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manual extractor: increased FPS,

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sort by final : now you can specify target number of images,
converter: fix seamless mask and exception,
huge refactoring
This commit is contained in:
iperov 2019-02-28 11:56:31 +04:00
parent 7db469a1da
commit 438213e97c
30 changed files with 1834 additions and 1718 deletions
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19
samples/SampleLoader.py
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@ -2,7 +2,6 @@ import traceback
from enum import IntEnum
import cv2
import numpy as np
from tqdm import tqdm
from pathlib import Path
from utils import Path_utils
@ -14,6 +13,7 @@ from .Sample import SampleType
from facelib import FaceType
from facelib import LandmarksProcessor
from interact import interact as io
class SampleLoader:
cache = dict()
@ -29,7 +29,7 @@ class SampleLoader:
if sample_type == SampleType.IMAGE:
if datas[sample_type] is None:
datas[sample_type] = [ Sample(filename=filename) for filename in tqdm( Path_utils.get_image_paths(samples_path), desc="Loading", ascii=True ) ]
datas[sample_type] = [ Sample(filename=filename) for filename in io.progress_bar_generator( Path_utils.get_image_paths(samples_path), "Loading") ]
elif sample_type == SampleType.FACE:
if datas[sample_type] is None:
@ -55,16 +55,17 @@ class SampleLoader:
def upgradeToFaceSamples ( samples ):
sample_list = []
for s in tqdm( samples, desc="Loading", ascii=True ):
for s in io.progress_bar_generator(samples, "Loading"):
s_filename_path = Path(s.filename)
try:
if s_filename_path.suffix == '.png':
dflimg = DFLPNG.load ( str(s_filename_path), print_on_no_embedded_data=True )
if dflimg is None: continue
dflimg = DFLPNG.load ( str(s_filename_path) )
elif s_filename_path.suffix == '.jpg':
dflimg = DFLJPG.load ( str(s_filename_path), print_on_no_embedded_data=True )
if dflimg is None: continue
dflimg = DFLJPG.load ( str(s_filename_path) )
else:
dflimg = None
if dflimg is None:
print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
continue
@ -87,7 +88,7 @@ class SampleLoader:
yaw_samples_len = len(yaw_samples)
sample_list = []
for i in tqdm( range(yaw_samples_len), desc="Sorting", ascii=True ):
for i in io.progress_bar_generator( range(yaw_samples_len), "Sorting"):
if yaw_samples[i] is not None:
for s in yaw_samples[i]:
s_t = []
@ -123,7 +124,7 @@ class SampleLoader:
yaws_sample_list = [None]*gradations
for i in tqdm( range(0, gradations), desc="Sorting", ascii=True ):
for i in io.progress_bar_generator(range(gradations), "Sorting"):
yaw = lowest_yaw + i*diff_rot_per_grad
next_yaw = lowest_yaw + (i+1)*diff_rot_per_grad