Added example for inference with the new util functions.

util_inference_example.py

+"""
+Usage: python util_inference_example.py image annotations
+
+Adapted from the dense_inference.py to demonstate the usage of the util
+functions.
+"""
+
+import sys
+import numpy as np
+import cv2
+import densecrf as dcrf
+import matplotlib.pylab as plt
+from skimage.segmentation import relabel_sequential
+
+from utils import compute_unary, create_pairwise_bilateral, \
+    create_pairwise_gaussian
+
+fn_im = sys.argv[1]
+fn_anno = sys.argv[2]
+
+##################################
+### 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)
+
+    # get unary potentials (neg log probability)
+    U = compute_unary(labels, M)
+    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 ###
+####################################
+Q = d.inference(5)
+map = np.argmax(Q, axis=0).reshape(img.shape[:2])
+
+res = map.astype('float32') * 255 / map.max()
+plt.imshow(res)
+plt.show()
+
+
+# Manually inference
+Q, tmp1, tmp2 = d.startInference()
+for i in range(5):
+    print("KL-divergence at {}: {}".format(i, d.klDivergence(Q)))
+    d.stepInference(Q, tmp1, tmp2)
+