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import csv
import numpy as np
import plotly.graph_objects as go
# To generate distorsed covariance matrixes
def genS(sds, R, distor=1):
# sds: standard deviations
# R: correlation matrix
# distor: level of distorsion
sdsdis = sds * distor
S = R * np.outer(sdsdis, sdsdis)
np.fill_diagonal(S, sdsdis ** 2)
return S
def get_datXYZ(gesto, data_path):
file_path = data_path + f"config{gesto}.csv"
with open(file_path, 'r') as file:
csv_reader = csv.reader(file)
data = list(csv_reader)
n = len(data)
coordinates = np.zeros((n, 21, 3))
for i, row in enumerate(data):
for j in range(21):
x, y, z = map(float, row[j*3 : (j+1)*3])
coordinates[i, j] = [x, y, z]
return coordinates
def plot_3d_points(points_21_3d, title):
# Create a scatter plot
colors = ['red', 'green', 'yellow', 'orange', 'blue']
colors = ["purple"]
colors += ["red"] * 4
colors += ["green"] * 4
colors += ["yellow"] * 4
colors += ["orange"] * 4
colors += ["blue"] * 4
fig = go.Figure(data=go.Scatter3d(
x=points_21_3d[:, 0],
y=points_21_3d[:, 1],
z=points_21_3d[:, 2],
mode='markers',
marker=dict(
size=5,
color=colors,
opacity=0.8
)
))
# Define the line segments
segments = [(0, 1), (1, 2), (2, 3), (3, 4), # Thumb, 4 segments
(0, 5), (5, 6), (6, 7), (7, 8), # Index, 4 segments
(0, 9), (9, 10), (10, 11), (11, 12), # Middle, 4 segments
(0, 13), (13, 14), (14, 15), (15, 16), # Ring, 4 segments
(0, 17), (17, 18), (18, 19), (19, 20)] # Little, 4 segments
# Create a Scatter3d trace for each segment and add to the figure
for segment in segments:
start_index, end_index = segment
start_point = points_21_3d[start_index]
end_point = points_21_3d[end_index]
line = go.Scatter3d(
x=[start_point[0], end_point[0]],
y=[start_point[1], end_point[1]],
z=[start_point[2], end_point[2]],
mode='lines',
line=dict(color='black', width=2),
name ="",
showlegend=False
)
fig.add_trace(line)
# Set axes labels and title
fig.update_layout(
scene=dict(
xaxis=dict(title='X'),
yaxis=dict(title='Y'),
zaxis=dict(title='Z')
),
dragmode = 'orbit',
title=title
)
# Return the figure
return fig |