def test_unary(self):
M = 3
U, P, N = 1. / M, 0.8, 0.2 / (M - 1) # Uniform, Positive, Negative
labels = np.array([
[0, 1, 2, 3],
[3, 2, 1, 0],
])
unary = -np.log(
np.array([
[U, P, N, N, N, N, P, U],
[U, N, P, N, N, P, N, U],
[U, N, N, P, P, N, N, U],
]))
np.testing.assert_almost_equal(
utils.compute_unary(labels, M, GT_PROB=P), unary)
def refine_crf(im, lb, gtProb=0.5, posTh=None, negTh=None, crfParams=0):
"""
[ NOTE: Currently only supports n=2 i.e. FG/BG.
For general n: Remove this line: `MAP*=1/MAP.max()` at the end]
Convert a given video into number of shots
im: (h,w,c): 0-255: np.uint8: RGB
lb: (h,w): 0-255: int or float
=> int: it should have labels 1,..,n and must have one '0' special
label which is not a label, but the special value indicating
no-confidence region.
=> float: it should probabilities in [0,1]. Func will assign:
label=2 to region with prob>=posTh
label=1 to region with prob<=negTh
label=0 to region with negTh<prob<posTh
crfParams:
value: 0: default crf params
value: 1: deeplab crf params
value: 2: ccnn crf params
out: (h,w): np.uint8:
For n=2: output labels are 0 and 1
0 means BG or uncertain (i.e. lb=0,1)
1 means FG (i.e. lb=2)
For general n: Remove this line: `MAP*=1/MAP.max()` at the end
Then, label space is same as input i.e. in 0..n
"""
# Hard coded CRF parameters
iters = 5
if crfParams == 1:
# Deeplab Params
xy_gauss = 19
wt_gauss = 15
xy_bilateral = 61
rgb_bilateral = 10
wt_bilateral = 35
elif crfParams == 2:
# untuned ccnn params
xy_gauss = 6
wt_gauss = 6
xy_bilateral = 50
rgb_bilateral = 4
wt_bilateral = 5
else:
# Default Params
xy_gauss = 3
wt_gauss = 3
xy_bilateral = 80
rgb_bilateral = 13
wt_bilateral = 10
# take care of probability mask
if lb.dtype == np.float32 or lb.dtype == np.float64:
if posTh is None or negTh is None:
print('For probability mask, labels are not given !')
return
lb1 = np.zeros(lb.shape, dtype=np.uint8)
lb1[lb >= posTh] = 2
lb1[lb <= negTh] = 1
presentLb = np.unique(lb1)
if presentLb.size < 3:
if 2 not in presentLb:
y, x = int(lb.shape[0] / 2), int(lb.shape[1] / 2)
lb1[y - 1:y + 1, x - 1:x + 1] = 2 # center area as FG
if 1 not in presentLb:
lb1[0, :] = 1 # top row as BG
if 0 not in presentLb:
lb1[1, :] = 0 # second row: doesn't matter
lb = lb1
# convert to BGR
im = np.ascontiguousarray(im[..., ::-1])
# Compute the number of classes in the label image
M = len(set(lb.flat))
# Setup the 2D-CRF model
d = dcrf.DenseCRF2D(im.shape[1], im.shape[0], M)
# get unary potentials (neg log probability)
U = compute_unary(lb, M)
d.setUnaryEnergy(U)
# This adds the color-independent term, features are the locations only.
d.addPairwiseGaussian(sxy=(xy_gauss, xy_gauss), compat=wt_gauss)
# This adds the color-dependent term, i.e. features are (x,y,r,g,b).
d.addPairwiseBilateral(sxy=(xy_bilateral, xy_bilateral),
srgb=(rgb_bilateral, rgb_bilateral, rgb_bilateral),
rgbim=im,
compat=wt_bilateral)
# Do inference and compute map
Q = d.inference(iters)
MAP = np.argmax(Q, axis=0).astype('float32')
MAP *= 1 / MAP.max()
MAP = MAP.reshape(im.shape[:2])
out = MAP.astype('uint8')
return out
### Read images and annotation ###
##################################
img = cv2.imread(fn_im)
labels = relabel_sequential(cv2.imread(fn_anno, 0))[0].flatten()
M = 21 # 21 Classes to match the C++ example
###########################
### Setup the CRF model ###
###########################
use_2d = False
if use_2d:
# Example using the DenseCRF2D code
d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], M)
# get unary potentials (neg log probability)
U = compute_unary(labels, M)
d.setUnaryEnergy(U)
# This adds the color-independent term, features are the locations only.
d.addPairwiseGaussian(sxy=(3, 3),
compat=3,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# This adds the color-dependent term, i.e. features are (x,y,r,g,b).
d.addPairwiseBilateral(sxy=(80, 80),
srgb=(13, 13, 13),
rgbim=img,
compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
### Read images and annotation ###
##################################
img = cv2.imread(fn_im)
labels = relabel_sequential(cv2.imread(fn_anno, 0))[0].flatten()
M = 21 # 21 Classes to match the C++ example
###########################
### Setup the CRF model ###
###########################
use_2d = False
if use_2d:
# Example using the DenseCRF2D code
d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], M)
# get unary potentials (neg log probability)
U = compute_unary(labels, M)
d.setUnaryEnergy(U)
# This adds the color-independent term, features are the locations only.
d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# This adds the color-dependent term, i.e. features are (x,y,r,g,b).
d.addPairwiseBilateral(sxy=(80, 80), srgb=(13, 13, 13), rgbim=img,
compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
else:
# Example using the DenseCRF class and the util functions
d = dcrf.DenseCRF(img.shape[0] * img.shape[1], M)