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imagelib : apply_random_sharpen

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iperov 2021-04-22 18:16:42 +04:00
commit dcf146cc16
2 changed files with 47 additions and 28 deletions
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1
core/imagelib/__init__.py
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@ -22,6 +22,7 @@ from .blursharpen import LinearMotionBlur, blursharpen
from .filters import apply_random_rgb_levels, \
apply_random_hsv_shift, \
apply_random_sharpen, \
apply_random_motion_blur, \
apply_random_gaussian_blur, \
apply_random_nearest_resize, \

74
core/imagelib/filters.py
View file

@ -1,47 +1,65 @@
import numpy as np
from .blursharpen import LinearMotionBlur
from .blursharpen import LinearMotionBlur, blursharpen
import cv2
def apply_random_rgb_levels(img, mask=None, rnd_state=None):
if rnd_state is None:
rnd_state = np.random
np_rnd = rnd_state.rand
inBlack = np.array([np_rnd()*0.25 , np_rnd()*0.25 , np_rnd()*0.25], dtype=np.float32)
inWhite = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
inGamma = np.array([0.5+np_rnd(), 0.5+np_rnd(), 0.5+np_rnd()], dtype=np.float32)
outBlack = np.array([np_rnd()*0.25 , np_rnd()*0.25 , np_rnd()*0.25], dtype=np.float32)
outWhite = np.array([1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25, 1.0-np_rnd()*0.25], dtype=np.float32)
result = np.clip( (img - inBlack) / (inWhite - inBlack), 0, 1 )
result = ( result ** (1/inGamma) ) * (outWhite - outBlack) + outBlack
result = np.clip(result, 0, 1)
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_hsv_shift(img, mask=None, rnd_state=None):
if rnd_state is None:
rnd_state = np.random
h, s, v = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
h = ( h + rnd_state.randint(360) ) % 360
s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
result = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_sharpen( img, chance, kernel_max_size, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
sharp_rnd_kernel = rnd_state.randint(kernel_max_size)+1
result = img
if rnd_state.randint(100) < np.clip(chance, 0, 100):
if rnd_state.randint(2) == 0:
result = blursharpen(result, 1, sharp_rnd_kernel, rnd_state.randint(10) )
else:
result = blursharpen(result, 2, sharp_rnd_kernel, rnd_state.randint(50) )
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
mblur_rnd_kernel = rnd_state.randint(mb_max_size)+1
mblur_rnd_deg = rnd_state.randint(360)
@ -50,22 +68,22 @@ def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=Non
result = LinearMotionBlur (result, mblur_rnd_kernel, mblur_rnd_deg )
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_gaussian_blur( img, chance, kernel_max_size, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
result = img
if rnd_state.randint(100) < np.clip(chance, 0, 100):
gblur_rnd_kernel = rnd_state.randint(kernel_max_size)*2+1
result = cv2.GaussianBlur(result, (gblur_rnd_kernel,)*2 , 0)
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
@ -73,24 +91,24 @@ def apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINE
result = img
if rnd_state.randint(100) < np.clip(chance, 0, 100):
h,w,c = result.shape
trg = rnd_state.rand()
rw = w - int( trg * int(w*(max_size_per/100.0)) )
rh = h - int( trg * int(h*(max_size_per/100.0)) )
rw = w - int( trg * int(w*(max_size_per/100.0)) )
rh = h - int( trg * int(h*(max_size_per/100.0)) )
result = cv2.resize (result, (rw,rh), interpolation=interpolation )
result = cv2.resize (result, (w,h), interpolation=interpolation )
if mask is not None:
result = img*(1-mask) + result*mask
return result
def apply_random_nearest_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_NEAREST, mask=mask, rnd_state=rnd_state )
def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
return apply_random_resize( img, chance, max_size_per, interpolation=cv2.INTER_LINEAR, mask=mask, rnd_state=rnd_state )
def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
@ -98,14 +116,14 @@ def apply_random_jpeg_compress( img, chance, mask=None, rnd_state=None ):
result = img
if rnd_state.randint(100) < np.clip(chance, 0, 100):
h,w,c = result.shape
quality = rnd_state.randint(10,101)
ret, result = cv2.imencode('.jpg', np.clip(img*255, 0,255).astype(np.uint8), [int(cv2.IMWRITE_JPEG_QUALITY), quality] )
if ret == True:
result = cv2.imdecode(result, flags=cv2.IMREAD_UNCHANGED)
result = result.astype(np.float32) / 255.0
if mask is not None:
result = img*(1-mask) + result*mask
return result