def kmean_statistics(images, options, kmax=10, nsamples=250):
global_times = np.zeros((kmax-1))
global_scores = np.zeros((kmax-1))
global_iterations = np.zeros((kmax-1))
for ix, input in enumerate(images[:nsamples]):
local_times = []
local_scores = []
local_iterations = []
kms = km.KMeans(input, 1, options)
for k in range(1, kmax+1):
start = time.time()
kms.K = k
kms.fit()
score = kms.perform_score()
end = time.time()
elapsed = end - start
global_times[k-2] += elapsed
global_scores[k-2] += score
global_iterations[k-2] += kms.num_iter
# local_scores.append(score)
# local_iterations.append(kms.num_iter)
# local_times.append(elapsed)
# print("Results for image " + str(ix) + " with k=" + str(k))
# print("Score: " + str(score))
# print("Iterations needed: " + str(kms.num_iter))
# print("Elapsed time: " + str(elapsed))
# print("")
# visualize_k_means(kms, input.shape)
# score_series = pd.Series(local_scores, index=list(range(2,kmax+1)), name="Score")
# score_series.plot(legend=True)
# plt.show()
# iterations_series = pd.Series(local_iterations, index=list(range(2,kmax+1)), name="Iterations")
# iterations_series.plot(legend=True)
# plt.show()
# time_series = pd.Series(local_times, index=list(range(2,kmax+1)), name="Time")
# time_series.plot(legend=True)
# plt.show()
global_scores /= images.shape[0]
global_iterations /= images.shape[0]
global_times /= images.shape[0]
score_series = pd.Series(global_scores, index=list(range(1,kmax+1)), name="Score")
score_series.plot(legend=True)
plt.show()
iterations_series = pd.Series(global_iterations, index=list(range(1,kmax+1)), name="Iterations")
iterations_series.plot(legend=True)
plt.show()
time_series = pd.Series(global_times, index=list(range(1,kmax+1)), name="Time")
time_series.plot(legend=True)
plt.show()
def kmeans_statistics(images, KMax):
times = []
iterations = []
wcds = []
for i in range(2, KMax):
km = Kmeans.KMeans(images, i)
time1 = time.time()
iterations_needed = km.fit()
times.append(time.time() - time1)
iterations.append(iterations_needed)
wcds.append(km.whitinClassDistance())
return times, iterations, wcds
def get_color_predictions(images, max_k):
# preds = np.empty((len(images), k), dtype='<U8')
preds = []
for ix, input in enumerate(images):
# S'ha observat que el nombre d'iteracions necessàries era proper a k*5.
# Si el sobrepassa, és que no està essent eficient
# La tolerància podria ser 0.05 però no val la pena
kms = km.KMeans(input, 1, {"km_init": "kmeans++", "max_iter": max_k*5, "threshold": 0.35, "fitting": "DB", "tolerance": 0.1, "background_mask": 250})
kms.find_bestK(max_k)
kms.fit()
preds.append(km.get_colors(kms.centroids))
return np.array(preds)
def get_kmeans_accuracy(kmeans_labels_test, images, KMax, max_images_to_use,
options):
plt.clf()
accerted_ratios_for_all_images = []
print("estimated time: 1 minute")
if len(used_kmeans_images) != max_images_to_use:
for i in range(len(used_kmeans_images), max_images_to_use):
number_to_use = random.randint(0, images.shape[0])
used_kmeans_images.append(number_to_use)
time1 = time.time()
for number_to_use in used_kmeans_images:
accerted_ratios = []
for j in range(2, KMax):
km = Kmeans.KMeans(images[number_to_use], j, options)
km.fit()
returned_from_kmeans_color_labels = Kmeans.get_colors(km.centroids)
accerted = get_color_accuracy(kmeans_labels_test[number_to_use],
returned_from_kmeans_color_labels)
#visualize_k_means(km, images[number_to_use].shape)
accerted_ratios.append(accerted)
accerted_ratios_for_all_images.append(accerted_ratios)
for i in range(len(used_kmeans_images)):
plt.scatter(list(range(2, KMax)),
accerted_ratios_for_all_images[i],
label="image " + str(used_kmeans_images[i]))
plt.legend()
plt.title("KMeans accerted % " + options["km_init"] + " ratio")
plt.xlabel("K")
plt.ylabel("accerted % ratios kmeans")
plt.savefig(output_folder + "kmeans " + options["km_init"] +
" Accerted.png")
print(time.time() - time1)
start = time.time()
# color_chosen = input('Choose a color')
# query_color = Retrieval_by_color(test_imgs, test_color_labels, 'White')
# visualize_retrieval(query_color, len(query_color))
# query_shape = Retrieval_by_shape(test_imgs, test_class_labels, 'Dresses')
# visualize_retrieval(query_shape, len(query_shape))
# query_combined = Retrieval_combined(test_imgs, test_color_labels, test_class_labels, 'White', 'Dresses')
# visualize_retrieval(query_combined, 20)
elem_kmeans = []
elem_colors_kmeans = []
for img in test_imgs:
elem_kmeans.append(km.KMeans(img))
elem_kmeans[-1].find_bestK_improved(4, 'FISHER_REAL_CENT')
elem_colors_kmeans.append(km.get_colors(elem_kmeans[-1].centroids))
# Kmean_statistics(km.KMeans(test_imgs[0]), 4)
'''
elem_knn = KNN.KNN(train_imgs, train_class_labels)
# start = time.time()
porcentaje_shape = Get_shape_accuracy(elem_knn.predict(test_imgs, 4), test_class_labels)
# print("tiempo: ", time.time() - start)
print(porcentaje_shape)
''' '''
for propio, solucion in zip(sorted(elem_colors_kmeans), sorted(test_color_labels)):
print(propio)
