def graphcut(self, label_costs, l=100):
num_classes = len(self.colors_present)
#calculate pariwise potiential costs (distance between color classes)
pairwise_costs = np.zeros((num_classes, num_classes))
for ii in range(num_classes):
for jj in range(num_classes):
c1 = np.array(self.label_to_color_map[ii])
c2 = np.array(self.label_to_color_map[jj])
pairwise_costs[ii,jj] = np.linalg.norm(c1-c2)
label_costs_int32 = (100*label_costs).astype('int32')
pairwise_costs_int32 = (l*pairwise_costs).astype('int32')
vv_int32 = (self.g).astype('int32')
vh_int32 = (self.g).astype('int32')
#vv_int32 = (1/np.clip(self.g,0.00001,10000)).astype('int32')
#vh_int32 = (1/np.clip(self.g,0.00001,10000)).astype('int32')
#perform graphcut optimization
new_labels = pygco.cut_simple_vh(label_costs_int32, pairwise_costs_int32, vv_int32, vh_int32, n_iter=10, algorithm='swap')
#new_labels = pygco.cut_simple(label_costs_int32, pairwise_costs_int32, algorithm='swap')
return new_labels
def graph_cut(self,im):
color_costs = np.zeros((self.num_colors,self.num_colors))
for x in range(self.num_colors):
for y in range(self.num_colors):
color_costs[x][y] = np.linalg.norm(self.colors[x]-self.colors[y])
color_costs = (color_costs).astype('int32')
temp = np.hstack(np.array([-1.0*self.distances[i] for i in range(self.num_colors)]))
new_shape = (im.shape[0],im.shape[1],self.num_colors)
label_affinity = (100*temp.reshape(new_shape)).astype('int32')
blurred = cv2.GaussianBlur(im, (0, 0), 3)
vh = cv2.Sobel(blurred, -1, 1, 0)
vv = cv2.Sobel(blurred, -1, 0, 1)
edges = (0.5*vv + 0.5*vh).astype('int32')
gc = pygco.cut_simple_vh(label_affinity, color_costs, edges, edges, n_iter=10, algorithm='swap')
return gc
