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SAE/SAEHD:

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removed option 'apply random ct'

added option
   Color transfer mode apply to src faceset. ( none/rct/lct/mkl/idt, ?:help skip: none )
   Change color distribution of src samples close to dst samples. Try all modes to find the best.
before was lct mode, but sometime it does not work properly for some facesets.
This commit is contained in:
Colombo 2019-10-15 11:22:19 +04:00
parent 5a2eefaa5b
commit 73511a072a
3 changed files with 29 additions and 22 deletions
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19
samplelib/SampleProcessor.py
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@ -37,7 +37,7 @@ opts:
'resolution' : N
'motion_blur' : (chance_int, range) - chance 0..100 to apply to face (not mask), and max_size of motion blur
'apply_ct' : bool
'ct_mode' :
'normalize_tanh' : bool
"""
@ -125,7 +125,7 @@ class SampleProcessor(object):
gaussian_blur = opts.get('gaussian_blur', None)
random_hsv_shift = opts.get('random_hsv_shift', None)
apply_ct = opts.get('apply_ct', False)
ct_mode = opts.get('ct_mode', 'None')
normalize_tanh = opts.get('normalize_tanh', False)
img_type = SPTF.NONE
@ -244,14 +244,23 @@ class SampleProcessor(object):
img_bgr = img[...,0:3]
img_mask = img[...,3:4]
if apply_ct and ct_sample is not None:
if ct_mode is not None and ct_sample is not None:
if ct_sample_bgr is None:
ct_sample_bgr = ct_sample.load_bgr()
ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR )
img_bgr = imagelib.linear_color_transfer (img_bgr, ct_sample_bgr_resized)
img_bgr = np.clip( img_bgr, 0.0, 1.0)
if ct_mode == 'lct':
img_bgr = imagelib.linear_color_transfer (img_bgr, ct_sample_bgr_resized)
img_bgr = np.clip( img_bgr, 0.0, 1.0)
elif ct_mode == 'rct':
img_bgr = imagelib.reinhard_color_transfer ( np.clip( (img_bgr*255).astype(np.uint8), 0, 255),
np.clip( (ct_sample_bgr_resized*255).astype(np.uint8), 0, 255) )
img_bgr = np.clip( img_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
elif ct_mode == 'mkl':
img_bgr = imagelib.color_transfer_mkl (img_bgr, ct_sample_bgr_resized)
elif ct_mode == 'idt':
img_bgr = imagelib.color_transfer_idt (img_bgr, ct_sample_bgr_resized)
if random_hsv_shift:
rnd_state = np.random.RandomState (sample_rnd_seed)