||||
optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3, name='opt'),
|
||||
data_format=nn.data_format)
|
||||
|
||||
if self.is_training:
|
||||
|
|
@ -111,38 +101,33 @@ class SkinSegModel(ModelBase):
|
|||
|
||||
# initializing sample generators
|
||||
cpu_count = min(multiprocessing.cpu_count(), 8)
|
||||
src_dst_generators_count = cpu_count // 2
|
||||
src_generators_count = cpu_count // 2
|
||||
dst_generators_count = cpu_count // 2
|
||||
src_generators_count = int(src_generators_count * 1.5)
|
||||
|
||||
"""
|
||||
|
||||
srcdst_generator = SampleGeneratorFaceXSeg([self.training_data_src_path, self.training_data_dst_path],
|
||||
debug=self.is_debug(),
|
||||
batch_size=self.get_batch_size(),
|
||||
resolution=resolution,
|
||||
face_type=self.face_type,
|
||||
generators_count=src_dst_generators_count,
|
||||
data_format=nn.data_format)
|
||||
|
||||
src_generator = SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR_RANDOM_HSV_SHIFT, 'border_replicate':False, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'random_bilinear_resize':(25,75), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.NONE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=src_generators_count )
|
||||
"""
|
||||
src_generator = SampleGeneratorFaceSkinSegDataset(self.training_data_src_path,
|
||||
debug=self.is_debug(),
|
||||
batch_size=self.get_batch_size(),
|
||||
resolution=resolution,
|
||||
face_type=self.face_type,
|
||||
generators_count=src_generators_count,
|
||||
data_format=nn.data_format)
|
||||
|
||||
sample_process_options=SampleProcessor.Options(random_flip=False),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=src_generators_count,
|
||||
raise_on_no_data=False )
|
||||
dst_generator = SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'random_bilinear_resize':(25,75), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
sample_process_options=SampleProcessor.Options(random_flip=False),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=dst_generators_count,
|
||||
raise_on_no_data=False )
|
||||
|
||||
|
||||
if not dst_generator.is_initialized():
|
||||
io.log_info(f"\nTo view the model on unseen faces, place any image faces in {self.training_data_dst_path}.\n")
|
||||
|
||||
self.set_training_data_generators ([src_generator, dst_generator])
|
||||
self.set_training_data_generators ([srcdst_generator, src_generator, dst_generator])
|
||||
|
||||
#override
|
||||
def get_model_filename_list(self):
|
||||
|
|
@ -154,6 +139,8 @@ class SkinSegModel(ModelBase):
|
|||
|
||||
#override
|
||||
def onTrainOneIter(self):
|
||||
|
||||
|
||||
image_np, mask_np = self.generate_next_samples()[0]
|
||||
loss = self.train (image_np, mask_np)
|
||||
|
||||
|
|
@ -163,8 +150,8 @@ class SkinSegModel(ModelBase):
|
|||
def onGetPreview(self, samples):
|
||||
n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
|
||||
|
||||
src_samples, dst_samples = samples
|
||||
image_np, mask_np = src_samples
|
||||
srcdst_samples, src_samples, dst_samples = samples
|
||||
image_np, mask_np = srcdst_samples
|
||||
|
||||
I, M, IM, = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([image_np,mask_np] + self.view (image_np) ) ]
|
||||
M, IM, = [ np.repeat (x, (3,), -1) for x in [M, IM] ]
