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iperov 2019-04-25 14:46:39 +04:00
parent c0a63addd4
commit 0e088f6415
4 changed files with 272 additions and 123 deletions
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70
models/Model_DEV_POSEEST/Model.py
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@ -7,25 +7,33 @@ from facelib import PoseEstimator
from samplelib import *
from interact import interact as io
import imagelib
class Model(ModelBase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs,
ask_write_preview_history=False,
super().__init__(*args, **kwargs,
ask_write_preview_history=False,
ask_target_iter=False,
ask_sort_by_yaw=False,
ask_random_flip=False,
ask_src_scale_mod=False)
#override
def onInitializeOptions(self, is_first_run, ask_override):
def onInitializeOptions(self, is_first_run, ask_override):
yn_str = {True:'y',False:'n'}
default_face_type = 'f'
if is_first_run:
self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower()
else:
self.options['face_type'] = self.options.get('face_type', default_face_type)
def_train_bgr = self.options.get('train_bgr', True)
if is_first_run or ask_override:
self.options['train_bgr'] = io.input_bool ("Train bgr? (y/n, ?:help skip: %s) : " % (yn_str[def_train_bgr]), def_train_bgr)
else:
self.options['train_bgr'] = self.options.get('train_bgr', def_train_bgr)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
@ -34,9 +42,9 @@ class Model(ModelBase):
self.resolution = 128
self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
self.pose_est = PoseEstimator(self.resolution,
FaceType.toString(self.face_type),
self.pose_est = PoseEstimator(self.resolution,
FaceType.toString(self.face_type),
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True)
@ -45,42 +53,44 @@ class Model(ModelBase):
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
self.set_training_data_generators ([
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution, 'motion_blur':(25, 1) },
output_sample_types=[ {'types': (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution, 'motion_blur':(25, 1) },
{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution },
{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M, t.FACE_MASK_FULL), 'resolution':self.resolution },
{'types': (t.IMG_PITCH_YAW_ROLL,)}
]),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, generators_count=4,
sample_process_options=SampleProcessor.Options( rotation_range=[0,0] ), #random_flip=True,
output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE), 'resolution':self.resolution },
{'types': (t.IMG_PITCH_YAW_ROLL,)}
])
])
#override
def onSave(self):
def onSave(self):
self.pose_est.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, pitch_yaw_roll = generators_samples[0]
pitch_loss,yaw_loss,roll_loss = self.pose_est.train_on_batch( target_src, pitch_yaw_roll )
target_srcw, target_src, target_srcm, pitch_yaw_roll = generators_samples[0]
bgr_loss, pitch_loss,yaw_loss,roll_loss = self.pose_est.train_on_batch( target_srcw, target_src, target_srcm, pitch_yaw_roll, skip_bgr_train=not self.options['train_bgr'] )
return ( ('bgr_loss', bgr_loss), ('pitch_loss', pitch_loss), ('yaw_loss', yaw_loss), ('roll_loss', roll_loss) )
return ( ('pitch_loss', pitch_loss), ('yaw_loss', yaw_loss), ('roll_loss', roll_loss) )
#override
def onGetPreview(self, generators_samples):
test_src = generators_samples[0][0][0:4] #first 4 samples
test_pyr_src = generators_samples[0][1][0:4]
test_src = generators_samples[0][1][0:4] #first 4 samples
test_pyr_src = generators_samples[0][3][0:4]
test_dst = generators_samples[1][0][0:4]
test_pyr_dst = generators_samples[1][1][0:4]
h,w,c = self.resolution,self.resolution,3
h_line = 13
result = []
for name, img, pyr in [ ['training data', test_src, test_pyr_src], \
['evaluating data',test_dst, test_pyr_dst] ]:
@ -91,18 +101,18 @@ class Model(ModelBase):
img_info = np.ones ( (h,w,c) ) * 0.1
lines = ["%s" % ( str(pyr[i]) ),
"%s" % ( str(pyr_pred[i]) ) ]
lines_count = len(lines)
for ln in range(lines_count):
for ln in range(lines_count):
img_info[ ln*h_line:(ln+1)*h_line, 0:w] += \
imagelib.get_text_image ( (h_line,w,c), lines[ln], color=[0.8]*c )
hor_imgs.append ( np.concatenate ( (
img[i,:,:,0:3],
img_info
), axis=1) )
result += [ (name, np.concatenate (hor_imgs, axis=0)) ]
result += [ (name, np.concatenate (hor_imgs, axis=0)) ]
return result