def slice_imgs(imgs, count, size=224, transform=None, overscan=False, micro=None):
def map(x, a, b):
return x * (b-a) + a
rnd_size = torch.rand(count)
rnd_offx = torch.rand(count)
rnd_offy = torch.rand(count)
sz = [img.shape[2:] for img in imgs]
sz_min = [torch.min(torch.tensor(s)) for s in sz]
if overscan is True:
sz = [[2*s[0], 2*s[1]] for s in list(sz)]
imgs = [pad_up_to(imgs[i], sz[i], type='centr') for i in range(len(imgs))]
sliced = []
for i, img in enumerate(imgs):
cuts = []
for c in range(count):
if micro is True: # both scales, micro mode
csize = map(rnd_size[c], 64, max(size, 0.25*sz_min[i])).int()
elif micro is False: # both scales, macro mode
csize = map(rnd_size[c], 0.5*sz_min[i], 0.98*sz_min[i]).int()
else: # single scale
csize = map(rnd_size[c], 112, 0.98*sz_min[i]).int()
offsetx = map(rnd_offx[c], 0, sz[i][1] - csize).int()
offsety = map(rnd_offy[c], 0, sz[i][0] - csize).int()
cut = img[:, :, offsety:offsety + csize, offsetx:offsetx + csize]
cut = F.interpolate(cut, (size,size), mode='bicubic', align_corners=False) # bilinear
if transform is not None:
cut = transform(cut)
cuts.append(cut)
sliced.append(torch.cat(cuts, 0))
return sliced
def slice_imgs(imgs,
count,
size=224,
transform=None,
align='uniform',
micro=1.):
def map(x, a, b):
return x * (b - a) + a
rnd_size = torch.rand(count)
if align == 'central': # normal around center
rnd_offx = torch.clip(torch.randn(count) * 0.2 + 0.5, 0., 1.)
rnd_offy = torch.clip(torch.randn(count) * 0.2 + 0.5, 0., 1.)
else: # uniform
rnd_offx = torch.rand(count)
rnd_offy = torch.rand(count)
sz = [img.shape[2:] for img in imgs]
sz_max = [torch.min(torch.tensor(s)) for s in sz]
if align == 'overscan': # add space
sz = [[2 * s[0], 2 * s[1]] for s in list(sz)]
imgs = [
pad_up_to(imgs[i], sz[i], type='centr') for i in range(len(imgs))
]
sliced = []
for i, img in enumerate(imgs):
cuts = []
sz_max_i = max(size, 0.25 * sz_max[i]) if micro is True else sz_max[i]
if micro is True: # both scales, micro mode
sz_min_i = size // 4
elif micro is False: # both scales, macro mode
sz_min_i = 0.5 * sz_max[i]
else: # single scale
sz_min_i = size if torch.rand(1) < micro else 0.9 * sz_max[i]
for c in range(count):
csize = map(rnd_size[c], sz_min_i, sz_max_i).int()
offsetx = map(rnd_offx[c], 0, sz[i][1] - csize).int()
offsety = map(rnd_offy[c], 0, sz[i][0] - csize).int()
cut = img[:, :, offsety:offsety + csize, offsetx:offsetx + csize]
cut = F.interpolate(cut, (size, size),
mode='bicubic',
align_corners=False) # bilinear
if transform is not None:
cut = transform(cut)
cuts.append(cut)
sliced.append(torch.cat(cuts, 0))
return sliced