|
||||
|
|
@ -174,9 +161,23 @@ class SkinSegModel(ModelBase):
|
|||
result = []
|
||||
st = []
|
||||
for i in range(n_samples):
|
||||
ar = I[i]*M[i]+0.5*I[i]*(1-M[i])+0.5*green_bg*(1-M[i]), IM[i], I[i]*IM[i] + green_bg*(1-IM[i])
|
||||
ar = I[i]*M[i]+0.5*I[i]*(1-M[i])+0.5*green_bg*(1-M[i]), IM[i], I[i]*IM[i]+0.5*I[i]*(1-IM[i]) + 0.5*green_bg*(1-IM[i])
|
||||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
result += [ ('SkinSeg training faces', np.concatenate (st, axis=0 )), ]
|
||||
result += [ ('XSeg training faces', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
if len(src_samples) != 0:
|
||||
src_np, = src_samples
|
||||
|
||||
|
||||
D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([src_np] + self.view (src_np) ) ]
|
||||
DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
|
||||
|
||||
st = []
|
||||
for i in range(n_samples):
|
||||
ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
|
||||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
|
||||
result += [ ('XSeg src faces', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
if len(dst_samples) != 0:
|
||||
dst_np, = dst_samples
|
||||
|
|
@ -187,11 +188,11 @@ class SkinSegModel(ModelBase):
|
|||
|
||||
st = []
|
||||
for i in range(n_samples):
|
||||
ar = D[i], DM[i], D[i]*DM[i]+ green_bg*(1-DM[i])
|
||||
ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
|
||||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
|
||||
result += [ ('SkinSeg unseen faces', np.concatenate (st, axis=0 )), ]
|
||||
result += [ ('XSeg dst faces', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
return result
|
||||
|
||||
Model = SkinSegModel
|
||||
Model = XSegModel
|
||||
|
|
@ -6,4 +6,5 @@ ffmpeg-python==0.1.17
|
|||
scikit-image==0.14.2
|
||||
scipy==1.4.1
|
||||
colorama
|
||||
labelme==4.2.9
|
||||
tensorflow-gpu==1.13.2
|
||||
|
|
@ -27,6 +27,7 @@ class Sample(object):
|
|||
'shape',
|
||||
'landmarks',
|
||||
'ie_polys',
|
||||
'seg_ie_polys',
|
||||
'eyebrows_expand_mod',
|
||||
'source_filename',
|
||||
'person_name',
|
||||
|
|
@ -40,6 +41,7 @@ class Sample(object):
|
|||
shape=None,
|
||||
landmarks=None,
|
||||
ie_polys=None,
|
||||
seg_ie_polys=None,
|
||||
eyebrows_expand_mod=None,
|
||||
source_filename=None,
|
||||
person_name=None,
|
||||
|
|
@ -52,6 +54,7 @@ class Sample(object):
|
|||
self.shape = shape
|
||||
self.landmarks = np.array(landmarks) if landmarks is not None else None
|
||||
self.ie_polys = IEPolys.load(ie_polys)
|
||||
self.seg_ie_polys = IEPolys.load(seg_ie_polys)
|
||||
self.eyebrows_expand_mod = eyebrows_expand_mod
|
||||
self.source_filename = source_filename
|
||||
self.person_name = person_name
|
||||
|
|
@ -88,6 +91,7 @@ class Sample(object):
|
|||
'shape': self.shape,
|
||||
'landmarks': self.landmarks.tolist(),
|
||||
'ie_polys': self.ie_polys.dump(),
|
||||
'seg_ie_polys': self.seg_ie_polys.dump(),
|
||||
'eyebrows_expand_mod': self.eyebrows_expand_mod,
|
||||
'source_filename': self.source_filename,
|
||||
'person_name': self.person_name
|
||||
|
|
|
|||
|
|
@ -1,260 +0,0 @@
|
|||
import multiprocessing
|
||||
import pickle
|
||||
import time
|
||||
import traceback
|
||||
from enum import IntEnum
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from core import imagelib, mplib, pathex
