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SampleProcessor: add denoise filter

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iperov 2021-08-20 17:06:43 +04:00
parent b256b07e03
commit e41f87e682
1 changed files with 8 additions and 6 deletions
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14
samplelib/SampleProcessor.py
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@ -108,6 +108,7 @@ class SampleProcessor(object):
transform = opts.get('transform', False)
motion_blur = opts.get('motion_blur', None)
gaussian_blur = opts.get('gaussian_blur', None)
denoise_filter = opts.get('denoise_filter', False)
random_bilinear_resize = opts.get('random_bilinear_resize', None)
random_rgb_levels = opts.get('random_rgb_levels', False)
random_hsv_shift = opts.get('random_hsv_shift', False)
@ -150,6 +151,7 @@ class SampleProcessor(object):
img = np.zeros ( sample_bgr.shape[0:2]+(1,), dtype=np.float32)
if sample_face_type == FaceType.MARK_ONLY:
raise NotImplementedError()
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
@ -181,6 +183,7 @@ class SampleProcessor(object):
elif sample_type == SPST.FACE_IMAGE:
img = sample_bgr
if random_rgb_levels:
random_mask = sd.random_circle_faded ([w,w], rnd_state=np.random.RandomState (sample_rnd_seed) ) if random_circle_mask else None
img = imagelib.apply_random_rgb_levels(img, mask=random_mask, rnd_state=np.random.RandomState (sample_rnd_seed) )
@ -207,9 +210,6 @@ class SampleProcessor(object):
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate)
img = np.clip(img.astype(np.float32), 0, 1)
if motion_blur is not None:
random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+2)) if random_circle_mask else None
@ -222,9 +222,11 @@ class SampleProcessor(object):
if random_bilinear_resize is not None:
random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+4)) if random_circle_mask else None
img = imagelib.apply_random_bilinear_resize(img, *random_bilinear_resize, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+4) )
if denoise_filter:
d_size = ( (max(*img.shape[:2]) // 128) + 1 )*2 +1
img = cv2.bilateralFilter( np.clip(img*255, 0,255).astype(np.uint8), d_size, 80, 80).astype(np.float32) / 255.0
# Transform from BGR to desired channel_type
if channel_type == SPCT.BGR:
out_sample = img