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"""
Adapted from the inference.py to demonstate the usage of the util functions.
"""
import sys
import numpy as np
import pydensecrf.densecrf as dcrf
# Get im{read,write} from somewhere.
try:
from cv2 import imread, imwrite
except ImportError:
# Note that, sadly, skimage unconditionally import scipy and matplotlib,
# so you'll need them if you don't have OpenCV. But you probably have them.
from skimage.io import imread, imsave
imwrite = imsave
# TODO: Use scipy instead.
from pydensecrf.utils import unary_from_labels, create_pairwise_bilateral, create_pairwise_gaussian
if len(sys.argv) != 4:
print("Usage: python {} IMAGE ANNO OUTPUT".format(sys.argv[0]))
print("")
print("IMAGE and ANNO are inputs and OUTPUT is where the result should be written.")
print("If there's at least one single full-black pixel in ANNO, black is assumed to mean unknown.")
sys.exit(1)
fn_im = sys.argv[1]
fn_anno = sys.argv[2]
fn_output = sys.argv[3]
##################################
### Read images and annotation ###
##################################
img = imread(fn_im)
# Convert the annotation's RGB color to a single 32-bit integer color 0xBBGGRR
anno_rgb = imread(fn_anno).astype(np.uint32)
anno_lbl = anno_rgb[:,:,0] + (anno_rgb[:,:,1] << 8) + (anno_rgb[:,:,2] << 16)
# Convert the 32bit integer color to 1, 2, ... labels.
# Note that all-black, i.e. the value 0 for background will stay 0.
colors, labels = np.unique(anno_lbl, return_inverse=True)
# But remove the all-0 black, that won't exist in the MAP!
HAS_UNK = 0 in colors
if HAS_UNK:
print("Found a full-black pixel in annotation image, assuming it means 'unknown' label, and will thus not be present in the output!")
print("If 0 is an actual label for you, consider writing your own code, or simply giving your labels only non-zero values.")
colors = colors[1:]
#else:
# print("No single full-black pixel found in annotation image. Assuming there's no 'unknown' label!")
# And create a mapping back from the labels to 32bit integer colors.
colorize = np.empty((len(colors), 3), np.uint8)
colorize[:,0] = (colors & 0x0000FF)
colorize[:,1] = (colors & 0x00FF00) >> 8
colorize[:,2] = (colors & 0xFF0000) >> 16
# Compute the number of classes in the label image.
# We subtract one because the number shouldn't include the value 0 which stands
# for "unknown" or "unsure".
n_labels = len(set(labels.flat)) - int(HAS_UNK)
print(n_labels, " labels", (" plus \"unknown\" 0: " if HAS_UNK else ""), set(labels.flat))
###########################
### Setup the CRF model ###
###########################
use_2d = False
# use_2d = True
if use_2d:
print("Using 2D specialized functions")
# Example using the DenseCRF2D code
d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], n_labels)
# get unary potentials (neg log probability)
U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=HAS_UNK)
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:
print("Using generic 2D functions")
# Example using the DenseCRF class and the util functions
d = dcrf.DenseCRF(img.shape[1] * img.shape[0], n_labels)
# get unary potentials (neg log probability)
U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=HAS_UNK)
d.setUnaryEnergy(U)
# This creates the color-independent features and then add them to the CRF
feats = create_pairwise_gaussian(sdims=(3, 3), shape=img.shape[:2])
d.addPairwiseEnergy(feats, compat=3,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# This creates the color-dependent features and then add them to the CRF
feats = create_pairwise_bilateral(sdims=(80, 80), schan=(13, 13, 13),
img=img, chdim=2)
d.addPairwiseEnergy(feats, compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
####################################
### Do inference and compute MAP ###
####################################
# Run five inference steps.
Q = d.inference(5)
# Find out the most probable class for each pixel.
MAP = np.argmax(Q, axis=0)
# Convert the MAP (labels) back to the corresponding colors and save the image.
# Note that there is no "unknown" here anymore, no matter what we had at first.
MAP = colorize[MAP,:]
imwrite(fn_output, MAP.reshape(img.shape))
# Just randomly manually run inference iterations
Q, tmp1, tmp2 = d.startInference()
for i in range(5):
print("KL-divergence at {}: {}".format(i, d.klDivergence(Q)))
d.stepInference(Q, tmp1, tmp2)
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