|
||||
from core.imagelib import sd
|
||||
from core.cv2ex import *
|
||||
from core.interact import interact as io
|
||||
from core.joblib import SubprocessGenerator, ThisThreadGenerator
|
||||
from facelib import LandmarksProcessor
|
||||
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor, SampleType)
|
||||
|
||||
class MaskType(IntEnum):
|
||||
none = 0,
|
||||
cloth = 1,
|
||||
ear_r = 2,
|
||||
eye_g = 3,
|
||||
hair = 4,
|
||||
hat = 5,
|
||||
l_brow = 6,
|
||||
l_ear = 7,
|
||||
l_eye = 8,
|
||||
l_lip = 9,
|
||||
mouth = 10,
|
||||
neck = 11,
|
||||
neck_l = 12,
|
||||
nose = 13,
|
||||
r_brow = 14,
|
||||
r_ear = 15,
|
||||
r_eye = 16,
|
||||
skin = 17,
|
||||
u_lip = 18
|
||||
|
||||
|
||||
|
||||
MaskType_to_name = {
|
||||
int(MaskType.none ) : 'none',
|
||||
int(MaskType.cloth ) : 'cloth',
|
||||
int(MaskType.ear_r ) : 'ear_r',
|
||||
int(MaskType.eye_g ) : 'eye_g',
|
||||
int(MaskType.hair ) : 'hair',
|
||||
int(MaskType.hat ) : 'hat',
|
||||
int(MaskType.l_brow) : 'l_brow',
|
||||
int(MaskType.l_ear ) : 'l_ear',
|
||||
int(MaskType.l_eye ) : 'l_eye',
|
||||
int(MaskType.l_lip ) : 'l_lip',
|
||||
int(MaskType.mouth ) : 'mouth',
|
||||
int(MaskType.neck ) : 'neck',
|
||||
int(MaskType.neck_l) : 'neck_l',
|
||||
int(MaskType.nose ) : 'nose',
|
||||
int(MaskType.r_brow) : 'r_brow',
|
||||
int(MaskType.r_ear ) : 'r_ear',
|
||||
int(MaskType.r_eye ) : 'r_eye',
|
||||
int(MaskType.skin ) : 'skin',
|
||||
int(MaskType.u_lip ) : 'u_lip',
|
||||
}
|
||||
|
||||
MaskType_from_name = { MaskType_to_name[k] : k for k in MaskType_to_name.keys() }
|
||||
|
||||
class SampleGeneratorFaceSkinSegDataset(SampleGeneratorBase):
|
||||
def __init__ (self, root_path, debug=False, batch_size=1, resolution=256, face_type=None,
|
||||
generators_count=4, data_format="NHWC",
|
||||
**kwargs):
|
||||
|
||||
super().__init__(debug, batch_size)
|
||||
self.initialized = False
|
||||
|
||||
|
||||
aligned_path = root_path /'aligned'
|
||||
if not aligned_path.exists():
|
||||
raise ValueError(f'Unable to find {aligned_path}')
|
||||
|
||||
obstructions_path = root_path / 'obstructions'
|
||||
|
||||
obstructions_images_paths = pathex.get_image_paths(obstructions_path, image_extensions=['.png'], subdirs=True)
|
||||
|
||||
samples = SampleLoader.load (SampleType.FACE, aligned_path, subdirs=True)
|
||||
self.samples_len = len(samples)
|
||||
|
||||
pickled_samples = pickle.dumps(samples, 4)
|
||||
|
||||
if self.debug:
|
||||
self.generators_count = 1
|
||||
else:
|
||||
self.generators_count = max(1, generators_count)
|
||||
|
||||
if self.debug:
|
||||
self.generators = [ThisThreadGenerator ( self.batch_func, (pickled_samples, obstructions_images_paths, resolution, face_type, data_format) )]
|
||||
else:
|
||||
self.generators = [SubprocessGenerator ( self.batch_func, (pickled_samples, obstructions_images_paths, resolution, face_type, data_format), start_now=False ) \
|
||||
for i in range(self.generators_count) ]
|
||||
|
||||
SubprocessGenerator.start_in_parallel( self.generators )
|
||||
|
||||
self.generator_counter = -1
|
||||
|
||||
self.initialized = True
|
||||
|
||||
#overridable
|
||||
def is_initialized(self):
|
||||
return self.initialized
|
||||
|
||||
