Merge pull request #4 from kevin-keraudren/example-dense-inference

Example dense inference

dense_inference.py

+#!/usr/bin/python
+
+"""
+Usage: python dense_inference.py image annotations output
+
+Adapted from the original C++ example: densecrf/examples/dense_inference.cpp
+http://www.philkr.net/home/densecrf Version 2.2
+"""
+
+import numpy as np
+import cv2
+import densecrf as dcrf
+from skimage.segmentation import relabel_sequential
+import sys
+
+img = cv2.imread(sys.argv[1], 1)
+labels = relabel_sequential(cv2.imread(sys.argv[2], 0))[0].flatten()
+output = sys.argv[3]
+
+M = labels.max() + 1  # number of labels
+
+# Setup the CRF model
+d = dcrf.DenseCRF2D(img.shape[0], img.shape[1], M)
+
+# Certainty that the ground truth is correct
+GT_PROB = 0.5
+
+# Simple classifier that is 50% certain that the annotation is correct
+u_energy = -np.log(1.0 / M)
+n_energy = -np.log((1.0 - GT_PROB) / (M - 1))
+p_energy = -np.log(GT_PROB)
+
+U = np.zeros((M, img.shape[0] * img.shape[1]), dtype='float32')
+U[:, labels > 0] = n_energy
+U[labels, np.arange(U.shape[1])] = p_energy
+U[:, labels == 0] = u_energy
+d.setUnaryEnergy(U)
+
+d.addPairwiseGaussian(sxy=3, compat=3)
+d.addPairwiseBilateral(sxy=80, srgb=13, rgbim=img, compat=10)
+
+# Do the inference
+res = np.argmax(d.inference(5), axis=0).astype('float32')
+
+res *= 255 / res.max()
+res = res.reshape(img.shape[:2])
+cv2.imwrite(output, res.astype('uint8'))