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fix sample processor

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iperov 2021-10-09 13:56:53 +04:00
commit f8469fe4d7
3 changed files with 36 additions and 127 deletions
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11
core/imagelib/warp.py
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@ -104,10 +104,11 @@ def gen_pts(W, H, rnd_state=None):
return pts1, pts2
def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None ):
def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None, warp_rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
if warp_rnd_state is None:
warp_rnd_state = np.random
rw = None
if w < 64:
rw = w
@ -120,13 +121,13 @@ def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5,
p_flip = flip and rnd_state.randint(10) < 4
#random warp V1
cell_size = [ w // (2**i) for i in range(1,4) ] [ rnd_state.randint(3) ]
cell_size = [ w // (2**i) for i in range(1,4) ] [ warp_rnd_state.randint(3) ]
cell_count = w // cell_size + 1
grid_points = np.linspace( 0, w, cell_count)
mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
mapy = mapx.T
mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2), rnd_state=warp_rnd_state )*(cell_size*0.24)
mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2), rnd_state=warp_rnd_state )*(cell_size*0.24)
half_cell_size = cell_size // 2
mapx = cv2.resize(mapx, (w+cell_size,)*2 )[half_cell_size:-half_cell_size,half_cell_size:-half_cell_size].astype(np.float32)
mapy = cv2.resize(mapy, (w+cell_size,)*2 )[half_cell_size:-half_cell_size,half_cell_size:-half_cell_size].astype(np.float32)

6
core/randomex.py
View file

@ -1,12 +1,14 @@
import numpy as np
def random_normal( size=(1,), trunc_val = 2.5 ):
def random_normal( size=(1,), trunc_val = 2.5, rnd_state=None ):
if rnd_state is None:
rnd_state = np.random
len = np.array(size).prod()
result = np.empty ( (len,) , dtype=np.float32)
for i in range (len):
while True:
x = np.random.normal()
x = rnd_state.normal()
if x >= -trunc_val and x <= trunc_val:
break
result[i] = (x / trunc_val)