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 ):
|
||||
pickled_samples, obstructions_images_paths, resolution, face_type, data_format = param
|
||||
|
||||
samples = pickle.loads(pickled_samples)
|
||||
|
||||
obstructions_images_paths_len = len(obstructions_images_paths)
|
||||
shuffle_o_idxs = []
|
||||
o_idxs = [*range(obstructions_images_paths_len)]
|
||||
|
||||
shuffle_idxs = []
|
||||
idxs = [*range(len(samples))]
|
||||
|
||||
random_flip = True
|
||||
rotation_range=[-10,10]
|
||||
scale_range=[-0.05, 0.05]
|
||||
tx_range=[-0.05, 0.05]
|
||||
ty_range=[-0.05, 0.05]
|
||||
|
||||
o_random_flip = True
|
||||
o_rotation_range=[-180,180]
|
||||
o_scale_range=[-0.5, 0.05]
|
||||
o_tx_range=[-0.5, 0.5]
|
||||
o_ty_range=[-0.5, 0.5]
|
||||
|
||||
random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
|
||||
motion_blur_chance, motion_blur_mb_max_size = 25, 5
|
||||
gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
|
||||
|
||||
bs = self.batch_size
|
||||
while True:
|
||||
batches = [ [], [] ]
|
||||
|
||||
n_batch = 0
|
||||
while n_batch < bs:
|
||||
try:
|
||||
if len(shuffle_idxs) == 0:
|
||||
shuffle_idxs = idxs.copy()
|
||||
np.random.shuffle(shuffle_idxs)
|
||||
|
||||
idx = shuffle_idxs.pop()
|
||||
|
||||
sample = samples[idx]
|
||||
|
||||
img = sample.load_bgr()
|
||||
h,w,c = img.shape
|
||||
|
||||
mask = np.zeros ((h,w,1), dtype=np.float32)
|
||||
sample.ie_polys.overlay_mask(mask)
|
||||
|
||||
warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
|
||||
|
||||
if face_type == sample.face_type:
|
||||
if w != resolution:
|
||||
img = cv2.resize( img, (resolution, resolution), cv2.INTER_LANCZOS4 )
|
||||
mask = cv2.resize( mask, (resolution, resolution), cv2.INTER_LANCZOS4 )
|
||||
else:
|
||||
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
|
||||
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
|
||||
mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
|
||||
|
||||
if len(mask.shape) == 2:
|
||||
mask = mask[...,None]
|
||||
|
||||
if obstructions_images_paths_len != 0:
|
||||
# apply obstruction
|
||||
if len(shuffle_o_idxs) == 0:
|
||||
shuffle_o_idxs = o_idxs.copy()
|
||||
np.random.shuffle(shuffle_o_idxs)
|
||||
o_idx = shuffle_o_idxs.pop()
|
||||
o_img = cv2_imread (obstructions_images_paths[o_idx]).astype(np.float32) / 255.0
|
||||
oh,ow,oc = o_img.shape
|
||||
if oc == 4:
|
||||
ohw = max(oh,ow)
|
||||
scale = resolution / ohw
|
||||
|
||||
#o_img = cv2.resize (o_img, ( int(ow*rate), int(oh*rate), ), cv2.INTER_CUBIC)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
mat = cv2.getRotationMatrix2D( (ow/2,oh/2),
|
||||
np.random.uniform( o_rotation_range[0], o_rotation_range[1] ),
|
||||
1.0 )
|
||||
|
||||
mat += np.float32( [[0,0, -ow/2 ],
|
||||
[0,0, -oh/2 ]])
|
||||
mat *= scale * np.random.uniform(1 +o_scale_range[0], 1 +o_scale_range[1])
|
||||
mat += np.float32( [[0, 0, resolution/2 + resolution*np.random.uniform( o_tx_range[0], o_tx_range[1] ) ],
|
||||
[0, 0, resolution/2 + resolution*np.random.uniform( o_ty_range[0], o_ty_range[1] ) ] ])
|
||||
|
||||
|
||||
o_img = cv2.warpAffine( o_img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
|
||||
|
||||
if o_random_flip and np.random.randint(10) < 4:
|
||||
o_img = o_img[:,::-1,...]
