AntoineH/smart_augmentation
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Evite redefinition inutile de prob dist + Fix mineur transformation

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Harle, Antoine (Contracteur) 2020-01-27 17:29:25 -05:00
parent 923ef7b85e
commit a2135e4709
2 changed files with 44 additions and 41 deletions
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54
higher/smart_aug/transformations.py
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@ -429,31 +429,31 @@ def auto_contrast(x):
x = int_image(x) #Expect image in the range of [0, 1]
#print('Start',x[0])
for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
#print(img.shape)
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
#print(chan.shape)
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
#print(img.shape)
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
#print(chan.shape)
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
# find lowest/highest samples after preprocessing
for lo in range(256):
if hist[lo]:
break
for hi in range(255, -1, -1):
if hist[hi]:
break
if hi <= lo:
# don't bother
pass
else:
scale = 255.0 / (hi - lo)
offset = -lo * scale
for ix in range(256):
n_ix = int(ix * scale + offset)
if n_ix < 0: n_ix = 0
elif n_ix > 255: n_ix = 255
# find lowest/highest samples after preprocessing
for lo in range(256):
if hist[lo]:
break
for hi in range(255, -1, -1):
if hist[hi]:
break
if hi <= lo:
# don't bother
pass
else:
scale = 255.0 / (hi - lo)
offset = -lo * scale
for ix in range(256):
n_ix = int(ix * scale + offset)
if n_ix < 0: n_ix = 0
elif n_ix > 255: n_ix = 255
chan[chan==ix]=n_ix
x[im_idx, chan_idx]=chan
chan[chan==ix]=n_ix
x[im_idx, chan_idx]=chan
#print('End',x[0])
return float_image(x)
@ -468,9 +468,9 @@ def equalize(x):
x = int_image(x) #Expect image in the range of [0, 1]
#print('Start',x[0])
for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image
#print(img.shape)
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
#print(chan.shape)
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
#print(img.shape)
for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel
#print(chan.shape)
hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
return float_image(x)