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XSeg sample generator: additional sample augmentation for training

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iperov 2021-04-30 16:49:32 +04:00
commit 081d8faa45
3 changed files with 26 additions and 7 deletions
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1
core/imagelib/__init__.py
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@ -21,6 +21,7 @@ from .SegIEPolys import *
from .blursharpen import LinearMotionBlur, blursharpen
from .filters import apply_random_rgb_levels, \
apply_random_overlay_triangle, \
apply_random_hsv_shift, \
apply_random_sharpen, \
apply_random_motion_blur, \

22
core/imagelib/filters.py
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@ -128,7 +128,29 @@ def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
return result
def apply_random_overlay_triangle( img, max_alpha, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
h,w,c = img.shape
pt1 = [rnd_state.randint(w), rnd_state.randint(h) ]
pt2 = [rnd_state.randint(w), rnd_state.randint(h) ]
pt3 = [rnd_state.randint(w), rnd_state.randint(h) ]
alpha = rnd_state.uniform()*max_alpha
tri_mask = cv2.fillPoly( np.zeros_like(img), [ np.array([pt1,pt2,pt3], np.int32) ], (alpha,)*c )
if rnd_state.randint(2) == 0:
result = np.clip(img+tri_mask, 0, 1)
else:
result = np.clip(img-tri_mask, 0, 1)
if mask is not None:
result = img*(1-mask) + result*mask
return result
def _min_resize(x, m):
if x.shape[0] < x.shape[1]:
s0 = m