|
||||
|
||||
o_mask = o_img[...,3:4]
|
||||
o_mask[o_mask>0] = 1.0
|
||||
|
||||
|
||||
o_mask = cv2.erode (o_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1 )
|
||||
o_mask = cv2.GaussianBlur(o_mask, (5, 5) , 0)[...,None]
|
||||
|
||||
img = img*(1-o_mask) + o_img[...,0:3]*o_mask
|
||||
|
||||
o_mask[o_mask<0.5] = 0.0
|
||||
|
||||
|
||||
#import code
|
||||
#code.interact(local=dict(globals(), **locals()))
|
||||
mask *= (1-o_mask)
|
||||
|
||||
|
||||
#cv2.imshow ("", np.clip(o_img*255, 0,255).astype(np.uint8) )
|
||||
#cv2.waitKey(0)
|
||||
|
||||
|
||||
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
|
||||
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
|
||||
|
||||
|
||||
img = np.clip(img.astype(np.float32), 0, 1)
|
||||
mask[mask < 0.5] = 0.0
|
||||
mask[mask >= 0.5] = 1.0
|
||||
mask = np.clip(mask, 0, 1)
|
||||
|
||||
|
||||
img = imagelib.apply_random_hsv_shift(img, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
|
||||
if data_format == "NCHW":
|
||||
img = np.transpose(img, (2,0,1) )
|
||||
mask = np.transpose(mask, (2,0,1) )
|
||||
|
||||
batches[0].append ( img )
|
||||
batches[1].append ( mask )
|
||||
|
||||
n_batch += 1
|
||||
except:
|
||||
io.log_err ( traceback.format_exc() )
|
||||
|
||||
yield [ np.array(batch) for batch in batches]
|
||||
148
samplelib/SampleGeneratorFaceXSeg.py
Normal file
148
samplelib/SampleGeneratorFaceXSeg.py
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
import multiprocessing
|
||||
import pickle
|
||||
import time
|
||||
import traceback
|
||||
from enum import IntEnum
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from core import imagelib, mplib, pathex
|
||||
from core.imagelib import sd
|
||||
from core.cv2ex import *
|
||||
from core.interact import interact as io
|
||||
from core.joblib import SubprocessGenerator, ThisThreadGenerator
|
||||
from facelib import LandmarksProcessor
|
||||
from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor, SampleType)
|
||||
|
||||
class SampleGeneratorFaceXSeg(SampleGeneratorBase):
|
||||
def __init__ (self, paths, debug=False, batch_size=1, resolution=256, face_type=None,
|
||||
generators_count=4, data_format="NHWC",
|
||||
**kwargs):
|
||||
|
||||
super().__init__(debug, batch_size)
|
||||
self.initialized = False
|
||||
|
||||
samples = []
|
||||
for path in paths:
|
||||
samples += SampleLoader.load (SampleType.FACE, path)
|
||||
|
||||
seg_samples = [ sample for sample in samples if sample.seg_ie_polys.get_total_points() != 0]
|
||||
seg_samples_len = len(seg_samples)
|
||||
if seg_samples_len == 0:
|
||||
raise Exception(f"No segmented faces found.")
|
||||
else:
|
||||
io.log_info(f"Using {seg_samples_len} segmented samples.")
|
||||
|
||||
pickled_samples = pickle.dumps(seg_samples, 4)
|
||||
|
||||
if self.debug:
|
||||
self.generators_count = 1
|
||||
else:
|
||||
self.generators_count = max(1, generators_count)
|
||||
|
||||
if self.debug:
|
||||
self.generators = [ThisThreadGenerator ( self.batch_func, (pickled_samples, resolution, face_type, data_format) )]
|
||||
else:
|
||||
self.generators = [SubprocessGenerator ( self.batch_func, (pickled_samples, resolution, face_type, data_format), start_now=False ) \
|
||||
for i in range(self.generators_count) ]
|
||||
|
||||
SubprocessGenerator.start_in_parallel( self.generators )
|
||||
|
||||
self.generator_counter = -1
|
||||
|
||||
self.initialized = True
|
||||
|
||||
#overridable
|
||||
def is_initialized(self):
|
||||
return self.initialized
|
||||
|
||||
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 ):
|
||||
pickled_samples, resolution, face_type, data_format = param
|
||||
|
||||
samples = pickle.loads(pickled_samples)
|
||||
|
||||
shuffle_idxs = []
