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run_me_also.py
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run_me_also.py
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import os
import matplotlib.pyplot as plt
import random
import h5py
import numpy as np
import warnings
from sklearn.neighbors import kneighbors_graph
from sklearn.cluster import SpectralClustering
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift
from sklearn.cluster import estimate_bandwidth
from sklearn.mixture import GaussianMixture
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import DBSCAN
from sklearn.metrics import silhouette_score
from sklearn.ensemble import IsolationForest
warnings.filterwarnings('ignore', '.*Graph is not fully connected*')
warnings.filterwarnings('ignore', '.*number of connected components of the connectivity*')
print('reading Street_sequence...')
file_name = "Object Motion Data (mat files)/Street_sequence.h5"
f = h5py.File(file_name, "r")
#events = f['events']
#frame = f['frame']
frame_idx = f['frame_idx']
frame_ts = f['frame_ts']
#x = events[:,1]
x = np.load("street_x.npy").astype(int)
#y = events[:,2]
y = np.load("street_y.npy").astype(int)
#ts= events[:,0]
ts = np.load("street_ts.npy")
ts = ts*0.000001
print(ts)
#pol=events[:,3]
pol = np.load("street_pol.npy").astype(int)
#np.save("street_img_ts.npy", np.asarray(frame_ts)*0.000001)
dvs_ts = np.asarray(frame_idx, dtype=np.int32)
n = len(dvs_ts)
last = 0
ALL = len(pol)
NEIGHBORS = 25
j = len(dvs_ts)
for i in range(1, len(dvs_ts)):
xx = '0000000000'
yy = str(i)
file_name = xx[:len(xx) - len(yy)] + yy
print('img : ', i)
selected_events = []
last = dvs_ts[i-1] if i>0 else 0
idx = dvs_ts[i]
#print('Total : ', idx-last+1)
jump = (idx-last+1)//3500 if (idx-last+1) > 3500 else 1
#for i in range(0, ALL)[last:idx:jump]:
# selected_events.append([y[i], x[i], ts[i] * 0.0001, pol[i] * 0])
selected_events = np.load("results/"+str(j)+"/selected_events_/" + file_name + ".npy")
#selected_events = np.asarray(selected_events)
#print('removing noise...')
#cleaned_events = IsolationForest(random_state=0, n_jobs=-1, contamination=0.05).fit(selected_events)
#unwanted_events = cleaned_events.predict(selected_events)
#selected_events = selected_events[np.where(unwanted_events == 1, True, False)]
#if len(selected_events) >= 3500:
# selected_events = selected_events[:3500]
#print('Selected : ', len(selected_events))
#print('graph construction...')
adMat = kneighbors_graph(selected_events, n_neighbors=NEIGHBORS)
#max_score_sc = -20
max_score_gmm = -20
#opt_clusters_sc = 2
opt_clusters_gmm = 2
scores_gmm = list(np.load("results/"+str(j)+"/predict_k/gmm/"+file_name+".npy"))
opt_clusters_sc = scores_gmm.index(max(scores_gmm))+2
#scores_gmm = []
#print('predicting number of clusters...')
#for CLUSTERS in range(2, 11):
#clustering_sc = SpectralClustering(n_clusters=CLUSTERS, random_state=0,
# affinity='precomputed_nearest_neighbors',
# n_neighbors=NEIGHBORS, assign_labels='kmeans',
# n_jobs=1).fit_predict(adMat)
#clustering_gmm = GaussianMixture(n_components=CLUSTERS,
# random_state=0).fit_predict(selected_events)
#ssc=silhouette_score(selected_events, clustering_sc)
#scores_sc.append(ssc)
#scores_gmm.append(silhouette_score(selected_events, clustering_gmm))
#if scores_sc[-1] > max_score_sc:
# max_score_sc = scores_sc[-1]
# opt_clusters_sc = CLUSTERS
#if scores_gmm[-1] > max_score_gmm:
# max_score_gmm = scores_gmm[-1]
# opt_clusters_gmm = CLUSTERS
print('clustering...')
#clustering_sc = SpectralClustering(n_clusters=opt_clusters_sc, random_state=0,
# affinity='precomputed_nearest_neighbors',
# n_neighbors=NEIGHBORS, assign_labels='kmeans',
# n_jobs=-1).fit_predict(adMat)
#print(max(scores_sc))
clustering_gmm = GaussianMixture(n_components=opt_clusters_gmm,
random_state=0).fit_predict(selected_events)
#clustering_db = DBSCAN(eps=5, min_samples=10).fit_predict(selected_events)
#BW = estimate_bandwidth(selected_events)
#clustering_ms = MeanShift(bandwidth=BW).fit_predict(selected_events)
#print('saving results...')
#np.save(os.path.join('results/200/predict_k/spectral',
# file_name + '.npy'),
# np.asarray(scores_sc))
#np.save(os.path.join('results/200/predict_k/gmm',
# file_name + '.npy'),
# np.asarray(scores_gmm))
#np.save(os.path.join('results/200/selected_events',
# file_name + '.npy'),
# selected_events)
#np.save(os.path.join('results/200/clusters/spectral',
# file_name + '.npy'),
# clustering_sc)
#np.save(os.path.join('results/200/clusters/meanshift',
# file_name + '.npy'),
# clustering_ms)
#np.save(os.path.join('results/200/clusters/dbscan',
# file_name + '.npy'),
# clustering_db)
np.save(os.path.join('results/200/clusters/gmm',
file_name + '.npy'),
clustering_gmm)
print('done')