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50f892d57d
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw. Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster. added 4.2.other) data_src/dst util faceset pack.bat Packs /aligned/ samples into one /aligned/samples.pak file. After that, all faces will be deleted. 4.2.other) data_src/dst util faceset unpack.bat unpacks faces from /aligned/samples.pak to /aligned/ dir. After that, samples.pak will be deleted. Packed faceset load and work faster.
100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
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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FACE_BEGIN = 1
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FACE = 1 #aligned face unsorted
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FACE_TEMPORAL_SORTED = 2 #sorted by source filename
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FACE_END = 2
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QTY = 5
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class Sample(object):
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__slots__ = ['sample_type',
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'filename',
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'person_id',
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'face_type',
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'shape',
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'landmarks',
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'ie_polys',
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'pitch_yaw_roll',
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'eyebrows_expand_mod',
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'source_filename',
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'mirror',
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'fanseg_mask_exist',
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'_filename_offset_size',
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]
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def __init__(self, sample_type=None, filename=None, person_id=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=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.person_id = person_id
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self.face_type = face_type
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self.shape = shape
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self.landmarks = np.array(landmarks) if landmarks is not None else None
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self.ie_polys = ie_polys
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self.pitch_yaw_roll = pitch_yaw_roll
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self.eyebrows_expand_mod = eyebrows_expand_mod
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self.source_filename = source_filename
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self.mirror = mirror
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self.fanseg_mask_exist = fanseg_mask_exist
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self._filename_offset_size = None
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def set_filename_offset_size(self, filename, offset, size):
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self._filename_offset_size = (filename, offset, size)
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def copy_and_set(self, sample_type=None, filename=None, person_id=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, mirror=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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person_id=person_id if person_id is not None else self.person_id,
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face_type=face_type if face_type is not None else self.face_type,
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shape=shape if shape is not None else self.shape,
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landmarks=landmarks if landmarks is not None else self.landmarks.copy(),
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ie_polys=ie_polys if ie_polys is not None else self.ie_polys,
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pitch_yaw_roll=pitch_yaw_roll if pitch_yaw_roll is not None else self.pitch_yaw_roll,
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eyebrows_expand_mod=eyebrows_expand_mod if eyebrows_expand_mod is not None else self.eyebrows_expand_mod,
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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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fanseg_mask_exist=fanseg_mask_exist if fanseg_mask_exist is not None else self.fanseg_mask_exist)
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def read_raw_file(self, filename=None):
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if self._filename_offset_size is not None:
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filename, offset, size = self._filename_offset_size
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with open(filename, "rb") as f:
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f.seek( offset, 0)
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return f.read (size)
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else:
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with open(filename, "rb") as f:
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return f.read()
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def load_bgr(self):
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img = cv2_imread (self.filename, loader_func=self.read_raw_file).astype(np.float32) / 255.0
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if self.mirror:
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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), loader_func=self.read_raw_file )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath), loader_func=self.read_raw_file )
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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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