|
||||
idxs = [*range(len(samples))]
|
||||
|
||||
random_flip = True
|
||||
rotation_range=[-10,10]
|
||||
scale_range=[-0.05, 0.05]
|
||||
tx_range=[-0.05, 0.05]
|
||||
ty_range=[-0.05, 0.05]
|
||||
|
||||
random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
|
||||
motion_blur_chance, motion_blur_mb_max_size = 25, 5
|
||||
gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
|
||||
|
||||
bs = self.batch_size
|
||||
while True:
|
||||
batches = [ [], [] ]
|
||||
|
||||
n_batch = 0
|
||||
while n_batch < bs:
|
||||
try:
|
||||
if len(shuffle_idxs) == 0:
|
||||
shuffle_idxs = idxs.copy()
|
||||
np.random.shuffle(shuffle_idxs)
|
||||
idx = shuffle_idxs.pop()
|
||||
|
||||
sample = samples[idx]
|
||||
|
||||
img = sample.load_bgr()
|
||||
h,w,c = img.shape
|
||||
|
||||
mask = np.zeros ((h,w,1), dtype=np.float32)
|
||||
sample.seg_ie_polys.overlay_mask(mask)
|
||||
|
||||
warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
|
||||
|
||||
if face_type == sample.face_type:
|
||||
if w != resolution:
|
||||
img = cv2.resize( img, (resolution, resolution), cv2.INTER_LANCZOS4 )
|
||||
mask = cv2.resize( mask, (resolution, resolution), cv2.INTER_LANCZOS4 )
|
||||
else:
|
||||
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
|
||||
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
|
||||
mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )
|
||||
|
||||
if len(mask.shape) == 2:
|
||||
mask = mask[...,None]
|
||||
|
||||
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
|
||||
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
|
||||
|
||||
img = np.clip(img.astype(np.float32), 0, 1)
|
||||
mask[mask < 0.5] = 0.0
|
||||
mask[mask >= 0.5] = 1.0
|
||||
mask = np.clip(mask, 0, 1)
|
||||
|
||||
if np.random.randint(2) == 0:
|
||||
img = imagelib.apply_random_hsv_shift(img, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
else:
|
||||
img = imagelib.apply_random_rgb_levels(img, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
|
||||
img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
|
||||
|
||||
if data_format == "NCHW":
|
||||
img = np.transpose(img, (2,0,1) )
|
||||
mask = np.transpose(mask, (2,0,1) )
|
||||
|
||||
batches[0].append ( img )
|
||||
batches[1].append ( mask )
|
||||
|
||||
n_batch += 1
|
||||
except:
|
||||
io.log_err ( traceback.format_exc() )
|
||||
|
||||
yield [ np.array(batch) for batch in batches]
|
||||
|
|
@ -75,6 +75,7 @@ class SampleLoader:
|
|||
shape,
|
||||
landmarks,
|
||||
ie_polys,
|
||||
seg_ie_polys,
|
||||
eyebrows_expand_mod,
|
||||
source_filename,
|
||||
) in result:
|
||||
|
|
@ -84,6 +85,7 @@ class SampleLoader:
|
|||
shape=shape,
|
||||
landmarks=landmarks,
|
||||
ie_polys=ie_polys,
|
||||
seg_ie_polys=seg_ie_polys,
|
||||
eyebrows_expand_mod=eyebrows_expand_mod,
|
||||
source_filename=source_filename,
|
||||
))
|
||||
|
|
@ -177,6 +179,7 @@ class FaceSamplesLoaderSubprocessor(Subprocessor):
|
|||
dflimg.get_shape(),
|
||||
dflimg.get_landmarks(),
|
||||
dflimg.get_ie_polys(),
|
||||
dflimg.get_seg_ie_polys(),
|
||||
dflimg.get_eyebrows_expand_mod(),
|
||||
dflimg.get_source_filename() )
|
||||
|
||||
|
|
|
|||
|
|
@ -9,5 +9,5 @@ from .SampleGeneratorFaceTemporal import SampleGeneratorFaceTemporal
|
|||
from .SampleGeneratorImage import SampleGeneratorImage
|
||||
from .SampleGeneratorImageTemporal import SampleGeneratorImageTemporal
|
||||
from .SampleGeneratorFaceCelebAMaskHQ import SampleGeneratorFaceCelebAMaskHQ
|
||||
from .SampleGeneratorFaceSkinSegDataset import SampleGeneratorFaceSkinSegDataset
|
||||
from .SampleGeneratorFaceXSeg import SampleGeneratorFaceXSeg
|
||||
from .PackedFaceset import PackedFaceset
|
||||
Loading…
Add table
Add a link
Reference in a new issue