# Create CRF model and add potentials
# =============================================================================
#zero_unsure: whether zero is a class, if its False, it means zero canb be any of other classes
# =============================================================================
# crf = crf_model.DenseCRF(
# num_classes = 3,
# zero_unsure = True, # The number of output classes
# unary_potential=unary,
# pairwise_potentials=[bilateral_pairwise, gaussian_pairwise],
# use_2d = 'rgb-2d' #'rgb-1d' or 'rgb-2d' or 'non-rgb'
# )
# =============================================================================
crf = crf_model.DenseCRF(
num_classes=5,
zero_unsure=False, # The number of output classes
unary_potential=unary,
pairwise_potentials=[bilateral_pairwise, gaussian_pairwise],
use_2d='rgb-1d' #'rgb-1d' or 'rgb-2d' or 'non-rgb'
)
def crfing(rootpath, image, probabilities, count):
# =============================================================================
# Set the CRF model
# =============================================================================
#label_source: whether label is from softmax, or other type of label.
crf.set_image(
image=image,
probabilities=probabilities,
colour_axis=
-1, # The axis corresponding to colour in the image numpy shape
bilateral_pairwise = potentials.BilateralPotential(
sdims=10,
schan=0.01,
compatibility=10,
kernel=dense_crf.DIAG_KERNEL,
normalization=dense_crf.NORMALIZE_SYMMETRIC)
# =============================================================================
# Create CRF model and add potentials
# =============================================================================
#zero_unsure: whether zero is a class, if its False, it means zero canb be any of other classes
crf = crf_model.DenseCRF(
num_classes=2, # The number of output classes
zero_unsure=False,
unary_potential=unary,
pairwise_potentials=bilateral_pairwise,
use_2d='non-rgb' #'rgb-1d' or 'rgb-2d' or 'non-rgb'
)
# =============================================================================
# Load image and probabilities, use the original progect code
# =============================================================================
H, W, NLABELS = 400, 512, 2
# This creates a gaussian blob...
pos = np.stack(np.mgrid[0:H, 0:W], axis=2)
rv = multivariate_normal([H // 2, W // 2], (H // 4) * (W // 4))
probs = rv.pdf(pos)
# ...which we project into the range [0.4, 0.6]
probs = (probs - probs.min()) / (probs.max() - probs.min())
probs = 0.5 + 0.2 * (probs - 0.5)