DBSCAN을 사용하여 시선 데이터 클러스터링

gaze_clustering.py

import numpy as np
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2

# 총 200개의 좌표 생성
number_of_points = [20, 10, 20, 10, 100, 40]
x_ranges = [(0.3, 0.7), (0.4, 0.5), (0.2, 0.5), (0.9, 1.0), (0.7, 0.8), (0.2, 0.3)]
y_ranges = [(0.3, 0.9), (0.2, 0.5), (0.2, 0.6), (0.1, 0.2), (0.2, 0.7), (0.1, 0.3)]
points = []

for i in range(6):
  for j in range(number_of_points[i]):
    x = np.random.uniform(low=x_ranges[i][0], high=x_ranges[i][1])
    y = np.random.uniform(low=y_ranges[i][0], high=y_ranges[i][1])
    points.append((x, y))

# 노이즈 추가
noise = np.random.uniform(low=0.0, high=0.15, size=(200, 2))

# 시선 더미 데이터
gaze_data = points + noise
print(gaze_data)




# DBSCAN 알고리즘 사용하여 시선데이터 클러스터링
dbscan = DBSCAN(eps=0.1, min_samples=20)
labels = dbscan.fit_predict(gaze_data)

# 클러스터 개수 출력
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
print(f"Number of clusters found: {n_clusters}")