def convert_colorspace(inputs, colorspace, inplace=False):
if colorspace.lower() == inputs.colorspace.lower():
if not inplace:
return inputs.im
return
im_out = np.empty_like(inputs.im)
dst = np.ascontiguousarray(np.empty_like(inputs.im[0]))
for ix, im in enumerate(inputs.im):
util.convert_colorspace(im, src_space=inputs.colorspace,
dst_space=colorspace, dst=dst)
im_out[ix] = dst
if inplace:
inputs.im = im_out
inputs.colorspace = colorspace
else:
return im_out
def grab_test_image(key='astro', space='rgb'):
from netharn.util import convert_colorspace
import cv2
if key == 'astro':
url = 'https://i.imgur.com/KXhKM72.png'
elif key == 'carl':
url = 'https://i.imgur.com/oHGsmvF.png'
else:
raise KeyError(key)
fpath = ub.grabdata(url)
bgr = cv2.imread(fpath)
image = convert_colorspace(bgr, space, src_space='bgr')
return image
def grab_test_image(key='astro', space='rgb'):
"""
Args:
key (str): which test image to grab. Valid choices are:
astro - an astronaught
carl - Carl Sagan
paraview - ParaView logo
stars - picture of stars in the sky
space (str): which colorspace to return in (defaults to RGB)
Example:
>>> for key in grab_test_image.keys():
... grab_test_image(key)
"""
from netharn.util import convert_colorspace
import cv2
fpath = grab_test_image_fpath(key)
bgr = cv2.imread(fpath)
image = convert_colorspace(bgr, space, src_space='bgr')
return image
def load_inputs(dset, index):
"""
Ignore:
>>> inputs, task = cifar_inputs(train=False)
>>> workdir = ub.ensuredir(ub.truepath('~/data/work/cifar'))
>>> dset = CIFAR_Wrapper(inputs, task, workdir, 'LAB')
>>> dset._make_normalizer('independent')
>>> index = 0
>>> im, gt = dset.load_inputs(index)
Example:
>>> inputs, task = cifar_inputs(train=False)
>>> workdir = ub.ensuredir(ub.truepath('~/data/work/cifar'))
>>> dset = CIFAR_Wrapper(inputs, task, workdir, 'RGB')
>>> index = 0
>>> im, gt = dset.load_inputs(index)
>>> from netharn.util import mplutil
>>> mplutil.qtensure()
>>> dset = CIFAR_Wrapper(inputs, task, workdir, 'RGB')
>>> dset.augment = True
>>> im, gt = dset.load_inputs(index)
>>> mplutil.imshow(im, colorspace='rgb')
>>> dset = CIFAR_Wrapper(inputs, task, workdir, 'LAB')
>>> dset.augment = True
>>> im, gt = dset.load_inputs(index)
>>> mplutil.imshow(im, colorspace='LAB')
"""
assert dset.inputs.colorspace.lower() == 'rgb', (
'we must be in rgb for augmentation')
im = dset.inputs.im[index]
if dset.inputs.gt is not None:
gt = dset.inputs.gt[index]
else:
gt = None
if dset.augment:
# Image augmentation must be done in RGB
# Augment intensity independently
# im = dset.im_augment(im)
# Augment geometry consistently
# params = dset.rand_aff.random_params()
# im = dset.rand_aff.warp(im, params, interp='cubic', backend='cv2')
im = util.convert_colorspace(im,
src_space=dset.inputs.colorspace,
dst_space='rgb')
# Do augmentation in uint8 RGB
im = (im * 255).astype(np.uint8)
im = dset.augmenter.augment_image(im)
im = (im / 255).astype(np.float32)
im = util.convert_colorspace(im,
src_space='rgb',
dst_space=dset.output_colorspace)
else:
im = util.convert_colorspace(im,
src_space=dset.inputs.colorspace,
dst_space=dset.output_colorspace)
# Do centering of inputs
if dset.center_inputs:
im = dset.center_inputs(im)
return im, gt