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XSeg training generator: added additional sample augmentations

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iperov 2021-04-10 09:59:49 +04:00
parent 457a39c093
commit d676a365f7
1 changed files with 14 additions and 6 deletions
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18
samplelib/SampleGeneratorFaceXSeg.py
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@ -79,6 +79,7 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
random_bilinear_resize_chance, random_bilinear_resize_max_size_per = 25,75
motion_blur_chance, motion_blur_mb_max_size = 25, 5
gaussian_blur_chance, gaussian_blur_kernel_max_size = 25, 5
random_jpeg_compress_chance = 25
def gen_img_mask(sample):
img = sample.load_bgr()
@ -130,14 +131,15 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
bg_img, bg_mask = gen_img_mask(bg_sample)
bg_wp = imagelib.gen_warp_params(resolution, True, rotation_range=[-180,180], scale_range=[-0.10, 0.10], tx_range=[-0.10, 0.10], ty_range=[-0.10, 0.10] )
bg_img = imagelib.warp_by_params (bg_wp, bg_img, can_warp=False, can_transform=True, can_flip=True, border_replicate=False)
bg_img = imagelib.warp_by_params (bg_wp, bg_img, can_warp=False, can_transform=True, can_flip=True, border_replicate=True)
bg_mask = imagelib.warp_by_params (bg_wp, bg_mask, can_warp=False, can_transform=True, can_flip=True, border_replicate=False)
c_mask = (1-bg_mask) * (1-mask)
img = img*(1-c_mask) + bg_img * c_mask
c_mask = 1.0 - (1-bg_mask) * (1-mask)
rnd = np.random.uniform()
img = img*(c_mask) + img*(1-c_mask)*rnd + bg_img*(1-c_mask)*(1-rnd)
warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
img = imagelib.warp_by_params (warp_params, img, can_warp=True, can_transform=True, can_flip=True, border_replicate=True)
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False)
img = np.clip(img.astype(np.float32), 0, 1)
@ -152,7 +154,13 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
img = imagelib.apply_random_motion_blur( img, motion_blur_chance, motion_blur_mb_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_gaussian_blur( img, gaussian_blur_chance, gaussian_blur_kernel_max_size, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
if np.random.randint(2) == 0:
img = imagelib.apply_random_nearest_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
else:
img = imagelib.apply_random_bilinear_resize( img, random_bilinear_resize_chance, random_bilinear_resize_max_size_per, mask=sd.random_circle_faded ([resolution,resolution]))
img = imagelib.apply_random_jpeg_compress( img, random_jpeg_compress_chance, mask=sd.random_circle_faded ([resolution,resolution]))
if data_format == "NCHW":
img = np.transpose(img, (2,0,1) )