def calculate_cluster(camera, camera_mat, quantile):
bandwidth = estimate_bandwidth(camera_mat,
quantile=quantile,
n_samples=500)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(camera_mat)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
camera_clustered = camera.copy()
camera_clustered_value = camera.copy()
camera_mat_clustered = camera_mat.copy()
camera_mat_clustered_value = camera_mat.copy()
for point, pointb, value in zip(camera_mat_clustered,
camera_mat_clustered_value, labels):
point[2] = value
pointb[2] = cluster_centers[value, 2]
camera_clustered[point[0], point[1]] = value
camera_clustered_value[point[0], point[1]] = cluster_centers[value, 2]
image = {
"image": camera_clustered_value,
"quantile": quantile,
"clusters": n_clusters_
}
return image
'LassoLarsCV':LassoLarsCV(), 'LassoLarsIC':LassoLarsIC(), 'LatentDirichletAllocation':LatentDirichletAllocation(), 'LedoitWolf':LedoitWolf(), 'LinearDiscriminantAnalysis':LinearDiscriminantAnalysis(), 'LinearRegression':LinearRegression(), 'LinearSVC':LinearSVC(), 'LinearSVR':LinearSVR(), 'LocallyLinearEmbedding':LocallyLinearEmbedding(), 'LogisticRegression':LogisticRegression(), 'LogisticRegressionCV':LogisticRegressionCV(), 'MDS':MDS(), 'MLPClassifier':MLPClassifier(), 'MLPRegressor':MLPRegressor(), 'MaxAbsScaler':MaxAbsScaler(), 'MeanShift':MeanShift(), 'MinCovDet':MinCovDet(), 'MinMaxScaler':MinMaxScaler(), 'MiniBatchDictionaryLearning':MiniBatchDictionaryLearning(), 'MiniBatchKMeans':MiniBatchKMeans(), 'MiniBatchSparsePCA':MiniBatchSparsePCA(), 'MultiTaskElasticNet':MultiTaskElasticNet(), 'MultiTaskElasticNetCV':MultiTaskElasticNetCV(), 'MultiTaskLasso':MultiTaskLasso(), 'MultiTaskLassoCV':MultiTaskLassoCV(), 'MultinomialNB':MultinomialNB(), 'NMF':NMF(), 'NearestCentroid':NearestCentroid(), 'NearestNeighbors':NearestNeighbors(), 'Normalizer':Normalizer(), 'NuSVC':NuSVC(),
