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rename samples to samplelib

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iperov 2019-03-27 10:44:13 +04:00
parent 773e8d80e0
commit 3cf3bb786e
14 changed files with 9 additions and 15 deletions
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samplelib/SampleLoader.py Normal file
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import traceback
from enum import IntEnum
import cv2
import numpy as np
from pathlib import Path
from utils import Path_utils
from utils.DFLPNG import DFLPNG
from utils.DFLJPG import DFLJPG
from .Sample import Sample
from .Sample import SampleType
from facelib import FaceType
from facelib import LandmarksProcessor
from interact import interact as io
class SampleLoader:
cache = dict()
@staticmethod
def load(sample_type, samples_path, target_samples_path=None):
cache = SampleLoader.cache
if str(samples_path) not in cache.keys():
cache[str(samples_path)] = [None]*SampleType.QTY
datas = cache[str(samples_path)]
if sample_type == SampleType.IMAGE:
if datas[sample_type] is None:
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:
datas[sample_type] = SampleLoader.upgradeToFaceSamples( [ Sample(filename=filename) for filename in Path_utils.get_image_paths(samples_path) ] )
elif sample_type == SampleType.FACE_YAW_SORTED:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceYawSortedSamples( SampleLoader.load(SampleType.FACE, samples_path) )
elif sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if datas[sample_type] is None:
if target_samples_path is None:
raise Exception('target_samples_path is None for FACE_YAW_SORTED_AS_TARGET')
datas[sample_type] = SampleLoader.upgradeToFaceYawSortedAsTargetSamples( SampleLoader.load(SampleType.FACE_YAW_SORTED, samples_path), SampleLoader.load(SampleType.FACE_YAW_SORTED, target_samples_path) )
elif sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
if datas[sample_type] is None:
datas[sample_type] = SampleLoader.upgradeToFaceCloseToSelfSamples( SampleLoader.load(SampleType.FACE, samples_path) )
return datas[sample_type]
@staticmethod
def upgradeToFaceSamples ( samples ):
sample_list = []
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) )
elif s_filename_path.suffix == '.jpg':
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
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw ( dflimg.get_landmarks() )
sample_list.append( s.copy_and_set(sample_type=SampleType.FACE,
face_type=FaceType.fromString (dflimg.get_face_type()),
shape=dflimg.get_shape(),
landmarks=dflimg.get_landmarks(),
pitch=pitch,
yaw=yaw) )
except:
print ("Unable to load %s , error: %s" % (str(s_filename_path), traceback.format_exc() ) )
return sample_list
@staticmethod
def upgradeToFaceCloseToSelfSamples (samples):
yaw_samples = SampleLoader.upgradeToFaceYawSortedSamples(samples)
yaw_samples_len = len(yaw_samples)
sample_list = []
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 = []
for n in range(2000):
yaw_idx = np.clip ( i-10 +np.random.randint(20), 0, yaw_samples_len-1 )
if yaw_samples[yaw_idx] is None:
continue
yaw_idx_samples_len = len(yaw_samples[yaw_idx])
yaw_idx_sample = yaw_samples[yaw_idx][ np.random.randint(yaw_idx_samples_len) ]
if s.filename == yaw_idx_sample.filename:
continue
s_t.append ( yaw_idx_sample )
if len(s_t) >= 50:
break
if len(s_t) == 0:
s_t = [s]
sample_list.append( s.copy_and_set(close_target_list = s_t) )
return sample_list
@staticmethod
def upgradeToFaceYawSortedSamples( samples ):
lowest_yaw, highest_yaw = -1.0, 1.0
gradations = 64
diff_rot_per_grad = abs(highest_yaw-lowest_yaw) / gradations
yaws_sample_list = [None]*gradations
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
yaw_samples = []
for s in samples:
s_yaw = s.yaw
if (i == 0 and s_yaw < next_yaw) or \
(i < gradations-1 and s_yaw >= yaw and s_yaw < next_yaw) or \
(i == gradations-1 and s_yaw >= yaw):
yaw_samples.append ( s.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED) )
if len(yaw_samples) > 0:
yaws_sample_list[i] = yaw_samples
return yaws_sample_list
@staticmethod
def upgradeToFaceYawSortedAsTargetSamples (s, t):
l = len(s)
if l != len(t):
raise Exception('upgradeToFaceYawSortedAsTargetSamples() s_len != t_len')
b = l // 2
s_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in s] ) == 1 )[:,0]
t_idxs = np.argwhere ( np.array ( [ 1 if x != None else 0 for x in t] ) == 1 )[:,0]
new_s = [None]*l
for t_idx in t_idxs:
search_idxs = []
for i in range(0,l):
search_idxs += [t_idx - i, (l-t_idx-1) - i, t_idx + i, (l-t_idx-1) + i]
for search_idx in search_idxs:
if search_idx in s_idxs:
mirrored = ( t_idx != search_idx and ((t_idx < b and search_idx >= b) or (search_idx < b and t_idx >= b)) )
new_s[t_idx] = [ sample.copy_and_set(sample_type=SampleType.FACE_YAW_SORTED_AS_TARGET,
mirror=True,
yaw=-sample.yaw,
landmarks=LandmarksProcessor.mirror_landmarks (sample.landmarks, sample.shape[1] ))
for sample in s[search_idx]
] if mirrored else s[search_idx]
break
return new_s