Find points in curve

Can you share some ideas of how to find curve points (orange color marked places) like shown in picture:

I've tried this code:

result = []
    for i in range(len(df)):
        if i == 0 or df['y'].iloc[i] != df['y'].iloc[i - 1]:
            result.append(df.iloc[i])
            continue
        if i < len(df) - 1 and df['y'].iloc[i] != df['y'].iloc[i + 1]:
            result.append(df.iloc[i])

but the problem is, my 'y' isn't always equal between breakpoints and returns too many values. Could you give some advice, how to achieve my goal?

Used data for picture:

time = np.arange(0, 2200, 100)
values = np.array([-0.1, 0, 0.13, 0.27, 0.27, 0.4, 0.27, 0.27, 0.13, 0.13,
                   0.01, 0.01, -0.13, -0.13, -0.27, -0.4, -0.4, -0.27, -0.13, -0.13, 0, 0])

full_time = np.arange(0, 2200, 1)
full_values = np.interp(full_time, time, values)

Here is the full code:

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema

time = np.arange(0, 2200, 100)
values = np.array([
    -0.1, 0, 0.13, 0.27, 0.27, 0.4, 0.27, 0.27, 0.13, 0.13,
     0.01, 0.01, -0.13, -0.13, -0.27, -0.4, -0.4, -0.27, -0.13, -0.13, 0, 0
])

x = np.arange(0, 2200, 1)
y = np.interp(x, time, values)

dy = np.gradient(y)
d2y = np.gradient(dy)

curvature = np.abs(d2y) / (1 + dy**2)**1.5

threshold = 0.0005
corner_indices = np.where(curvature > threshold)[0]

filtered_corners = [corner_indices[0]]
for idx in corner_indices[1:]:
    if idx - filtered_corners[-1] > 10:
        filtered_corners.append(idx)

filtered_corners = [0] + filtered_corners + [len(x) - 1]
filtered_corners = sorted(set(filtered_corners))

plt.figure(figsize=(12, 6))
plt.plot(x, y, label='Interpolated Curve')
plt.plot(x[filtered_corners], y[filtered_corners], 'o', color='orange', label='Corners')
plt.xlabel('x')
plt.ylabel('y')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

Output: