sample car detection with lidar

detection.py

from hdbscan import HDBSCAN
from sklearn import metrics
from sklearn.preprocessing import StandardScaler

import numpy as np
import scipy.spatial
import seaborn as sns

xy_lim = ((-60, 60), (0, 60))
n = 12
x, y = map(lambda x: x.compressed(), record[n:n + 1].cartesian(*xy_lim))
X = np.vstack((x, y)).transpose()

hdb = HDBSCAN(min_cluster_size=40, metric='euclidean').fit(X)
hdb_labels = hdb.labels_
hdb_unique_labels = set(hdb_labels)

colors = sns.husl_palette(len(hdb_unique_labels))
fig, ax = plt.subplots()
ax.plot(x, y, '*', color='black', zorder=0)
for i in range(len(hdb_unique_labels)):
    index = hdb_labels == i
    
    if len(index):
        hull = scipy.spatial.ConvexHull(X[index])
        if hull.area < 10:
            ax.scatter(x[index], y[index], color=colors[i], zorder=1)
    
plt.show()