def datosParaGraficarKmeans(minSize=2,maxSize=100, step=1, runs=200, nclust = 5, it = 10):
totalC=[] #arreglo con el promedio de comparaciones para cada tamaño del arreglo
totalM=[] #arreglo con el promedio de movimientos
img_clstr = km.K_means(n_clusters = nclust, iterations = it)
for size in range(minSize, maxSize, step):
sum_mov = 0
sum_comp = 0
for i in range(runs):
test_array = createIntArray(size)
img_clstr.fit(test_array)
sum_mov += img_clstr.mov
sum_comp += img_clstr.comp
totalM.append(sum_mov/runs)
totalC.append(sum_comp/runs)
return totalC, totalM
def classify_images(self, img_array, nclust, it, plot_3d = False, listnames = None):
self.image_cluster = km.K_means(n_clusters = nclust, iterations = it)
self.image_cluster.fit(img_array)
self.cluster_map['values'] = img_array
self.cluster_map['labels'] = self.image_cluster.labels_
self.cluster_map['filename'] = listnames
self.complete_center = [False] * len(self.image_cluster.cluster_centers_)
#To plot dominant colors and centers
if plot_3d:
colors = ['red', 'green', 'blue', 'cyan', 'orange']
color_list = []
for label in self.image_cluster.labels_:
color_list.append(colors[int(label)])
fig = plt.figure(2)
ax = Axes3D(fig)
img_array = np.array(img_array)
centers = self.image_cluster.cluster_centers_
ax.scatter(img_array[:, 0], img_array[:, 1], img_array[:, 2], c = color_list)
ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], marker='*', c='#050505', s=500)
